Tamper-proof major hazard source monitoring method based on identifier analysis

Abstract

The invention discloses a tamper-proof major hazard source monitoring method based on identification analysis. According to the invention, through data analysis and monitoring and setting of a tamper-proof mechanism, comprehensive monitoring and data security protection of a dangerous source are realized, so that a remarkable comprehensive effect is brought. According to the method and the system, the readability and the practicability of the data are improved, the accuracy and the real-time performance of the data are ensured through a self-adaptive analysis rule and a real-time monitoring technology, a tamper-proof mechanism enhances the safety of the data in the transmission process through data encryption and digital signature, and the integrity and the authenticity of the data are ensured. Dynamic threshold adjustment and risk probability based alarm systems help more accurately identify and cope with potential security threats. The dangerous source monitoring efficiency is improved, the safety, reliability and practicability of the system are enhanced, powerful technical support is provided for dangerous source safety management, continuous optimization and improvement of the monitoring method are facilitated, and the dangerous source safety management level is continuously improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of hazard source monitoring, and specifically is a tamper-proof major hazard source monitoring method based on identification resolution. Background Art

[0002] Major hazard source monitoring refers to the systematic work of identifying, evaluating, monitoring and managing potential hazard sources that may cause major casualties, property losses and environmental damage. This work plays a vital role in preventing and reducing major accidents. The main contents of major hazard source monitoring include: Hazard source identification: Identify the hazard sources that may cause accidents through safety evaluation, risk assessment and other means, such as the storage tank area of ​​chemical enterprises, the use of toxic and hazardous substances, etc. Risk assessment: Quantitative or qualitative evaluation of the identified hazard sources to determine the degree of harm and scope of impact they may cause. Monitoring measures: According to the results of risk assessment, formulate corresponding monitoring measures, including technical measures, management measures and emergency measures. Real-time monitoring: Use sensors, cameras and other equipment to monitor major hazard sources in real time 24 hours a day to ensure timely detection of abnormal situations. Information management: Establish a major hazard source information database to realize centralized management and sharing of information and provide support for decision-making. Emergency plans and organize emergency drills to ensure that they can respond quickly and effectively when accidents occur.

[0003] However, traditional data parsing methods may rely on predefined rules. When the data format or content changes, the rules may need to be manually updated, which may lead to parsing errors or delays. At the same time, existing monitoring systems may lack effective security audit mechanisms, resulting in uncertainty and risks in system operation. Summary of the invention

[0004] The purpose of the present invention is to provide a tamper-proof major hazard source monitoring method based on identification resolution in order to solve the above-mentioned problems.

[0005] The technical solution adopted by the present invention is as follows: a method for monitoring major tamper-proof hazard sources based on identification resolution, the method comprising the following steps:

[0006] S1: Identify and classify hazardous sources and determine monitoring targets: Identify and classify major hazardous sources, including chemical companies, oil and gas pipelines, and nuclear facilities;

[0007] S2: Build an identification resolution system, select DNS identification resolution technology to build an identification resolution server: deploy identification resolution software to ensure stable operation of the server; configure resolution rules: set resolution rules according to the characteristics of the hazard source, ensure that the identification code corresponds to the hazard source information one by one, and set corresponding thresholds according to the risk level of the hazard source;

[0008] S3: Install sensors for data collection and transmission, and install various sensors at the hazardous source site, including temperature, pressure, and flow;

[0009] S4: Perform data analysis and monitoring. After receiving the data, the identification analysis server analyzes the data into readable information according to the preset analysis rules, and then sets the threshold and alarms. When the monitoring data exceeds the threshold, the system automatically alarms;

[0010] S5: Set up anti-tampering mechanism, perform data encryption and digital signature, and conduct security audits on the system regularly to ensure safe and reliable operation of the system;

[0011] S6: Set up emergency response and disposal, and formulate emergency plans: formulate corresponding emergency plans for different types of hazardous sources;

[0012] S7: Continuous optimization and improvement, data analysis: Regularly analyze the monitoring data to identify potential risks, and then end the entire anti-tampering major hazard source monitoring process based on identity resolution.

[0013] In a preferred embodiment, in step S1, a globally unique identification code is assigned to each hazard source, which is one of a two-dimensional code or an RFID.

[0014] Establish a hazard source database: enter the basic information, geographical location, and risk level data of the hazard source into the database.

[0015] In a preferred embodiment, in step S5, the data encryption process is as follows: select an initial value IV. Encrypt the IV using AES to obtain the first ciphertext block. XOR the plaintext with the ciphertext block to obtain the ciphertext. Increment the IV and repeat the above steps. The decryption process of the AES-CTR variant is as follows: use the same IV and key to perform AES encryption to obtain a ciphertext block. XOR the ciphertext with the ciphertext block to obtain the plaintext. Increment the IV and repeat the above steps.

[0016] The encryption process of the AES-GCM variant is:

[0017] S1. Select an initial value IV.

[0018] S2. Use AES to encrypt the plaintext to obtain the ciphertext.

[0019] S3. Calculate the GCM tag (GMAC) for data integrity verification.

[0020] S4. Transmit the IV, ciphertext and GCM tag together.

[0021] The decryption process of S5.AES-GCM variant is:

[0022] S6. Separate the IV, ciphertext, and GCM tag from the received data.

[0023] S7. Use AES to decrypt the ciphertext to obtain the plaintext.

[0024] S8. Recalculate the GCM tag and compare it with the received GCM tag to ensure data integrity.

[0025] In step S4, the AES encryption formula is:

[0026] C=AES(K,P)

[0027] Among them, C is the ciphertext, K is the key, and P is the plaintext.

[0028] The AES decryption formula is:

[0029] P=AES^(-1)(K,C)

[0030] Among them, P is plaintext, K is the key, and C is the ciphertext;

[0031] The formula for the XOR operation in the AES-CTR variant is:

[0032] C_i=P_iXORAES(K,IV_i)

[0033] Where C_i is the i-th ciphertext block, P_i is the i-th plaintext block, and IV_i is the i-th IV value.

[0034] The GMAC calculation formula for the AES-GCM variant is:

[0035] GMAC=AES_GCM_MAC(K,A,C)

[0036] Where GMAC is the GCM tag, K is the key, A is the Additional Authentication Data (AAD), and C is the ciphertext.

[0037] In a preferred embodiment, in step S6, the digital signature process includes:

[0038] Signature generation: Data hash: Use a hash function to generate a fixed-length hash value for the data packet to be sent. Signature operation: Use the sender's private key to encrypt the hash value and generate a digital signature.

[0039] Signature attachment: Attach the generated digital signature to the data packet and send it to the identity resolution server.

[0040] Signature verification: Receiving data: After the identity resolution server receives the data packet, it separates the digital signature and data content. Hash verification: Use the same hash function to generate a new hash value for the data content. Signature decryption: Use the sender's public key to decrypt the digital signature and obtain the original hash value. Hash comparison: Compare the decrypted hash value with the newly generated hash value. If the two are the same, the data integrity is verified; otherwise, the data may have been tampered with.

[0041] In a preferred embodiment, in step S6, the security audit process includes:

[0042] Audit strategy formulation: Develop an audit strategy based on the safety requirements of the hazard source monitoring system, including audit frequency, audit content, and audit responsible persons.

[0043] Logging: The system automatically records all security-related events, including user logins, data access, system configuration changes, and alarm events, in log files.

[0044] Log collection: Log files are collected regularly to ensure the integrity and immutability of the logs.

[0045] Audit analysis: Analyze log files to identify abnormal behaviors, potential security threats, and system vulnerabilities. Use audit tools to perform automated analysis and generate audit reports.

[0046] Report and feedback: The audit results are compiled into a report and submitted to the management and security team. Based on the problems found in the audit, improvement measures are proposed and the rectification status is tracked.

[0047] Continuous monitoring: Adjust monitoring strategies and system configurations based on audit results, and continuously monitor the security status of the system.

[0048] In a preferred embodiment, in step S4, data parsing uses a machine learning algorithm to automatically learn and optimize parsing rules to adapt to different types of data formats;

[0049] Let D be the original data, R be the parsing rule, A be the algorithm model, and D′ be the parsed data, then D′=A(D,R);

[0050] Data anomaly detection detects data anomalies in real time during the parsing process, and uses the Isolation Forest algorithm to identify outliers. Let L be the path length, c(n) be the subsampling size, and the anomaly score calculation formula is:

[0051] s(x,L)=2-L(x) / c(n)E[L].

[0052] In a preferred embodiment, in step S4, real-time monitoring is combined with AR technology to overlay real-time monitoring data in the field of vision of the monitoring personnel to improve the intuitiveness of monitoring, and the monitoring data is displayed in the form of virtual tags at the location corresponding to the hazard source using the AR device head-mounted display;

[0053] Real-time monitoring uses time series analysis and machine learning to predict future data trends and provide early warning of potential risks. Let Xt be the time series data, X^t+1 be the predicted value, and f be the prediction model, then X^t+1=f(Xt).

[0054] In a preferred embodiment, in step S4, the threshold setting and dynamic threshold adjustment in the alarm dynamically adjusts the threshold according to historical data and current environmental conditions: let T be the threshold, α be the adjustment coefficient, σ be the standard deviation of the data, then the dynamic threshold T′=T+ασ;

[0055] The alarm system based on risk probability in threshold setting and alarm uses Bayesian network to evaluate the risk probability of alarm events and determine the urgency of the alarm. Let P(E|D) be the probability of event E occurring under data D, and P(D) be the probability of data D. Then

[0056] P(E)=∑DP(E|D)P(D).

[0057] In a preferred embodiment, in step S4, the monitoring center displays various parameters of the dangerous source in real time, so that the monitoring personnel can grasp the on-site situation. At the same time, according to the risk level of the dangerous source, a corresponding threshold is set. When the monitoring data exceeds the threshold, the system automatically alarms to remind the monitoring personnel to take corresponding measures.

[0058] In a preferred embodiment, in step S7, the monitoring system and identification resolution technology are optimized and upgraded according to the data analysis results to improve the monitoring effect. At the same time, the training of relevant personnel is strengthened to improve safety awareness.

[0059] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0060] 1. In the present invention, data analysis and monitoring can automatically adjust the analysis rules according to data features through machine learning algorithms, which means that even if the data format changes, the system can adapt quickly without human intervention. At the same time, through the automated analysis process, the possibility of human error is reduced, and the accuracy and reliability of the data are improved. Real-time monitoring and early warning are combined with AR technology, so that monitoring personnel can intuitively see the monitoring data in the actual environment. This immersive experience can greatly improve the understanding and response speed of the hazardous source status. At the same time, using time series analysis and machine learning, the system can predict future data trends, which helps to intervene before potential problems become actual threats. Dynamic threshold adjustment enables the system to automatically adjust the monitoring standards according to actual conditions, which is particularly important for responding to emergencies and seasonal changes. The alarm system based on risk probability can more accurately identify which alarms require immediate response, thereby reducing unnecessary interference and improving response efficiency.

[0061] 2. In the present invention, an anti-tampering mechanism is set up to ensure that the data cannot be decrypted even if it is intercepted during transmission by using advanced encryption standards (such as AES), thereby protecting the data from being leaked. By securely generating, storing and rotating keys, the system further enhances the security of the data. Through digital signatures, the receiver can verify that the data has not been tampered with during transmission and that it does come from the claimed sender. The application of hash functions ensures the integrity of the data, and any tampering with the data will result in a change in the hash value, which will be detected. Security audits provide a detailed record of system operations, which helps to track potential security incidents and strengthen the system's defense mechanisms. Through regular security audits and transparent security measures, the system establishes a high degree of trust between users and managers, improves the anti-tampering ability of the method, and ensures the security and reliability of the overall method.

[0062] 3. In the present invention, comprehensive monitoring of hazardous sources and data security protection are achieved through data analysis and monitoring as well as the setting of anti-tampering mechanisms, thus bringing about significant comprehensive effects. First, this method improves the readability and practicality of the data, and ensures the accuracy and real-time nature of the data through adaptive parsing rules and real-time monitoring technology. Secondly, the anti-tampering mechanism enhances the security of data during transmission through data encryption and digital signatures, and ensures the integrity and authenticity of the data. In addition, dynamic threshold adjustment and the alarm system based on risk probability improve the flexibility and response efficiency of the system, and help to more accurately identify and respond to potential security threats. In general, this method not only improves the effectiveness of hazardous source monitoring, enhances the safety, reliability and practicality of the system, but also provides strong technical support for the safety management of hazardous sources, helps to continuously optimize and improve monitoring methods, and continuously improves the level of safety management of hazardous sources. DETAILED DESCRIPTION

[0063] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0064] Embodiment:

[0065] A method for monitoring major tamper-proof hazard sources based on identification resolution, the method comprising the following steps:

[0066] S1: Identify and classify hazardous sources and determine monitoring targets: Identify and classify major hazardous sources, such as chemical companies, oil and gas pipelines, nuclear facilities, etc.;

[0067] S2: Build an identification resolution system, select DNS identification resolution technology to build an identification resolution server: deploy identification resolution software to ensure stable operation of the server. Configure resolution rules: according to the characteristics of the hazard source, set resolution rules to ensure that the identification code corresponds to the hazard source information one by one, and set the corresponding threshold according to the risk level of the hazard source;

[0068] S3: Install sensors for data collection and transmission, and install various sensors at the hazardous source site, including temperature, pressure, and flow;

[0069] S4: Perform data analysis and monitoring. After receiving the data, the identification analysis server analyzes the data into readable information according to the preset analysis rules, and then sets the threshold and alarms. When the monitoring data exceeds the threshold, the system automatically alarms;

[0070] S5: Set up anti-tampering mechanism, perform data encryption and digital signature, and conduct security audits on the system regularly to ensure safe and reliable operation of the system;

[0071] S6: Set up emergency response and disposal, and formulate emergency plans: formulate corresponding emergency plans for different types of hazardous sources.

[0072] S7: Continuous optimization and improvement, data analysis: Regularly analyze the monitoring data to identify potential risks, and then end the entire anti-tampering major hazard source monitoring process based on identity resolution.

[0073] In step S1, a globally unique identification code is assigned to each hazard source, which is one of a two-dimensional code or an RFID.

[0074] Establish a hazard source database: enter the basic information, geographical location, risk level and other data of the hazard source into the database.

[0075] In step S5, the data encryption process is as follows: select an initial value IV (usually a random number). Encrypt the IV using AES to obtain the first ciphertext block. XOR the plaintext with the ciphertext block to obtain the ciphertext. Increment the IV (can be a counter or other means) and repeat the above steps. The decryption process of the AES-CTR variant is as follows: use the same IV and key to perform AES encryption to obtain a ciphertext block. XOR the ciphertext with the ciphertext block to obtain the plaintext. Increment the IV and repeat the above steps.

[0076] The encryption process of the AES-GCM variant is:

[0077] S1. Select an initial value IV.

[0078] S2. Use AES to encrypt the plaintext to obtain the ciphertext.

[0079] S3. Calculate the GCM tag (GMAC) for data integrity verification.

[0080] S4. Transmit the IV, ciphertext and GCM tag together.

[0081] The decryption process of S5.AES-GCM variant is:

[0082] S6. Separate the IV, ciphertext, and GCM tag from the received data.

[0083] S7. Use AES to decrypt the ciphertext to obtain the plaintext.

[0084] S8. Recalculate the GCM tag and compare it with the received GCM tag to ensure data integrity.

[0085] In step S4, the AES encryption formula is:

[0086] C=AES(K,P)

[0087] Among them, C is the ciphertext, K is the key, and P is the plaintext.

[0088] The AES decryption formula is:

[0089] P=AES^(-1)(K,C)

[0090] Among them, P is plaintext, K is the key, and C is the ciphertext;

[0091] The formula for the XOR operation in the AES-CTR variant is:

[0092] C_i=P_iXORAES(K,IV_i)

[0093] Where C_i is the i-th ciphertext block, P_i is the i-th plaintext block, and IV_i is the i-th IV value.

[0094] The GMAC calculation formula for the AES-GCM variant is:

[0095] GMAC=AES_GCM_MAC(K,A,C)

[0096] Where GMAC is the GCM tag, K is the key, A is the additional authentication data (AAD), and C is the ciphertext.

[0097] In step S6, the digital signature process includes:

[0098] Signature generation: Data hash: Use a hash function (such as SHA-256) to generate a fixed-length hash value for the data packet to be sent. Signature operation: Use the sender's private key to encrypt the hash value and generate a digital signature.

[0099] Signature attachment: Attach the generated digital signature to the data packet and send it to the identity resolution server.

[0100] Signature verification: Receiving data: After the identity resolution server receives the data packet, it separates the digital signature and data content. Hash verification: Use the same hash function to generate a new hash value for the data content. Signature decryption: Use the sender's public key to decrypt the digital signature and obtain the original hash value. Hash comparison: Compare the decrypted hash value with the newly generated hash value. If the two are the same, the data integrity is verified; otherwise, the data may have been tampered with.

[0101] In step S6, the security audit process includes:

[0102] Audit strategy formulation: Develop audit strategies based on the safety requirements of the hazard monitoring system, including audit frequency, audit content, and audit responsible persons.

[0103] Logging: The system automatically records all security-related events, including user logins, data accesses, system configuration changes, alarm events, etc., in log files.

[0104] Log collection: Log files are collected regularly to ensure the integrity and immutability of the logs.

[0105] Audit analysis: Analyze log files to identify abnormal behaviors, potential security threats, and system vulnerabilities. Use audit tools to perform automated analysis and generate audit reports.

[0106] Report and feedback: The audit results are compiled into a report and submitted to the management and security team. Based on the problems found in the audit, improvement measures are proposed and the rectification status is tracked.

[0107] Continuous monitoring: Adjust monitoring strategies and system configurations based on audit results, and continuously monitor the security status of the system.

[0108] In step S4, data parsing uses a machine learning algorithm to automatically learn and optimize parsing rules to adapt to different types of data formats;

[0109] Let D be the original data, R be the parsing rule, A be the algorithm model, and D′ be the parsed data, then D′=A(D,R);

[0110] Data anomaly detection detects data anomalies in real time during the parsing process, and uses the Isolation Forest algorithm to identify outliers. Let L be the path length, c(n) be the subsampling size, and the anomaly score calculation formula is:

[0111] s(x,L)=2-L(x) / c(n)E[L].

[0112] In step S4, real-time monitoring is combined with AR technology to overlay real-time monitoring data in the field of vision of the monitoring personnel to improve the intuitiveness of monitoring. The monitoring data is displayed in the form of virtual tags at the location corresponding to the hazard source using the AR device head-mounted display;

[0113] Real-time monitoring uses time series analysis and machine learning to predict future data trends and provide early warning of potential risks. Let Xt be the time series data, X^t+1 be the predicted value, and f be the prediction model, then X^t+1=f(Xt).

[0114] In step S4, the threshold setting and dynamic threshold adjustment in the alarm dynamically adjusts the threshold according to historical data and current environmental conditions: let T be the threshold, α be the adjustment coefficient, σ be the standard deviation of the data, then the dynamic threshold T′=T+ασ;

[0115] The alarm system based on risk probability in threshold setting and alarm uses Bayesian network to evaluate the risk probability of alarm events and determine the urgency of the alarm. Let P(E|D) be the probability of event E occurring under data D, and P(D) be the probability of data D. Then

[0116] P(E)=∑DP(E|D)P(D).

[0117] In step S4, the monitoring center displays various parameters of the dangerous source in real time, so that the monitoring personnel can grasp the on-site situation. At the same time, according to the risk level of the dangerous source, the corresponding threshold is set. When the monitoring data exceeds the threshold, the system automatically alarms and reminds the monitoring personnel to take corresponding measures.

[0118] In step S7, the monitoring system and identification resolution technology are optimized and upgraded according to the data analysis results to improve the monitoring effect. At the same time, the training of relevant personnel is strengthened to improve safety awareness, ensure the effective implementation of the monitoring method, and continuously improve the safety management level of hazardous sources.

[0119] In the present invention, data analysis and monitoring can automatically adjust the analysis rules according to data characteristics through machine learning algorithms, which means that even if the data format changes, the system can adapt quickly without human intervention. At the same time, through the automated analysis process, the possibility of human error is reduced and the accuracy and reliability of the data are improved. Real-time monitoring and early warning are combined with AR technology, so that monitoring personnel can intuitively see the monitoring data in the actual environment. This immersive experience can greatly improve the understanding and response speed of the hazardous source status. At the same time, using time series analysis and machine learning, the system can predict future data trends, which helps to intervene before potential problems become actual threats. Dynamic threshold adjustment enables the system to automatically adjust the monitoring standards according to actual conditions, which is particularly important for responding to emergencies and seasonal changes. The alarm system based on risk probability can more accurately identify which alarms require immediate response, thereby reducing unnecessary interference and improving response efficiency.

[0120] In the present invention, an anti-tampering mechanism is set up to ensure that the data cannot be decrypted even if it is intercepted during transmission by using an advanced encryption standard (such as AES), thereby protecting the data from being leaked. By securely generating, storing and rotating keys, the system further enhances the security of the data. Through digital signatures, the recipient can verify that the data has not been tampered with during transmission and is indeed from the claimed sender. The application of hash functions ensures the integrity of the data, and any tampering with the data will result in a change in the hash value, which is detected. Security audits provide a detailed record of system operations, which helps to track potential security incidents and strengthen the system's defense mechanisms. Through regular security audits and transparent security measures, the system establishes a high degree of trust between users and managers, improves the anti-tampering ability of the method, and ensures the safety and reliability of the overall method.

[0121] In the present invention, comprehensive monitoring of hazardous sources and data security protection are achieved through data analysis and monitoring as well as setting up an anti-tampering mechanism, thereby bringing about a significant comprehensive effect. First, this method improves the readability and practicality of the data, and ensures the accuracy and real-time nature of the data through adaptive parsing rules and real-time monitoring technology. Secondly, the anti-tampering mechanism enhances the security of data during transmission through data encryption and digital signatures, and ensures the integrity and authenticity of the data. In addition, dynamic threshold adjustment and an alarm system based on risk probability improve the flexibility and response efficiency of the system, and help to more accurately identify and respond to potential security threats. In general, this method not only improves the effectiveness of hazardous source monitoring, enhances the safety, reliability and practicality of the system, but also provides strong technical support for the safety management of hazardous sources, helps to continuously optimize and improve monitoring methods, and continuously improves the level of safety management of hazardous sources.

[0122] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the term "comprise" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0123] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for monitoring major tamper-proof hazard sources based on identification resolution, characterized in that: The method comprises the following steps: S1: Identify and classify hazardous sources and determine monitoring targets: Identify and classify major hazardous sources, including chemical companies, oil and gas pipelines, and nuclear facilities; S2: Build an identification resolution system, select DNS identification resolution technology to build an identification resolution server: deploy identification resolution software to ensure stable operation of the server; configure resolution rules: set resolution rules according to the characteristics of the hazard source, ensure that the identification code corresponds to the hazard source information one by one, and set corresponding thresholds according to the risk level of the hazard source; S3: Install sensors for data collection and transmission, and install various sensors at the hazardous source site, including temperature, pressure, and flow; S4: Perform data analysis and monitoring. After receiving the data, the identification analysis server analyzes the data into readable information according to the preset analysis rules, and then sets the threshold and alarms. When the monitoring data exceeds the threshold, the system automatically alarms; S5: Set up anti-tampering mechanism, perform data encryption and digital signature, and conduct security audits on the system regularly to ensure safe and reliable operation of the system; S6: Set up emergency response and disposal, and formulate emergency plans: formulate corresponding emergency plans for different types of hazardous sources; S7: Continuous optimization and improvement, data analysis: Regularly analyze the monitoring data to identify potential risks, and then end the entire anti-tampering major hazard source monitoring process based on identity resolution.

2. The method for monitoring major tamper-proof sources of danger based on identification resolution as claimed in claim 1, characterized in that: In the step S1, a globally unique identification code is assigned to each hazard source, which is one of a QR code or an RFID; Establish a hazard source database: enter the basic information, geographical location, and risk level data of the hazard source into the database.

3. The method for monitoring major tamper-proof hazard sources based on identification resolution as claimed in claim 1, characterized in that: In step S5, the data encryption process is: select an initial value IV; use AES to encrypt IV to obtain the first ciphertext block; perform XOR operation on the plaintext and the ciphertext block to obtain the ciphertext; increment IV and repeat the above steps; the decryption process of the AES-CTR variant is: use the same IV and key to perform AES encryption to obtain the ciphertext block; perform XOR operation on the ciphertext and the ciphertext block to obtain the plaintext; increment IV and repeat the above steps; The encryption process of the AES-GCM variant is: S1. Select an initial value IV; S2. Use AES to encrypt the plaintext to obtain the ciphertext; S3. Calculate the GCM tag (GMAC) for data integrity verification; S4. Transmit the IV, ciphertext and GCM tag together; The decryption process of S5.AES-GCM variant is: S6. Separate the IV, ciphertext and GCM tag from the received data; S7. Use AES to decrypt the ciphertext to obtain the plaintext; S8. Recalculate the GCM tag and compare it with the received GCM tag to ensure data integrity; In step S4, the AES encryption formula is: C=AES(K,P) Among them, C is the ciphertext, K is the key, and P is the plaintext; The AES decryption formula is: P=AES^(-1)(K,C) Among them, P is plaintext, K is the key, and C is the ciphertext; The formula for the XOR operation in the AES-CTR variant is: C_i=P_iXORAES(K,IV_i) Where C_i is the i-th ciphertext block, P_i is the i-th plaintext block, and IV_i is the i-th IV value; The GMAC calculation formula for the AES-GCM variant is: GMAC=AES_GCM_MAC(K,A,C) Where GMAC is the GCM tag, K is the key, A is the Additional Authentication Data (AAD), and C is the ciphertext.

4. The method for monitoring major tamper-proof hazard sources based on identification resolution as claimed in claim 1, characterized in that: In step S6, the digital signature process includes: Signature generation: Data hashing: Use a hash function to generate a fixed-length hash value for the data packet to be sent; Signature operation: Use the sender's private key to encrypt the hash value and generate a digital signature; Signature attachment: attach the generated digital signature to the data packet and send it to the identity resolution server; Signature verification: Receiving data: After the identity resolution server receives the data packet, it separates the digital signature and data content; Hash verification: Use the same hash function to generate a new hash value for the data content; Signature decryption: Use the sender's public key to decrypt the digital signature to obtain the original hash value; Hash comparison: Compare the decrypted hash value with the newly generated hash value. If the two are the same, the data integrity is verified; otherwise, the data may have been tampered with.

5. The method for monitoring major tamper-proof hazard sources based on identification resolution as claimed in claim 1, characterized in that: In step S6, the security audit process includes: Audit strategy formulation: formulate audit strategies according to the safety requirements of the hazard source monitoring system, including audit frequency, audit content, and audit responsible persons; Logging: The system automatically records all security-related events, including user logins, data access, system configuration changes, and alarm events, in a log file; Log collection: Collect log files regularly to ensure the integrity and immutability of the logs; Audit analysis: Analyze log files to identify abnormal behaviors, potential security threats, and system vulnerabilities; use audit tools to perform automated analysis and generate audit reports; Report and feedback: organize the audit results into reports and submit them to the management and security team; propose improvement measures based on the problems found in the audit and track the rectification; Continuous monitoring: Adjust monitoring strategies and system configurations based on audit results, and continuously monitor the security status of the system.

6. The method for monitoring major tamper-proof hazard sources based on identification resolution as claimed in claim 1, characterized in that: In step S4, data parsing uses a machine learning algorithm to automatically learn and optimize parsing rules to adapt to different types of data formats; Let D be the original data, R be the parsing rule, A be the algorithm model, and D′ be the parsed data, then D′=A(D,R); Data anomaly detection detects data anomalies in real time during the parsing process, and uses the Isolation Forest algorithm to identify outliers. Let L be the path length, c(n) be the subsampling size, and the anomaly score calculation formula is: s(x,L)=2-L(x) / c(n)E[L].

7. The method for monitoring major tamper-proof hazard sources based on identification resolution as claimed in claim 1, characterized in that: In step S4, real-time monitoring is combined with AR technology to overlay real-time monitoring data in the field of vision of the monitoring personnel to improve the intuitiveness of monitoring. The monitoring data is displayed in the form of virtual tags at the location corresponding to the hazard source using the AR device head-mounted display; Real-time monitoring uses time series analysis and machine learning to predict future data trends and provide early warning of potential risks. Let Xt be the time series data, X^t+1 be the predicted value, and f be the prediction model, then X^t+1=f(Xt).

8. The method for monitoring major tamper-proof hazard sources based on identification resolution as claimed in claim 1, characterized in that: In step S4, the threshold setting and dynamic threshold adjustment in the alarm dynamically adjusts the threshold according to historical data and current environmental conditions: let T be the threshold, α be the adjustment coefficient, σ be the standard deviation of the data, then the dynamic threshold T′=T+ασ; The alarm system based on risk probability in threshold setting and alarm uses Bayesian network to evaluate the risk probability of alarm events and determine the urgency of the alarm. Let P(E|D) be the probability of event E occurring under data D, and P(D) be the probability of data D, then P(E) = ∑DP(E|D)P(D).

9. The method for monitoring major tamper-proof hazard sources based on identification resolution as claimed in claim 1, characterized in that: In step S4, the monitoring center displays various parameters of the hazardous source in real time, so that the monitoring personnel can understand the on-site situation. At the same time, according to the risk level of the hazardous source, the corresponding threshold is set. When the monitoring data exceeds the threshold, the system automatically alarms to remind the monitoring personnel to take corresponding measures.

10. The method for monitoring major tamper-proof hazard sources based on identification resolution as claimed in claim 1, characterized in that: In step S7, the monitoring system and identification resolution technology are optimized and upgraded according to the data analysis results to improve the monitoring effect; at the same time, the training of relevant personnel is strengthened to improve safety awareness.