Execution scene adjustment method based on intelligent rollback mechanism

Abstract

The invention provides an execution scene adjustment method based on an intelligent rollback mechanism. The execution scene adjustment method comprises the steps of obtaining arithmetic unit information, generating and encrypting revision information, recognizing a target execution scene based on a standard matching degree, and verifying the correctness of the revision information. And by comparing the security level of the architecture, the revision information is ensured to adapt to the execution environment. The verification process comprises the step of comparing a revision record with a master control record so as to ensure consistency and safety. According to the method, an intelligent rollback mechanism is utilized, abnormity is automatically detected, the version is recovered to the previous version under the triggering condition, meanwhile, revision information is optimized in combination with machine learning, updating reliability and adaptability are improved, and the risk of wrong updating is reduced.

Description

Technical Field

[0001] The present invention relates to the field of computer system management, and in particular to an execution scenario adjustment method based on an intelligent rollback mechanism. Background Art

[0002] In the field of computer system management, especially in the technical practice of adjusting and updating execution scenarios, existing technologies usually rely on preset rules or manual intervention to execute system configuration changes. Traditional methods often adopt a static configuration management model, in which system administrators or developers modify and update execution scenarios based on predefined policies. The advantage of this method is its predictability, but it also has certain limitations, especially when flexible adaptation to different execution environments is required, there is often a lack of effective automation means.

[0003] During the execution scenario adjustment process, it is usually necessary to ensure the security and compliance of the changed content. In the prior art, a common practice is to rely on access control mechanisms or permission management systems to verify the source and credibility of revision information. However, these mechanisms usually rely on fixed permission settings and cannot dynamically adjust policies to meet the needs of different execution scenarios. In addition, traditional security verification methods are mainly based on predefined rules, and cannot accurately evaluate the adaptability of revision information for complex execution environments and multi-level architectures.

[0004] In order to improve the accuracy of execution scenario adjustments, the prior art usually uses version management systems or change log recording functions to track the adjustments of different versions. These systems allow administrators to go back to historical versions and manually restore to the previous state to reduce the risk of update failure. However, the manual rollback process relies on human judgment, which can make the recovery process more cumbersome, and in a complex distributed environment, manual rollback cannot guarantee the state consistency of all system components.

[0005] In the process of dealing with changes in execution scenarios, traditional methods usually focus on static matching based on preset rules to decide whether to accept a certain revision information. This method can be relatively effective in fixed architectures, but in dynamic environments or execution scenarios with multiple security levels, it may be insufficiently adaptable due to insufficient flexibility of the rules. In addition, in the process of matching revision information with execution scenarios, existing technologies often lack a detailed comparison of the security level of the architecture, which can cause some execution scenarios to accept revision information that does not meet security standards, or mistakenly reject appropriate updates.

[0006] In terms of exception handling, current technology mainly relies on system monitoring tools to detect abnormal situations, such as abnormal system load and frequent recording errors. Once an abnormality is found, the administrator usually needs to intervene manually to analyze the cause of the failure and decide whether to roll back to the previous version. However, the traditional rule-based anomaly detection method can have lags, and the execution of abnormal rollbacks usually lacks automated support, resulting in a slow response speed of the system during exception handling. In addition, existing methods usually fail to use historical data to conduct in-depth analysis of the causes of update failures, making it difficult to form an effective optimization mechanism, which in turn causes similar errors to occur repeatedly in the future.

[0007] In summary, the existing technology still has certain limitations in terms of the security, adaptability and exception handling efficiency of execution scene adjustment. With the increasing complexity of computer system architecture and the increasing demand for automated management, how to improve the intelligence level of execution scene adjustment and reduce the impact of update failures has become an urgent problem to be solved in this field. Summary of the invention

[0008] In order to solve the above problems in the prior art, the present invention proposes an execution scenario adjustment method based on an intelligent rollback mechanism, characterized in that the method comprises the following steps: Step 1: Acquire information of at least one computing unit, wherein the computing unit communicates with a data storage module, and the data storage module contains an operation command; Step 2: Generate revision information for one or more execution scenarios; Step 3: embedding an encrypted identifier corresponding to the source of the revision information in the revision information; Step 4: Identifying at least one execution scenario for receiving the revision information, Step 5: Verify the at least one execution scenario. Step 6: Based on the verification result, send the revision information to the at least one execution scenario to adjust the at least one execution scenario, wherein the adjustment includes an intelligent rollback mechanism, and the intelligent rollback mechanism includes: Automatic detection trigger conditions are set, including abnormal record fluctuations, system load exceeding the threshold, and incompatibility warnings caused by revision information; once the abnormal detection is triggered, the system immediately starts the intelligent rollback mechanism, restores to the previous version, and re-evaluates the adaptability of the revision information; combined with machine learning, the failed revision information is analyzed, and the adaptability of subsequent revision information is optimized based on historical data.

[0009] The computing unit includes a computing node, a server or a virtual machine instance.

[0010] The revision information includes adjustment of configuration parameters, optimization of execution strategy and update of security strategy.

[0011] The step 3 specifically includes: Obtain the revision information to be embedded in the encrypted identifier, and generate a unique identifier based on the timestamp, revision number and hash value of the revision content; Signing the revision information using an asymmetric encryption algorithm, wherein the asymmetric encryption algorithm includes a private key and a public key; Encrypting the hash value of the revision information by using a private key to generate an encrypted identifier, and attaching the encrypted identifier to the revision information to form a binding relationship between the revision information and the encrypted identifier; During the revision information verification process, the encrypted identifier is decrypted using the public key and the hash value of the revision information is recalculated; The decrypted hash value is compared with the recalculated hash value. If the comparison is consistent, it is confirmed that the revision information has not been tampered with. If the comparison is inconsistent, the revision information is rejected.

[0012] The identification in step 4 includes: Determine the specification matching degree of one or more execution scenarios; and identify the at least one execution scenario based on the specification matching degree, wherein identifying the at least one execution scenario based on the specification matching degree includes: comparing the architectural security level of the revision information with the architectural security level of the at least one execution scenario, and identifying the at least one execution scenario based on the comparison result.

[0013] The verification of step 5 includes: receiving a revision record related to the at least one execution scenario; comparing the revision record with a master control record; and verifying the at least one execution scenario based on the comparison result.

[0014] The method further includes setting automatic detection trigger conditions for the intelligent rollback mechanism, wherein the trigger conditions include abnormal record fluctuations, system load exceeding a set threshold, and incompatibility warnings of revision information; Calculate the abnormal index that triggers rollback based on the intelligent rollback decision model ,when Exceeding the preset rollback trigger threshold , the system starts the intelligent rollback mechanism, restores the execution scene to the previous version, and reloads the restored execution scene.

[0015] The abnormal index The calculation formula is:

[0016] in, Indicates the number of abnormal records detected per unit time; Indicates the average number of records per unit time during normal operation; Indicates the current load ratio of the CPU; Indicates the current load ratio of memory; Indicates the current load ratio of the network; Score the compatibility risk of the revision information, is the weight coefficient.

[0017] The rollback operation includes: According to the change records and stored historical version data, select the last stable version for recovery; Pause the affected service instance; reload the restored execution scenario and perform a self-check.

[0018] The machine learning algorithm establishes a revision information adaptability evaluation model based on historical revision information, execution environment parameters and failure cases, and optimizes the revision information parameter configuration, execution strategy and adaptability checking mechanism.

[0019] Beneficial effects: The present invention provides a method for identifying execution scenarios based on specification matching, which improves the adaptability of revision information, ensures that the update process meets the security level requirements of the execution environment, and reduces system anomalies caused by incompatibility. By embedding encrypted identifiers, the credibility of revision information is enhanced, enabling the system to effectively identify the source of information, avoid unauthorized modifications, and improve overall security and data integrity. The present invention uses a verification mechanism to compare execution scenarios to ensure the correctness and consistency of revision information, reduce system risks caused by erroneous updates, and improve system stability. Using an intelligent rollback mechanism, it is possible to quickly restore to the previous version when an anomaly is detected, reduce the impact of failures, and combine machine learning to optimize revision information to improve the reliability and adaptability of subsequent updates. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present application, but do not constitute an improper limitation of the present invention. In the drawings: Figure 1 A flow chart of a method for adjusting an execution scenario is shown. DETAILED DESCRIPTION

[0021] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments, wherein the illustrative embodiments and descriptions are only used to explain the present invention but are not intended to limit the present invention.

[0022] Example 1 The present invention provides a method for adjusting an execution scenario, and the specific implementation of the present invention is described in detail below in conjunction with embodiments.

[0023] In this embodiment, a method for adjusting an execution scenario is provided. Figure 1 As shown, the specific steps include: Step 1: Obtain information of at least one computing unit, wherein the computing unit is a computing node, a server or a virtual machine instance. The computing unit communicates with a data storage module, which contains operation commands and stores historical revision information and execution scenario configuration data.

[0024] Step 2: Based on the requirements of the current execution environment, generate revision information for one or more execution scenarios. The revision information includes updates of configuration parameters, execution policies or security policies to meet the requirements of different scenarios.

[0025] Step 3: Embed an encrypted identifier corresponding to the source of the revision information in the generated revision information to ensure the integrity and authenticity of the revision information. The encrypted identifier is generated using an asymmetric encryption algorithm, allowing the system to confirm whether the information has been authorized to be modified during the subsequent verification process.

[0026] Step 4: Identify at least one execution scenario for receiving the revision information, the identification process comprising: Step 4.1: Determine the specification matching degree of one or more execution scenarios. The specification matching degree is calculated by the system according to a preset matching rule to determine the compatibility of the revision information with the execution scenario.

[0027] Step 4.2: Identify the at least one execution scenario based on the specification match, wherein the identification process includes: comparing the architectural security level of the revision information with the architectural security level of the at least one execution scenario, and identifying an adapted execution scenario based on the comparison result to ensure that the revision information does not reduce the security of the system.

[0028] Step 5: Verify the at least one execution scenario, the verification process comprising: Step 5.1: Receive a revision record related to the at least one execution scenario to ensure traceability of the change process.

[0029] Step 5.2: Compare the revision record with the master record to determine whether the revision information complies with the system's management policy and change record requirements.

[0030] Step 5.3: Verify the at least one execution scenario based on the comparison result to prevent revision information that does not meet the specification from being applied to the execution environment.

[0031] Step 6: Based on the verification result, the revision information is sent to the at least one execution scenario, and the execution scenario is adjusted. The adjustment process includes an intelligent rollback mechanism, which specifically includes: Step 6.1: Set the automatic detection trigger conditions, including abnormal record fluctuations, system load exceeding the threshold, incompatibility warnings caused by revision information, etc. Once an abnormal situation is detected, the system automatically enters the exception handling mode.

[0032] Step 6.2: When anomaly detection is triggered, the system immediately starts the intelligent rollback mechanism, restores the execution scenario to the previous version, and re-evaluates the adaptability of the revision information to reduce the impact of erroneous updates on system stability.

[0033] Step 6.3: Combine machine learning algorithms to analyze failed revision information and optimize the adaptability of subsequent revision information based on historical data. This optimization process reduces the probability of future erroneous updates and increases the success rate of execution scenario adjustments.

[0034] The present invention realizes efficient adjustment of execution scenarios through intelligent matching, security verification and machine learning optimization mechanisms, improves the security and reliability of the system, and reduces the risk of erroneous updates.

[0035] The specific implementation methods of each step are as follows: Step 1 of the present application provides a method for obtaining information of at least one computing unit, wherein the computing unit is a computing node, a server, or a virtual machine instance. The information acquisition process of the computing unit needs to ensure that the system has a comprehensive perception of the execution environment and provide stable data support to support subsequent execution scenario adjustments.

[0036] In this embodiment, the information of the computing unit is acquired through the data acquisition module built into the system. The data acquisition module adopts a polling method, an event triggering method or an asynchronous acquisition method based on a message queue to meet the needs of different computing environments. In the polling method, the system actively requests the information of the computing unit at a preset time interval and updates it to the data storage module. In the event triggering method, when the computing unit changes (such as adding, deleting or updating the configuration), the system automatically triggers the data acquisition operation. In the message queue-based method, the state change of the computing unit is written into the queue and parsed and stored by the data storage module.

[0037] The information obtained includes, but is not limited to, the identification information of the computing unit, computing resource status, network connection information, and security policy configuration. Identification information is used to uniquely identify the computing unit, including but not limited to device ID, IP address, host name, or virtual machine UUID. Computing resource status includes CPU occupancy, memory usage, disk storage status, and network bandwidth load. Network connection information involves the communication between the computing unit and external components, including connection ports, transmission protocols, and traffic statistics. Security policy configuration is used to describe the access control rules, identity authentication methods, and data encryption policies of the computing unit.

[0038] The computing unit and the data storage module communicate with each other. The data storage module adopts a distributed storage architecture to support information storage and management in a large-scale computing environment. The core functions of the data storage module include data writing, updating, index query and historical version management. Data writing is guaranteed to be consistent through a transaction mechanism to ensure the integrity of the computing unit information. Data updates are stored in an incremental manner to reduce the storage burden and improve retrieval efficiency. Index query supports fast retrieval based on multi-dimensional conditions, including computing unit ID, timestamp, resource occupancy status, etc. Historical version management is used to store change records of computing unit information to support backtracking query and exception analysis.

[0039] The data storage module includes operation commands. The operation commands are used to manage the information interaction of the computing unit, including data pulling, data writing, information comparison and status update. The data pulling command is used to actively obtain the latest status data of the computing unit and synchronize it to the storage module. The data writing command is used to store the newly acquired data in the storage module and compare the historical data to identify the change point. The information comparison command is used to check the match between the current status of the computing unit and the target configuration to evaluate the adjustment requirements. The status update command is used to record the new computing unit status after the system performs the change operation and mark the current status as the latest version.

[0040] The data storage module stores historical revision information and execution scenario configuration data. Historical revision information includes change records, revision time, revision reasons, and execution results of computing units. This information supports the system to analyze change trends and is used to formulate abnormal rollback strategies. Execution scenario configuration data includes the execution strategy, load balancing scheme, and security constraints applicable to the current computing unit. This configuration data is used to guide the system to select the optimal solution when adjusting the execution scenario to ensure the reasonable allocation of computing resources and the effective execution of security policies.

[0041] Step 2 of this application provides a method for generating revision information based on the requirements of the current execution environment, ensuring that the system can adapt to different execution scenarios and make corresponding adjustments. The generation process of revision information involves analyzing the state of the execution environment, determining the revision content, and formatting the revision information to ensure the integrity and adaptability of the revision information.

[0042] In the process of generating revision information, the requirements of the current execution environment are first analyzed. The requirements of the execution environment are evaluated based on the system operation status, task scheduling strategy, resource allocation and security policy requirements. The system operation status includes parameters such as CPU occupancy, memory usage, and network load. The task scheduling strategy involves the priority, concurrency and execution time limit of the computing task. The resource allocation reflects the availability of computing resources, storage resources and bandwidth resources in the execution scenario. The security policy requirements involve access control, data encryption and identity authentication mechanisms.

[0043] Based on the needs of the execution environment, determine the specific content of the revision information. The revision information includes the adjustment of configuration parameters, optimization of execution strategies, and update of security strategies. The adjustment of configuration parameters involves the resource quota of computing nodes, task priority setting, and record level. The optimization of execution strategies includes the adjustment of task scheduling methods, optimization of load balancing strategies, and update of fault recovery strategies. The update of security strategies involves the adjustment of user access rights, changes in data transmission encryption methods, and updates of anomaly detection rules.

[0044] Revision information needs to be formatted when it is generated to ensure that the revision information can be correctly parsed and applied by the system. Formatting includes data structuring, field standardization, and version management. Data structuring ensures that the revision information has a clear hierarchy, field standardization ensures the compatibility of revision information between different system components, and version management is used to record revision information of different versions to support historical backtracking and version control.

[0045] Step 3 of the present application provides a method for embedding an encrypted identifier corresponding to the source of the revision information in the generated revision information to ensure the integrity and source authenticity of the revision information.

[0046] After the revision information is generated, the system first assigns a unique identifier to the information, which is generated based on the timestamp, revision number and hash value of the revision content to ensure the uniqueness of the revision information. Subsequently, the system signs the revision information through an asymmetric encryption algorithm to generate an encrypted identifier.

[0047] In this embodiment, the asymmetric encryption algorithm adopts a public key infrastructure (PKI) system, which includes a private key and a public key. The system uses the private key to sign the revision information, generates an encrypted identifier, and attaches the identifier to the revision information. Specifically, the system first calculates a hash value for the revision information to obtain a summary of a fixed length. Then, the private key is used to encrypt the hash value to generate a digital signature, i.e., an encrypted identifier. The process is expressed as:

[0048] in, is the encryption identifier, represents the encryption function, For revision information The hash value of Is the private key used for encryption.

[0049] After the encryption identifier is embedded in the revision information, the revision information and its encryption identifier form a binding relationship to ensure that the subsequent system verifies the authenticity of its source. During the verification process, the system uses the public key to decrypt the encryption identifier and recalculate the hash value of the revision information. If the two are consistent, it is confirmed that the revision information has not been tampered with. The verification process is expressed as:

[0050] in, is the decryption function, is the public key. If the decryption result is consistent with the original hash value, it means that the revision information is complete and reliable, otherwise the system rejects the revision information.

[0051] In step 4 of the present application, in the process of identifying the execution scenario, the specification matching degree of one or more execution scenarios is first determined. The specification matching degree is an important indicator to measure the degree of adaptation between the revision information and the execution scenario, and is calculated by the system according to the preset matching rules. The matching rules are set according to the functional requirements, system architecture, resource configuration, security level and other factors of the execution scenario to ensure that the revision information is applied in the applicable execution scenario.

[0052] The calculation of the specification matching degree is based on a multi-factor weighted evaluation model, which quantitatively scores the key features of the execution scenario and conducts comparative analysis in combination with the applicable conditions of the revision information. The matching degree calculation is expressed as:

[0053] in, To standardize the matching degree, For the The adaptation score of the matching indicators, is the corresponding weight coefficient, is the total number of factors involved in the matching rule. After the matching degree is calculated, the system compares the calculation result with the set adaptation threshold. If the matching degree meets or exceeds the set threshold, the execution scenario is considered suitable for receiving revision information.

[0054] Based on the determination of the specification matching degree, at least one execution scenario is further identified, wherein the identification process includes comparing the architectural security level of the revision information with the architectural security level of the execution scenario to ensure that the security requirements of the revision information match the execution environment.

[0055] The comparison of architecture security levels involves the comparison of multiple security parameters, including but not limited to access control policies, data encryption standards, identity authentication mechanisms, and system protection capabilities. The security level assessment adopts a hierarchical identification method. A hierarchical system in which Indicates the lowest security level. Indicates the highest security level, then the security level of the revised information The following conditions must be met to be applicable to the security level of the target execution scenario :

[0056] If the security level of the revised information is lower than the security level of the target execution scenario, , the system refuses to apply the revision information in the execution scenario and prompts a warning message that the security policy does not match.

[0057] In step 5 of the present application, during the verification process, the revision record related to the at least one execution scenario is first received. The revision record contains key data such as the specific content of the revision information, revision time, revision initiator, revision reason and execution status. The system obtains historical revision records through the record collection module or the database query interface and stores them in the version management database to ensure the traceability of the change process. The storage of revision records adopts a unique index identification mechanism to ensure that revision information of different versions will not be confused.

[0058] After receiving the revision record, the revision record is compared with the master record to determine whether the revision information complies with the system's management policy and change record requirements. The master record is the global version control information maintained by the system, including the baseline configuration of the execution scenario, approved revision content, and historical change records. During the comparison process, the system first checks the legitimacy of the revision record, including whether it is initiated by an authorized subject, whether it complies with the security policy, and whether it has gone through a complete approval process. Then, the system compares the revision information with the execution scenario version in the master record to determine whether it complies with the expected change path. If the content of the revision information does not match the standard configuration in the master record or is modified without authorization, the system rejects the application of the revision information and records the exception record.

[0059] After completing the comparison analysis, the at least one execution scenario is verified based on the comparison results to prevent revision information that does not meet the specifications from being applied to the execution environment. The system adopts a multi-level verification strategy. First, a consistency check is performed to check whether the revision information meets the standard format and data structure requirements of the execution scenario. Secondly, an integrity check is performed to verify whether the revision information contains all necessary parameters and ensure that the parameter values ​​are within the range defined by the system. Finally, an adaptability check is performed to apply the revision information to the test environment or sandbox environment, simulate the running status of the execution scenario, and evaluate the impact of the revision information on system performance, security, and stability.

[0060] Step 6 of the present application provides a method for sending revision information to the execution scene based on the verification result and completing the execution scene adjustment. The method improves the stability and reliability of the system through an intelligent rollback mechanism and reduces the impact of erroneous revision information on the execution environment.

[0061] After the verification is completed, the system determines whether to send the revision information to the execution scenario based on the verification results. If the revision information meets the specification matching and security requirements of the execution scenario, it will be applied to the target execution environment and the status of the current execution scenario will be updated. During the adjustment of the execution scenario, the system will record all change operations and generate change records for subsequent backtracking and analysis.

[0062] In order to ensure the stability of the execution scenario adjustment, this implementation method utilizes an intelligent rollback mechanism and first sets the automatic detection trigger conditions. The system continuously monitors the running status of the execution scenario and evaluates the impact of the revision information based on multiple indicators. Trigger conditions include but are not limited to abnormal record fluctuations, system load exceeding the set threshold, incompatibility warnings caused by revision information, etc. Abnormal record fluctuations are detected by the record analysis module, and the system will make a comprehensive judgment on the recorded abnormality type, error frequency and impact range. When the system load exceeds the preset threshold, the system determines whether the revision information imposes a burden on the system operation by monitoring the usage rate of resources such as CPU, memory, and network bandwidth. In addition, if the revision information is incompatible with existing system components or dependencies, the system will automatically generate a warning and mark the revision information as a high-risk change.

[0063] In this embodiment, an intelligent rollback decision model is used to calculate the comprehensive abnormality index that triggers the rollback. The anomaly index is calculated by combining the record anomaly score, system load score, and compatibility risk score. Its mathematical expression is as follows:

[0064] in: Indicates the number of abnormal records detected per unit time; Indicates the average number of records per unit time during normal operation; , , Respectively represents the current load ratio of CPU, memory and network (value range ); Score the compatibility risk of the revision information in the range ,The higher value indicates a greater incompatibility risk; , , It is the weight coefficient, which is set according to the specific application scenario to ensure the reasonable weight of different influencing factors in the calculation of the anomaly index.

[0065] when Exceeding the preset rollback trigger threshold When a new file is executed, the system immediately starts the intelligent rollback mechanism to ensure the stability of the execution environment.

[0066] When anomaly detection is triggered, the system immediately starts the intelligent rollback mechanism to restore the execution scenario to the previous version to reduce the impact of erroneous updates on system stability. The rollback operation is performed based on the version control system. The system will select the last stable version for recovery based on the change records and stored historical version data. During the rollback process, the system will suspend the affected service instances to avoid new conflicts or data corruption during the rollback process. After the rollback is completed, the system reloads the restored execution scenario and performs a self-check to ensure that the rolled-back version can run normally.

[0067] While performing intelligent rollback, the system combines machine learning algorithms to analyze failed revision information and optimize the adaptability of subsequent revision information based on historical data. The system establishes a revision information adaptability assessment model by collecting historical revision information, execution environment parameters, and failure cases. The model identifies the main reasons for revision information failure based on feature extraction methods and predicts problems that may occur in future revision information. The optimization process includes adjusting the parameter configuration of revision information, improving execution strategies, and enhancing the adaptability check mechanism. By continuously training and optimizing the model, the system can improve the adaptability of future revision information, reduce the probability of erroneous updates, and increase the success rate of execution scenario adjustments.

[0068] The above description is only a preferred embodiment of the present invention, so all equivalent changes or modifications made according to the structure, characteristics and principles described in the scope of the patent application of the present invention are included in the scope of the patent application of the present invention.

Claims

1. A method for adjusting execution scenarios based on an intelligent rollback mechanism, characterized in that: The method comprises the following steps: Step 1: Acquire information of at least one computing unit, wherein the computing unit communicates with a data storage module, and the data storage module contains an operation command; Step 2: Generate revision information for one or more execution scenarios; Step 3: embedding an encrypted identifier corresponding to the source of the revision information in the revision information; Step 4: Identifying at least one execution scenario for receiving the revision information, Step 5: Verify the at least one execution scenario. Step 6: Based on the verification result, send the revision information to the at least one execution scenario to adjust the at least one execution scenario, wherein the adjustment includes an intelligent rollback mechanism, and the intelligent rollback mechanism includes: Set automatic detection trigger conditions, including abnormal record fluctuations, system load exceeding the threshold, and incompatibility warnings caused by revision information; once the abnormal detection is triggered, the system immediately starts the intelligent rollback mechanism, restores to the previous version, and re-evaluates the adaptability of the revision information; combines machine learning to analyze failed revision information and optimize the adaptability of subsequent revision information based on historical data.

2. The execution scenario adjustment method based on the intelligent rollback mechanism according to claim 1, characterized in that: The computing unit includes a computing node, a server or a virtual machine instance.

3. The method for adjusting execution scenarios based on an intelligent rollback mechanism according to claim 1, characterized in that: The revision information includes adjustment of configuration parameters, optimization of execution strategy and update of security strategy.

4. The execution scenario adjustment method based on the intelligent rollback mechanism according to claim 1, characterized in that: The step 3 specifically includes: Obtain the revision information to be embedded in the encrypted identifier, and generate a unique identifier based on the timestamp, revision number and hash value of the revision content; Signing the revision information using an asymmetric encryption algorithm, wherein the asymmetric encryption algorithm includes a private key and a public key; Encrypting the hash value of the revision information by using a private key to generate an encrypted identifier, and attaching the encrypted identifier to the revision information to form a binding relationship between the revision information and the encrypted identifier; During the revision information verification process, the encrypted identifier is decrypted using the public key and the hash value of the revision information is recalculated; The decrypted hash value is compared with the recalculated hash value. If the comparison is consistent, it is confirmed that the revision information has not been tampered with. If the comparison is inconsistent, the revision information is rejected.

5. The execution scenario adjustment method based on the intelligent rollback mechanism according to claim 1, characterized in that: The identification of step 4 includes: Determine the specification matching degree of one or more execution scenarios; and identify the at least one execution scenario based on the specification matching degree, wherein identifying the at least one execution scenario based on the specification matching degree includes: comparing the architectural security level of the revision information with the architectural security level of the at least one execution scenario, and identifying the at least one execution scenario based on the comparison result.

6. The method for adjusting execution scenarios based on an intelligent rollback mechanism according to claim 1, characterized in that: The verification of step 5 includes: receiving a revision record related to the at least one execution scenario; comparing the revision record with a master control record; and verifying the at least one execution scenario based on the comparison result.

7. The method for adjusting execution scenarios based on an intelligent rollback mechanism according to claim 1, characterized in that: The method further includes setting automatic detection trigger conditions for the intelligent rollback mechanism, wherein the trigger conditions include abnormal record fluctuations, system load exceeding a set threshold, and incompatibility warnings of revision information; Calculate the abnormal index that triggers rollback based on the intelligent rollback decision model ,when Exceeding the preset rollback trigger threshold , the system starts the intelligent rollback mechanism, restores the execution scene to the previous version, and reloads the restored execution scene.

8. The method for adjusting execution scenarios based on an intelligent rollback mechanism according to claim 7, characterized in that: The abnormal index The calculation formula is: in, Indicates the number of abnormal records detected per unit time; Indicates the average number of records per unit time during normal operation; Indicates the current load ratio of the CPU; Indicates the current load ratio of memory; Indicates the current load ratio of the network; Score the compatibility risk of the revision information, is the weight coefficient.

9. The execution scenario adjustment method based on the intelligent rollback mechanism according to claim 7, characterized in that: The rollback operation includes: According to the change records and stored historical version data, select the last stable version for recovery; Pause the affected service instance; reload the restored execution scenario and perform a self-check.

10. The method for adjusting execution scenarios based on an intelligent rollback mechanism according to claim 1, characterized in that: The machine learning algorithm establishes a revision information adaptability evaluation model based on historical revision information, execution environment parameters and failure cases, and optimizes the revision information parameter configuration, execution strategy and adaptability checking mechanism.

Images