A cloud platform-based industry chain standard digital management method and system

Abstract

The application discloses a kind of based on cloud platform's industry chain standard digital management method and system, it is related to digital management technical field.The rule metadata and physical variable data of standard rule to be deployed of cloud platform are collected, variable traceability analysis is carried out to rule metadata and physical variable data, the external physical variable dependency record and dependency relationship mapping table of standard rule to be deployed are obtained, and then the traceability topology graph is constructed;In the edge node of cloud platform, in combination with traceability topology graph, rule determination is executed, and the structured traceability record of each edge node is obtained;Structured traceability record is collected and cross-standard conflict detection is carried out, and the cross-standard conflict event set is obtained, the conflict resolution result is obtained by cause-effect tracing analysis to cross-standard conflict event set;Cloud platform combines conflict resolution result, carries out industry chain standard digital evidence storage, and obtains standard digital evidence storage package, improves the accuracy, automation degree and compliance credibility of standard collaborative management.

Description

Technical Field

[0001] This invention relates to the field of digital management technology, specifically to a cloud platform-based digital management method and system for industry chain standards. Background Technology

[0002] With the rapid development of industrial digitalization and intelligent manufacturing, enterprises face challenges in supply chain management, including standard compliance, information interoperability, and end-to-end traceability. The supply chain standard digital management system constructs a full-chain standard knowledge graph covering raw materials, processing and manufacturing, components, and final products. This enables deep association between multiple standard clauses and multi-dimensional information such as material attributes, processing parameters, equipment models, supplier qualifications, and testing indicators. The core tasks of the supply chain standard digital management system are to automatically determine the standard compliance of each link in the entire supply chain; provide intelligent recommendations based on multi-dimensional data; and implement traceable management of the entire process, thereby supporting enterprises in achieving efficient and accurate standardized management and digital governance.

[0003] In actual operation, the digital management system for industry chain standards may experience cross-standard execution conflicts caused by physical causal transmission. When multiple independent standards are digitized simultaneously and distributed to edge nodes for independent execution, the correct execution of one standard rule may indirectly trigger the judgment condition of another standard rule by changing physical variables in the industry chain, causing the latter to judge the legitimate physical change caused by the former as non-compliant. For example, a safety standard requires opening a pressure relief valve to protect pipeline safety. The pressure relief action leads to an increase in steam emissions, and the environmental standard immediately detects the unapproved emission behavior and marks it as a violation. In this scenario, both standards are executed correctly within their respective logical frameworks, but the execution action of the safety standard becomes the direct physical cause of the environmental standard's judgment of non-compliance. The system thus falls into a logical deadlock of "executing the safety standard leads to environmental violations, while meeting the environmental standard prevents the execution of safety protection."

[0004] When such conflicts caused by physical causal transmission cannot be identified and resolved, legitimate operations may be wrongly judged as non-compliant with standards. Automated control systems may switch back and forth between performing safety protection and avoiding environmental violations due to contradictory judgments. Operators' trust in alarms decreases, and they may be forced to shut down automatic execution functions or reduce the association density of knowledge graphs, thereby weakening the system's automatic judgment capabilities and reducing the reliability and overall operational efficiency of digital governance in the industrial chain. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a cloud-based digital management method and system for industry chain standards, which can effectively solve the problems mentioned in the background technology.

[0006] To achieve the above objectives, the first aspect of the present invention is implemented through the following technical solution: a digital management method for industry chain standards based on a cloud platform, comprising collecting rule metadata and physical variable data of standard rules to be deployed on the cloud platform, performing variable source analysis on the rule metadata and physical variable data to obtain external physical variable dependency records and dependency relationship mapping tables of the standard rules to be deployed, and then constructing a source topology diagram of the standard rules to be deployed; On the edge nodes of the cloud platform, the rule determination is performed by combining the traceability topology graph of the standard rules to be deployed, and the structured traceability records of each edge node are obtained. Structured traceability records of each edge node are collected for cross-standard conflict detection to obtain a set of cross-standard conflict events. Causal tracing analysis is then performed on the set of cross-standard conflict events to obtain conflict resolution results. The cloud platform combines the conflict resolution results to perform digital evidence storage of industry chain standards, resulting in a standard digital evidence storage package for the cloud platform.

[0007] Furthermore, the method for collecting rule metadata and physical variable data of the standard rules to be deployed on the cloud platform is as follows: The rule metadata for standard rules to be deployed on the cloud platform includes rule identifier, condition vector, physical variable name, decision action, and physical effect representation of the decision action; The physical variable data for the standard rules to be deployed on the cloud platform includes a physical variable archive table and a sensor chain archive table.

[0008] Furthermore, the method for performing variable source tracing analysis on rule metadata and physical variable data is as follows: The cloud platform extracts the condition vector of the standard rule to be deployed, and matches it item by item with the physical variable archive table to obtain the external physical variable dependency record of the standard rule to be deployed.

[0009] Furthermore, the method for obtaining the external physical variable dependency records and dependency mapping table of the standard rule to be deployed is as follows: All external physical variable dependency records of the standard rules to be deployed are summarized into a dependency mapping table. Each external physical variable dependency record in the dependency mapping table includes the rule identifier of the standard rule to be deployed, the physical effect representation of the judgment action of the target rule corresponding to the standard rule to be deployed, the name of the target physical variable, the rule package identifier to which the target physical variable belongs, and the target rule identifier of the target physical variable in the rule package.

[0010] Furthermore, the method for constructing the source topology graph of the standard rules to be deployed is as follows: For each external physical variable dependency record in the dependency mapping table, the physical causal consistency of the physical effect representation of the decision action of the standard rule to be deployed and the physical effect representation of the decision action of the target rule is verified to obtain the dependency relationship between the standard rule to be deployed and the target rule. Using rules as nodes and dependencies as directed edges, a source topology graph is constructed to obtain the standard rules to be deployed.

[0011] Furthermore, the method for obtaining the structured traceability records of each edge node is as follows: The edge node extracts the corresponding sensor identifier, sampling period, and data preprocessing method from the sensor chain archive table based on the target physical variable name of the current rule node, and continuously collects real-time sampling data of the target physical variable according to the sampling period; Based on real-time sampling data of the target physical variables, a record of whether the event was triggered or not is generated as the rule determination result; After generating the judgment trigger record, the edge node extracts the associated rule node corresponding to the current rule node based on the source tracing topology graph and obtains the corresponding execution status. It performs consistency verification on the physical causal relationship between the current rule node and the associated rule node, generates the causal relationship verification result, and then encapsulates it into a structured source tracing record.

[0012] Furthermore, the method for collecting structured source records of each edge node for cross-standard conflict detection is as follows: The cloud platform continuously receives structured traceability records uploaded by each edge node, performs time association matching on all structured traceability records, and determines whether two structured traceability records meet the time co-occurrence condition. Perform subject association matching on structured traceability records that meet the time co-occurrence condition, and determine whether two structured traceability records meet the subject association condition; Perform rule-based correlation analysis on structured traceability records that meet the subject association conditions to determine whether two structured traceability records meet the conflict conditions. Structured source records that simultaneously meet the conditions of temporal co-occurrence, subject association, and conflict determination are combined into cross-standard conflict events, and then summarized to obtain a set of cross-standard conflict events.

[0013] Furthermore, the method for conducting causal tracing analysis on a set of cross-standard conflict events is as follows: The cloud platform extracts all structured source records corresponding to cross-standard conflict events from the cross-standard conflict event set, and establishes cross-standard causal relationship paths based on the source topology graph; Perform causality verification on the association rule nodes in the cross-standard causal association path to obtain the corresponding causal association verification results; Perform defense integrity analysis on structured attribution records in cross-standard conflict events to generate defense integrity scores corresponding to the cross-standard conflict events; Based on the causal relationship verification results and the defense integrity score, conflict resolution analysis is performed to generate conflict resolution results.

[0014] Furthermore, the method for obtaining the standard digital evidence storage package of the cloud platform is as follows: The cloud platform extracts the conflict resolution results, performs evidence storage and integration processing to obtain a standardized evidence storage dataset, and then constructs a cross-standard causal attribution chain; Perform hash digest calculation on the structured traceability records in the standardized evidence storage dataset to obtain the record hash value, and construct a structured traceability hash index table; The structured traceability hash index table, cross-standard causal attribution chain, and standardized evidence storage dataset are encapsulated into a standard digital evidence storage package on the cloud platform.

[0015] A second aspect of this invention provides a cloud-based digital management system for industry chain standards, comprising: The source tracing analysis module is used to collect rule metadata and physical variable data of the standard rules to be deployed on the cloud platform, perform variable source tracing analysis on the rule metadata and physical variable data, obtain the external physical variable dependency records and dependency relationship mapping table of the standard rules to be deployed, and then construct the source tracing topology diagram of the standard rules to be deployed. The rule determination module is used to perform rule determination on the edge nodes of the cloud platform, in combination with the traceability topology map of the standard rules to be deployed, to obtain the structured traceability records of each edge node; The conflict detection module is used to collect structured traceability records of each edge node to perform cross-standard conflict detection, obtain a set of cross-standard conflict events, perform causal tracing analysis on the set of cross-standard conflict events, and obtain conflict resolution results. The digital evidence storage module is used by the cloud platform to combine the conflict resolution results with the standard digital evidence storage of the industry chain, resulting in a standard digital evidence storage package for the cloud platform.

[0016] The present invention has the following beneficial effects: This invention addresses the problems of incomplete dependencies and difficulty in identifying cross-standard physical coupling caused by isolated rule modeling in a multi-standard parallel industry chain. It constructs a unified mapping system between rule metadata and physical variable data, and introduces variable source analysis and dependency mapping mechanisms. This elevates the relationship between rules from traditional text matching or logical reasoning to a causal relationship modeling method based on the transmission path of physical variables. A global physical variable archive table enables cross-rule full-set retrieval, avoiding dependency omissions caused by local rule perspectives, thereby improving the completeness of cross-standard rule association identification. Simultaneously, by determining the physical effect representation of actions, the rule execution results are mapped to the direction and type of physical quantity changes, giving the relationship between rules a clear physical semantic basis and enhancing the interpretability and consistency of rule associations under a multi-standard system.

[0017] This invention addresses the problems of high noise levels in industrial field data and unstable sensor sampling leading to false or missed rule triggers. It constructs a data quality control and judgment input generation mechanism based on edge nodes. By performing outlier removal, valid sample quantity verification, and sliding window filtering on real-time sampled data, the impact of random disturbances on rule judgment is reduced, improving input data stability. Furthermore, multi-dimensional indicators such as sensor chain record integrity, valid sample quantity, and causal consistency are introduced to construct a defense strength evaluation system for structured traceability records. This enables a quantifiable assessment of the credibility of rule execution results, effectively solving the problem of traditional rule engines relying solely on a single threshold judgment and lacking evidentiary support. It provides rule triggering with a multi-source data verification basis, thereby improving the reliability and anti-false judgment capability of automatic decision-making at the edge.

[0018] This invention addresses the problems of inefficient and inconsistent cross-standard conflicts due to the inability to automatically identify and resolve them, reliance on manual adjudication, or fixed priorities. It constructs a cross-standard conflict detection mechanism based on temporal co-occurrence, subject association, and conflict determination. Furthermore, it introduces a joint resolution method combining physical causal tracing and defense integrity scoring. By establishing physical causal paths between rules through a source topology graph, conflict judgment is transformed from logical rule conflict to physical factual conflict analysis, improving the deep accuracy of conflict identification. In conflict resolution, causal attribution and causal exemption mechanisms enable automatic backtracking of conflict sources, avoiding conflict adjudication based on subjective priorities. Simultaneously, blockchain evidence storage ensures immutable records throughout the entire process, enhancing the credibility, transparency, and traceability of conflict handling. This solves the problems of uncontrollable conflicts and inefficient governance in the collaborative execution of multiple standards.

[0019] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of the method flow of the present invention.

[0021] Figure 2 This is a schematic diagram of the system module connections of the present invention. Detailed Implementation

[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] Please see Figure 1 As shown, the first aspect of the present invention provides a technical solution: a digital management method for industry chain standards based on a cloud platform, comprising collecting rule metadata and physical variable data of standard rules to be deployed on the cloud platform, performing variable source analysis on the rule metadata and physical variable data to obtain external physical variable dependency records and dependency relationship mapping tables of the standard rules to be deployed, and then constructing a source topology diagram of the standard rules to be deployed.

[0024] The method for collecting rule metadata and physical variable data of standard rules to be deployed on the cloud platform is as follows: The rule metadata for standard rules to be deployed on the cloud platform includes rule identifier, condition vector, physical variable name, decision action, and physical effect representation of the decision action.

[0025] The rule identifier for a standard rule to be deployed on the cloud platform is used to represent the unique rule record number of the standard rule to be deployed in the cloud platform. The cloud platform generates the corresponding rule identifier based on the rule creation time, rule package number, and target physical variable name. The condition vector represents all combinations of judgment conditions corresponding to the standard rule to be deployed. The cloud platform extracts fields such as the target physical variable name from the rule text and combines them according to a preset field order to generate the condition vector. The physical variable name represents the target monitoring object corresponding to the standard rule to be deployed. The physical variable name is determined based on the sensor acquisition type in the sensor chain archive table. The judgment action represents the execution processing content of the standard rule to be deployed after the judgment conditions are met. The cloud platform generates the corresponding judgment action based on the action field in the rule configuration file. The physical effect representation of the judgment action represents the direction and type of change of the target physical variable caused by the judgment action. The cloud platform performs statistical analysis on the change trend of the target physical variable corresponding to the judgment action based on the target physical variable change results in historical traceability records to obtain the corresponding physical effect representation of the judgment action.

[0026] The physical variable data for the standard rules to be deployed on the cloud platform includes the physical variable archive table and the sensor chain archive table archived on the cloud platform.

[0027] The physical variable archive table stores the names of target physical variables, target rule identifiers, and associated rule package identifiers corresponding to all standard rules. The cloud platform generates the physical variable archive table by archiving fields based on the rule metadata of the archived standard rules. The sensor chain archive table stores the sensor identifiers, sampling periods, and data preprocessing methods corresponding to the target physical variables. Edge nodes generate corresponding sensor chain archive tables based on sensor operation records, historical sampling records, and data preprocessing records.

[0028] The physical variable archive table aggregates the complete set of target physical variables governed by all registered standard rules on the cloud platform, providing a global matching scope for variable source tracing analysis. This ensures that newly registered rules can traverse all existing rules for dependency detection, avoiding the omission of potential cross-standard physical coupling relationships due to incomplete search scope. The sensor chain archive table records the correspondence between each target physical variable and its measuring sensor, as well as the data preprocessing method. This allows edge nodes to directly obtain the required sensor configuration information during rule judgment and execution, eliminating the need for re-querying or manual specification for each judgment, thus ensuring the real-time performance and automation of edge node judgment and execution.

[0029] The method for performing variable source tracing analysis on rule metadata and physical variable data is as follows: The cloud platform extracts the condition vector of the standard rule to be deployed. Each physical variable name referenced in the condition vector is matched item by item with the target physical variable names corresponding to all target rules in the physical variable archive table. When a match is successful, the physical effect representation of the judgment action of the target rule to be deployed, the target physical variable name, the rule package identifier to which the target physical variable belongs, and the target rule identifier of the target physical variable in the rule package are recorded as the external physical variable dependency record of the standard rule to be deployed. No record is made when the match fails.

[0030] In one specific embodiment, the cloud platform extracts the condition vector of the standard rule RULE-A1 to be deployed. This condition vector only references one physical variable name, "Steam Emission". It then performs a line-by-line matching between "Steam Emission" and the target physical variable names corresponding to all target rules in the physical variable archive table. The physical variable archive table stores two target rule records: target rule RULE-B1 corresponds to "Steam Emission", and target rule RULE-C1 corresponds to "Cooling Rate". After line-by-line matching, "Steam Emission" matches the target physical variable name of RULE-B1, resulting in a successful match; however, it does not match the target physical variable name of RULE-C1, resulting in a failed match.

[0031] The method to obtain the external physical variable dependency records and dependency mapping table of the standard rule to be deployed is as follows: All external physical variable dependency records of the standard rules to be deployed are summarized into a dependency mapping table. Each external physical variable dependency record in the dependency mapping table includes the rule identifier of the standard rule to be deployed, the physical effect representation of the judgment action of the target rule corresponding to the standard rule to be deployed, the name of the target physical variable, the rule package identifier to which the target physical variable belongs, and the target rule identifier of the target physical variable in the rule package.

[0032] In a specific embodiment, the cloud platform extracts the physical effect representation of the judgment action of the successfully matched target rule RULE-B1. This representation is recorded as {Action mode: forced opening of the discharge valve, physical effect direction: positive increase, affected physical quantity: steam emission}. The target physical variable name "steam emission" is extracted, the rule package identifier "environmental standard rule package" to which the target physical variable belongs is extracted, and the target rule identifier "RULE-B1" of the target physical variable in the rule package is extracted. The above four pieces of information, together with the rule identifier RULE-A1 of the standard rule to be deployed, are recorded as an external physical variable dependency record of the standard rule RULE-A1 to be deployed.

[0033] The physical effect representation of the judgment action of the target rule corresponding to the standard rule to be deployed is used to characterize the direction and type of change of the target physical variable after the target rule executes the judgment action. The cloud platform extracts the real-time sampling data changes of the target physical variable before and after the execution of the judgment action based on the historical traceability records corresponding to the target rule, and generates the corresponding physical effect representation of the judgment action based on the trend of the target physical variable change. The target physical variable name is used to characterize the target monitoring object corresponding to the target rule. The cloud platform matches the target physical variable field in the physical variable archive table with the sensor acquisition type in the sensor chain archive table to obtain the corresponding target physical variable name. The rule package identifier to which the target physical variable belongs is used to characterize the standard rule set to which the target rule belongs. The cloud platform generates the corresponding rule package identifier based on the rule package archive record and rule package registration record corresponding to the target rule. The target rule identifier of the target physical variable in the rule package is used to characterize the unique rule record number of the target rule in the corresponding rule package. The cloud platform performs field association encoding processing based on the rule creation time, target physical variable name, and rule package identifier corresponding to the target rule to generate the corresponding target rule identifier.

[0034] The method for constructing the source topology graph of the standard rules to be deployed is as follows: For each external physical variable dependency record in the dependency mapping table, the physical causal consistency of the physical effect representation of the decision action of the standard rule to be deployed and the physical effect representation of the decision action of the target rule is checked. When the decision action of the standard rule to be deployed directly changes the target physical variable of the target rule, it is determined that there is a physical causal relationship between the standard rule to be deployed and the target rule. The dependency relationship between the standard rule to be deployed and the target rule is marked as a strong dependency relationship and recorded as a strong dependency edge. When the decision action of the standard rule to be deployed only has an indirect effect on the target physical variable of the target rule, it is determined that there is a physical causal relationship between the standard rule to be deployed and the target rule. The dependency relationship between the standard rule to be deployed and the target rule is marked as a weak dependency relationship and recorded as a weak dependency edge. Otherwise, it is determined that there is no physical causal relationship between the standard rule to be deployed and the target rule.

[0035] In a specific embodiment, the decision action of the standard rule RULE-A1 to be deployed is "open the pressure relief valve," and the physical quantity of influence recorded in its physical effect characterization is "steam emission." The target physical variable name of the target rule RULE-B1 is also "steam emission." Upon comparison, the physical quantity of influence is consistent with the name of the target physical variable. Furthermore, according to the physical effect characterization, the execution of the decision action of RULE-A1 directly causes the value of "steam emission" to jump from zero to a non-zero value. The decision action of the standard rule to be deployed directly changes the target physical variable of the target rule. The cloud platform marks this dependency as a strong dependency and records it as a strong dependency edge. If the physical quantity of influence of the standard rule RULE-C1 to be deployed is "workshop air pressure," while the name of the target physical variable of the target rule RULE-D1 is "reflow oven cooling rate," the names are inconsistent. However, the workshop air pressure indirectly causes a change in the cooling rate by changing the pressure difference inside and outside the furnace and the convective heat transfer coefficient. Based on the physical causal knowledge base, the cloud platform determines that the decision action of the standard rule to be deployed only has an indirect impact on the target physical variable, and thus marks it as a weak dependency and records it as a weak dependency edge.

[0036] By comparing the physical quantities affected by the actions determined by the standard rules to be deployed with the target physical variables governed by the target rules, and determining whether the influence is a direct change or an indirect transmission, the text matching relationship between rules is transformed into a physical causal relationship. This distinguishes between strong and weak dependencies, enabling the system to accurately identify which conflicts between rules are caused by direct physical causality and must be automatically resolved, and which relationships between rules are only indirectly affected and can be used as auxiliary references. This provides a basis for the hierarchical handling of subsequent conflict resolution, avoiding over-resolution or under-resolution caused by using a uniform processing strategy for all dependencies.

[0037] When the decision action of the standard rule to be deployed does not affect the target physical variable of the target rule, it is determined that there is no physical causal relationship between the standard rule to be deployed and the target rule, and the dependency relationship between the standard rule to be deployed and the target rule is removed from the dependency mapping table.

[0038] The cloud platform uses each rule as a node and strong and weak dependencies (retained in the dependency mapping table) as directed edges to construct a source tracing topology graph for the standard rules to be deployed. In this graph, a strong dependency edge from rule node A to rule node B indicates that the decision action of rule node A directly changes the target physical variable of rule node B, while a weak dependency edge from rule node A to rule node B indicates that the decision action of rule node A indirectly affects the target physical variable of rule node B. The source tracing topology graph, with rules as nodes and physical causal dependencies as directed edges, integrates the scattered physical transmission paths between rules into a global directed graph.

[0039] On the edge nodes of the cloud platform, the rule determination is performed by combining the traceability topology graph of the standard rules to be deployed, and the structured traceability records of each edge node are obtained.

[0040] The standard rules to be deployed are deployed to the cloud platform. The cloud platform then distributes the standard rules to the edge nodes. The edge nodes extract the corresponding sensor identifier, sampling period, and data preprocessing method from the sensor chain archive table based on the target physical variable name corresponding to the current rule node.

[0041] Real-time sampling data of the target physical variables are continuously collected according to the sampling period.

[0042] Real-time sampling data of the target physical variable refers to the sequence of measurement values ​​continuously read by the edge node from the physical sensor corresponding to the sensor identifier according to the sampling period recorded in the sensor chain archive table. Each measurement value reflects the instantaneous value of the physical variable at the current sampling moment.

[0043] When the number of valid samples of real-time sampling data exceeds the preset threshold for the number of valid samples, the edge nodes perform filtering processing on the real-time sampling data according to the data preprocessing method to obtain the judgment input value of the target physical variable.

[0044] The effective sampling quantity refers to the total number of valid measurements in the real-time sampled data after data quality verification. Data quality verification is performed by the edge nodes for each measurement value according to preset outlier judgment rules. When the measurement value is within the sensor's range and the deviation from the previous sample value does not exceed the preset effective deviation threshold, it is judged as a valid sample value and included in the effective sampling quantity. When the measurement value exceeds the sensor's range or the deviation exceeds the preset effective deviation threshold, it is judged as an invalid sample value and discarded. The data preprocessing method is used to characterize the set of processing rules for noise suppression and fluctuation smoothing of real-time sampled data. The edge nodes perform sliding window smoothing and outlier correction processing on the valid sampled data according to the data preprocessing method to obtain denoised sequence data. The judgment input value is used to characterize the current state of the target physical variable after data preprocessing. The edge nodes take the statistical center value within the time window of the denoised sequence data as the judgment input value.

[0045] Edge nodes perform rule determination based on the current rule node's determination threshold and the determination input value, obtaining the rule determination result. When the determination input value meets the current rule node's determination threshold comparison condition, the edge node generates a corresponding rule determination trigger record, recorded as the current rule node's rule determination result; when the determination input value does not meet the current rule node's determination threshold comparison condition, the edge node generates a corresponding rule non-triggered record, recorded as the current rule node's rule determination result. By generating determination input values ​​from raw sensor data after data quality verification and filtering, the interference of sensor noise, transient disturbances, and abnormal sampling values ​​on the rule determination results is minimized, ensuring that every rule triggering or non-triggered determination conclusion has sufficient data quality support, avoiding false triggering or missed triggering due to data quality issues.

[0046] In a specific embodiment, the edge node performs rule judgment based on the judgment threshold "pipeline pressure greater than 1.6MPa" of the current rule node RULE-L1 and the judgment input value; the edge node extracts the real-time sampling data of the three pressure sensors PT-001, PT-002 and PT-003 corresponding to the target physical variable "pipeline pressure" from the sensor chain archive table at 14:30:15, and after median filtering, takes the median value to obtain the judgment input value of 1.67MPa. The edge node compares the input value of 1.67 MPa with the threshold value of 1.6 MPa. Since 1.67 MPa is greater than 1.6 MPa, the input value satisfies the threshold comparison condition of the current rule node RULE-A1. The edge node generates a judgment trigger record for rule RULE-L1. The content of the judgment trigger record is {Rule ID: RULE-L1, Judgment Timestamp: 14:30:15:200 milliseconds, Judgment Input Value: 1.67 MPa, Rule Judgment Result: Triggered, Judgment Action: Open the pressure relief valve}, which is recorded as the rule judgment result of the current rule node.

[0047] After generating the judgment trigger record, the edge node extracts all strong and weak dependency edges corresponding to the current rule node based on the source topology graph, and records them as the associated rule nodes corresponding to the current rule node. Based on the strong and weak dependency edges, the target physical variables and execution status of the associated rule nodes are obtained. The execution status of the associated rule nodes is used to characterize the running behavior result status of the target rule at the current moment.

[0048] The method for obtaining the execution status of associated rule nodes is as follows: After generating a judgment trigger record, each edge node sends the rule identifier, judgment action, judgment timestamp, and target physical variable change direction in the judgment trigger record to all dependent edge nodes in the source topology map with the current rule node as the dependent party through the message forwarding service of the cloud platform; Each edge node maintains an associated rule status cache table locally, receives and stores the execution status information of associated rule nodes sent by other edge nodes, and when it is necessary to obtain the execution status of associated rule nodes, it first reads from the local cache table. If the execution status record of the corresponding rule does not exist in the cache table, it sends a status query request to the cloud platform, which retrieves it from the corresponding edge node and returns it.

[0049] Edge nodes perform consistency checks on the physical causal relationships between the current rule node and associated rule nodes. When the execution state of the associated rule node is consistent with the direction of change of the target physical variable corresponding to the current rule node, the edge node marks the causal relationship as valid. When the execution state of the associated rule node is inconsistent with the direction of change of the target physical variable corresponding to the current rule node, the edge node marks the causal relationship as invalid. When the two directions are consistent, it indicates that the change of the physical variable governed by the associated rule node is indeed caused by the judgment action of the current rule node, and the physical causal chain is verified in direction. When the two directions are inconsistent, it indicates that although the dependency relationship between the two is recorded in the source topology graph, the physical causal transmission has not occurred or has been offset by other factors under the current actual working conditions.

[0050] In one specific embodiment, the edge node performs a consistency check on the physical causal relationship between the current rule node RULE-L1 and the associated rule node RULE-R1: the physical effect recorded in the physical effect representation of the judgment action of the current rule node RULE-L1 is "positively increasing", and the change direction of the target physical variable "steam emission" of the associated rule node RULE-R1 is also positively increasing. Since the change directions of the two are consistent, the edge node marks the causal relationship check result between the current rule node RULE-L1 and the associated rule node RULE-R1 as a valid causal relationship.

[0051] The edge node further encapsulates the sensor identifier, effective sampling quantity, rule judgment result, judgment input value of the target physical variable, execution status of associated rule nodes, causal correlation verification result between the current rule node and associated rule nodes, and current judgment timestamp in a structured manner to generate a structured traceability record. The structured traceability record includes the rule identifier, effective sampling quantity, target physical variable name, judgment input value, rule judgment result, sensor chain record, set of associated rule nodes, causal correlation verification result, and judgment timestamp of the current rule node. The edge node stores the structured traceability record in the local traceability record library and uploads it to the cloud platform simultaneously. The sensor chain record refers to the snapshot of the running status of all sensors corresponding to the physical variables on which the current rule judgment depends at the time of this judgment trigger.

[0052] Structured traceability records of each edge node are collected for cross-standard conflict detection, resulting in a set of cross-standard conflict events. Causal tracing analysis is then performed on the set of cross-standard conflict events to obtain conflict resolution results.

[0053] The method for collecting structured source traceability records of each edge node for cross-standard conflict detection is as follows: The cloud platform continuously receives structured traceability records uploaded by each edge node and archives them in a structured manner according to the judgment timestamp, target physical variable name, set of associated rule nodes, and rule judgment results. Time association matching is performed on all structured traceability records. When the difference between the judgment timestamps of two structured traceability records is less than or equal to the preset conflict time threshold, the two structured traceability records are determined to meet the time co-occurrence condition.

[0054] For structured traceability records that meet the time co-occurrence condition, subject association matching is performed. When the target physical variable names corresponding to two structured traceability records are the same, or when the association rule node sets corresponding to two structured traceability records have the same rule identifier, it is determined that the two structured traceability records meet the subject association condition.

[0055] For structured traceability records that meet the subject association condition, the system performs rule-based judgment result correlation analysis. When the judgment actions corresponding to the rule judgment results of two structured traceability records have opposite physical effects in the physical effect representation of the judgment actions, the two structured traceability records are judged to meet the judgment conflict condition. Structured traceability records that simultaneously meet the time co-occurrence condition, subject association condition, and judgment conflict condition are combined into cross-standard conflict events, and a set of cross-standard conflict events is obtained. The definition of cross-standard conflict is expanded from simple logical contradiction to compliance semantic contradiction, enabling the system to identify deep value conflicts between safety standards and environmental protection standards, such as "one standard requires emissions, while the other prohibits emissions," which cannot be detected in traditional rule conflict detection.

[0056] The method for determining opposite physical interaction relationships is as follows: The cloud platform sequentially performs physical direction determination and semantic direction determination. Physical direction determination is performed when the physical effect of the first record's action is positively increasing and the physical effect of the second record's action is negatively decreasing; in this case, an opposite physical interaction relationship is directly determined. Semantic direction determination is performed when the physical direction determination is not met; further, it is determined that the physical effect of the first record's action is either positively increasing or negatively decreasing, while the second record's action is a negative marking action such as "mark violation" or "mark unqualified," and the target physical variable acted upon by the first record's action has the same name as the target physical variable governed by the second record; in this case, an opposite physical interaction relationship is determined. When neither physical direction determination nor semantic direction determination is met, an opposite physical interaction relationship is determined not to exist.

[0057] In one specific embodiment, the cloud platform selects two structured traceability records. The first record is RULE-L2, whose rule determination result is triggered, and the physical effect of the determined action is characterized as "opening the pressure relief valve, resulting in a positive increase in steam emissions." The second record is RULE-B2, whose rule determination result is triggered, and the physical effect of the determined action is characterized as "closing the discharge valve, resulting in a negative decrease in steam emissions." The physical effects of the two records are positive increase and negative decrease, respectively. When the directional attributes show an opposite relationship, the determination conflict condition is met, and the two structured traceability records are combined into a cross-standard conflict event.

[0058] The method for causal tracing analysis of cross-standard conflict event sets is as follows: For each cross-standard conflict event in the cross-standard conflict event set, causal tracing analysis is performed. The cloud platform extracts sensor chain records, effective sampling quantity, association rule node set, causal association verification results, and judgment input values ​​from all structured tracing records corresponding to the cross-standard conflict event. Based on strong and weak dependency edges in the tracing topology graph, cross-standard causal association paths are established. Using these edges, directed causal association paths are established between the structured tracing records within the conflict event, clarifying the physical causal transmission order between the judgments of the conflicting parties. This transforms conflict resolution from a normative question of "which standard takes precedence" into a factual judgment question of "which judgment's physical cause can be traced back to the correct execution of another judgment." This allows the system to naturally resolve conflicts by revealing the physical causal chain of judgments without prioritizing the standards themselves.

[0059] For each association rule node in the cross-standard causal relationship path, a causal verification is performed. When the direction of change of the target physical variable corresponding to the current structured tracing record is consistent with the direction of the physical effect representation of the judgment action corresponding to the association rule node, and the judgment timestamp corresponding to the current structured tracing record is later than the judgment timestamp corresponding to the association rule node, the cloud platform determines that there is a valid causal relationship between the current association rule node and the current structured tracing record, and updates the corresponding causal relationship verification result to indicate that the causal relationship is established. When the direction of change of the target physical variable corresponding to the current structured tracing record is opposite to the direction of the physical effect representation of the judgment action corresponding to the association rule node, or the judgment timestamp corresponding to the current structured tracing record is earlier than the judgment timestamp corresponding to the association rule node, the cloud platform determines that there is no valid causal relationship between the current association rule node and the current structured tracing record, and updates the corresponding causal relationship verification result to indicate that the causal relationship is not established. The comparison of judgment timestamps adopts the temporal relationship judgment. Only when the judgment timestamp of the current structured tracing record is strictly later than the judgment timestamp of the association rule node can the temporal premise that "the execution of the association rule is the cause of the current rule triggering" be established. If the timestamps of the two events are equal or cannot be distinguished at the preset minimum time resolution, the timing relationship is deemed unsatisfactory, and the causal association is not established. This timing judgment avoids erroneous judgments due to causal reversal caused by clock synchronization errors or concurrent events.

[0060] For each structured source tracing record in a cross-standard conflict event, a defense integrity analysis is performed. The cloud platform counts the sensor chain record completeness, effective sampling quantity, number of association rule nodes, and number of causal associations of association rule nodes corresponding to each structured source tracing record. The record defense strength index value of the structured source tracing record is calculated. The record defense strength index value of the structured source tracing record is used to quantify the credible support strength and traceable proof capability of the current structured source tracing record for the current rule judgment result under the constraints of sensor chain coverage integrity, sampling validity, and cross-rule causal consistency.

[0061] Sensor chain record completeness characterizes the extent to which the sensor chain records actually invoked in the structured traceability record cover the complete set of sensors corresponding to the target physical variable in the sensor chain archive table. It is obtained by comparing the set of sensor identifiers in the structured traceability record with the standard set of sensor identifiers corresponding to the target physical variable in the sensor chain archive table through a cloud platform, and statistically analyzing the ratio of the number of successfully matched sensor identifiers to the total number of standard identifiers.

[0062] In this embodiment, the record defense strength index value of the structured source tracing record can be obtained through the following analysis method, with the specific analysis conditions as follows: ; In the formula, This represents the strength index value of the record defense of the structured source tracing record. This indicates the completeness of the sensor chain record in the structured traceability record. This represents the weighting coefficient corresponding to the preset sensor chain record completeness. This indicates the number of valid samples in the structured traceability records. This represents the weighting coefficient corresponding to the preset number of valid samples. This indicates the number of causal relationships that are established between the association rule nodes in the structured source tracing records. This indicates the number of association rule nodes in the structured traceability record. This represents the weighting coefficient corresponding to the pre-defined proportion of causal relationships.

[0063] When the record defense strength index value of a structured source tracing record is greater than or equal to a preset record defense strength index threshold, the corresponding structured source tracing record is marked as a high-credibility source tracing record; when the record defense strength index value of a structured source tracing record is less than the preset record defense strength index threshold, the corresponding structured source tracing record is marked as a low-credibility source tracing record. Based on the ratio of the number of high-credibility source tracing records to low-credibility source tracing records in a cross-standard conflict event, a defense integrity score is generated for that cross-standard conflict event. The defense integrity score is used to quantify the overall credibility and causal explanatory completeness of the cross-standard conflict event supported by multi-source structured source tracing evidence. The ratio obtained by dividing the number of high-credibility source tracing records in a cross-standard conflict event by the sum of the number of high-credibility source tracing records and the number of low-credibility source tracing records is the defense integrity score. When all structured source traces in a cross-standard conflict are highly credible source traces, the defense integrity score is one point, indicating that each judgment in the conflict has sufficient evidence to support its defense in the three dimensions of sensor chain coverage, sampling validity, and causal consistency. When the proportion of highly credible source traces decreases, the defense integrity score decreases accordingly, indicating that there are weaknesses in the defense basis of the conflict.

[0064] Based on the causal correlation verification results and the defense integrity score, conflict resolution analysis is performed. When the physical effect of the judgment action corresponding to the first structured source record is the direct triggering cause of the change in the target physical variable corresponding to the second structured source record, and the defense integrity score corresponding to the first structured source record is greater than the defense integrity score corresponding to the second structured source record, the cloud platform will remark the judgment result corresponding to the second structured source record as a causal exemption state and generate the corresponding causal attribution record.

[0065] The platform does not rely on a preset standard priority table, nor does it directly report conflicts to manual adjudication. Instead, it automatically resolves conflicts based on the physical causal relationship between two structured source records and the completeness of their respective chains of evidence. When the physical effect of the judgment action in the first record is confirmed as the direct trigger for the change in the target physical variable of the second record, it indicates that the "non-compliance" judgment of the second record is not caused by defects in the production process at the physical fact level, but by the correct compliance execution of the first record. At this point, it is further required that the defense completeness score of the first record be greater than that of the second record. The purpose is to ensure the reliability of the attribution basis. That is, only when the defense evidence chain of the record that is the target of the attribution is more sufficient than that of the record being attributed can the attribution conclusion have a defensible factual basis. This allows the system to naturally resolve cross-standard compliance conflicts based on physical facts and the strength of the defense, avoiding supply chain disruptions caused by delays in manual adjudication and standard applicability disputes caused by preset priorities.

[0066] The meaning of causal exemption status is that the marked judgment record still holds within the logical framework of the standard clause. That is, the judgment conditions of the record are indeed met and the judgment conclusion is correct at the technical level. However, at the compliance attribution level, the triggering cause of the judgment is traced back to the correct execution of another standard, rather than the judgment object itself having quality defects or management oversights.

[0067] The causal attribution record includes the rule identifier of the current rule node, the rule identifier of the associated rule node, the name of the target physical variable, the judgment timestamp, and the causal association verification result. When all the structured source trace records in a cross-standard conflict event have the same defense integrity score, and there is no causal association between all the structured source trace records, the cloud platform marks the current cross-standard conflict event as an event that cannot be automatically resolved and generates the corresponding conflict analysis record.

[0068] The cloud platform extracts the rule identifier, target physical variable name, and judgment timestamp of the current rule node from the structured source records marked as causal exemption status. It also extracts the rule identifier of the associated rule node from the structured source records of the associated rule nodes confirmed as direct triggering causes. After performing physical causal consistency verification, the platform updates the status flag indicating that the causal association is established and writes it to the causal association verification result field. The causal attribution record, through these five fields, fully records the complete attribution information chain: "which rule was exempted, the exemption was due to the execution of which rule, which physical variable was involved, when it occurred, and whether the causal association was verified."

[0069] When the strength of the defenses of all parties in a conflict is completely equal and there is no physical causal connection, it indicates that the conflict cannot be automatically resolved through comparison of the strength of the defenses or tracing of physical causality. The credible support for each party's judgment is consistent, and there is no situation where the conclusion of one judgment can be attributed to the other judgment at the level of physical facts. In this case, the conflict is marked as an event that cannot be automatically resolved, a complete conflict analysis record is generated, all defense evidence and causal path information are retained, and then it is pushed to manual adjudication.

[0070] The causal attribution records, conflict analysis records, and all structured source tracing records corresponding to cross-standard conflict events are structured and encapsulated to generate conflict resolution results. The conflict resolution results include cross-standard conflict event identifiers, a set of structured source tracing records, a defense integrity score, causal attribution records, causal exemption status, and conflict analysis records.

[0071] The cloud platform combines the conflict resolution results to perform digital evidence storage of industry chain standards, resulting in a standard digital evidence storage package for the cloud platform.

[0072] The method to obtain the standard digital evidence package from the cloud platform is as follows: The cloud platform collects cross-standard conflict event identifiers, structured source tracing record sets, defense integrity scores, causal attribution records, causal exemption status, and conflict analysis records from the conflict resolution results, performs evidence integration processing, and obtains a standardized evidence dataset. The evidence integration processing includes performing unified field mapping processing, unified data type processing, and rule identifier association processing on each data.

[0073] The unified field mapping process involves the cloud platform mapping the names of each field in the conflict resolution results to standardized field names defined in the pre-defined evidence data dictionary. The unified data type processing involves converting the values ​​of each field to the unified data type specified in the evidence data dictionary. The rule identifier association process involves associating and verifying the rule identifiers referenced in the structured traceability records with the rule identifiers in the standard rule base to ensure consistency between the rule identifiers referenced in the evidence data and the actual deployed standard rules. Through these three standardization steps, it is ensured that the resolution results from different batches, different industry chain links, and different conflict event types can be written to the blockchain in a unified data format, avoiding difficulties in evidence retrieval and verification due to inconsistent data formats.

[0074] A hash digest is calculated for each structured traceability record in the standardized evidence storage dataset to obtain the record hash value of the corresponding structured traceability record. The record hash value is then indexed and mapped according to the rule identifier, the name of the target physical variable, and the judgment timestamp to generate a structured traceability hash index table.

[0075] The hash digest calculation uses the SHA-256 algorithm, taking the complete field content of the structured traceability record as input and outputting a fixed-length hash value. The principle behind hash digest calculation is that, through the one-wayness and collision resistance of cryptographic hash functions, any tampering with the content of the structured traceability record will result in a change in the hash value. This allows for verification of the integrity of the stored data in subsequent blockchain evidence storage by comparing the hash values.

[0076] The cloud platform combines causal attribution records and causal exemption status in the standardized evidence dataset to construct a cross-standard causal attribution chain. The cross-standard causal attribution chain fully records the physical causal path that leads to the conflict resolution conclusion.

[0077] Starting with the structured source tracing record upstream in the causal chain of a cross-standard conflict event, the chain is constructed by sequentially extracting each causal association state as an association rule node along the strong dependency edge direction in the source tracing topology graph. The rule identifier, physical effect representation of the judgment action, direction of change of the target physical variable, and judgment timestamp of each association rule node are linked in chronological order to form a causal attribution chain. This cross-standard causal attribution chain fully records the complete physical causal path from the source rule node that triggers the security protection action, through the transmission of intermediate physical variables, to the final target rule node marked as unqualified. This elevates the conflict resolution conclusion from "a judgment automatically made by the system" to "a conclusion supported by a complete chain of causal evidence."

[0078] The cloud platform encapsulates the structured source hash index table, the cross-standard causal attribution chain, and the standardized evidence storage dataset into the cloud platform's standard digital evidence storage package; the standard digital evidence storage package includes cross-standard conflict event identifiers, structured source hash index tables, cross-standard causal attribution chains, defense integrity scores, causal attribution records, and causal exemption status.

[0079] The standard digital evidence package is synchronously sent to the corresponding edge node and stored in the cloud platform standard digital evidence repository; based on the cross-standard conflict event identifier, the standard digital evidence package is classified and archived, and a mapping relationship between the cross-standard conflict event identifier and the standard digital evidence package is established.

[0080] A second aspect of this invention provides a cloud-based digital management system for industry chain standards, comprising: The source tracing analysis module is used to collect rule metadata and physical variable data of the standard rules to be deployed on the cloud platform, perform variable source tracing analysis on the rule metadata and physical variable data, obtain the external physical variable dependency records and dependency relationship mapping table of the standard rules to be deployed, and then construct the source tracing topology graph of the standard rules to be deployed.

[0081] The rule determination module is used to perform rule determination on the edge nodes of the cloud platform in combination with the traceability topology map of the standard rules to be deployed, so as to obtain the structured traceability records of each edge node.

[0082] The conflict detection module is used to collect structured traceability records of each edge node to perform cross-standard conflict detection, obtain a set of cross-standard conflict events, perform causal tracing analysis on the set of cross-standard conflict events, and obtain conflict resolution results.

[0083] The digital evidence storage module is used by the cloud platform to combine the conflict resolution results with the standard digital evidence storage of the industry chain, resulting in a standard digital evidence storage package for the cloud platform.

[0084] It should be noted that a cloud-based industrial chain standard digital management method and system also includes a digital management database, which is used to store the judgment threshold of the current rule node, the judgment threshold comparison conditions of the current rule node, the preset effective sampling quantity threshold, the preset conflict time threshold, the preset effective deviation threshold, the record defense strength index threshold, the weight coefficient corresponding to the integrity of sensor chain records, the weight coefficient corresponding to the effective sampling quantity, the weight coefficient corresponding to the causal association establishment ratio, and the evidence data dictionary.

[0085] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0086] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention.

Claims

1. A cloud-based digital management method for industry chain standards, characterized in that, include: Collect rule metadata and physical variable data of standard rules to be deployed on the cloud platform, perform variable source analysis on the rule metadata and physical variable data, obtain external physical variable dependency records and dependency relationship mapping tables of standard rules to be deployed, and then construct the source topology graph of standard rules to be deployed. On the edge nodes of the cloud platform, the rule determination is performed by combining the traceability topology graph of the standard rules to be deployed, and the structured traceability records of each edge node are obtained. Structured traceability records of each edge node are collected for cross-standard conflict detection to obtain a set of cross-standard conflict events. Causal tracing analysis is then performed on the set of cross-standard conflict events to obtain conflict resolution results. The method for collecting structured source tracing records of each edge node to perform cross-standard conflict detection is as follows: The cloud platform continuously receives structured traceability records uploaded by each edge node, performs time association matching on all structured traceability records, and determines whether two structured traceability records meet the time co-occurrence condition. Perform subject association matching on structured traceability records that meet the time co-occurrence condition, and determine whether two structured traceability records meet the subject association condition; Perform rule-based correlation analysis on structured traceability records that meet the subject association conditions to determine whether two structured traceability records meet the conflict conditions. Structured source records that simultaneously meet the time co-occurrence condition, subject association condition, and conflict determination condition are combined into cross-standard conflict events, and then summarized to obtain a set of cross-standard conflict events. The method for causal tracing analysis of cross-standard conflict event sets is as follows: The cloud platform extracts all structured source records corresponding to cross-standard conflict events from the cross-standard conflict event set, and establishes cross-standard causal relationship paths based on the source topology graph; Perform causality verification on the association rule nodes in the cross-standard causal association path to obtain the corresponding causal association verification results; Perform defense integrity analysis on structured attribution records in cross-standard conflict events to generate defense integrity scores corresponding to the cross-standard conflict events; Based on the causal relationship verification results and the defense integrity score, conflict resolution analysis is performed to generate conflict resolution results; The cloud platform combines the conflict resolution results to perform digital evidence storage of industry chain standards, resulting in a standard digital evidence storage package for the cloud platform.

2. The method for digital management of industry chain standards based on a cloud platform according to claim 1, characterized in that, The method for collecting rule metadata and physical variable data of standard rules to be deployed on the cloud platform is as follows: The rule metadata for standard rules to be deployed on the cloud platform includes rule identifier, condition vector, physical variable name, decision action, and physical effect representation of the decision action; The physical variable data for the standard rules to be deployed on the cloud platform includes a physical variable archive table and a sensor chain archive table.

3. The method for digital management of industry chain standards based on a cloud platform according to claim 2, characterized in that, The method for performing variable source tracing analysis on rule metadata and physical variable data is as follows: The cloud platform extracts the condition vector of the standard rule to be deployed, and matches it item by item with the physical variable archive table to obtain the external physical variable dependency record of the standard rule to be deployed.

4. The method for digital management of industry chain standards based on a cloud platform according to claim 3, characterized in that, The method for obtaining the external physical variable dependency records and dependency mapping table of the standard rule to be deployed is as follows: All external physical variable dependency records of the standard rules to be deployed are summarized into a dependency mapping table. Each external physical variable dependency record in the dependency mapping table includes the rule identifier of the standard rule to be deployed, the physical effect representation of the judgment action of the target rule corresponding to the standard rule to be deployed, the name of the target physical variable, the rule package identifier to which the target physical variable belongs, and the target rule identifier of the target physical variable in the rule package.

5. The method for digital management of industry chain standards based on a cloud platform according to claim 4, characterized in that, The method for constructing the source topology graph of the standard rules to be deployed is as follows: For each external physical variable dependency record in the dependency mapping table, the physical causal consistency of the physical effect representation of the decision action of the standard rule to be deployed and the physical effect representation of the decision action of the target rule is verified to obtain the dependency relationship between the standard rule to be deployed and the target rule. Using rules as nodes and dependencies as directed edges, a source topology graph is constructed to obtain the standard rules to be deployed.

6. The method for digital management of industry chain standards based on a cloud platform according to claim 1, characterized in that, The method for obtaining the structured source records of each edge node is as follows: The edge node extracts the corresponding sensor identifier, sampling period, and data preprocessing method from the sensor chain archive table based on the target physical variable name of the current rule node, and continuously collects real-time sampling data of the target physical variable according to the sampling period; Based on real-time sampling data of the target physical variables, a record of whether the event was triggered or not is generated as the rule determination result; After generating the judgment trigger record, the edge node extracts the associated rule node corresponding to the current rule node based on the source tracing topology graph and obtains the corresponding execution status. It performs consistency verification on the physical causal relationship between the current rule node and the associated rule node, generates the causal relationship verification result, and then encapsulates it into a structured source tracing record.

7. The method for digital management of industry chain standards based on a cloud platform according to claim 1, characterized in that, The method for obtaining the standard digital evidence storage package of the cloud platform is as follows: The cloud platform extracts the conflict resolution results, performs evidence storage and integration processing to obtain a standardized evidence storage dataset, and then constructs a cross-standard causal attribution chain; Perform hash digest calculation on the structured traceability records in the standardized evidence storage dataset to obtain the record hash value, and construct a structured traceability hash index table; The structured traceability hash index table, cross-standard causal attribution chain, and standardized evidence storage dataset are encapsulated into a standard digital evidence storage package on the cloud platform.

8. A cloud-based digital management system for supply chain standards, used to implement the cloud-based digital management method for supply chain standards as described in any one of claims 1-7, characterized in that, include: The source tracing analysis module is used to collect rule metadata and physical variable data of the standard rules to be deployed on the cloud platform, perform variable source tracing analysis on the rule metadata and physical variable data, obtain the external physical variable dependency records and dependency relationship mapping table of the standard rules to be deployed, and then construct the source tracing topology diagram of the standard rules to be deployed. The rule determination module is used to perform rule determination on the edge nodes of the cloud platform in combination with the traceability topology map of the standard rules to be deployed, so as to obtain the structured traceability records of each edge node. The conflict detection module is used to collect structured traceability records of each edge node to perform cross-standard conflict detection, obtain a set of cross-standard conflict events, perform causal tracing analysis on the set of cross-standard conflict events, and obtain conflict resolution results. The digital evidence storage module is used by the cloud platform to combine the conflict resolution results with the standard digital evidence storage of the industry chain, resulting in a standard digital evidence storage package for the cloud platform.

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