An execution scenario adjustment system based on edge computing

Abstract

This invention provides an execution scenario adjustment system based on edge computing, comprising at least one computing unit and a data storage module. The computing unit generates revision information and embeds encrypted identifiers, identifies suitable execution scenarios based on specification matching, and verifies them. During the adjustment process, an edge computing optimization mechanism is employed, deploying lightweight computing units near the execution scenario to achieve local processing and preliminary verification of the revision information. The information is only synchronized to the central server after it has been adapted, reducing communication latency and improving system real-time performance. This system is suitable for distributed application scenarios with low latency and high real-time requirements.

Description

Technical Field

[0001] This invention relates to the field of edge computing technology, specifically to an execution scenario adjustment system based on edge computing, which is particularly suitable for application scenarios in distributed systems that require low latency and high real-time performance. Background Technology

[0002] When adjusting the execution of tasks or scenarios in a distributed system, it is often necessary to handle the collaboration and synchronization of massive numbers of nodes, which is a common challenge in the industry. Especially in application areas with high real-time requirements, relying solely on a central server to distribute instructions to each node in a one-way manner can easily lead to communication congestion or increased latency, making it difficult to meet the requirements for stable and efficient operation.

[0003] Conventional technologies mostly employ centralized deployment, managing and controlling operations through pre-defined, unified command flow paths. While this approach may meet basic needs when the number of nodes is small, as network complexity and the number of nodes increase, the types and quantities of commands that the central node needs to process explode, highlighting the bottleneck at single points of failure. Furthermore, network outages or abnormal conditions can prevent nodes from receiving the correct adjustment commands in a timely manner, further amplifying the inherent vulnerabilities of centralized architectures.

[0004] However, this solution suffers from significant latency and insufficient security verification. The single-center management model is prone to resource contention under high concurrency, making it difficult to promptly transmit and verify the actual execution status of each node. Without an effective local response mechanism, update commands cannot be issued quickly if the central node is overloaded or communication links are congested, leading to delays in execution and adjustments. For applications requiring frequent configuration updates or stringent security verification, such latency and lack of verification pose significant risks.

[0005] When the aforementioned problems cannot be resolved in a timely manner, some critical business processes may be forced to interrupt or experience execution chaos, severely impacting the overall reliability and availability of the system. In systems with wide distribution and high real-time requirements, if necessary response and verification cannot be performed at the network edge or other critical locations, centralized management models often struggle to balance the dual demands of timeliness and security, thus significantly restricting system scalability and subsequent upgrades. Summary of the Invention

[0006] To address the aforementioned problems in the prior art, this invention proposes an execution scenario adjustment system based on edge computing. The system includes: at least one computing unit; and a data storage module that communicates with the at least one computing unit. The data storage module contains operation commands that configure the computing unit to: 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; identify at least one execution scenario for receiving the revision information; verify the at least one execution scenario; and, based on the verification result, send the revision information to the at least one execution scenario to adjust the at least one execution scenario. The adjustment employs edge computing optimization, which includes:

[0007] Lightweight computing units are deployed on edge computing nodes, enabling them to process revision information and perform initial verification locally. The revision information is only synchronized to the central server after the edge computing unit confirms that the revision information is compatible.

[0008] The computing unit includes multiple computing cores, and each computing core performs load balancing through a task scheduling mechanism.

[0009] The computing unit generates revision information based on the received system status data and the execution scenario operation status, and calculates revision parameters according to the matching rules; the data storage module stores the revision information, and the data storage module includes operation commands, matching rules and historical revision records.

[0010] The identification includes:

[0011] Determine the specification fit of one or more execution scenarios; and

[0012] Identifying 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:

[0013] The architecture security level of the revised information is compared with the architecture security level of the at least one execution scenario, and the at least one execution scenario is identified based on the comparison result;

[0014] The architectural security level of the revised information is determined by predefined security policies, including but not limited to data integrity level, access control level, and execution permission level.

[0015] The verification includes:

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

[0017] The revision record is compared with the master control record; and the at least one execution scenario is verified based on the comparison results.

[0018] The revision record includes the past adjustment history of the execution scenario, the applied revision information, the adjustment effective time, and the execution status; the master control record includes the global adjustment strategy, the expected system status, and the standard version of the historical revisions.

[0019] The edge computing unit includes:

[0020] The data parsing module parses the adjustment parameters, target execution scenario identifier, and encryption identifier in the revision information, and uses a predefined data structure;

[0021] The rule matching module performs adaptability testing on the revision information, matches the revision information with the execution scenario rules, and calculates the adaptability between the revision information and the target execution scenario using the matching degree calculation formula.

[0022] The security assessment module detects the security level of the revised information and confirms its integrity based on public key decryption or hash verification.

[0023] The matching degree M s The calculation formula is:

[0024]

[0025] in,

[0026] S c To revise the information and the parameter compatibility score for the execution scenario,

[0027] P r Score the current load status of the execution scenario.

[0028] T l The task execution priority is scored for the execution scenario.

[0029] w1, w2, and w3 are different weighting coefficients.

[0030] λ1, λ2, λ3 are nonlinear adjustment exponents.

[0031] D f To correct historical discrepancies between information and execution scenarios,

[0032] θ is the bias smoothing factor.

[0033] The edge computing optimization described above is applicable to distributed systems.

[0034] Beneficial effects:

[0035] This invention utilizes an edge computing optimization mechanism to make the processing of revision information more efficient, reducing communication latency during execution scenario adjustments and improving system real-time performance and response speed. Using encrypted identifiers to mark revision information effectively enhances data security and reliability, ensuring the traceability of the revision information's source and reducing the risk of errors or malicious tampering. The execution scenario identification method based on specification matching allows revision information to more accurately adapt to different execution scenarios, improving the accuracy and adaptability of system adjustments and reducing unnecessary resource consumption. Local processing and verification by edge computing units reduces the computational burden on the central server, optimizes the system architecture, and makes it suitable for distributed application scenarios with stringent requirements for low latency and high real-time performance. Attached Figure Description

[0036] 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:

[0037] Figure 1 A schematic diagram of the system structure for adjusting execution scenarios based on edge computing according to the present invention is shown. Detailed Implementation

[0038] 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.

[0039] like Figure 1 As shown, this embodiment provides a system for adjusting execution scenarios based on edge computing. Specifically, this embodiment provides a system for adjusting execution scenarios based on edge computing. The system includes at least one computing unit 101 and a data storage module 102 that communicates with the computing unit. The computing unit 101 is responsible for processing computational tasks related to execution scenario adjustment and accessing stored operation commands and revision information through the data storage module 102. This system adopts an edge computing optimization mechanism, enabling revision information to be processed at computing nodes close to the execution scenario, thereby improving the real-time performance and efficiency of the adjustment.

[0040] The computing unit 101 employs a high-performance computing chip or embedded processor and can be configured with a dedicated computing acceleration module to improve execution efficiency. In a specific implementation, the computing unit 101 may include multiple computing cores, each capable of independently running computing tasks and dynamically load balancing through a task scheduling mechanism to optimize computing resource allocation. This computing unit interacts with the data storage module 102 via a standard data communication protocol, supporting both synchronous and asynchronous data access modes to adapt to the computing needs of different scenarios.

[0041] The data storage module 102 adopts a high-reliability storage architecture, which may include a cache storage unit and a long-term storage unit. The cache storage unit is used to store data that needs to be accessed frequently in the short term, such as operation commands and recent revision information, to reduce data access latency. The long-term storage unit is used to store historical revision records and configuration files for execution scenarios to support the data accumulation and management required for long-term system operation. In terms of storage management, this data storage module supports an index-based fast query mechanism and can employ data compression and block storage technologies to improve data retrieval efficiency and storage space utilization.

[0042] The processing unit 101 accesses stored operation commands and revision information through the data storage module 102, specifically in two modes: data retrieval and data push. In data retrieval mode, the processing unit 101 periodically or on-demand requests the latest revision information from the data storage module 102 and performs local caching and parsing upon receiving the data. In data push mode, when the storage module 102 detects new revision information being written, it automatically sends a notification to the processing unit 101 to trigger the data update process. These two modes can be combined according to the actual application requirements of the system to balance the real-time nature of data access and the utilization of system resources.

[0043] This system employs an edge computing optimization mechanism, enabling revision information to be processed on computing nodes close to the execution scenario, thereby improving the real-time performance and efficiency of adjustments. To this end, a lightweight computing unit is deployed near the execution scenario. This unit can be a standalone edge computing device or an embedded computing module. This computing unit possesses local data processing capabilities, enabling it to receive revision information from the computing unit 101 and perform preliminary verification and data processing. Specifically, the computing unit checks the completeness, format specifications, and preliminary adaptability of the revision information to ensure the accuracy and stability of subsequent adjustment operations.

[0044] During data transmission, to reduce communication latency and improve system reliability, the arithmetic unit 101 employs differential data transmission, sending only the revised information of the changes, rather than the complete scenario adjustment data. This reduces the amount of data transmitted and improves the system's real-time response capability. Furthermore, the system can adopt a layered data verification mechanism, including data integrity verification at the transport layer and data consistency verification at the application layer, to ensure that the revised information is not erroneous or corrupted during transmission and storage.

[0045] Overall, this system can efficiently and stably execute edge computing-based scenario adjustment tasks, reducing the computational burden on the central server while improving the real-time performance and adaptability of the adjustments, thereby meeting the requirements of distributed systems for low latency and high reliability.

[0046] Regarding the specific functional implementation:

[0047] (1) Generation and storage of revision information

[0048] The computation unit 101 is responsible for generating revision information for adjusting the execution scenario. The process of generating revision information includes receiving system status data, analyzing the current operating status of the execution scenario, and calculating revision parameters according to matching rules. In specific implementations, the computation unit 101 uses a rule engine or machine learning model to analyze the operating status of the execution scenario and generates corresponding revision information based on the analysis results. The revision information includes adjustment parameters, control commands, timestamps, and other necessary execution data to ensure that the adjustment task can be correctly executed in the target execution scenario.

[0049] During the revision information generation process, the processing unit 101 embeds a corresponding encrypted identifier from the source using a hash algorithm or public-key encryption algorithm to ensure data security and traceability. The generation method of the encrypted identifier can include a key-based signature mechanism, such as using SHA-256 for hash calculation and combining it with RSA or ECC (elliptic curve cryptography) for digital signature. This encrypted identifier ensures the integrity of the revision information and prevents unauthorized modification. At the receiving end, the execution scenario can use the corresponding public key for signature verification to confirm the authenticity and integrity of the revision information.

[0050] Revision information is stored in data storage module 102, which manages and maintains the data required for execution scenario adjustments. Data storage module 102 employs a hierarchical storage structure to improve data access efficiency and system stability. The storage module includes three core data categories: operation commands, matching rules, and historical revision records. Operation commands indicate the adjustment method for execution scenario 103, matching rules determine the scope of application of the revision information, and historical revision records store previous adjustment data to support version management and retrospective analysis.

[0051] The data storage module 102 employs an indexing mechanism to organize the stored data, accelerating data retrieval. The indexing mechanism can utilize a B+ tree structure or a hash index to reduce query latency and improve the speed of retrieving revision information. Furthermore, to ensure data reliability, the data storage module 102 features a redundant backup mechanism, allowing the stored data to be distributed across multiple nodes to prevent data loss due to single points of failure. When writing new revision information, the system employs atomic operations to ensure data storage consistency; that is, if a storage operation is interrupted due to an anomaly, the data storage module can roll back to the previous stable state, preventing data corruption or incomplete storage.

[0052] The data storage module 102 also supports data synchronization and version management mechanisms. Whenever the computation unit 101 generates new revision information, this information is assigned a unique version number and stored in the historical revision record. The version management mechanism allows the system to revert to a specific version when necessary, enabling recovery operations in case of anomalies during execution scenario adjustments. The synchronization mechanism ensures that the data storage module 102 maintains data consistency across multiple computation units 101, avoiding adjustment conflicts caused by asynchronous storage on different nodes.

[0053] Through the above technical solutions, the computing unit 101 can efficiently generate revision information and ensure data security and traceability. Meanwhile, the data storage module 102 employs an efficient storage and indexing mechanism to provide data support for adjustments to the execution scenario, and ensures data integrity and consistency through version management and synchronization mechanisms.

[0054] (2) Matching and identification of execution scenarios

[0055] The processing unit 101 identifies the target execution scenario suitable for receiving the revision information based on the specification matching degree of the execution scenario. To ensure the accuracy and efficiency of the matching process, the processing unit 101 adopts a hierarchical parsing and dynamic matching strategy to ensure that the revision information can be adapted to the target execution scenario.

[0056] During the matching process, the processing unit 101 first parses the architectural security level of the revision information. The architectural security level of the revision information is determined by predefined security policies, including but not limited to data integrity level, access control level, and execution permission level. The processing unit 101 extracts this security level from the revision information and compares it with the security level of the execution scenario. The security level of the execution scenario is set by the system during the initialization phase and can be dynamically adjusted according to its operating status. During the comparison process, the processing unit 101 uses security level verification rules, such as using a hierarchical structure for permission mapping or employing a weighted matching method, to determine whether the revision information is applicable to the target execution scenario.

[0057] During the matching degree calculation process, the calculation unit 101 combines the operation rules of the execution scenario with the current state to calculate the matching degree, and filters out suitable execution scenarios based on a preset threshold. The operation rules are stored in the data storage module and include the functional scope of the execution scenario, acceptable data formats, and adjustment parameter ranges. The calculation unit 101 reads the operation rules from the data storage module and compares them with the adjustment content in the revision information to ensure that the revision information conforms to the operation constraints of the execution scenario.

[0058] The calculation of the current state involves the operating parameters of the execution scenario, including but not limited to system load, resource utilization, and task execution status. The computing unit 101 obtains the current operating parameters by real-time monitoring or calling the status interface of the execution scenario, and calculates the matching degree using a mathematical model.

[0059] After the matching score is calculated, the processing unit 101 compares the matching score with a preset threshold. If the matching score is higher than the threshold, the execution scenario is identified as the target execution scenario and proceeds to the subsequent verification stage. If the matching score is lower than the threshold, the revision information will not be applied to the execution scenario, and other possible target execution scenarios will continue to be screened.

[0060] Through the above matching and recognition methods, the operation unit 101 can ensure the adaptability of the revision information, avoid instability of the execution scenario due to incorrect matching, and improve the accuracy and reliability of the execution adjustment.

[0061] (3) Validation of the execution scenario

[0062] After determining the target execution scenario, the computing unit 101 further verifies it to ensure that the state of the execution scenario meets the adjustment requirements. The verification process involves steps such as data collection, comparison calculation, and matching degree evaluation to ensure the applicability of the revised information and the effectiveness of the adjustment.

[0063] The processing unit 101 first receives the revision records from the execution scenario. These revision records contain information such as the execution scenario's past adjustment history, applied revision information, adjustment effective time, and execution status. These records are stored in the data storage module 102 and support indexing by timestamp or adjustment version for quick data retrieval. The processing unit 101 accesses the data storage module 102 to extract the revision records related to the current adjustment and parses them.

[0064] After parsing, the processing unit 101 compares the revision record of the execution scenario with the master control record. The master control record is stored in the central control system and contains the global adjustment strategy, the expected system state, and the standard version of historical revisions. During the comparison, the processing unit 101 uses a rule matching algorithm or a difference analysis algorithm to check whether the execution scenario already contains the current revision information or whether there is any historical revision content that conflicts with the current adjustment target. For example, when the revision information previously applied to the execution scenario is incompatible with the current revision information, the processing unit 101 can mark the scenario as inapplicable and terminate the adjustment process.

[0065] The matching degree calculation employs a multi-dimensional evaluation method, including but not limited to the consistency of revised content, the compliance of execution status, and system stability assessment. The consistency of revised content refers to the degree of matching between the revised record and the master control record, calculated using the following formula:

[0066]

[0067] in,

[0068] C m To score for consistency of the revised content,

[0069] R c This refers to the set of adjustment parameters included in the current revision information.

[0070] R m This is the set of adjustment parameters that have been applied in the master control record.

[0071] ∩ represents the intersection operation of two sets.

[0072] The execution status compliance measure whether the current state of the execution scenario meets the adjustment requirements, including factors such as resource availability and task progress. For example, if the current task load of the execution scenario is too high, the adjustment may cause system instability, and the computing unit 101 will lower the matching score or directly exclude the execution scenario.

[0073] Finally, the calculation unit 101 calculates the matching degree based on the consistency score of the revised content and the compliance score of the execution status, and compares it with a preset threshold. If the matching degree is higher than the set threshold, the target execution scenario is confirmed as the adjustment target, and the revision information is sent out. If the matching degree is lower than the threshold, the calculation unit 101 terminates the adjustment and records the reason for failure for subsequent analysis and optimization.

[0074] Through the above verification mechanism, the operation unit 101 can ensure the adaptability of the revision information, avoid instability caused by incompatible adjustments or abnormal system states, and improve the reliability and security of the execution scenario adjustment.

[0075] (4) Edge computing optimization

[0076] Before adjustments are implemented, the revised information is first processed and preliminarily verified locally by an edge computing unit. The edge computing unit is deployed near the execution scenario to reduce data transmission latency and improve the real-time performance and reliability of the revised information processing.

[0077] The edge computing unit employs a lightweight computing mechanism to reduce computing resource consumption and improve data processing efficiency. This unit includes a data parsing module, a rule matching module, and a security assessment module, which are used to perform format parsing, compatibility testing, and security level verification on the revised information, respectively. The data parsing module receives the revised information and parses the adjustment parameters, target execution scenario identifiers, and encryption identifiers contained within. During the parsing process, the system uses a predefined data structure to ensure that the revised information can be parsed in a standardized format, thereby guaranteeing data integrity and consistency.

[0078] The rule matching module performs an adaptability test on the revised information to determine whether it meets the operational requirements of the execution scenario. During the matching process, the edge computing unit uses pre-stored execution scenario rules, including hardware compatibility constraints, adjustment parameter ranges, and task execution priorities. The matching degree calculation employs a weighted scoring mechanism to evaluate the adaptability between the revised information and the target execution scenario.

[0079] Traditional matching degree calculation methods often use linear weighted summation to score multiple matching factors. This solution employs an improved matching degree calculation formula based on non-linear weighting combined with dynamic adjustment factors to more accurately adapt to the characteristics of different execution scenarios. The specific matching degree score M is... s The calculation is as follows:

[0080]

[0081] in,

[0082] S c To revise the information and the parameter compatibility score for the execution scenario,

[0083] P r Score the current load status of the execution scenario.

[0084] T l The task execution priority is scored for the execution scenario.

[0085] w1, w2, and w3 are different weighting coefficients, which are adjusted according to specific application requirements.

[0086] λ1, λ2, and λ3 are nonlinear adjustment exponents that can be dynamically adjusted according to system optimization requirements.

[0087] D f To revise the historical adjustment deviation between the revised information and the execution scenario, that is, the magnitude of parameter change between the previous adjustment and the current revised information.

[0088] θ is the deviation smoothing factor, used to control the impact of adjustment deviation on the matching degree.

[0089] By employing an exponential approach to adjust the scoring, certain key parameters can have a greater impact on the matching degree under specific conditions, thus improving the flexibility of the matching calculation. This is achieved through an exponential decay factor. The matching degree is dynamically corrected to prevent the system from fluctuating due to too frequent adjustments, making the adjustment process smoother and more stable.

[0090] The security assessment module is used to detect the security level of the revised information, ensuring it meets the security requirements of the execution scenario. During the assessment, the system extracts the encrypted identifier from the revised information and confirms the data integrity through public key decryption or hash verification. The edge computing unit also checks whether the revised information contains unauthorized instructions or abnormal parameters. If a security risk is detected, execution is refused and an alarm message is generated.

[0091] Revised information is only confirmed as compatible and synchronized to the central server after the edge computing unit passes adaptability testing and security assessment. During synchronization, the system employs an incremental data update mechanism, transmitting only necessary revisions to reduce communication overhead. Furthermore, data transmission uses an encrypted channel to prevent data tampering or leakage during transmission.

[0092] Through the above optimizations, the edge computing unit can quickly verify the revised information at a location close to the execution scenario, reducing system latency and improving the security and reliability of the execution adjustments.

[0093] (5) Implementation and adjustment of revised information

[0094] After verification by the edge computing unit, the revised information is officially distributed to the target execution scenario and dynamically adjusted according to its current state. The distribution of revised information employs an asynchronous transmission mechanism to reduce network congestion and ensure reliable data delivery. The system can use message queues or a publish-subscribe-based transmission protocol to improve the scalability and stability of data transmission.

[0095] Upon receiving the revision information, the target execution scenario parses the information and extracts the adjustment parameters contained within. This parsing process is completed by the task scheduling module or control logic unit within the execution scenario to ensure that the adjustment parameters accurately match the operational requirements of the execution scenario. After parameter parsing, the execution scenario determines its adjustment strategy based on its current state. For example, if the execution scenario currently has a high task load, a phased adjustment strategy can be adopted to avoid system instability caused by parameter adjustments.

[0096] The local computing unit is deployed within the execution scenario and is responsible for real-time parameter optimization and adjustment. Based on feedback data from the execution scenario, this unit dynamically corrects and adjusts the parameters to ensure that the adjustments meet expectations. Feedback data includes, but is not limited to, key performance indicators such as task execution efficiency, resource utilization, and system response time. The local computing unit employs an incremental adjustment method, calculating the optimized adjustment parameters based on changes in the execution scenario's feedback.

[0097] During the adjustment process, the system monitors the operational status of the execution scenario through a periodic sampling mechanism and decides whether to continue adjusting or roll back to the previous valid parameter set based on the set stability judgment criteria. If the system performance improves after adjustment, the optimized parameter set will be stored as the new baseline parameters; if the adjustment causes abnormal fluctuations or exceeds the preset range, the system will automatically roll back to the previous stable state and generate an adjustment failure record.

[0098] The above-mentioned mechanism for executing and adjusting revised information ensures the stability and adaptability of the adjustment process, improves the dynamic response capability of the execution scenario, and reduces system risks caused by improper parameter adjustments.

[0099] 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. An execution scenario adjustment system based on edge computing, characterized in that: The system includes: at least one computing unit; and a data storage module that communicates with the at least one computing unit. The data storage module contains operation commands that configure the computing unit to: 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; identify at least one execution scenario for receiving the revision information; verify the at least one execution scenario; and, based on the verification result, send the revision information to the at least one execution scenario to adjust the at least one execution scenario. The adjustment employs edge computing optimization, which includes: Lightweight computing units are deployed on edge computing nodes, enabling them to process revision information and perform initial verification locally. The revision information is only synchronized to the central server after the edge computing unit confirms that the revision information is compatible. The identification includes: Determine the specification fit of one or more execution scenarios; and Identifying 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: The architecture security level of the revised information is compared with the architecture security level of the at least one execution scenario, and the at least one execution scenario is identified based on the comparison result; The matching degree calculation formula is as follows: Among them, M s For matching degree; S c To revise the information and the parameter compatibility score for the execution scenario, P r Score the current load status of the execution scenario. T l The task execution priority is scored for the execution scenario. w1, w2, and w3 are different weighting coefficients. λ1, λ2, λ3 are nonlinear adjustment exponents. D f To correct historical discrepancies between information and execution scenarios, θ is the bias smoothing factor.

2. The execution scenario adjustment system based on edge computing as described in claim 1, characterized in that: The computing unit includes multiple computing cores, and each computing core performs load balancing through a task scheduling mechanism.

3. The execution scenario adjustment system based on edge computing as described in claim 1, characterized in that: The computing unit generates revision information based on the received system status data and the execution scenario operation status, and calculates revision parameters according to the matching rules; the data storage module stores the revision information, and the data storage module includes operation commands, matching rules and historical revision records.

4. The execution scenario adjustment system based on edge computing as described in claim 1, characterized in that: The architectural security level of the revised information is determined by predefined security policies, including but not limited to data integrity level, access control level, and execution permission level.

5. The execution scenario adjustment system based on edge computing as described in claim 1, characterized in that: The verification includes: Receive revision records related to the at least one execution scenario; The revision record is compared with the master control record; and the at least one execution scenario is verified based on the comparison results.

6. The execution scenario adjustment system based on edge computing as described in claim 5, characterized in that: The revision record includes the past adjustment history of the execution scenario, the applied revision information, the adjustment effective time, and the execution status; the master control record includes the global adjustment strategy, the expected system status, and the standard version of the historical revisions.

7. The execution scenario adjustment system based on edge computing as described in claim 1, characterized in that: The edge computing unit includes: The data parsing module parses the adjustment parameters, target execution scenario identifier, and encryption identifier in the revision information, and uses a predefined data structure; The rule matching module performs adaptability testing on the revision information, matches the revision information with the execution scenario rules, and calculates the adaptability between the revision information and the target execution scenario using the matching degree calculation formula. The security assessment module detects the security level of the revised information and confirms its integrity based on public key decryption or hash verification.

8. The execution scenario adjustment system based on edge computing as described in claim 1, characterized in that: The edge computing optimization described above is applicable to distributed systems.

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