A system for adjusting execution scenarios based on a multi-level matching mechanism

Abstract

This invention provides a system for adjusting execution scenarios based on a multi-level matching mechanism, including a computing unit and a data storage module for information exchange. The computing unit generates revision information and embeds encrypted identifiers to ensure information security. The system employs a multi-level matching mechanism, sequentially based on security level filtering, historical data analysis, and real-time environmental parameter matching, to improve the accuracy of the adaptation between the revision information and the execution scenario. By verifying the execution scenario, the correctness and consistency of the revision information are ensured, and the execution scenario is adjusted based on the verification results. This invention improves the accuracy of information transmission and enhances the system's stability and intelligence.

Description

Technical Field

[0001] This invention relates to the field of computer systems, and more specifically, to a system for adjusting execution scenarios based on a multi-level matching mechanism. Background Technology

[0002] In the fields of computer systems and intelligent control, adjustments to execution scenarios typically rely on preset rules or strategies. These rules are mainly based on static configurations or simple conditional logic to determine the methods of information transmission and scenario adaptation. Existing systems usually employ fixed access control mechanisms, performing basic filtering based on the security level of the execution scenario to ensure the security of information transmission. However, this filtering method is often rather simplistic and struggles to accurately adapt to dynamic changes in the execution environment. This can lead to situations where necessary revisions are not received in a timely manner, or information is mistakenly transmitted to unsuitable execution scenarios.

[0003] During the adaptation process for execution scenarios, some systems attempt to introduce matching methods based on historical data, such as using previous update records to determine the compatibility of the current scenario. However, these methods often rely on fixed parameters or simple statistical analysis, making it difficult to effectively handle complex environmental variables and accurately predict the adaptation status of execution scenarios. Especially when multiple execution scenarios are involved, traditional methods cannot fully consider the mutual influence between different scenarios, thus affecting the overall system stability and optimization effect.

[0004] Furthermore, existing technologies primarily rely on manual configuration or rule-based automated processing during information transmission and execution scenario adjustments. These methods often struggle to dynamically adapt to changes in the actual operating environment, limiting the applicability of information updates. For example, when the operating environment undergoes sudden changes, the system may be unable to adjust the transmission strategy of revision information in a timely manner, thus affecting the normal operation of the execution scenario. Simultaneously, while some technical solutions possess basic verification mechanisms, they mainly rely on simple comparison methods, such as merely comparing revision records with stored original data. This makes it difficult to achieve in-depth verification for complex application scenarios, potentially leading to erroneous revision information transmission or inaccurate execution scenario identification.

[0005] With the development of computer systems and the improvement of their intelligence, existing execution scenario adjustment solutions still have room for improvement in terms of accuracy, adaptability, and security. On the one hand, how to improve the matching accuracy of revision information while ensuring security has become a technical issue of concern in the industry; on the other hand, how to introduce a more intelligent adaptation mechanism to enhance the dynamic adjustment capability of execution scenarios has also become a direction that existing technical solutions urgently need to improve. Summary of the Invention

[0006] To address the aforementioned problems in the prior art, this invention proposes a system for adjusting execution scenarios based on a multi-level matching mechanism. The system includes: at least one computing unit; and a data storage module that communicates with the computing unit. The computing unit is configured as follows:

[0007] Generate revision information for one or more execution scenarios; embed an encrypted identifier corresponding to the source of the revision information in the revision information;

[0008] At least one execution scenario for receiving the revision information is identified based on a multi-level matching mechanism;

[0009] Verify at least one of the execution scenarios;

[0010] Based on the verification results, the revision information is sent to the at least one execution scenario to adjust the at least one execution scenario.

[0011] The multi-level matching mechanism includes:

[0012] The first layer of matching filters based on the basic security level of the execution scenario;

[0013] The second layer of matching uses machine learning models to analyze the compatibility between historical update data and the current execution scenario;

[0014] The third layer of matching, based on parameters of the real-time operating environment, ensures that the revised information is transmitted only to execution scenarios that meet specific operating conditions.

[0015] The methods by which the computing unit verifies the execution scenario include:

[0016] Receive revision records related to the execution scenario;

[0017] Compare the revised records with the master control records;

[0018] The execution scenario was verified based on the comparison results.

[0019] The first layer of matching further includes:

[0020] The system retrieves the security policies for execution scenarios from the data storage module; filters execution scenarios based on preset security level requirements; marks the security level of revision information and compares it with the security level of the target execution scenario; and removes the execution scenario when the security level of the target execution scenario is lower than the requirements of the revision information.

[0021] The second layer of matching further includes:

[0022] Retrieve historical update data and operation records for execution scenarios;

[0023] Based on the historical update data and operation records, a compatibility assessment model is constructed, and the compatibility assessment model is trained using a supervised learning method.

[0024] Extract key features of the execution scenario, including applicable software version, historical success rate, and execution error;

[0025] The compatibility score for the execution scenario is calculated based on the compatibility evaluation model. The compatibility score is derived from historical success rate, software version compatibility, and execution error.

[0026] The calculated compatibility score is compared with a preset threshold.

[0027] The compatibility score S c The calculation method is as follows:

[0028]

[0029] in,

[0030] T s Historical success rate;

[0031] V r To revise the software version number to which the information applies, V c V represents the software version number of the current execution scenario. max This is the maximum software version number supported by the system.

[0032] E c E represents the historical execution error of the current execution scenario. max This represents the maximum acceptable execution error.

[0033] w1, w2, and w3 are weighting coefficients.

[0034] The third layer of matching further includes:

[0035] Obtain the real-time runtime environment parameters of the target execution scenario;

[0036] The adaptability score for the execution scenario is calculated, and the adaptability score is calculated based on CPU load, memory utilization, network load and the running status of key tasks;

[0037] The adaptability score is compared with a preset adaptability threshold.

[0038] When the adaptability score meets the preset adaptability threshold, the revision information is allowed to be transmitted to the execution scenario; otherwise, the execution scenario is removed.

[0039] The compatibility score S r The calculation method is as follows:

[0040]

[0041] in,

[0042] L c For the CPU load of the current execution scenario, L max This represents the maximum CPU load allowed by the system.

[0043] M c M represents the memory usage of the current execution scenario. max This represents the maximum allowed memory usage of the system.

[0044] N c N represents the network load of the current execution scenario. max The maximum network load allowed by the system;

[0045] T c This is a state variable indicating whether there are critical tasks running in the current execution scenario;

[0046] w′1, w′2, w′3, w4′ are weighting coefficients.

[0047] The comparison includes version consistency of revision information, parameter matching degree, execution time window, and status feedback of the execution scenario after revision.

[0048] The adjustment of at least one execution scenario includes: dynamically modifying system parameters, loading new execution rules, or adjusting the operating mode.

[0049] Beneficial effects:

[0050] This invention improves the accuracy of matching revision information with execution scenarios through a multi-level matching mechanism. Based on a combination of security level filtering, historical data analysis, and real-time environmental parameter matching, information transmission is more precise, avoiding incorrect matching and unnecessary updates.

[0051] By embedding revision information with encrypted identifiers, the reliability of the information source is ensured, data security is enhanced, and unauthorized information tampering or misuse is effectively prevented.

[0052] By verifying the execution scenario, the reliability of the revision record can be accurately identified, and the correctness of the update can be improved by comparing it with the master control record, ensuring that the adjustment of the execution scenario meets expectations and improving the stability and intelligence level of the system. Attached Figure Description

[0053] The accompanying drawings, which are provided to further illustrate the invention and form part of this application, are not intended to unduly limit the invention. In the drawings:

[0054] Figure 1A schematic diagram of the structure of a system that adjusts the execution scenario based on a multi-level matching mechanism is shown. Detailed Implementation

[0055] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. The illustrative embodiments and descriptions are only used to explain the present invention and are not intended to limit the present invention.

[0056] Example 1: Execution Scenario Adjustment System Based on Multi-Level Matching Mechanism

[0057] like Figure 1 As shown, this embodiment provides a system for adjusting execution scenarios based on a multi-level matching mechanism. The system includes at least one computing unit 101 and a data storage module 102 that communicates with it. The computing unit 101 is used to perform computing tasks and to perform information generation, matching, verification, and transmission operations based on the operation commands in the data storage module 102.

[0058] In this embodiment, the computing unit 101 first generates revision information for one or more execution scenarios, and embeds an encrypted identifier corresponding to the information source in the revision information. The encrypted identifier is used to verify the source and integrity of the revision information, ensuring that the data is not tampered with or misused during transmission.

[0059] This embodiment provides a multi-level matching mechanism to improve the accuracy of the adaptation between revision information and execution scenarios.

[0060] The first layer of matching filters based on the basic security level of the execution scenario. The processing unit 101 retrieves the security policies stored in the data storage module 102 and eliminates execution scenarios that do not meet the security level requirements, ensuring that sensitive information is not mistakenly transmitted to scenarios with low security levels.

[0061] The second layer of matching uses a machine learning model to analyze the compatibility between historical update data and the current execution scenario. The computing unit 101 uses the historical update data recorded in the storage module 102 to calculate the matching degree between the revised information and the current execution scenario through model training and inference, thereby improving the accuracy of information transmission and reducing unnecessary updates.

[0062] The third layer of matching filters based on parameters of the real-time operating environment. The computing unit 101 obtains the current environmental parameters of the execution scenario, such as system load, operating status, or specific configuration, and compares them with the applicable conditions of the revision information to ensure that the revision information is transmitted only to the execution scenario that meets the specific operating conditions, so as to optimize the adaptability of the execution scenario.

[0063] To ensure the correct transmission and application of revision information, this embodiment further provides a verification mechanism for the execution scenario. The processing unit 101 first receives the revision record related to the target execution scenario and compares the revision record with the master control record to verify whether the revision information has been correctly applied. If the verification passes, the system allows the revision information to take effect; if the comparison results are inconsistent, a rollback mechanism or error message is triggered to ensure the stability and security of the execution scenario.

[0064] Based on the above verification results, the computing unit 101 ultimately decides whether to send revision information to the target execution scenario to adjust the parameters or operating status of that execution scenario. Through this mechanism, the system can achieve accurate information transmission, improve the adaptability of the execution scenario, and reduce system risks caused by erroneous updates.

[0065] Specific Implementation of the Operation Unit 101 in Example 2

[0066] The arithmetic unit 101 is configured to generate revision information and embed an encrypted identifier corresponding to the information source in the revision information to ensure the security, integrity, and traceability of the information. The generation of revision information is based on the system's operating status, policy adjustment requirements, or user-input configuration parameters. The generated revision information includes, but is not limited to, parameter updates, logic adjustments, policy optimizations, or other instruction sets that need to be applied to the execution scenario.

[0067] After the revision information is generated, the processing unit 101 performs an embedding operation of the encryption identifier. The encryption identifier is used to ensure the source verification of the revision information and prevent unauthorized information tampering or forgery. In this embodiment, the encryption identifier is generated using a public key infrastructure (PKI) system, and the revision information is authenticated through digital signature. Specifically, the processing unit 101 uses the private key stored in the data storage module 102 to sign the revision information, generates a digital signature, and then appends it to the data structure of the revision information.

[0068] The encryption of the revised information employs various techniques. For example, a specific field is added to the standard format of the revised information to store the digital signature, or a hash function is used to calculate the digest value of the revised information, and the digest value is signed using a private key to form an immutable identity. The computation unit 101 uses the SHA-256 or SHA-512 hash algorithm to calculate the digest of the revised information and uses encryption algorithms such as RSA and ECC (elliptic curve cryptography) to perform digital signature, ensuring the uniqueness and integrity of the revised information.

[0069] Before revising the information for transmission or storage, the processing unit 101 further encrypts it to improve data security. Encryption methods include symmetric encryption (such as AES-256) or asymmetric encryption (such as RSA). Symmetric encryption is suitable for efficient transmission, while asymmetric encryption is suitable for secure authentication. The processing unit 101 selects a suitable encryption scheme according to system requirements and marks the corresponding encryption algorithm in the header or metadata section of the revised information so that the receiver can correctly decrypt and verify the validity of the encryption identifier.

[0070] When the target execution scenario receives the revision information, the computing unit 101 calls the public key stored in the data storage module 102 to verify the embedded encrypted identifier. Specifically, the receiver verifies the digital signature to determine whether the revision information originates from a trusted computing unit and checks whether the revision information has been tampered with during transmission. If verification fails, the system triggers an exception handling mechanism, such as discarding the revision information or sending a security warning to the management module, thereby preventing the execution of erroneous revisions.

[0071] Through the above-mentioned technical means, this embodiment ensures the integrity and traceability of the revision information, prevents unauthorized modifications, and improves the security and stability of the system.

[0072] This invention provides a multi-level matching mechanism to improve the accuracy of matching revision information with execution scenarios. This matching mechanism includes preliminary screening based on security levels, compatibility analysis based on historical data, and final screening based on real-time runtime environment parameters, to ensure the accurate transmission and execution of revision information.

[0073] The first layer of matching filters based on the basic security level of the execution scenario. The processing unit 101 retrieves security policies related to the execution scenario from the data storage module 102 and eliminates execution scenarios that do not meet the predefined security level requirements. Security levels are divided based on system-defined access permissions, data sensitivity, or business rules, typically using a multi-level permission model, such as high security level (e.g., core system), medium security level (e.g., controlled subsystem), and low security level (e.g., ordinary user environment). The processing unit 101 marks the revision information with a security level and determines whether the revision information should be transmitted to the target execution scenario by comparing its security level with that of the target execution scenario. If the security level of the target execution scenario is lower than the requirements of the revision information, the scenario will be eliminated to prevent security risks or unauthorized access.

[0074] The second layer of matching employs a machine learning model to analyze the compatibility between historical update data and the current execution scenario. The computation unit 101 utilizes historical update data and operation records from the data storage module 102 to establish a compatibility evaluation model. This model uses supervised learning methods, such as decision trees, random forests, or deep neural networks, to train on past revision information applications and combines feature engineering to extract key features of the execution scenario, such as applicable software version, historical success rate, and execution time. During the actual matching process, the computation unit 101 inputs the current revision information into the trained compatibility evaluation model, calculates the matching score, and determines whether to push revision information to the execution scenario based on a preset threshold.

[0075] To further enhance matching accuracy, this embodiment proposes a compatibility score calculation formula to optimize the matching score calculation process. The formula is as follows:

[0076]

[0077] in,

[0078] S c The compatibility score for the execution scenario ranges from [0,1]. A higher score indicates a higher degree of matching between the execution scenario and the revision information.

[0079] T s The historical success rate is the success rate of this execution scenario when similar revision information was applied in the past, ranging from [0,1].

[0080] V r To revise the software version number to which the information applies, V c V represents the software version number of the current execution scenario. max This is the maximum software version number supported by the system. This item measures version compatibility; a value closer to 1 indicates smaller software version differences.

[0081] E c E represents the historical execution error of the current execution scenario, indicating the degree of failure when applying revision information in the past. max To maximize the acceptable execution error, scenarios with a high number of abnormal executions will receive lower scores.

[0082] w1, w2, and w3 are weighting coefficients used to balance the influence of different factors on the compatibility score, and are optimized and adjusted through training data.

[0083] This formula provides a more accurate matching score calculation method by simultaneously combining historical success rates, software version compatibility, and execution errors, avoiding the limitations of existing technologies that rely on only a single feature for evaluation. Furthermore, the formula employs normalization processing to ensure that data of different dimensions are calculated within a unified scoring system, thus improving the stability of the score.

[0084] During the matching process, the computing unit 101 first calculates the S of each target execution scenario. c Value, and compared with the preset threshold S threshold Compare them. If S c ≥S threshold If the condition is met, the execution scenario is considered compatible and the revision information will be pushed to it; otherwise, the execution scenario is excluded to reduce execution failures or system anomalies caused by incompatibility.

[0085] The calculation method provided by this invention makes the matching score calculation more intelligent and adaptable, making the transmission of revision information more targeted, thereby improving the efficiency and accuracy of execution scenario adjustments.

[0086] In the third-layer matching process of this embodiment, to ensure the real-time adaptability of the revision information to the execution scenario, the third-layer matching is based on parameters of the real-time operating environment for filtering. The computing unit 101 obtains the current operating status of the execution scenario from the data storage module 102 or the system monitoring module, including but not limited to system load, CPU utilization, memory usage, network connection status, and a list of running tasks. The computing unit 101 compares these real-time parameters with the applicable conditions of the revision information to determine whether to allow the revision information to be transmitted to the execution scenario. For example, some revision information may require the execution scenario to be within a specific load range (such as CPU utilization below 80%), or to be applied only when there are no critical tasks.

[0087] To further optimize the accuracy and flexibility of matching, this embodiment proposes an adaptability scoring formula based on multi-dimensional real-time environmental weight calculation. The formula is as follows:

[0088]

[0089] in,

[0090] S r The performance is rated for the suitability of the execution scenario, ranging from [0,1]. A higher score indicates that the current state of the execution scenario is more suitable for receiving revision information.

[0091] L c For the CPU load of the current execution scenario, L max This represents the maximum CPU load allowed by the system. This item is used to measure whether the execution scenario exceeds the load limit.

[0092] M c M represents the memory usage of the current execution scenario. max Set the maximum allowed memory usage to ensure sufficient system resources.

[0093] N cN represents the network load of the current execution scenario. max This is the maximum network load allowed by the system, ensuring that transmission is not affected by network bottlenecks.

[0094] T c This is a state variable indicating whether a critical task is running in the current execution scenario. If no critical task is running, then T... c =1, otherwise T c =0, ensuring that the revised information will not affect the execution of critical tasks.

[0095] w′1, w′2, w′3, and w′4 are weighting coefficients used to adjust the impact of different environmental parameters on the adaptability score. The weights are optimized based on the system characteristics.

[0096] In the actual matching process, the computing unit 101 calculates S for each execution scenario. r The value, and the preset adaptation threshold S threshold Compare. If S r ≥S threshold If the execution scenario meets the real-time environmental requirements for revising information, then the information transmission is allowed; otherwise, the execution scenario is excluded to ensure the stability of the system and the optimization and adaptability of the execution scenario.

[0097] Compared to existing methods that rely solely on a single environmental parameter for matching, the adaptability scoring formula proposed in this embodiment employs a normalized calculation method. This ensures that parameters across different dimensions are weighted uniformly, preventing the bias caused by a single parameter affecting the overall adaptability score. Furthermore, by considering network load conditions, it ensures that revision information transmission is not hindered by bandwidth issues, thus improving system stability. A key task identifier variable T is used. c This ensures that the revised information does not affect important tasks in the execution scenario, thereby improving the security and reliability of system operation.

[0098] The arithmetic unit 101 can more intelligently select suitable execution scenarios, improve the accuracy of revision information adaptation, and ensure that the system applies updates under optimal conditions, thereby improving overall operating efficiency.

[0099] In this embodiment, the second-layer matching and the third-layer matching are interconnected and work together to ensure that the revision information accurately matches the target execution scenario in terms of both compatibility and real-time adaptability, thereby improving the accuracy of the transmission of revision information, the stability of execution, and the overall optimization level of the system.

[0100] The second layer of matching primarily focuses on the historical compatibility of the execution scenario, specifically whether the revision information has been successfully applied in the target execution scenario and whether it conforms to long-term characteristics such as the version and historical success rate of that execution scenario. This layer of matching provides a global filtering mechanism to ensure that revision information is not sent to historically incompatible execution scenarios, thereby reducing the possibility of execution failure.

[0101] The third layer of matching is based on the real-time operating environment, ensuring that the current execution scenario has the capability to execute the revised information at the current point in time. For example, even if an execution scenario is historically compatible with the revised information (through the second layer of matching), the revised information will not be transmitted if the current system load is too high, the network is unstable, or a critical task is running (the third layer of matching fails), to prevent system resource conflicts or service interruptions. Only execution scenarios that pass both the second and third layer of matching will receive the revised information and execute the adjustments.

[0102] In addition, the second-level matching first filters, only when S c Exceeding threshold S threshold Only when the execution scenario meets the requirements will the next step of real-time environment matching begin. Only when S... r Exceeding threshold S threshold Only in specific execution scenarios will the revised information be ultimately transmitted.

[0103] In this embodiment, a verification mechanism for the execution scenario is provided to ensure the correct transmission and application of revision information. This mechanism compares the revision record and the master control record to determine whether the revision information has been executed correctly, thereby ensuring the stability of the system and the accuracy of the data.

[0104] After the revision information is transmitted to the target execution scenario, the processing unit 101 first retrieves the revision record related to the execution scenario from the data storage module 102. The revision record includes, but is not limited to, the content of the revision information, the transmission time, the identifier of the target execution scenario, the revision execution status, and the system status of the execution scenario after the revision information is applied.

[0105] Meanwhile, the processing unit 101 extracts the corresponding baseline information from the master control record. The master control record is the standard data source for the application status of revision information maintained by the system, and typically includes the revision content set by the system, the expected execution status, reference record information, and the revision history approved by the system administrator. The master control record is stored in the data storage module 102 or a dedicated version control system, and is updated and maintained by the system administrator.

[0106] The processing unit 101 compares the revision record with the master control record item by item, verifying aspects including but not limited to version consistency of the revision information, parameter matching degree, execution time window, and status feedback of the execution scenario after revision. If the comparison result shows that the revision record is consistent with the master control record, the system confirms that the revision information has been correctly applied, allows the revision information to officially take effect, and records the successful application for subsequent query or tracking.

[0107] If the comparison results show inconsistencies between the revision record and the master control record, such as incorrect transmission of revision information, abnormal execution scenario status, or unexpected system behavior after revision, the arithmetic unit 101 triggers an exception handling process. The exception handling process includes, but is not limited to, the following: First, the system sends an error message to the administrator, detailing the cause of the error and the comparison results, and providing feasible solutions. Second, the system invokes the rollback mechanism to restore the execution scenario to its state before the application of the revision information, preventing erroneous revisions from affecting system operation. The rollback mechanism is based on pre-set snapshot data or historical configuration records to ensure the execution scenario is restored to a stable state. Furthermore, the system analyzes the specific stage where the error occurred through execution records and optimizes the transmission strategy of revision information in subsequent matching processes to reduce the recurrence of similar problems.

[0108] The execution scenario verification mechanism in this embodiment can effectively ensure the correctness of the revised information, reduce system risks caused by incorrect information transmission or execution failure, and improve system stability and data consistency.

[0109] Based on the verification results of the execution scenario, the computing unit 101 ultimately decides whether to send revision information to the target execution scenario to adjust the parameters or running status of the execution scenario. The process of transmitting revision information and adjusting the execution scenario involves multiple steps to ensure accurate information transmission and stable execution.

[0110] After receiving the verification feedback from the execution scenario, the processing unit 101 first parses the verification results, including but not limited to whether the revision information has been correctly transmitted, whether the execution scenario has been updated as expected, and whether the revised system operation status meets the standards. If the verification results confirm that the revision information is applicable to the target execution scenario, the processing unit 101 performs the information transmission operation and officially sends the revision information to the target execution scenario.

[0111] The transmission of revised information employs an encrypted data channel to ensure that the information is not tampered with or intercepted during transmission. Before transmission, the processing unit 101 performs integrity verification on the revised information, including but not limited to hash verification and digital signature verification, to ensure that the content of the revised information is consistent with the initially generated information. The method of information transmission is selected based on the system architecture, such as based on message queues, remote calls, or direct writing to the configuration storage of the target execution scenario.

[0112] After the revised information successfully reaches the target execution scenario, the computing unit 101 triggers the execution adjustment process. This process adopts different adjustment methods depending on the type of revised information, such as dynamically modifying system parameters, loading new execution rules, or adjusting the operating mode. During the execution adjustment process, the computing unit 101 continuously monitors the operating status of the execution scenario to ensure the stable operation of the adjusted system.

[0113] After the adjustment is completed, the computing unit 101 records the revision execution record, including the specific content of the revision information, the execution time, and the status of the execution scenario after the adjustment, for subsequent analysis and tracking. If an abnormality occurs during the adjustment process, such as the target execution scenario failing to correctly parse the revision information, the system status being abnormal after execution, or the adjustment result not meeting expectations, the computing unit 101 triggers a rollback mechanism or error recovery strategy to ensure that the system is restored to its stable state before the adjustment.

[0114] Through the aforementioned mechanism for transmitting revised information and adjusting execution scenarios, the system can achieve accurate information transmission, improve the adaptability of execution scenarios, reduce system risks caused by erroneous updates, and thus ensure that revised information is reliably applied in appropriate execution scenarios.

[0115] The above description is only a preferred embodiment of the present invention. Therefore, all equivalent changes or modifications made to the structure, features and principles described in the claims of this patent application are included in the scope of this patent application.

Claims

1. A system for adjusting execution scenarios based on a multi-level matching mechanism, characterized in that: The system includes: at least one computing unit; and a data storage module that communicates with the computing unit; wherein the computing unit is configured as follows: Generate revision information for one or more execution scenarios; embed an encrypted identifier corresponding to the source of the revision information in the revision information; At least one execution scenario for receiving the revision information is identified based on a multi-level matching mechanism; Verify at least one of the execution scenarios; Based on the verification results, the revision information is sent to the at least one execution scenario to adjust the at least one execution scenario; The multi-level matching mechanism includes: The first layer of matching filters based on the security level of the execution scenario; The second layer of matching uses machine learning models to analyze the compatibility between historical update data and the current execution scenario; The third layer of matching, based on parameters of the real-time operating environment, ensures that the revised information is transmitted only to execution scenarios that meet the operating conditions; The first layer of matching further includes: The system retrieves the security policies for execution scenarios from the data storage module; filters execution scenarios based on preset security level requirements; labels revision information with security levels and compares them with the security levels of the target execution scenario; if the security level of the target execution scenario is lower than the requirements of the revision information, the execution scenario is removed. The second layer of matching further includes: Retrieve historical update data and operation records for execution scenarios; Based on the historical update data and operation records, a compatibility assessment model is constructed, and the compatibility assessment model is trained using a supervised learning method. Extract key features of the execution scenario, including applicable software version, historical success rate, and execution error; The compatibility score for the execution scenario is calculated based on the compatibility evaluation model. The compatibility score is derived from historical success rate, software version compatibility, and execution error. The calculated compatibility score is compared with a preset threshold. The compatibility score The calculation method is as follows: in, Historical success rate; To revise the software version number to which the information applies, This represents the software version number for the current execution scenario. This is the maximum software version number supported by the system. This refers to the historical execution error of the current execution scenario. This represents the maximum acceptable execution error. These are weighting coefficients; The third layer of matching further includes: Obtain the real-time runtime environment parameters of the target execution scenario; The adaptability score for the execution scenario is calculated, and the adaptability score is calculated based on CPU load, memory utilization, network load and the running status of key tasks; The adaptability score is compared with a preset adaptability threshold. When the adaptability score meets the preset adaptability threshold, the revision information is allowed to be transmitted to the execution scenario; otherwise, the execution scenario is removed. The compatibility score The calculation method is as follows: in, The CPU load for the current execution scenario. This represents the maximum CPU load allowed by the system. This represents the memory usage rate for the current execution scenario. This represents the maximum allowed memory usage of the system. Given the network load of the current execution scenario, The maximum network load allowed by the system; This is a state variable indicating whether there are critical tasks running in the current execution scenario; These are the weighting coefficients.

2. The system for adjusting execution scenarios based on a multi-level matching mechanism as described in claim 1, characterized in that: The methods by which the computing unit verifies the execution scenario include: Receive revision records related to the execution scenario; Compare the revised records with the master control records; The execution scenario is verified based on the verification results.

3. The system for adjusting execution scenarios based on a multi-level matching mechanism as described in claim 2, characterized in that: The verification content includes version consistency of revision information, parameter matching degree, execution time window, and status feedback of the execution scenario after revision.

4. The system for adjusting execution scenarios based on a multi-level matching mechanism as described in claim 1, characterized in that: The adjustment of at least one execution scenario includes: dynamically modifying system parameters, loading new execution rules, or adjusting the operating mode.

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