Energy consumption data security sharing method for coal mine energy management

Abstract

The invention discloses an energy consumption data security sharing method for coal mine energy management, which comprises the following steps: deploying an intelligent sensor and an edge computing unit in coal mine equipment, and carrying out real-time acquisition and preliminary anomaly detection on energy consumption data; differential compression processing is carried out on the detected data, encryption is carried out by adopting a mode of combining AES-GCM with a random one-time session key, and an integrity check code is generated at the same time; storing the encrypted data and the integrity check code in a distributed database, and recording a hash value of the encrypted data and an access control strategy in a block chain; dynamically adjusting the access authority based on the access control model of the role and the user behavior record; encrypted data are shared among authorized users through a secure transmission protocol, and traceability and abnormal operation monitoring are carried out on an access process. The coal mine energy consumption data sharing process is more transparent and safer, and powerful support is provided for guaranteeing compliance and safety in a large-scale user access environment.

Description

Technical Field

[0001] The present invention relates to the technical field of coal mine energy management and Internet of Things data security, and in particular to a method for securely sharing energy consumption data for coal mine energy management. Background Art

[0002] With the continuous expansion of coal mine production scale and the gradual improvement of automation, the demand for real-time monitoring and refined management of energy consumption data has become increasingly prominent. Traditional coal mine equipment energy consumption management usually relies on manual meter reading or simple data collection, which is difficult to meet the current high standards for data accuracy and security.

[0003] In recent years, with the widespread application of emerging technologies such as the Internet of Things (IoT), edge computing and blockchain, coal mine energy management has gradually developed in the direction of distribution, intelligence and traceability, and a variety of solutions integrating sensor networks and distributed ledger technologies have emerged; in this process, how to efficiently transmit, analyze and save huge energy consumption data has become a key challenge. Although some solutions can achieve data encryption and permission control, there are still many areas that need to be improved in dealing with real-time anomaly detection of large-scale heterogeneous data, ensuring data integrity and traceability, and combining edge computing for efficient processing.

[0004] Existing technologies only focus on a single link, such as using conventional symmetric encryption algorithms for data encryption, but ignoring the importance of data compression and anomaly identification at the front end; or only deploying access control policies on central servers, but lacking dynamic tracking of real-time user behavior, resulting in the inability to flexibly and timely adjust access rights; in addition, most of the current blockchain-based coal mine energy consumption data management solutions only upload basic transaction information to the chain, and fail to fully utilize distributed storage and smart contracts to deeply couple hash verification and user authorization processes, resulting in the risk of data tampering and insufficient audit transparency.

[0005] There is still much room for improvement in real-time verification and differential compression of large-scale coal mine energy consumption data on the collection side, high-security encryption and integrity verification on the transmission side, and dynamic authorization achieved by combining blockchain and behavioral scoring on the permission control side. It can be seen that the existing technical solutions are difficult to effectively balance system performance and security requirements in terms of front-end and back-end collaboration, heterogeneous data integration, and overall security auditing. Summary of the invention

[0006] The purpose of this section is to summarize some aspects of the embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the abstract of the specification and the title of the invention of this application to avoid blurring the purpose of this section, the abstract of the specification and the title of the invention, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0007] In view of the above existing problems, the present invention is proposed.

[0008] Therefore, the technical problem solved by the present invention is that the existing coal mine energy consumption data security sharing technology has the problem of being difficult to balance real-time anomaly detection, dynamic authority management and security traceability.

[0009] In order to solve the above technical problems, the present invention provides the following technical solutions: deploy intelligent sensors and edge computing units in coal mine equipment to collect energy consumption data in real time and perform preliminary anomaly detection;

[0010] The detected data is differentially compressed and encrypted using AES-GCM combined with a random one-time session key, while generating an integrity check code;

[0011] The encrypted data and the integrity check code are stored in a distributed database, and the hash value of the encrypted data and the access control policy are recorded in the blockchain;

[0012] Dynamically adjust access rights based on role-based access control models and user behavior records;

[0013] The encrypted data is shared among authorized users via a secure transmission protocol, and the access process is traced and abnormal operations are monitored.

[0014] As a preferred solution of the method for securely sharing energy consumption data for coal mine energy management described in the present invention, the real-time collection of energy consumption data includes:

[0015] Place several smart sensors on coal mining equipment;

[0016] Each sensor collects the energy consumption data periodically or continuously, and forms a raw data sequence through data preprocessing;

[0017] The energy consumption data includes current, voltage, power and flow.

[0018] As a preferred solution of the energy consumption data security sharing method for coal mine energy management described in the present invention, abnormality detection is performed on the original data sequence, including:

[0019] Splitting the original data sequence into multiple continuous windows according to a fixed time window, and performing statistical analysis on the data in each window respectively;

[0020] In the edge computing unit, a simplified model based on sliding average and coefficient of variation threshold is used to score the data in each window for abnormality, and the abnormality score Score is obtained. i .

[0021] As a preferred solution of the method for securely sharing energy consumption data for coal mine energy management described in the present invention, it also includes:

[0022] When the anomaly score Score i When the pre-set threshold is exceeded, the data is marked as suspicious and enters the secondary verification process;

[0023] When the anomaly score Score i If the value does not exceed the preset threshold, the data is marked as normal data and directly enters the next step of differential compression processing;

[0024] Among them, the secondary verification process is manual review.

[0025] As a preferred solution of the energy consumption data secure sharing method for coal mine energy management described in the present invention, the differential compression processing includes:

[0026] D i =S i -S i-1

[0027] Among them, S i is the i-th data, D i is the difference between the i-th data and the previous data.

[0028] As a preferred solution of the method for securely sharing energy consumption data for coal mine energy management described in the present invention, it includes:

[0029] Configure a hardware random number generator in the edge computing unit to generate a one-time session key k and a random initialization vector IV;

[0030] The one-time session key k is only used in the current batch data encryption process, and is then discarded and regenerated in the next batch;

[0031] The data block obtained by differential compression is denoted as P, and the data block is encrypted using the AES-GCM algorithm;

[0032] After encryption and verification code generation are completed, the ciphertext C and verification code Tag are output.

[0033] As a preferred solution of the method for securely sharing energy consumption data for coal mine energy management described in the present invention, the dynamic adjustment of access rights includes:

[0034] Several roles are pre-set in the system, each role corresponds to different access rights ranges, data operation levels, and executable functions;

[0035] When users register or access the system for the first time, they are assigned a corresponding role and registered in the blockchain smart contract;

[0036] The system continuously collects user behavior records when accessing data, submitting analysis results, or performing management operations. Each behavior record contains at least access time, operation type, and whether abnormal information is triggered;

[0037] Abnormal access times or suspicious behaviors are used as negative indicators, normal access times are used as positive indicators, and the user's current comprehensive trustworthiness is calculated based on a weighted summation mechanism. u ;

[0038] If the comprehensive trustworthiness Trust u The higher the value, the more credible the user's historical behavior is; otherwise, the less credible it is, that is, if there are multiple abnormal operations, the credibility is low;

[0039] When the user's trustworthiness is detected u When it falls below the trusted threshold, the smart contract automatically reduces the user's role permission level in the RBAC model. If the user maintains good behavior for a long time, the scope of access will be gradually increased.

[0040] As a preferred solution of the energy consumption data security sharing method for coal mine energy management described in the present invention, the comprehensive trustworthiness Trust is calculated. u The mathematical expression formula is:

[0041]

[0042] Among them, action u,j represents the credibility score of user u’s j-th access behavior, w j is the weight of the corresponding behavior, and n represents the number of behaviors.

[0043] Beneficial effects of the present invention:

[0044] 1. Reduce the large-scale transmission of invalid data, reduce the storage and computing load of the center, improve the overall data quality, and lay a reliable foundation for subsequent differential compression and secure sharing;

[0045] 2. While meeting the needs of high-efficiency transmission and large-scale data processing, it also ensures the two core security requirements of data encryption and integrity verification, thus providing reliable security guarantees for subsequent data storage and access;

[0046] 3. Ensure that energy consumption data is safe, reliable, and traceable during large-scale distribution and storage, greatly reducing the risks caused by single point failures or malicious tampering;

[0047] 4. While ensuring reasonable data sharing, it can minimize the risk of data leakage and realize flexible and adjustable security mechanisms to adapt to various user scenarios and security level requirements at the coal mine site;

[0048] 5. Based on the linkage of secure transmission protocols and audit mechanisms, the sharing process of coal mine energy consumption data is more transparent and secure, and can locate and deal with sudden safety incidents in a timely manner, providing strong support for ensuring compliance and security in large-scale user access environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0050] Figure 1 It is a flow chart of a method for securely sharing energy consumption data for coal mine energy management shown in the present invention;

[0051] Figure 2 Schematic diagram for comparing experimental indicators of the traditional method and the method of the present invention. DETAILED DESCRIPTION

[0052] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, but not all of the embodiments.

[0053] Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without making any creative work should fall within the scope of protection of the present invention.

[0054] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0055] According to an embodiment of the present invention, Figure 1 The flowchart shown in the figure is a method for securely sharing energy consumption data for coal mine energy management, which specifically includes the following steps:

[0056] S1. Deploy smart sensors and edge computing units in coal mine equipment to collect energy consumption data in real time and conduct preliminary anomaly detection. The following points need to be explained in this step:

[0057] Several intelligent sensors are placed on coal mine equipment (such as conveyor belts, coal mine ventilation systems, and power equipment). Each sensor regularly or continuously collects energy consumption data (such as current, voltage, power, and flow) to form a raw data sequence {x1, x2, …, x n};

[0058] Deploy edge computing units between sensors and backend servers to perform preliminary data processing close to the data source, thereby reducing the load on the central server and reducing network bandwidth usage;

[0059] Furthermore, anomaly detection is performed on the raw data collected by the sensor:

[0060] Split the original data sequence into multiple continuous intervals (windows) according to a fixed time window, and perform statistical analysis on the data in each window;

[0061] In the edge computing unit, a simplified model based on sliding average and coefficient of variation threshold is used to score the abnormality of the data in each window, which is recorded as Score. i ;

[0062] For example, for the i-th data point x i , the calculation formula is:

[0063]

[0064] in, represents the average value of all data in the current time window, σ win It represents the standard deviation of the data in the current time window, and α is an adjustable sensitivity coefficient, which is used to control the amplification or reduction of the degree of abnormality;

[0065] When Score i When the data exceeds the preset threshold, it is considered suspicious and enters the secondary verification process (such as manual review);

[0066] Data that is not marked as suspicious directly enters the next step (S2) of differential compression processing;

[0067] It is not difficult to understand that data marked as abnormal or suspicious is temporarily stored in an isolated cache, awaiting subsequent manual inspection, while the rest of the normal data is considered clean data and is directly passed to the next stage for differential compression processing.

[0068] After completing the above steps, the output result is a batch of qualified energy consumption data {S1, S2, …, S m}, and complete the isolation and temporary storage of abnormal data in the edge computing unit.

[0069] As an example, a large coal mine has about 100 large equipment (such as coal mine ventilators, electric drive devices, and mine hoists), which are distributed in several tunnels and control centers. Five sensors (including current, voltage, temperature, vibration, and flow types) are arranged at the key parts of each device, totaling 500 sensors. Each sensor collects raw data every 1 second and packages and transmits it once every minute. As a result, about 30,000 energy consumption or status-related data can be generated every minute (500 sensors × 60 seconds = 30,000 / minute). In addition, several edge computing servers (or industrial PCs) are deployed in the main tunnel control center and branch tunnels to process sensor data in real time.

[0070] For example, each edge computing unit takes 1 minute as a basic processing cycle. Within this 1 minute, it receives data from all sensors in the area, totaling about 3,000 to 5,000 items. For example, for temperature and current time series data, every 10 seconds is set as a small window. Each window contains about 500 data points. In each 10-second window, the average value is calculated. With standard deviation σ win ;

[0071] For example, in a window of 500 current data, x win =220A,σ win =5A, set α=2 as the sensitivity coefficient, if a data x i Score i If it exceeds the preset threshold (such as 5), it is judged as abnormal.

[0072] S2. Perform differential compression on the detected data, encrypt it using AES-GCM combined with a random one-time session key, and generate an integrity check code. The following points need to be explained in this step:

[0073] Taking the filtered qualified energy consumption data S1 as the benchmark, perform differential calculation on the subsequent data to obtain the differential sequence {D2, D3, …, D m}, illustratively, its mathematical calculation formula is:

[0074] D i =S i -S i-1

[0075] Among them, S i is the i-th data, D i is the difference between the i-th data and the previous data;

[0076] At the same time, the system retains data S1 for differential restoration processing. Exemplarily, the differential restoration process is:

[0077] S i =D i +S i-1

[0078] It is not difficult to understand that this differential method can effectively reduce data redundancy and occupy less space and bandwidth in the subsequent database and transmission process;

[0079] In order to further improve security and anti-attack capabilities, the embodiment of the present invention introduces a random one-time session key to encrypt data;

[0080] A hardware random number generator (RNG) or a security chip (such as a hardware security module HSM) is configured in the edge computing unit to generate high-quality random numbers for generating a one-time session key k and a random initialization vector IV;

[0081] The one-time session key k is only used in the encryption process of the current batch of data, and is then discarded and regenerated in the next batch to reduce the risk of subsequent data caused by key leakage;

[0082] The data block obtained by differential compression is recorded as P, and the data block is encrypted using the AES-GCM algorithm. For example, the encryption processing formula is:

[0083] C,Tag=AES-GCM k (IV,P)

[0084] Among them, C represents the encrypted ciphertext, Tag represents the message verification code (integrity check code) automatically generated by the AES-GCM algorithm during the encryption process, and AES-GCM k (IV, P) represents the process of encrypting the plaintext data P in AES-GCM mode and generating the verification tag using the session key k and the random initialization vector IV;

[0085] After the encryption and verification code generation are completed, the ciphertext C and the verification code Tag are used as the output results of step S2, and the next step S3 is entered for storage and hash calculation.

[0086] S3, storing the encrypted data and the integrity check code in a distributed database, and recording the hash value of the encrypted data and the access control policy in the blockchain. Among them, what needs to be explained in this step is:

[0087] Write the ciphertext C and the check code Tag output in step S2 into a scalable distributed database (such as a NoSQL database);

[0088] The system generates a corresponding storage path identifier Storage_ID to index or quickly locate the database node where the batch of ciphertext data is located;

[0089] Perform a one-way hash operation on the ciphertext C to obtain a hash value H. For example, the calculation formula is:

[0090] H=Hash(C)

[0091] Where Hash(·) is a secure hash algorithm (such as SHA-256) to ensure that the hash value is unique and collision-resistant;

[0092] In the smart contract deployed on the blockchain, the Storage_ID, hash value H, and initial access control policy (such as role authority level, access time limit) are written into the blockchain together to ensure that during subsequent access, each user can perform consistency verification against the hash value on the blockchain and the ciphertext stored in the database.

[0093] It should be noted that the distributed database only stores encrypted data and verification codes, while the blockchain stores key metadata such as access policies and hash values, and the two are indexed and mapped through Storage_ID.

[0094] S4. Dynamically adjust access rights based on role-based access control model and user behavior records.

[0095] Among them, what needs to be explained in this step is:

[0096] Set up several roles in the system in advance (such as "field operation and maintenance personnel", "data analyst", "coal mine safety inspector", "system administrator"), each role corresponds to a different access permission range, data operation level, and executable function;

[0097] When users register or access the system for the first time, they are assigned a corresponding role and registered in the blockchain smart contract;

[0098] The system continuously collects user behavior records when accessing data, submitting analysis results, or performing management operations. Each behavior record contains at least information about the access time, operation type, and whether an exception is triggered.

[0099] Abnormal access times or suspicious behaviors are used as negative indicators (penalty factors), normal access times are used as positive indicators (reward factors), and the user’s current comprehensive trustworthiness is calculated based on a weighted summation mechanism. u :

[0100]

[0101] Among them, action u,j represents the credibility score of user u’s jth behavior (access, operation), w j is the weight of the corresponding behavior, n represents the number of behaviors;

[0102] Comprehensive credibility Trust u The higher the value, the more credible the user's historical behavior is. Conversely, the less credible it is, that is, if there are multiple abnormal operations, the credibility is low.

[0103] When a user's trustworthiness is detected u When it drops below the trusted threshold (such as 0.5), the smart contract automatically reduces the user's role permission level in the RBAC model (role-based access control model). If a user maintains good behavior for a long time, his access scope will be gradually increased or the secondary verification steps will be reduced.

[0104] It should be noted that the above process is triggered and recorded in conjunction with the smart contract event mechanism on the blockchain to ensure the transparency and traceability of permission changes. After this step is completed, the latest permission configuration will be updated to the next step for secure data transmission and sharing.

[0105] S5. Share the encrypted data among authorized users via a secure transmission protocol, and trace the access process and monitor abnormal operations.

[0106] When a user requests access to energy consumption data from the system, the server will confirm the legitimacy of the access based on the user information and permission level on the blockchain. If the initial verification is passed, the two parties will generate a new temporary session key k through the elliptic curve key exchange algorithm (such as ECDH). ′ ;

[0107] The server uses Storage_ID to retrieve the corresponding ciphertext C and check code Tag from the distributed database, uses the session key k′ obtained in this negotiation to perform AES-GCM decryption on the ciphertext C, and performs integrity verification on the check code Tag (to ensure that the ciphertext has not been tampered with during transmission and storage);

[0108] If the decryption and integrity check are both passed, the server will send the decrypted energy consumption data to the user through a secure connection channel (HTTPS or TLS1.3);

[0109] At the same time, the system records the metadata of this access (timestamp, user role, credibility, access scope) and writes it back to the blockchain to form a complete access log chain;

[0110] If suspicious operations are detected during the transmission process or user use stage, an alert will be automatically triggered and a secondary verification (such as SMS or mobile phone token verification) will be forced on the next access;

[0111] If the frequency of a user's abnormal behavior continues to increase, the smart contract will automatically freeze or downgrade the user's access rights.

[0112] In an optional implementation, at the three key components of the edge computing unit, distributed database, and blockchain network, operation logs, encryption and decryption time, throughput, and anomaly detection indicators are collected, and visual analysis tools are used to count the delays, resource utilization, and data error rates of each link, and automatic load balancing is performed on the CPU and memory resources; when a node failure or network performance degradation is detected, an alarm is automatically triggered, and node expansion or traffic redistribution is performed at the smart contract layer to ensure the continuous stable operation and safe sharing of coal mine energy consumption data; after completing the above-mentioned traceability and monitoring, the entire system is periodically iterated and updated to form an adaptive security sharing platform to ensure that energy consumption data is still safe, reliable, and traceable in large-scale distribution and storage, reducing the risks caused by single point failures or malicious tampering.

[0113] In order to verify the authenticity of the beneficial effects of the method of the present invention in coal mine energy consumption data collection, anomaly detection, differential compression encryption, blockchain access control and security sharing processes, this embodiment is compared with the traditional method (relying on a central server for simple data collection and a fixed authority management mode), which can more intuitively show the effectiveness of the present invention in terms of differential compression rate, encryption and decryption time consumption, anomaly detection false alarm rate and dynamic role adjustment frequency;

[0114] Traditional methods only rely on central servers for simple data collection and fixed permission management modes. They lack differential compression and dynamic management of user roles, and fail to perform preprocessing on the edge. Such defects lead to redundant data occupying bandwidth, low efficiency of encryption and decryption, inability to filter abnormal data in time, and single permission control, making it difficult to balance large-scale real-time data management and security requirements.

[0115] Therefore, this experiment hopes to verify that the energy consumption data security sharing method for coal mine energy management proposed in the embodiment of the present invention can overcome the defects of the traditional method in actual application scenarios by comparing the above key indicators;

[0116] Specifically, six devices in the coal mine (marked as Device A, B, C, D, E, and F) were selected as the main test objects. Each device was equipped with 5 to 10 sensors for collecting energy consumption or status information of voltage, current, vibration, and temperature. An edge computing unit (industrial PC) was deployed near each device, configured with a 2.0GHz multi-core CPU and 4GB RAM. The central server has higher computing and storage capabilities for the management of distributed databases and blockchain nodes. Gigabit industrial Ethernet was used between the edge computing unit and the central server, with a delay of 5 to 10 milliseconds and sufficient bandwidth. However, occasional jitter may occur due to the influence of the mine environment. A lightweight security library supporting AES-GCM encryption and a real-time anomaly detection algorithm were deployed on the edge side. A NoSQL distributed database and a consortium blockchain network were built on the central side, and the access control model was implemented in the form of smart contracts.

[0117] Traditional method implementation process: collect raw data from each device once per second, perform simple symmetric encryption on the central server, use the same key before each encryption, adopt fixed role settings (such as administrator, visitor), and record the access process in a simple log;

[0118] The implementation process of the method of the present invention is as follows: on the edge side, a sliding average and coefficient of variation test is performed on each piece of sensor data, suspicious data is marked separately, and the next step of processing is performed only on normal data. Real-time or batch data is first differentially compressed, and then encrypted using AES-GCM combined with a random one-time session key and an integrity check code is generated. The ciphertext and the check code are stored in a NoSQL database, and the hash value and access control policy (based on smart contracts and role definitions) are registered on the blockchain at the same time. The role authority is automatically upgraded or reduced according to the user behavior scoring mechanism, and data is shared through a secure transmission protocol;

[0119] Collect experimental data and calculate experimental indicators:

[0120] Differential compression ratio: Under the same amount and duration of data collection conditions, compare the reduction ratio of data volume (KB or MB) after using differential compression and without using differential compression;

[0121] Encryption and decryption time: measures the time (in milliseconds) from data packaging to ciphertext generation and decryption and restoration, including the AES-GCM algorithm and one-time session key distribution;

[0122] Anomaly detection false alarm rate: within a certain period (such as 12 hours), the false alarm rate of the preliminary anomaly detection algorithm is counted against the actual situation of the sensor data;

[0123] Frequency of dynamic role adjustment: whether the smart contract changes the user role permissions under different loads or different user access scenarios, and the number of changes.

[0124] Reference Figure 2 In the figure, the dotted line represents the trend of the traditional method, and the solid line represents the trend of the method of the present invention. Figure 2 As shown in the figure, in terms of differential compression rate, the traditional method is basically equal to 0%, indicating that it does not use differential algorithm and the data volume is huge, while the method of the present invention is generally above 30%, and can reach up to 36%, which significantly reduces the pressure of data transmission and storage, and helps to reduce the load of the central server and the network;

[0125] In terms of encryption and decryption time consumption, in the AES-CBC mode, the method of the present invention combines multiple data processing at the center side, and the average time consumption is concentrated at about 18 milliseconds, thereby improving data processing efficiency;

[0126] In terms of anomaly detection false alarm rate, the traditional method has no real-time screening mechanism on the edge side, ignores anomaly detection or is based on offline processing, resulting in a false alarm rate of up to 8% to 12% in this experiment. In the case of many devices and complex data, it will consume additional manpower and computing resources. In contrast, the method of the present invention uses an effective sliding detection mechanism on the edge side, so that the false alarm rate can be controlled within the range of 3% to 6%, and obviously distorted data can be removed in time, thereby improving the accuracy of subsequent data analysis and processing.

[0127] In terms of the frequency of dynamic role adjustment, the traditional method lacks real-time analysis and management of user roles, and only has fixed permissions or manual adjustments. The method of the present invention can quickly respond to changes in role permission requirements when multiple shifts are switched or special tasks are requested, making the safe sharing of coal mine energy consumption data more targeted and avoiding resource abuse and unauthorized risks.

[0128] The method of the present invention performs data preprocessing and encryption on the edge side, combines distributed database and blockchain for storage and permission adjustment on the center side, and applies a secure transmission protocol to achieve sharing. It shows better performance in key performance indicators, especially in improving data processing efficiency, reducing network redundancy, strengthening security traceability, and supporting flexible role access management.

[0129] The data preprocessing method of the aforementioned original data, the writing and consistency verification method of the blockchain can be carried out using methods and means in the existing technology and will not be repeated in this example.

[0130] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for securely sharing energy consumption data for coal mine energy management, characterized in that: include: Deploy smart sensors and edge computing units in coal mine equipment to collect energy consumption data in real time and conduct preliminary anomaly detection; The detected data is differentially compressed and encrypted using AES-GCM combined with a random one-time session key, while generating an integrity check code; The encrypted data and the integrity check code are stored in a distributed database, and the hash value of the encrypted data and the access control policy are recorded in the blockchain; Dynamically adjust access rights based on role-based access control models and user behavior records; The encrypted data is shared among authorized users via a secure transmission protocol, and the access process is traced and abnormal operations are monitored.

2. The method for securely sharing energy consumption data for coal mine energy management according to claim 1, characterized in that: The real-time collection of energy consumption data includes: Place several smart sensors on coal mining equipment; Each sensor collects the energy consumption data periodically or continuously, and forms a raw data sequence through data preprocessing; The energy consumption data includes current, voltage, power and flow.

3. The method for securely sharing energy consumption data for coal mine energy management according to claim 2, characterized in that: Performing anomaly detection on the original data sequence includes: Splitting the original data sequence into multiple continuous windows according to a fixed time window, and performing statistical analysis on the data in each window respectively; In the edge computing unit, a simplified model based on sliding average and coefficient of variation threshold is used to score the data in each window for abnormality, and the abnormality score Score is obtained. i .

4. The method for securely sharing energy consumption data for coal mine energy management according to claim 3 is characterized in that: Also includes: When the anomaly score Score i When the pre-set threshold is exceeded, the data is marked as suspicious and enters the secondary verification process; When the anomaly score Score i If the value does not exceed the preset threshold, the data is marked as normal data and directly enters the next step of differential compression processing; Among them, the secondary verification process is manual review.

5. The method for securely sharing energy consumption data for coal mine energy management according to claim 1 or 4, characterized in that: The differential compression process comprises: D i =S i -S i-1 Among them, S i is the i-th data, D i is the difference between the i-th data and the previous data.

6. The method for securely sharing energy consumption data for coal mine energy management according to claim 1, characterized in that: AES-GCM is used to encrypt data in combination with a random one-time session key, and an integrity check code is generated at the same time, including: Configure a hardware random number generator in the edge computing unit to generate a one-time session key k and a random initialization vector IV; The one-time session key k is only used in the current batch data encryption process, and is then discarded and regenerated in the next batch; The data block obtained by differential compression is denoted as P, and the data block is encrypted using the AES-GCM algorithm; After encryption and verification code generation are completed, the ciphertext C and verification code Tag are output.

7. The method for securely sharing energy consumption data for coal mine energy management according to claim 1, characterized in that: The dynamically adjusting the access rights includes: Several roles are pre-set in the system, each role corresponds to different access rights ranges, data operation levels, and executable functions; When users register or access the system for the first time, they are assigned a corresponding role and registered in the blockchain smart contract; The system continuously collects user behavior records when accessing data, submitting analysis results, or performing management operations. Each behavior record contains at least access time, operation type, and whether abnormal information is triggered; Abnormal access times or suspicious behaviors are used as negative indicators, normal access times are used as positive indicators, and the user's current comprehensive trustworthiness is calculated based on a weighted summation mechanism. u ; If the comprehensive trustworthiness Trust u The higher the value, the more credible the user's historical behavior is; otherwise, the less credible it is, that is, if there are multiple abnormal operations, the credibility is low; When the user's trustworthiness is detected u When it falls below the trusted threshold, the smart contract automatically reduces the user's role permission level in the RBAC model. If the user maintains good behavior for a long time, the scope of access will be gradually increased.

8. The method for securely sharing energy consumption data for coal mine energy management according to claim 7, characterized in that: Calculate the comprehensive trustworthiness Trust u The mathematical expression formula is: Among them, action u,j represents the credibility score of user u’s j-th access behavior, w j is the weight of the corresponding behavior, and n represents the number of behaviors.

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