Research on Monitoring and Early Warning of the Mine Backfill System Based on Blockchain Technology

Round 1

Reviewer 1 Report

The paper deals with the Monitoring and Early Warning of Blockchain Technology in the Mine Backfill System. However, I found so many concerns in this paper. 

1. The research objective is not clear. 

2. What makes you go with blockchain technology? What are the existing problems in traditional monitoring systems?

3. I am confused with the methodology. The explanations are chaotic and not clear.

4. I couldn't find comprehensive explanations for the methodology section. 

5. In all figure captions, I could find "This is figure." What is the meaning of it?

6. Title is also so vague and confusing

7. Literature study is poor, and the problem statement is not provided properly.

8. Organization of the paper is missing.

9. I could observe only theoretical explanations throughout the paper, and there is no proper evidence.

10. The result section is very weak. Support the claims with the proper results and justifications.

11. When the iteration progresses, why does the accuracy of the systems show oscillations?

12. How are the hyperparameters of CNN tuned?

13. Results need better illustration with graphical proofs. 

Author Response

Response to Reviewer 1 Comments

 

Dear Editors and Reviewers:

Thank you for your letter and for the Reviewers' comments concerning our manuscript entitled “Research on Monitoring and Early Warning of Mine Backfill System based on Blockchain Technology” (ID: applsci-2090166). Those comments are all valuable and very helpful for revising and improving our paper, as well as the important guiding significance to our researches. We have studied comments carefully and have made correction which we hope can meet with approval. The main corrections in the paper and the responds to the reviewer’ s comments are as flowing:

 

Point 1: The research objective is not clear.

 

Response 1: We are grateful for the suggestion. To express more clearly, we have added a more detailed interpretation regarding drawbacks of mine filling process monitoring technology. This problem is modified in Section 1 (lin35~51), and the details are as follows,

“At present, the filling mining method has been popularized in China's major mines, and the monitoring technology in the process of mine filling has made great progress [4,5], but there are still several disadvantages to this method: (1) Intelligent monitoring and early warning technology of mines is still in an initial stage of developing artificial intelligence, computing, big data mining and other technologies to initially give the mine the ability to observe and perceive, through data analysis and processing, in order to achieve intelligent judgment and prediction, but there is still a lack of self-adaptive mine monitoring equipment, limiting the mine’ s safety situational awareness, its ability to monitor information sharing system collaboration, and other technical developments. (2) The traditional mine monitoring database management system adopts centralized storage, and the data center is responsible for accessing and sharing data. Both data providers and data users interact with the data center and upload or obtain data according to the data interface. However, with the continuous development of intelligent monitoring technology, the mine’s intelligent monitoring system tends to be complicated, and large amounts of monitoring data are generated. Due to the limitations of the central management mode of the mine database, the security of monitoring data transmission and access cannot be safely guaranteed.”

 

Point 2: What makes you go with blockchain technology? What are the existing problems in traditional monitoring systems?

 

Response 2: We are extremely grateful to reviewer for pointing out this problem. Blockchain has the characteristics of decentralization, non-tampering, openness and transparency, and traceability, which can ensure the security performance in the data transmission process. Therefore, we select the blockchain technology. The traditional monitoring system is based on the original system for hardware reference different research, does not involve the overall software specific research. This problem is modified in Section 1 (lin52~61), and the details are as follows,

“At present, most of the research on mine monitoring systems is based on the combination of software and hardware. For example, Wang Tianjun [6] proposed a server architecture based on cloud computing and adopted an AES data encryption algorithm to improve data security. Sun Changjun et al. [7] proposed a safety monitoring system based on a CAN bus, which can detect anomalies of underground intelligent nodes and send out early warning information. He Xiaowu [8] proposed a data center architecture, using different sensors to build different monitoring systems and employing an adaptive noise reduction method to monitor early warning in a mine filling system. However, these are based on the original system for different hardware references, not involving overall software-specific research.”

 

Point 3: I am confused with the methodology. The explanations are chaotic and not clear.

 

Response 3: We give an overall introduction to the method in this paper in the part of model design. This problem is modified in Section 3 (lin158~265), and the details are as follows,

“3. Model design

3.1. Over-limit warning model

Based on transfer learning, this paper builds an over-limit early warning model to realize the over-limit prediction of operating equipment in the mine filling system. The model is mainly composed of two parts: pre-training and parameter migration, as shown in Figure 2. The pre-training process uses the simulation data generated by the simulation system to train the neural network model, and the parameter migration process first freezes the convolutional pooling layer parameters of the pre-training model, before using a small amount of actual data to fine-tune the fully connected layer parameters of the pre-training model. Due to the factors that affect the collected data in the actual environment, such as the stability of the sensor, the impact resistance, and the use time of the acquisition equipment, the over-limit warning ability of the pre-trained model may decrease, and the accuracy of the over-limit prediction needs to be improved through the transfer learning of a small amount of data.

Figure 2. Flow chart of over-limit prediction method.

3.2. Data management model

This paper builds a data management model of a mine filling system based on blockchain, realizes the storage and retrieval of data in the operation of mine filling system, and ensures the safety and reliability of data transmission. The model mainly includes three stages of data upload, storage and retrieval. The specific steps are shown in Figure 3.

Figure 3. Data management model architecture based on blockchain.

The first part is the data upload and pre-preparation stage, and the main steps are as follows.

Step 1: The trust center determines the candidate of the administrator (including the dispatcher and the inspector) and stores the name, position, area of responsibility and other relevant information of the candidate. The dispatcher is appointed to encrypt, store and retrieve data. The inspector can detect the loyalty of the dispatcher.

Step 2: The mine filling system uploads all data during operation to the over-limit warning model for detection.

Step 3: The over-limit warning model predicts the future operation data of the mine filling system equipment, detects the over-limit data, and uploads the operation data and warning data to the dispatcher for storage.

The second part is the data storage phase, where the main steps are as follows.

Step 4: After receiving the monitoring data, the dispatcher encrypts the data with a public key and uploads it to the IPFS for storage.

Step 5: The IPFS receives the monitoring data for storage, generates the corresponding file identifier CID, and returns it to the dispatcher.

Step 6: The dispatcher stores the received CID on the blockchain.

Step 7: The inspector makes an inspection request to the dispatcher.

Step 8: The original data and encrypted hash values are sent by the dispatcher to the inspector.

Step 9: The inspector generates the hash value stored on the IPFS through the public key and the original data, and compares it with the received encrypted hash value to test the credibility of the dispatcher: if the two values are consistent, the dispatcher is internally loyal; if the two values are inconsistent, it indicates that there is a defection within the dispatcher, and the trust center needs to reappoint the dispatcher.

The data of this stage are stored by the combination of the blockchain and the IPFS. The specific process is shown in Figure 4. The monitoring data generated in the over-limit warning model are stored in the IPFS, and the file identifier CID returned by the IPFS is stored in the blockchain. The administrator node initiates a data storage request and calls the smart contract to query whether the administrator node has storage rights. If so, the encrypted monitoring data of the administrator node are stored in the IPFS and the returned CID is stored on the blockchain; if not, the data storage fails.

Figure 4. Data management model architecture based on blockchain.

The third part is the data retrieval stage, including administrator query data and user query data.

The main steps for administrators to query data are as follows.

Step 10: The dispatcher initiates a data retrieval request to the trust center.

Step 11: The trust center delivers the data access credentials to the dispatcher.

Step 12: The dispatcher extracts the CID value of the data to be queried from the blockchain based on the access credentials.

Step 13: With the obtained CID value, the dispatcher obtains the encrypted corresponding data from the IPFS and decrypts the original data with the private key.

The main steps of user query data are as follows.

Step 14: The user initiates a data retrieval request to the trust center.

Step 15: The trust center hands over the data access credentials to the user.

Step 16: The user hands over the data access credentials to the dispatcher.

Step 17: The dispatcher extracts the CID value of the data to be queried from the blockchain based on the access credentials.

Step 18: The dispatcher obtains the encrypted corresponding data from the IPFS by virtue of the obtained CID value and decrypts it with the private key to obtain the original data.

Step 19: The dispatcher sends the decrypted data to the user.

In this stage, the IPFS is combined with the blockchain to store the monitoring data. The monitoring data generated by the over-limit warning model are stored in the IPFS, and the CID returned by IPFS is stored on the blockchain to reduce the blockchain volume while ensuring security in the data storage process. However, after the monitoring data are stored in the IPFS, they cannot be directly queried from Fabric. It is necessary for the query node to obtain the address hash value of the query information from the blockchain and then obtain the corresponding data from IPFS according to the hash value. The query process of the administrator node is shown in Figure 5(a). The administrator node initiates a data retrieval request and calls the smart contract to verify whether the administrator node has authority for the query. If the administrator node has retrieval authority, the trust center issues a data access credential to the administrator. According to the credential, the CID that is needed to query the monitoring data is returned from the blockchain, the encrypted monitoring data are retrieved from the IPFS according to the CID value, and then the original monitoring data are obtained after decryption using the private key. If the administrator node does not have retrieval rights, verification does not occur, the trust center refuses to issue data access credentials, and the administrator cannot obtain the required hash value.

The user query data need to send a data retrieval request to the trust center. After verifying its identity, the obtained data access credentials are sent to the administrator. The administrator obtains the original monitoring data based on the credentials and sends them to the user. The specific data retrieval process is shown in Figure 5(b). The user node initiates a data query request and consults the smart contract to verify whether the user node has query authority. If the user node authentication is passed, a data access credential issued by the trust center is obtained, and the user sends the credential to the administrator. The administrator obtains the corresponding CID from the blockchain according to the credential and then requests the corresponding encrypted data from the IPFS according to the CID, before using the private key to decrypt and send the original monitoring data to the user. If the user node authentication is not passed, the data query fails.

(a)	(b)

Figure 5. Data query process: (a) Flow chart of administrator query the data (b) Flow chart of user query data. ”

 

Point 4: I couldn't find comprehensive explanations for the methodology section.

 

Response 4: Thank you for your comments. We give a comprehensive explanation of the methods in the article. This problem is modified in Section 1 (lin91~115), and the details are as follows,

“Aiming at solving the problems of fewer data samples and the low data storage safety performance of collecting equipment in a mine filling system, this paper constructs a mine filling over-limit early warning system based on the blockchain and transfer learning, which mainly includes the following two models:  

(1) Over-limit warning model: Based on transfer learning, an over-limit warning model is established to collect the operation data generated by the equipment during the operation of the mine filling system. The convolutional neural network is used to predict the future change in the equipment operation data over a certain period of time, and all the data are detected to obtain the time of the future over-limit situation of different equipment.

(2) Data management model: Data storage and retrieval are realized by combining the blockchain and the IPFS. After the over-limit data in operation are detected by the over-limit early warning model, the administrator stores the over-limit data on the IPFS and stores the returned file identifier on the blockchain to ensure the security performance of data storage. At the same time, alliance chain technology is used to design the data retrieval process of different users. The administrator can directly retrieve the required data after obtaining the access credentials given by the third-party trust institution, and the user needs to obtain the required data from the administrator after obtaining the access credentials issued by the trust institution to ensure the performance of data transmission in secure. ”

 

Point 5: In all figure captions, I could find "This is figure." What is the meaning of it?

 

Response 5: Because our understanding of the template format is wrong, mistakenly believe that the title of each picture need to specify "This is figure" , now we has been corrected to delete it.

 

Point 6: Title is also so vague and confusing.

 

Response 6: We are grateful for the suggestion. We changed the title from “Research on Monitoring and Early Warning of Blockchain Technology in Mine Backfill System” to “Research on Monitoring and Early Warning of Mine Backfill System based on Blockchain Technology”, indicating that the main content of this paper is to apply blockchain technology to the monitoring and early warning of mine filling system.

 

Point 7: Literature study is poor, and the problem statement is not provided properly.

 

Response 7: According to your suggestions, we added the literature of mine monitoring system research, and corrected the questions raised. This problem is modified in Section 1 (lin52~90), and the details are as follows,

“At present, most of the research on mine monitoring systems is based on the combination of software and hardware. For example, Wang Tianjun [6] proposed a server architecture based on cloud computing and adopted an AES data encryption algorithm to improve data security. Sun Changjun et al. [7] proposed a safety monitoring system based on a CAN bus, which can detect anomalies of underground intelligent nodes and send out early warning information. He Xiaowu [8] proposed a data center architecture, using different sensors to build different monitoring systems and employing an adaptive noise reduction method to monitor early warning in a mine filling system. However, these are based on the original system for different hardware references, not involving overall software-specific research.

The huge amount of data generated during the mine filling process is stored on the blockchain, which causes the blockchain to be too large. Therefore, many scholars have proposed a method of combining the IPFS with the blockchain to store data and reduce the volume of the blockchain. Miao Qi [9] proposed a blockchain storage optimization scheme based on IPFS, which limits the blockchain’s capacity by reducing the size of the upper chain data. Zhu Yanxia et al. [10] used IPFS to build a storage and distribution system for the decentralized media asset platform in the media service center. The files were distributed for storage and transmission using the IPFS protocol, which achieved both the secure storage of media data and CDN network optimization and reduced resource storage costs. Gao Wentao et al. [11] used the characteristics of blockchain decentralization and non-tampering combined with the IPFS to jointly maintain the distributed and reliable storage of music data and built nodes in the alliance chain scenario for data verification. The Ethereum consensus mechanism avoids computational mining to reduce the consensus time of the entire network’s nodes.

Recently, machine learning has been increasingly used to detect faults during system operation [12-13]. As a typical method of machine learning, transfer learning can train existing models through small sample data to obtain training models suitable for different scenarios. Xie Xuyang et al. [14] designed a convolutional neural network model training method based on transfer learning, which can automatically extract effective fault features from vibration signals and complete fault diagnosis of electric pumps using only a small amount of sample data. Based on the idea of classification learning, Shi Bingbo [15] proposed a fault diagnosis method based on transfer learning to achieve automatic and efficient diagnosis of equipment faults on the basis of fault diagnosis knowledge representation, fault diagnosis algorithm research, and similarity calculation of fault vectors. Yuan Laohu et al. [16] proposed a rolling bearing fault diagnosis method based on AlexNet and transfer learning. The collected data were set as training samples and trained in the fine-tuned pre-trained AlexNet network. During the test, it was found that the method can still be used to diagnose the common fault types of rolling bearings when the marked fault data are scarce.”

 

Point 8: Organization of the paper is missing.

 

Response 8: Thank you for underlining this deficiency. We elaborate the overall structure of the article in the last paragraph of the introduction. This problem is modified in Section 1 (lin111~115), and the details are as follows,

“The rest of this article is arranged as follows. Section 1 introduces the preliminary knowledge. Section 2 describes the design goals and model framework of the mine filling over-limit warning system. Section 3 presents system experimental evaluation and theoretical analysis to explain the advantages of the model. Section 4 summarizes the paper. ”

 

Point 9: I could observe only theoretical explanations throughout the paper, and there is no proper evidence.

 

Response 9: We add the evaluation of the storage performance of the data management model in the system experiment and analysis evaluation part, which shows that the combination of blockchain and IPFS can further optimize the storage space and improve the storage performance. This problem is modified in Subsection 4.1 (line267~288), and the details are as follows,

“4.1. Storage performance analysis

In this paper, the original monitoring data information is stored in the IPFS system, and the file identifier generated by the IPFS is stored in the blockchain system, which greatly reduces the length of the blockchain ledger.

The calculation formula of the storage space compression ratio is shown in Equation (1).

(1)

whererepresents the data volume of all block headers in the blockchain, represents the number of all transactions in the blockchain, represents the size of the IPFS hash corresponding to each transaction, and represents the original data volume of each transaction.

Among them, the block header takes up 80 bytes, each block contains at least 500 transactions on average, and each transaction takes up at least 250 bytes on average, but an IPFS hash value only takes up 46 bytes. It can be estimated that the optimization of the storage space of this solution is considerable [22]. It is assumed that the average size of each monitoring data point is 1MB. As each object in the IPFS can store up to 256 kB of data, each monitoring data point needs four IPFS objects, and the hash value generated by each data object is 46 B. Thus, the data on the chain are reduced to about 1 / 5698 of the original. The reduction in size of blockchain ledger data can optimize the storage space, improve the storage rate of monitoring data, and reduce the access threshold of blockchain nodes. The number of nodes that meet the conditions increases, which makes it more difficult for malicious nodes to modify the ledger, improving the security performance of the system.”

 

Point 10: The result section is very weak. Support the claims with the proper results and justifications.

 

Response 10: In the system experiment and analysis evaluation part, we increase the evaluation of the storage performance of the data management model in this paper, and use the over-limit prediction results to describe the performance of the data early warning model, so as to prove the superior performance of the proposed method. This problem is modified in Subsection 4.1 (line277~288) and Subsection 4.3 (line345~356). The details are as follows,

“Among them, the block header takes up 80 bytes, each block contains at least 500 transactions on average, and each transaction takes up at least 250 bytes on average, but an IPFS hash value only takes up 46 bytes. It can be estimated that the optimization of the storage space of this solution is considerable [22]. It is assumed that the average size of each monitoring data point is 1MB. As each object in the IPFS can store up to 256 kB of data, each monitoring data point needs four IPFS objects, and the hash value generated by each data object is 46 B. Thus, the data on the chain are reduced to about 1 / 5698 of the original. The reduction in size of blockchain ledger data can optimize the storage space, improve the storage rate of monitoring data, and reduce the access threshold of blockchain nodes. The number of nodes that meet the conditions increases, which makes it more difficult for malicious nodes to modify the ledger, improving the security performance of the system.”

“The experimental environment of the over-limit warning model is an Anaconda virtual environment based on TensorFlow. The Python language is used for programming. The number of network layers in the model, the categories and construction order of different layers, the hidden layer parameters, and the learning rate are selected by random search method. Each batch processes 40 items of data, each performing 10 training cycles. Among them, 80 % of the samples are used as training data sets, and 20 % of the samples are used as simulation verification sets to detect the prediction effect of the model. The prediction results of the model are shown in Figure 7. The comparison between the predicted data and the actual data is shown in Table 1. The calculation shows that the mean absolute error of the model is 0.023, the mean square error is 0.001, and the correlation index R2 is 0.98, indicating that the regression effect of the model is good and the predicted results are more accurate. ”

 

Point 11: When the iteration progresses, why does the accuracy of the systems show oscillations?

 

Response 11: This is because the system does not fully converge due to improper parameter adjustment. By increasing the optimization data set, increasing the number of training iterations, and further reducing the learning rate, the accuracy curve is fully converged.

 

Point 12: How are the hyperparameters of CNN tuned?

 

Response 12: We choose random search method to adjust the iteration parameters, batch size, number of network layers, hidden layer parameters, learning rate, etc. This problem is modified in Subsection 4.3 (line347~352), and the details are as follows,

“The number of network layers in the model, the categories and construction order of different layers, the hidden layer parameters, and the learning rate are selected by random search method. Each batch processes 40 items of data, each performing 10 training cycles. Among them, 80 % of the samples are used as training data sets, and 20 % of the samples are used as simulation verification sets to detect the prediction effect of the model. ”

 

Point 13: Results need better illustration with graphical proofs.

 

Response 13: We add the formula derivation to prove the storage performance advantage of the system in the system experiment and analysis evaluation part. We modify the accuracy rate into the fault prediction effect diagram and add the comparison table between the predicted value and the actual value of the material position of the mixing tank. This problem is modified in Subsection 4.1 (line271~276) and Subsection 4.3 (line352~360). The details are as follows,

“The calculation formula of the storage space compression ratio is shown in Equation (1).

(1)

whererepresents the data volume of all block headers in the blockchain, represents the number of all transactions in the blockchain, represents the size of the IPFS hash corresponding to each transaction, and represents the original data volume of each transaction. ”

“The prediction results of the model are shown in Figure 7. The comparison between the predicted data and the actual data is shown in Table 1. The calculation shows that the mean absolute error of the model is 0.023, the mean square error is 0.001, and the correlation index R2 is 0.98, indicating that the regression effect of the model is good and the predicted results are more accurate.”

 

Figure 7. Model prediction result.

Table 1. Partial comparison results of actual value and predicted value of slurry level in mixing tank.

	Time	Predicted value	Actual value		Time	Predicted value	Actual value
1	11:32:55	65.32	66	11	11:33:45	49.22	50
2	11:33:00	63.35	64	12	11:33:50	48.80	50
3	11:33:05	61.44	62	13	11:33:55	48.35	50
4	11:33:10	59.44	60	14	11:34:00	47.80	50
5	11:33:15	57.40	58	15	11:34:05	46.99	48
6	11:33:20	55.43	56	16	11:34:10	45.85	46
7	11:33:25	53.64	54	17	11:34:15	44.36	44
8	11:33:30	52.09	52	18	11:34:20	42.38	42
9	11:33:35	50.76	50	19	11:34:25	40.34	40
10	11:33:40	49.82	50	20	11:34:30	38.21	38

Thank you for your careful review. We have tried our best to improve the manuscript and made some changes in the manuscript. Your careful review has helped to make our study clearer and more comprehensive. The manuscript also has been undergone extensive English revisions. We use a paid editing service at https://www.mdpi.com/authors/english. 

Once again, thank you very much for your comments and suggestions.

 

If there is anything else we should do, please do not hesitate to let us know.

 

Best Regards.

 

Yours sincerely,

 

Jingtao Huo

Author Response File: Author Response.docx

Reviewer 2 Report

Warning of Blockchain Technology in Mine Backfill System   Suggestions for paper improvement are below:

·        The abstract should be reinforced (with more findings and results).

·        The last paragraph in the introduction section should be a short structure of the paper (several sentences for each section).

·        The methodology of the paper is not clear. This is the major lack of this paper.

·        Mentioned directly affects the results.

·        The separate section Practical and theoretical implications (or Discussion) is missing.

·        Conclusion section is not on a satisfactory level.

o   Avoid numbering in conclusions.

o   Future research directions are very weak (line 449).

o   Limitations of this research are missing.

 

·        Practical and theoretical contributions must be more clearly highlighted. 

Author Response

Response to Reviewer 2 Comments

 

Dear Editors and Reviewers:

Thank you for your letter and for the Reviewers' comments concerning our manuscript entitled “Research on Monitoring and Early Warning of Blockchain Technology in Mine Backfill System” (ID: applsci-2090166). Those comments are all valuable and very helpful for revising and improving our paper, as well as the important guiding significance to our researches. We have studied comments carefully and have made correction which we hope can meet with approval. The main corrections in the paper and the responds to the reviewer’s comments are as flowing:

 

Point 1: The abstract should be reinforced (with more findings and results).

 

Response 1: Thank you for your suggestion. As suggested by reviewer, we have added validation of model storage performance and model warning results to the summary. This problem is modified in abstract (lin13~26), and the details are as follows,

“Abstract: At present, the mine filling system is a mostly automated operation process, and it has many problems, such as centralized data storage and low safety performance. Aiming at solving the above problems, this paper proposes a mine filling over-limit warning system based on blockchain to achieve the functions of an equipment operation over-limit warning, data storage and data retrieval. Firstly, the original data model is trained based on transfer learning to obtain the over-limit early warning model and predict the over-limit of equipment operation. Then, the interplanetary file system (IPFS) storage device is used to store the running data and over-limit data, and the corresponding file identifier CID is stored on the blockchain. At the same time, alliance chain technology is used to set administrators and users to retrieve data based on credentials to ensure the privacy of the data retrieval process. System experiments and analysis evaluation show that the combination of blockchain and the IPFS to store monitoring data can effectively save storage space and improve the efficiency of storage and retrieval. The application of an over-limit early warning model can optimize the mine filling process and prolong the operational life of system equipment.”

 

Point 2: The last paragraph in the introduction section should be a short structure of the paper (several sentences for each section).

 

Response 2: We are grateful for the suggestion. As suggested by the reviewer, we have added an introduction to the structure of the thesis in the last paragraph of the introduction. This problem is modified in Section 1 (lin111~115), and the details are as follows,

“The rest of this article is arranged as follows. Section 1 introduces the preliminary knowledge. Section 2 describes the design goals and model framework of the mine filling over-limit warning system. Section 3 presents system experimental evaluation and theoretical analysis to explain the advantages of the model. Section 4 summarizes the paper.”

 

Point 3: The methodology of the paper is not clear. This is the major lack of this paper.

 

Response 3: We are extremely grateful to reviewer for pointing out this problem. In the introduction, we describe the methods and models used in this paper. This problem is modified in Section 1 (lin91~115), and the details are as follows,

“Aiming at solving the problems of fewer data samples and the low data storage safety performance of collecting equipment in a mine filling system, this paper constructs a mine filling over-limit early warning system based on the blockchain and transfer learning, which mainly includes the following two models:  

(1) Over-limit warning model: Based on transfer learning, an over-limit warning model is established to collect the operation data generated by the equipment during the operation of the mine filling system. The convolutional neural network is used to predict the future change in the equipment operation data over a certain period of time, and all the data are detected to obtain the time of the future over-limit situation of different equipment.

(2) Data management model: Data storage and retrieval are realized by combining the blockchain and the IPFS. After the over-limit data in operation are detected by the over-limit early warning model, the administrator stores the over-limit data on the IPFS and stores the returned file identifier on the blockchain to ensure the security performance of data storage. At the same time, alliance chain technology is used to design the data retrieval process of different users. The administrator can directly retrieve the required data after obtaining the access credentials given by the third-party trust institution, and the user needs to obtain the required data from the administrator after obtaining the access credentials issued by the trust institution to ensure the performance of data transmission in secure. “

 

Point 4: Mentioned directly affects the results.

 

Response 4: We have made changes to the mentioned content. And the analysis of model storage performance has been added to the paper. This problem is modified in Subsection 4.1 (line267~288), and the details are as follows,

“4.1. Storage performance analysis

In this paper, the original monitoring data information is stored in the IPFS system, and the file identifier generated by the IPFS is stored in the blockchain system, which greatly reduces the length of the blockchain ledger.

The calculation formula of the storage space compression ratio is shown in Equation (1).

(1)

whererepresents the data volume of all block headers in the blockchain, represents the number of all transactions in the blockchain, represents the size of the IPFS hash corresponding to each transaction, and represents the original data volume of each transaction.

Among them, the block header takes up 80 bytes, each block contains at least 500 transactions on average, and each transaction takes up at least 250 bytes on average, but an IPFS hash value only takes up 46 bytes. It can be estimated that the optimization of the storage space of this solution is considerable [22]. It is assumed that the average size of each monitoring data point is 1MB. As each object in the IPFS can store up to 256 kB of data, each monitoring data point needs four IPFS objects, and the hash value generated by each data object is 46 B. Thus, the data on the chain are reduced to about 1 / 5698 of the original. The reduction in size of blockchain ledger data can optimize the storage space, improve the storage rate of monitoring data, and reduce the access threshold of blockchain nodes. The number of nodes that meet the conditions increases, which makes it more difficult for malicious nodes to modify the ledger, improving the security performance of the system.”

 

Point 5: The separate section Practical and theoretical implications (or Discussion) is missing.

 

Response 5: It is indeed our negligence not to give a discussion of the contribution of this model in this paper. With the Reviewer’ s advice, the discussion has been added to the paper. This problem is modified in Section 5 (line361~390), and the details are as follows,

“5. Discussion

Blockchain technology and deep learning are applied to the mine filling process. The over-limit early warning system of mine filling equipment based on the blockchain proposed in this paper achieves the functions of over-limit early warning, data storage, and retrieval of equipment operation. The over-limit early warning model applies transfer learning to detect and warn of the over-limit data of the equipment during the operation of the mine filling system. The experimental results show that the accuracy of this method is high, and it can predict abnormal conditions in the operation of a mine filling system in advance. Based on the combination of the blockchain and the IPFS, the monitoring data are stored, which reduces the size of the blockchain and improves the performance of data transmission and retrieval. The alliance chain is used to standardize the functions of administrators and users. The dispatcher is responsible for storing and retrieving data. The inspector verifies the trust of the dispatcher. The user obtains the monitoring data from the dispatcher by means of data access credentials to ensure the security of the data retrieval process.

The application of the blockchain in a mine filling system gives the mine database the characteristics of being tamper-proof and decentralized, and it ensures security in the process of mine data storage and transmission. The application of deep learning models can predict the over-limit in the operation of equipment in advance and ensure safety in the operation of mine equipment. However, there are still limitations in the current research. It is difficult to obtain the operation data of each item of equipment in the actual mine filling system. This paper only verifies the monitoring and early warning effect of the equipment in the mine filling simulation system. The environment faced by the actual filling system equipment operation is relatively harsh, and the mutation probability of the equipment operation data is large, so it cannot prove that the proposed model is also applicable to the actual system. The performance of the blockchain and the IPFS combined storage has only been verified theoretically, and no simulation tests have been performed. It is impossible to verify the operating performance and energy consumption analysis more intuitively during storage and retrieval.”

 

Point 6: Conclusion section is not on a satisfactory level.

o   Avoid numbering in conclusions.

o   Future research directions are very weak (line 449).

o   Limitations of this research are missing.

 

Response 6: Thank you for this comment, the conclusions have been rewritten to increase description of limitations and future research directions. The number in the conclusion has also been removed. However, since the discussion part has analyzed the contribution and limitations of this paper, it is decided to add the content of the conclusion part to the discussion and delete the conclusion part.

 

Point 7: Practical and theoretical contributions must be more clearly highlighted. 

 

Response 7: According to your suggestions, we present the proposed method and its contribution in the discussion section. This problem is modified in Section 5 (line362~375), and the details are as follows,

“Blockchain technology and deep learning are applied to the mine filling process. The over-limit early warning system of mine filling equipment based on the blockchain proposed in this paper achieves the functions of over-limit early warning, data storage, and retrieval of equipment operation. The over-limit early warning model applies transfer learning to detect and warn of the over-limit data of the equipment during the operation of the mine filling system. The experimental results show that the accuracy of this method is high, and it can predict abnormal conditions in the operation of a mine filling system in advance. Based on the combination of the blockchain and the IPFS, the monitoring data are stored, which reduces the size of the blockchain and improves the performance of data transmission and retrieval. The alliance chain is used to standardize the functions of administrators and users. The dispatcher is responsible for storing and retrieving data. The inspector verifies the trust of the dispatcher. The user obtains the monitoring data from the dispatcher by means of data access credentials to ensure the security of the data retrieval process. “

 

Thank you for your careful review. We have tried our best to improve the manuscript and made some changes in the manuscript. Your careful review has helped to make our study clearer and more comprehensive. The manuscript also has been undergone extensive English revisions. We use a paid editing service at https://www.mdpi.com/authors/english. 

 

Once again, thank you very much for your comments and suggestions.

 

If there is anything else we should do, please do not hesitate to let us know.

 

Best Regards.

 

Yours sincerely,

 

Jingtao Huo

Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

Still, I feel the result section is weak. The authors should keep the discussion section separately and the conclusion and future section separately. 

Author Response

Dear Editors and Reviewers:

 

Thank you for your letter and for the Reviewers' comments concerning our manuscript entitled “Research on Monitoring and Early Warning of Mine Backfill System based on Blockchain Technology” (ID: applsci-2090166). Those comments are all valuable and very helpful for revising and improving our paper, as well as the important guiding significance to our researches. We have studied comments carefully and have made correction which we hope can meet with approval. The main corrections in the paper and the responds to the reviewer’ s comments are as flowing:

 

Point: Still, I feel the result section is weak. The authors should keep the discussion section separately and the conclusion and future section separately.

 

Response: We are grateful for the suggestion. We have added experiments on blockchain storage capacity comparisons in the storage performance analysis section. And we describe the discussion section and the conclusion section separately. This problem is modified in Section 4.1.2 (lin292~305), Section 5 (lin378~410) and Section 6 (lin411~433). The details are as follows,

“4.1.2. Experimental analysis

The data management scheme of IPFS and blockchain combined storage proposed in this paper. The blockchain layer is constructed by the Hyperledger Fabric project. The data storage performance test takes the administrator node as an example, and deploys a leader node, an orderer node and two general peer nodes. A leader node is responsible for data storage and communication with other organizational nodes. An orderer node completes the consensus of transaction sequencing. And deploys relevant chain codes in the organization.

The comparison between the system model designed in this paper and the original blockchain model to store the same data capacity is shown in Figure 6. The blocks generated by the system are reduced by more than 72 % compared with the original ordinary block, greatly reducing the capacity of the blockchain.

 

Figure 6. Blockchain capacity comparison.

5. Discussion

Aiming at the problems of incomplete early warning measures and low safety performance of database central management mode in the mine filling system, this paper applies blockchain technology and deep learning to mine filling process, and proposes an over-limit early warning system of mine filling equipment based on the blockchain, which achieves the functions of over-limit early warning, data storage, and retrieval of equipment operation. System experiment and analysis evaluation show that the block generated by combining IPFS with blockchain to store monitoring data is reduced by more than 72 % compared with the original ordinary block, which greatly optimizes the storage space. Transfer learning is applied to the over-limit early warning model, and the correlation index of the predicted operation monitoring data reaches 0.98 in the mine filling system, which can predict the over-limit situation of different equipment more accurate, and take measures to prolong the operational life of system equipment in advance.

However, there are still limitations in the current research. It is difficult to obtain the operation data of each item of equipment in the actual mine filling system. This paper only verifies the monitoring and early warning effect of the equipment in the mine filling simulation system. The environment faced by the actual filling system equipment operation is relatively harsh, and the mutation probability of the equipment operation data is large, so it cannot prove that the proposed model is also applicable to the actual system. The performance of the blockchain and the IPFS combined storage has only been verified theoretically and experimentally, without the simulation test of the whole process of storage and retrieval. It is impossible to verify the operating performance and energy consumption analysis more intuitively during storage and retrieval.

Future research will be based on the actual mine filling process, combined with the monitoring data of actual equipment operation to optimize the over-limit early warning model, and find a better method to achieve the prediction of over-limit data. The data management model will be based on the experimental test of operation performance and energy consumption analysis in the process of storage and retrieval to further demonstrates the scheme proposed in this paper. And the consensus algorithm applied in the alliance chain is optimized to improve the performance of data transmission and retrieval.

6. Conclusion

Blockchain technology and deep learning are applied to the mine filling process. The over-limit early warning system of mine filling equipment based on the blockchain proposed in this paper achieves the functions of over-limit early warning, data storage, and retrieval of equipment operation. The over-limit early warning model applies transfer learning to detect and warn of the over-limit data of the equipment during the operation of the mine filling system. The experimental results show that the accuracy of this method is high, and it can predict abnormal conditions in the operation of a mine filling system in advance. Based on the combination of the blockchain and the IPFS, the monitoring data are stored, which reduces the size of the blockchain and improves the performance of data transmission and retrieval. The alliance chain is used to standardize the functions of administrators and users. The dispatcher is responsible for storing and retrieving data. The inspector verifies the trust of the dispatcher. The user obtains the monitoring data from the dispatcher by means of data access credentials to ensure the security of the data retrieval process.

The application of the blockchain in a mine filling system gives the mine database the characteristics of being tamper-proof and decentralized, and it ensures security in the process of mine data storage and transmission. The application of deep learning models can predict the over-limit in the operation of equipment in advance and ensure safety in the operation of mine equipment. In the future work, it will be further combined with the actual mine filling process, deeply research the blockchain data management model, optimize the consensus algorithm, and improve the performance of monitoring data transmission and retrieval.”

 

We also add research literature on mine filling systems to improve background. It is modified in Section 1 (lin35~37). The details and the literature(lin454-459) are as follows,

“At present, the filling mining method has been popularized in China's major mines, the environmental protection of filling slurry and the visualization of slurry pipeline transportation have been studied in depth[4-6].”

“4. Chen Qiusong, Sun Shiyuan, Wang Yunming, et al, In-situ remediation of phosphogypsum in a cement-free pathway: Utilization of ground granulated blast furnace slag and NaOH pretreatment. Chemosphere, 2022, 137412.

5. Chen Qiusong, Sun Shiyuan, Wang Yunming, et al, The carbon uptake and mechanical property of cemented paste backfill carbonation curing for low concentration of CO2. Science of The Total Environment, 2022, 852: 158516.
6. Qin Xuebin, Shen Yutong, Li Mingqiao, et al, Visualization detection of slurry transportation pipeline based on electrical capacitance tomography in mining filling. Journal of Central South University, 2022, 29(11):3757-3766.”

 

We would love to thank you for allowing us to resubmit a revised copy of the manuscript. And we appreciate for Editors/Reviewers’ warm work earnestly, and hope that the correction will meet with approval.

 

Once again, thank you very much for your comments and suggestions.

 

If there is anything else we should do, please do not hesitate to let us know.

 

Best Regards.

 

Yours sincerely,

 

Jingtao Huo

Author Response File: Author Response.docx

Reviewer 2 Report

Acceptable for publication.

Author Response

Dear Editors and Reviewers:

 

Thank you for your letter and for the Reviewers' comments concerning our manuscript entitled “Research on Monitoring and Early Warning of Mine Backfill System based on Blockchain Technology” (ID: applsci-2090166).

 

We are very honored to receive your recognition of this work.

 

Once again, thank you very much for your comments and suggestions to improve the quality of the manuscript.

 

Best Regards.

 

Yours sincerely,

 

Jingtao Huo

Author Response File: Author Response.docx