A blockchain-based instrument equipment use information management method and system

Abstract

The application discloses a kind of based on blockchain's instrument equipment use information management method and system, method includes: obtaining preliminary access permission determination result;According to initial access permission determination result, obtain the access record of being rejected, form first operation log;The time stamp mark of first operation log is obtained, and subsequent access request is continuously tracked in combination with real-time monitoring, form second operation log, judge whether there is abnormal access mode, and output exception detection report;According to exception detection report, generate updated permission policy, and access request of equipment use information is compared in real time, form third operation log;Key field in third operation log is extracted and encrypted storage, form publicly accessible access summary, output to relevant system interface, according to access summary, access history of equipment use information is periodically checked.The application guarantees the integrity and traceability of operation record.

Description

Technical Field

[0001] This invention relates to the fields of blockchain technology and instrument and equipment management technology, and in particular to a blockchain-based method and system for managing instrument and equipment usage information. Background Technology

[0002] In modern science and technology and industry, the management of equipment usage information is a crucial link, directly affecting the efficient use of resources, operational safety, and data reliability. With the acceleration of intelligentization and digitalization, ensuring the security and transparency of equipment usage information has become a critical issue that cannot be ignored in industry development. Traditional management methods often rely on centralized systems, which, while achieving information recording and access control to some extent, suffer from drawbacks such as susceptibility to tampering, data opacity, and low management efficiency. These limitations make it difficult to completely eliminate security risks during equipment use.

[0003] Against this backdrop, current information management methods have revealed significant shortcomings, particularly in the effectiveness of access control. Traditional systems often fail to monitor and automatically respond to abnormal behavior in real time, increasing the risk of privilege abuse or unauthorized operations. Furthermore, the implementation of access control faces more specific challenges. First and foremost is ensuring the dynamic execution and real-time verification of permission rules. Traditional static rules struggle to cope with complex usage scenarios, easily leading to rule lag or vulnerabilities. This problem, in turn, directly results in another deeper dilemma: how to guarantee the authenticity and immutability of operation records based on dynamic verification. Once records are tampered with or lost, management trust will completely collapse. These two challenges are progressively more complex; the former determines the precision of control, while the latter concerns data reliability, together constituting the technical barriers that urgently need to be overcome in device management.

[0004] Therefore, how to achieve automated verification of access control through innovative technologies, and ensure the authenticity and immutability of operation records while executing dynamic rules, has become a key problem that this invention urgently needs to solve. Summary of the Invention

[0005] To address the technical problems existing in the prior art, this invention proposes a blockchain-based method and system for managing instrument and equipment usage information, thereby improving the security and reliability of access control.

[0006] On the one hand, to achieve the above objectives, the present invention provides a blockchain-based method for managing instrument and equipment usage information, comprising:

[0007] By using a pre-established permission rule library, device usage information is categorized and stored to generate an initial permission mapping table. Combined with real-time monitoring to obtain context data of the current access request, a preliminary access permission determination result is obtained.

[0008] Based on the initial access permission determination result, the legality of the access request is verified a second time to obtain the access denied record. The access denied record is written into the distributed ledger system to form the first operation log, and a corresponding timestamp identifier is generated at the same time.

[0009] Obtain the timestamp of the first operation log, combine it with real-time monitoring to continuously track subsequent access requests, form the second operation log, determine whether there is an abnormal access pattern, and output an anomaly detection report.

[0010] Based on the anomaly detection report, adjust the dynamic rules in the permission mapping table, generate an updated permission policy, and determine a new access control allocation scheme.

[0011] The updated permission policy is used to compare access requests for device usage information in real time. If a request that does not comply with the policy is detected, an alarm message is generated, details of the violation are output, and the details of the violation are added to the first operation log in conjunction with the distributed ledger system to form the third operation log.

[0012] Key fields are extracted from the third operation log and stored in an encrypted form to form a publicly verifiable access digest. This digest is then output to the relevant system interface. Based on the access digest, the access history of device usage information is periodically verified.

[0013] On the other hand, to achieve the above objectives, the present invention also provides a blockchain-based instrument and equipment usage information management system, comprising:

[0014] The permission classification and storage module is used to classify and store device usage information according to a pre-established permission rule base, generate an initial permission mapping table, and obtain the context data of the current access request by combining real-time monitoring to obtain the preliminary access permission determination result.

[0015] The dynamic verification module is used to perform a second verification on the legality of the access request based on the initial access permission determination result, obtain the access denied record, write the access denied record into the distributed ledger system to form the first operation log, and generate a corresponding timestamp identifier.

[0016] The anomaly detection module is used to obtain the timestamp identifier of the first operation log, continuously track subsequent access requests in conjunction with real-time monitoring, form the second operation log, determine whether there is an abnormal access pattern, and output an anomaly detection report.

[0017] The permission policy update module is used to adjust the dynamic rules in the permission mapping table according to the anomaly detection report, generate an updated permission policy, and determine a new access control allocation scheme.

[0018] The log appending module is used to compare access requests for device usage information in real time according to the updated permission policy. If a request that does not comply with the policy is detected, an alarm message is generated, the details of the violation operation are output, and the details of the violation operation are appended to the first operation log in combination with the distributed ledger system to form a third operation log.

[0019] The periodic verification module is used to extract key fields from the third operation log and store them in encryption to form a publicly verifiable access digest, which is then output to the relevant system interface. Based on the access digest, the access history of device usage information is periodically verified.

[0020] Compared with the prior art, the present invention has the following advantages and technical effects:

[0021] This invention pre-establishes a permission rule base and combines it with real-time monitoring to perform preliminary judgment and secondary verification of access requests. Denied access records are written to a distributed ledger system to form an immutable operation log. Subsequent access requests are continuously tracked, abnormal access patterns are detected, and permission policies are dynamically adjusted. Alarms are generated for violations and appended to the operation log. Through a management transparency mechanism, this invention encrypts and publicly stores key access information and periodically verifies access history to prevent tampering.

[0022] The method of this invention enables refined and dynamic access control of device usage information, improves the security and reliability of access control, and ensures the integrity and traceability of operation records. Attached Figure Description

[0023] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0024] Figure 1 This is a flowchart of a blockchain-based method for managing instrument and equipment usage information, according to an embodiment of the present invention.

[0025] Figure 2 This is a schematic diagram of the structure of a blockchain-based instrument and equipment usage information management system according to an embodiment of the present invention. Detailed Implementation

[0026] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0027] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0028] This embodiment proposes a blockchain-based method for managing instrument and equipment usage information, such as... Figure 1 ,include:

[0029] By using a pre-established permission rule library, device usage information is categorized and stored to generate an initial permission mapping table. Combined with real-time monitoring to obtain context data of the current access request, a preliminary access permission determination result is obtained.

[0030] Based on the initial access permission determination result, the legality of the access request is verified a second time to obtain the access denied record. The access denied record is written into the distributed ledger system to form the first operation log, and a corresponding timestamp is generated at the same time.

[0031] Obtain the timestamp of the first operation log, combine it with real-time monitoring to continuously track subsequent access requests, form the second operation log, determine whether there is an abnormal access pattern, and output an anomaly detection report.

[0032] Based on the anomaly detection report, adjust the dynamic rules in the permission mapping table, generate an updated permission policy, and determine a new access control allocation scheme.

[0033] The updated permission policy is used to compare access requests for device usage information in real time. If a request that does not comply with the policy is detected, an alarm message is generated, the details of the violation are output, and the details of the violation are added to the first operation log in combination with the distributed ledger system to form the third operation log.

[0034] Key fields are extracted from the third operation log and stored in an encrypted form to form a publicly verifiable access digest. This digest is then output to the relevant system interface, and the access history of device usage information is periodically verified based on the access digest.

[0035] Specifically, this embodiment pre-establishes a permission rule base and combines it with real-time monitoring to perform preliminary judgment and secondary verification of access requests. Denied access records are written to the distributed ledger system to form an immutable operation log. By continuously tracking subsequent access requests, abnormal access patterns are detected and permission policies are dynamically adjusted. Alarms are generated for violations and appended to the operation log. Through a management transparency mechanism, key access information is encrypted, stored, and made public, and access history is periodically verified to prevent tampering.

[0036] Furthermore, the preliminary access permission determination results include:

[0037] The instrument and equipment information is organized by a preset permission rule base, and the usage information is classified and stored. An initial mapping table is constructed and the classified data results are stored.

[0038] Based on the classified data results, the corresponding relationships in the initial mapping table are obtained, and combined with the permission rules in the rule base, the device information is initially matched to obtain a preliminary permission allocation scheme.

[0039] If the initial permission allocation scheme conflicts with the preset permission rule library, the context data of the access request is obtained through real-time monitoring, the conflicting parts are dynamically adjusted, and the adjusted permission allocation result is determined.

[0040] Based on the adjusted permission allocation results, a second verification is performed on the context data of the access request. If the verification result shows abnormal data, a preset threshold is used for comparison to determine whether it meets the access permission standard.

[0041] By comparing the results, the judgment conclusion of the access permission standard is obtained. If the judgment conclusion shows that it does not meet the standard, the context data is analyzed in depth, and the support vector machine algorithm is used to classify the abnormal data to obtain the classified abnormal data labels.

[0042] Based on the categorized abnormal data labels and the permission rules in the initial mapping table, the access requests are subject to final permission determination to determine the preliminary access permission determination results.

[0043] Specifically, the process involves initial extraction of contextual data, structuring key fields in access requests, and obtaining a pre-organized dataset. Based on this dataset, a pre-defined rule base is used for anomaly detection. If anomalies are detected, they are marked as data to be analyzed, resulting in a marked anomaly dataset. A support vector machine (SVM) algorithm is then used to classify the marked anomaly dataset, generating classified anomaly labels. These labels, combined with permission rules in a mapping table, are used to match and analyze access requests, determining if they meet permission standards. If not, the request is marked as restricted access, establishing the restricted access decision. Based on this decision, device information and usage information are compared. If inconsistencies are found, the relevant records are temporarily locked, resulting in a locked record status. Based on this locked record status, permission records are updated, and the updated permission data is stored in a mapping table, obtaining the updated permission mapping relationship. Finally, subsequent access requests are quickly validated using this updated permission mapping relationship to determine if they meet access conditions, resulting in a preliminary access permission determination.

[0044] Furthermore, obtain records of denied access, including:

[0045] By verifying the legitimacy of access requests and using a dynamic verification mechanism to analyze context data, if there is an inconsistency between the user identifier and the permission mapping table, an automated response process is triggered to obtain the denied access record.

[0046] Furthermore, the timestamp identifier of the first operation log is obtained, and subsequent access requests are continuously tracked in conjunction with real-time monitoring to form a second operation log. The system then determines whether abnormal access patterns exist and outputs an anomaly detection report, including:

[0047] The starting point of each access request is recorded by the timestamp of the first operation log, and a real-time monitoring mechanism is used to continuously track subsequent access requests to generate a second operation log containing a time series.

[0048] For the second operation log, extract the frequency and behavioral characteristics of access requests to determine whether there are any abnormal patterns. If the extracted access request frequency exceeds the preset threshold, it is marked as a potential abnormal pattern by the pattern judgment component and a preliminary abnormality indicator is output.

[0049] Based on the marked potential abnormal patterns, similar behavior data in historical logs are obtained, and the support vector machine algorithm is used for classification to obtain the classification results and determine whether they belong to abnormal access patterns.

[0050] Based on the classification results and anomaly detection logic, if the classification result indicates abnormal access, a detailed record containing timestamp identifiers and anomaly patterns will be generated.

[0051] Obtain the generated detailed records, use data integration tools to associate the abnormal patterns with the corresponding access request information, and output the abnormal detection report.

[0052] Specifically, access requests are continuously tracked using timestamps, and a real-time monitoring mechanism captures the dynamic data of each access request to obtain preliminary access sequence information. Based on the access sequence information, log analysis tools are used to extract access frequency and behavioral characteristic data to determine if there are any signs of deviation from the normal pattern. For the extracted access frequency and behavioral characteristic data, if the access frequency exceeds a preset threshold, it is marked as a potential abnormal pattern, and the corresponding timestamp and behavioral characteristic combination information is obtained. Similar behavioral data related to the potential abnormal pattern is obtained from historical logs, and a support vector machine algorithm is used to classify the similar behavioral data to determine whether it belongs to an abnormal access pattern. Based on the classification results, if it is determined to be an abnormal access pattern, detailed record data containing timestamps and abnormal pattern descriptions is generated. Through data integration tools, the detailed record data is associated with the context information of the access request to obtain a complete abnormal access information record.

[0053] Furthermore, based on the anomaly detection report, the dynamic rules in the permission mapping table are adjusted to generate an updated permission policy and determine a new access control allocation scheme, including:

[0054] Access behavior data related to permission abuse is extracted from anomaly detection reports, and data analysis tools are used to classify the behavior patterns to obtain preliminary risk assessment results.

[0055] Based on the preliminary risk assessment results, for high-risk access behaviors, the corresponding permission configuration information is obtained from the permission mapping table to determine whether there are vulnerabilities in the dynamic rules in the current configuration;

[0056] If there are vulnerabilities in the dynamic rules, the permission mapping table is modified to generate temporary permission constraints, resulting in an adjusted set of rules.

[0057] For the adjusted rule set, the support vector machine algorithm is used to verify the matching degree between access behavior and rules to determine whether the matching degree meets the preset requirements. If the matching degree does not meet the preset requirements, the rule set is adjusted a second time through the rule optimization tool to obtain the optimized permission policy content.

[0058] Based on the optimized permission policy, a new access control allocation scheme is generated, the final permission allocation logic is determined, and based on the final permission allocation logic, a data integration tool is used to associate the allocation scheme with access behavior data to determine a new access control allocation scheme.

[0059] Furthermore, the updated permission policy is used to compare access requests for device usage information in real time. If a request that does not comply with the policy is detected, an alarm is generated, and details of the violation are output, including:

[0060] The updated permission policy is used to obtain the latest policy requirements, and the received access requests are initially screened to determine whether the requests meet the basic constraints and obtain preliminary compliance judgment results.

[0061] Based on the preliminary compliance assessment results, for access requests that fail the screening, a real-time comparison tool is used to match and correlate them with device information to determine whether the request source is within the allowed range and to obtain the source verification conclusion.

[0062] If the source verification result shows that the request source is not within the allowed range, a corresponding alarm message is generated, and relevant violation operation data is extracted, and a detailed operation record is output, namely the violation operation details.

[0063] Specifically, for the generated alarm information and operation details, data is transmitted to the monitoring system through the information output interface to obtain a transmission confirmation status and determine whether the alarm information has been fully pushed. If the transmission confirmation status indicates that the push is incomplete, the alarm information and operation details are retransmitted through a backup transmission channel to determine the final push result. Based on the push result, a log recording tool is used to integrate and store the alarm information, violation operation data, and push status to obtain a complete access request processing archive. By extracting historical data from the access request processing archive, a support vector machine algorithm is used to analyze the patterns of violation operations to determine whether there are recurring violations and to obtain pattern analysis conclusions.

[0064] Furthermore, key fields are extracted from the third operation log and stored in an encrypted form to create a publicly verifiable access digest, which is then output to the relevant system interface, including:

[0065] The core field data in the third operation log is obtained, and the fields are grouped according to the preset classification criteria using a hierarchical filtering method to obtain a set of classified fields.

[0066] Based on the categorized set of fields, encryption technology is used to independently encrypt each field group, generating a corresponding encryption identifier to determine the encrypted field dataset.

[0067] For the encrypted field dataset, an access digest generation tool is used to transform it into structured digest information. Combined with public query requirements, an access digest record that can be called externally is generated.

[0068] Specifically, in a distributed ledger system, core fields include information such as operation time, operation type, and operation object. A transparency mechanism module can extract this data from logs, ensuring data traceability. A hierarchical filtering method can group these fields according to preset criteria, such as dividing them into regular operations and abnormal operations by operation type. Regular operations might have 100 records, and abnormal operations might have 20 records, facilitating subsequent processing.

[0069] For the categorized field sets, the application of encryption technology is crucial. Assuming each field group contains two types of data—operation time and operation object—the system assigns a different encryption algorithm and key to each group, generating a unique encryption identifier through the key management module. For example, identifier A is generated for the operation time group, and identifier B is generated for the operation object group. This independent encryption method ensures that the data cannot be easily cracked during transmission or storage.

[0070] Furthermore, after generating the access digest record that can be called externally, it also includes:

[0071] Determine if there is data loss or anomaly during transmission. If an anomaly is detected, trigger the retransmission mechanism to obtain the complete transmission result.

[0072] For the transmission results, an information verification tool is used to check the consistency between the access summary record and the data in the system interface. If the consistency verification passes, the field management process is confirmed to be complete, and the final interface data status is determined.

[0073] Based on the final interface data status, the public query request is authorized. If the request meets the preset authorization rules, the corresponding access summary record is opened and the query feedback information is obtained.

[0074] For query feedback information, a log tracking tool is used to record the operation details of each public query. Combined with the complete path of data extraction and information transmission, an operation traceability record is generated to determine the completeness of the traceability information.

[0075] Furthermore, based on the access summary, the access history of device usage information is periodically verified, including:

[0076] Retrieve key field data from the access history to determine if there are any abnormal modification traces;

[0077] If the field data in the access history is detected to be inconsistent with the preset verification rules, the tampering indication marking mechanism is triggered to determine the location of the suspected tampered record;

[0078] Based on the marked locations of suspected tampering records, extract the corresponding historical data from the backup storage to obtain the original operation record content;

[0079] For the extracted operation records, a pre-established comparison model is used to determine the differences with the current access history, identify the specific scope of tampering, and initiate a backtracking process based on the identified scope of tampering to obtain complete record data from the backup system and restore the tampered historical data.

[0080] Based on the recovered historical data, the storage content of the access records is updated, a verification log is generated, the complete process of this data recovery is recorded, and the verification log is classified and analyzed using the random forest algorithm to determine whether there is a pattern of repeated tampering and output the prediction results of potential risks.

[0081] Specifically, in the access record management of a distributed ledger system, periodically scanning the storage system to obtain key field data from the access history can effectively monitor data integrity. Assuming the system performs a full scan of the storage system every 24 hours, focusing on extracting fields such as operation time, user, and content, and comparing this information with preset rules, it can determine if there are any abnormal modification traces. For example, if the scan finds that the operation time of a record changed from October 1, 2023 to October 2, 2023, and there is no supporting operation log, the system will mark the record as suspected of tampering.

[0082] In the application of the comparison model, the differences between the extracted operation record content and the current access history are analyzed. Assuming the comparison reveals that the tampering is limited to the operation time field, while the operation user and operation content fields remain unaffected, the system will limit the scope of the tampering to the time field. This precise targeting helps in the targeted implementation of subsequent recovery work.

[0083] This embodiment also provides a blockchain-based instrument and equipment usage information management system, such as... Figure 2 ,include:

[0084] The permission classification and storage module is used to classify and store device usage information according to a pre-established permission rule base, generate an initial permission mapping table, and obtain the context data of the current access request by combining real-time monitoring to obtain the preliminary access permission determination result.

[0085] The dynamic verification module is used to perform a second verification on the legality of the access request based on the initial access permission determination result, obtain the access denied record, write the access denied record into the distributed ledger system to form the first operation log, and generate a corresponding timestamp identifier.

[0086] The anomaly detection module is used to obtain the timestamp identifier of the first operation log, continuously track subsequent access requests in conjunction with real-time monitoring, form the second operation log, determine whether there is an abnormal access pattern, and output an anomaly detection report.

[0087] The permission policy update module is used to adjust the dynamic rules in the permission mapping table according to the anomaly detection report, generate an updated permission policy, and determine a new access control allocation scheme.

[0088] The log appending module is used to compare access requests for device usage information in real time according to the updated permission policy. If a request that does not comply with the policy is detected, an alarm message is generated, the details of the violation operation are output, and the details of the violation operation are appended to the first operation log in combination with the distributed ledger system to form a third operation log.

[0089] The periodic verification module is used to extract key fields from the third operation log and store them in encryption to form a publicly verifiable access digest, which is then output to the relevant system interface. Based on the access digest, the access history of device usage information is periodically verified.

[0090] The above are merely preferred embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A blockchain-based method for managing instrument and equipment usage information, characterized in that, include: By using a pre-established permission rule library, device usage information is categorized and stored to generate an initial permission mapping table. Combined with real-time monitoring to obtain context data of the current access request, a preliminary access permission determination result is obtained. Based on the preliminary access permission determination result, the legality of the access request is verified a second time to obtain the access denied record. The access denied record is written into the distributed ledger system to form the first operation log, and a corresponding timestamp identifier is generated at the same time. Obtain the timestamp of the first operation log, combine it with real-time monitoring to continuously track subsequent access requests, form the second operation log, determine whether there is an abnormal access pattern, and output an anomaly detection report. Based on the anomaly detection report, adjust the dynamic rules in the permission mapping table, generate an updated permission policy, and determine a new access control allocation scheme. The updated permission policy is used to compare access requests for device usage information in real time. If a request that does not comply with the policy is detected, an alarm message is generated, details of the violation are output, and the details of the violation are added to the first operation log in conjunction with the distributed ledger system to form the third operation log. Key fields are extracted from the third operation log and stored in encryption to form a publicly verifiable access digest, which is then output to the relevant system interface. Based on the access digest, the access history of device usage information is periodically verified. The results of the preliminary access permission determination include: The instrument and equipment information is organized by a preset permission rule base, and the usage information is classified and stored. An initial mapping table is constructed and the classified data results are stored. Based on the classified data results, the corresponding relationships in the initial mapping table are obtained, and the device information is initially matched in combination with the permission rules in the rule base to obtain a preliminary permission allocation scheme. If the initial permission allocation scheme conflicts with the preset permission rule library, the context data of the access request is obtained through real-time monitoring, the conflicting part is dynamically adjusted, and the adjusted permission allocation result is determined. Based on the adjusted permission allocation results, a second verification is performed on the context data of the access request. If the verification result shows abnormal data, a preset threshold is used for comparison to determine whether it meets the access permission standard. By comparing the results, the judgment conclusion of the access permission standard is obtained. If the judgment conclusion shows that it does not meet the standard, the context data is analyzed in depth, and the support vector machine algorithm is used to classify the abnormal data to obtain the classified abnormal data labels. Based on the categorized abnormal data labels and the permission rules in the initial mapping table, the access requests are subject to final permission determination to determine the preliminary access permission determination results.

2. The method for managing instrument and equipment usage information based on blockchain according to claim 1, characterized in that, Obtaining the denied access record includes: By verifying the legitimacy of access requests and using a dynamic verification mechanism to analyze context data, if there is an inconsistency between the user identifier and the permission mapping table, an automated response process is triggered to obtain the denied access record.

3. The method for managing instrument and equipment usage information based on blockchain according to claim 1, characterized in that, Obtain the timestamp of the first operation log, combine it with real-time monitoring to continuously track subsequent access requests, generate the second operation log, determine whether there are abnormal access patterns, and output an anomaly detection report, including: The starting point of each access request is recorded by the timestamp identifier of the first operation log, and the subsequent access requests are continuously tracked by a real-time monitoring mechanism to generate a second operation log containing a time series. For the second operation log, extract the frequency and behavioral characteristics of access requests to determine whether there are any abnormal patterns. If the extracted access request frequency exceeds a preset threshold, it is marked as a potential abnormal pattern by the pattern judgment component and a preliminary abnormality identifier is output. Based on the marked potential abnormal patterns, similar behavior data in historical logs are obtained, and the support vector machine algorithm is used for classification to obtain the classification results and determine whether they belong to abnormal access patterns. Based on the classification results and anomaly detection logic, if the classification result indicates abnormal access, a detailed record containing timestamp identifiers and anomaly patterns will be generated. Obtain the generated detailed records, use data integration tools to associate the abnormal patterns with the corresponding access request information, and output the abnormal detection report.

4. The method for managing instrument and equipment usage information based on blockchain according to claim 1, characterized in that, Based on the anomaly detection report, adjust the dynamic rules in the permission mapping table, generate an updated permission policy, and determine a new access control allocation scheme, including: Access behavior data related to permission abuse is extracted from the anomaly detection report, and the behavior patterns are classified using data analysis tools to obtain preliminary risk assessment results; Based on the preliminary risk assessment results, for high-risk access behaviors, the corresponding permission configuration information is obtained from the permission mapping table to determine whether there are vulnerabilities in the dynamic rules in the current configuration; If there are vulnerabilities in the dynamic rules, the permission mapping table is modified to generate temporary permission constraints, resulting in an adjusted set of rules. For the adjusted rule set, the support vector machine algorithm is used to verify the matching degree between access behavior and rules to determine whether the matching degree meets the preset requirements. If the matching degree does not meet the preset requirements, the rule set is adjusted a second time through the rule optimization tool to obtain the optimized permission policy content. Based on the optimized permission policy, a new access control allocation scheme is generated, the final permission allocation logic is determined, and based on the final permission allocation logic, a data integration tool is used to associate the allocation scheme with access behavior data to determine the new access control right allocation scheme.

5. The method for managing instrument and equipment usage information based on blockchain according to claim 1, characterized in that, The updated permission policy is used to compare access requests for device usage information in real time. If a request that does not comply with the policy is detected, an alarm message is generated and details of the violation are output, including: The updated permission policy is used to obtain the latest policy requirements, and the received access requests are initially screened to determine whether the requests meet the basic constraints and obtain preliminary compliance judgment results. Based on the preliminary compliance assessment results, for access requests that fail the screening, a real-time comparison tool is used to match and correlate them with device information to determine whether the request source is within the allowed range and to obtain the source verification conclusion. If the source verification result shows that the request source is not within the allowed range, a corresponding alarm message is generated, and relevant violation operation data is extracted, and a detailed operation record is output, namely the violation operation details.

6. The method for managing instrument and equipment usage information based on blockchain according to claim 1, characterized in that, Extract key fields from the third operation log and encrypt and store them to form a publicly verifiable access digest, which is then output to the relevant system interface, including: The core field data in the third operation log is obtained, and the fields are grouped according to a preset classification standard using a hierarchical filtering method to obtain a set of classified fields. Based on the categorized set of fields, encryption technology is used to independently encrypt each field group, generating a corresponding encryption identifier to determine the encrypted field dataset. The encrypted field dataset is transformed into structured summary information using an access digest generation tool. Combined with public query requirements, this generates access digest records that can be accessed externally.

7. The method for managing instrument and equipment usage information based on blockchain according to claim 6, characterized in that, After generating the access digest record that can be called externally, the method further includes: Determine if there is data loss or anomaly during transmission. If an anomaly is detected, trigger the retransmission mechanism to obtain the complete transmission result. For the transmission results, an information verification tool is used to check the consistency between the access summary record and the data in the system interface. If the consistency verification passes, the field management process is confirmed to be complete, and the final interface data status is determined. Based on the final interface data status, the public query request is authorized. If the request meets the preset authorization rules, the corresponding access summary record is opened and the query feedback information is obtained. For query feedback information, a log tracking tool is used to record the operation details of each public query. Combined with the complete path of data extraction and information transmission, an operation traceability record is generated to determine the completeness of the traceability information.

8. The method for managing instrument and equipment usage information based on blockchain according to claim 1, characterized in that, Based on the access digest, the access history of device usage information is periodically verified, including: Retrieve key field data from the access history to determine if there are any abnormal modification traces; If the field data in the access history is detected to be inconsistent with the preset verification rules, the tampering indication marking mechanism is triggered to determine the location of the suspected tampered record; Based on the marked locations of suspected tampering records, extract the corresponding historical data from the backup storage to obtain the original operation record content; For the extracted operation records, a pre-established comparison model is used to determine the differences with the current access history, identify the specific scope of tampering, and initiate a backtracking process based on the identified scope of tampering to obtain complete record data from the backup system and restore the tampered historical data. Based on the recovered historical data, the storage content of the access records is updated, a verification log is generated, the complete process of this data recovery is recorded, and the verification log is classified and analyzed using the random forest algorithm to determine whether there is a pattern of repeated tampering and output the prediction results of potential risks.

9. A blockchain-based instrument and equipment usage information management system, used to implement the blockchain-based instrument and equipment usage information management method according to any one of claims 1-8, characterized in that, include: The permission classification and storage module is used to classify and store device usage information according to a pre-established permission rule base, generate an initial permission mapping table, and obtain the context data of the current access request by combining real-time monitoring to obtain the preliminary access permission determination result. The dynamic verification module is used to perform a second verification on the legality of the access request based on the preliminary access permission determination result, obtain the access denied record, write the access denied record into the distributed ledger system to form the first operation log, and generate a corresponding timestamp identifier. The anomaly detection module is used to obtain the timestamp identifier of the first operation log, continuously track subsequent access requests in conjunction with real-time monitoring, form the second operation log, determine whether there is an abnormal access pattern, and output an anomaly detection report. The permission policy update module is used to adjust the dynamic rules in the permission mapping table according to the anomaly detection report, generate an updated permission policy, and determine a new access control allocation scheme. The log appending module is used to compare access requests for device usage information in real time according to the updated permission policy. If a request that does not comply with the policy is detected, an alarm message is generated, the details of the violation operation are output, and the details of the violation operation are appended to the first operation log in combination with the distributed ledger system to form a third operation log. The periodic verification module is used to extract key fields from the third operation log and store them in encryption to form a publicly verifiable access digest, which is then output to the relevant system interface. Based on the access digest, the access history of device usage information is periodically verified. The results of obtaining preliminary access rights include: The instrument and equipment information is organized by a preset permission rule base, and the usage information is classified and stored. An initial mapping table is constructed and the classified data results are stored. Based on the classified data results, the corresponding relationships in the initial mapping table are obtained, and the device information is initially matched in combination with the permission rules in the rule base to obtain a preliminary permission allocation scheme. If the initial permission allocation scheme conflicts with the preset permission rule library, the context data of the access request is obtained through real-time monitoring, the conflicting part is dynamically adjusted, and the adjusted permission allocation result is determined. Based on the adjusted permission allocation results, a second verification is performed on the context data of the access request. If the verification result shows abnormal data, a preset threshold is used for comparison to determine whether it meets the access permission standard. By comparing the results, the judgment conclusion of the access permission standard is obtained. If the judgment conclusion shows that it does not meet the standard, the context data is analyzed in depth, and the support vector machine algorithm is used to classify the abnormal data to obtain the classified abnormal data labels. Based on the categorized abnormal data labels and the permission rules in the initial mapping table, the access requests are subject to final permission determination to determine the preliminary access permission determination results.

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