An ISO management system-based auditing evidence collection and electronic chapter management system

Abstract

The application discloses an auditing evidence collection and electronic chapter management system based on an ISO management system, relates to the field of ISO system auditing digitization technology, and discloses a time sequence and space coupled auditing evidence dynamic discrimination collection module and an evidence compliance degree mapped electronic chapter self-adaptive control module.Through a self-developed time sequence-space weighted traceability discrimination algorithm and an evidence compliance degree permission adaptive algorithm, the auditing evidence is collected in an all-around and traceable manner, and the dynamic linkage control of the electronic chapter permission, the signing action and the evidence compliance state is realized.The time sequence and space coupled auditing evidence dynamic discrimination collection module and the evidence compliance degree mapped electronic chapter self-adaptive control module form a closed-loop logic of "compliance collection first, linkage signing later and reverse error correction optimization", greatly improve the authenticity and integrity of the ISO auditing evidence, completely eliminate the risk of illegal use of the electronic chapter, and guarantee the legality and authority of the ISO system auditing result.

Description

Technical Field

[0001] This invention relates to the fields of digital auditing of ISO management systems and secure control of electronic signatures, specifically to an audit evidence collection and electronic seal management system based on ISO management systems. Background Technology

[0002] Certification audits, annual surveillance audits, and renewal audits of ISO management systems (including ISO 9001 Quality Management, ISO 27001 Information Security, ISO 14001 Environmental Management, and ISO 45001 Occupational Health and Safety) all rely on authentic, complete, and traceable audit evidence for review. The compliance of the audit evidence and the legality of electronic signatures directly determine the validity of the audit results, making them core components of ISO system certification. Currently, the industry faces two specific and urgent technical challenges in evidence collection and electronic signature management for ISO audits, severely hindering the level of digital compliance in audits.

[0003] First, existing audit evidence collection technologies suffer from specific problems such as being one-sided, fragmented, and lacking traceability. Currently, mainstream audit evidence collection methods fall into two categories: manual uploading and simplified semi-automatic collection, both of which have significant shortcomings. Manual collection relies on the auditor's subjective operation, and in remote audits, it is prone to issues such as deliberate framing and partial shooting, collecting only evidence of compliant scenarios while concealing details of on-site violations. Furthermore, the collected images, videos, ledgers, interview records, and other data are often in disorganized formats and have inconsistent timelines, lacking a unified temporal and spatial verification mechanism. Simplified semi-automatic collection systems only implement batch data storage functions and lack evidence verification algorithms, failing to identify tampered, expired, or one-sided evidence, resulting in a large amount of invalid evidence being mixed into the audit dataset. Simultaneously, existing technologies lack a mechanism for linking evidence with ISO audit clauses, making it impossible to quantify the completeness of evidence collection. This frequently leads to disputes over audit results and failed certification re-audits due to insufficient or distorted evidence.

[0004] Second, existing electronic seal management technology is completely disconnected from the evidence collection process, resulting in rigid static permissions and a high risk of unauthorized signatures. Traditional electronic seal management systems use a fixed permission configuration model, where auditor permissions remain unchanged once set, without being linked to the compliance status of real-time evidence verification. In actual audit scenarios, there are violations such as signing prematurely when evidence is incomplete or non-compliant, and signing without authorization. Furthermore, existing systems are unable to defend against attacks such as private key theft, forged signature images, and process tampering. Some certification bodies have been penalized by regulatory authorities for using fake electronic signatures (such as pasted images) and failing to follow evidence verification procedures for signing. In addition, existing technology lacks a reverse error correction mechanism; after signing, it is impossible to reverse-check evidence defects, resulting in a large number of non-compliant audit reports remaining, seriously affecting the authority and legitimacy of ISO system certification.

[0005] Existing publicly available technologies can only achieve basic data collection and signing functions, and cannot solve the core pain points in the above specific scenarios. Based on this, an audit evidence collection and electronic seal management system based on the ISO management system is designed. Summary of the Invention

[0006] The purpose of this invention is to provide an audit evidence collection and electronic seal management system based on the ISO management system, so as to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: an audit evidence collection and electronic seal management system based on the ISO management system, comprising a data storage module, a process control module, a time-space coupled dynamic identification and collection module for audit evidence, and an electronic seal adaptive control module for evidence compliance mapping; wherein, the data storage module is used to store ISO system audit standards and specifications, audit task information, collected audit evidence data, electronic seal permission parameters, and signature record logs; The process control module is used to coordinate the time nodes of the entire review process and realize the coordinated scheduling of evidence collection, compliance verification, electronic signature, and log retention. The time-space coupled dynamic identification and collection module for audit evidence is configured with a self-developed time-space weighted source identification algorithm, which is used to perform time-series correction, spatial completion, and authenticity source identification on multi-dimensional audit evidence collected offline and remotely captured online, eliminating one-sided, tampered, and invalid evidence, and outputting a standardized and compliant evidence set. The electronic seal adaptive control module for evidence compliance mapping adopts an evidence compliance permission adaptation algorithm, which works in conjunction with the dynamic identification and collection module for audit evidence. It receives a standardized set of compliant evidence and calculates the evidence compliance, dynamically matches and adjusts the electronic seal usage permissions of the auditors, and triggers a valid signing action only when the evidence compliance meets the standard. At the same time, it drives the evidence collection module to complete a second precise collection based on the evidence defect information.

[0008] Preferably, the specific implementation steps of the time-space coupled dynamic identification and collection module for audit evidence are as follows: Step A1: Synchronous Collection of Multi-Source Evidence: The time-space coupled dynamic identification and collection module for audit evidence synchronously collects evidence from the entire ISO audit scenario, including video / image evidence of equipment operation, work operations, and environmental conditions captured on-site, online ledger documents, system logs, and audit record data, and manually entered interview records and problem investigation notes. This achieves unified collection of evidence in multiple formats and scenarios, avoiding the problem of scenario omissions in traditional collection. Step A2, Time-Sequence Weight Correction Calculation: Retrieve the standard time-series nodes of the audit task. With review period Extract the collection timestamp of each piece of evidence. Substituting into the time series weight calculation formula, we get Evidence whose collection timeline deviates from the standard nodes is marked, and expired evidence added after the review task is completed and invalid evidence collected in advance are removed to solve the problem of disordered evidence timeline. Step A3, Spatial Integrity Completion Verification: Based on the corresponding ISO system standard, determine the number of standard scenario dimensions required for the current audit project. Count the number of effective scene dimensions actually collected. Calculate spatial integrity weights For evidence lacking spatial dimensions, an automatic list of collection defects is generated, marking uncovered review scenarios, providing precise guidance for secondary collection, and solving the problems of one-sided framing and missing scenes in traditional collection. Step A4, Quantification of Evidence Anomalies: Through image comparison, text deduplication, and data verification, detect whether the evidence contains anomalies including but not limited to tampering, splicing, and information conflicts, and quantify the anomaly deviation value. A higher deviation value indicates a more severe degree of abnormality in the evidence; Step A5, Compliance Scoring and Evidence Screening: Quantify the abnormal deviation values ​​obtained in Step A4. Substitute the time-series-space weighted source identification algorithm to calculate the compliance source identification score of a single piece of evidence. Evidence scoring above the system's preset compliance threshold is deemed valid and included in the standardized compliance evidence set; evidence scoring below the threshold is deemed invalid, automatically removed, and the reasons for the defects are recorded, thus completing the accurate identification of evidence.

[0009] Preferably, the specific implementation steps for compliance scoring and evidence screening in step A5 are as follows: Step A5.1: Retrieve standardized algorithm parameters and pre-calculated data: The time-space coupled dynamic identification and collection module for audit evidence automatically retrieves the weight factors corresponding to the current audit scenario. , , Satisfying the weight constraint conditions Simultaneously retrieve the time-series weighting coefficients that have already been calculated for each piece of evidence. Spatial integrity weighting coefficient Abnormal bias of evidence This ensures that all input parameters are valid quantized data after the aforementioned timing correction, spatial completion, and abnormal quantization, thus avoiding issues such as invalid parameters and timing misalignment. Step A5.2, Calculate the compliance tracing score for a single piece of evidence: Substitute the parameters retrieved in Step A5.1 into the time-series-spatial weighted tracing and identification algorithm formula: The compliance traceability score for a single piece of audit evidence is calculated. ;in Quantify the contribution of evidence to time-series compliance. Quantify the contribution of evidence to the completeness of the scene. As an abnormal penalty item, points are deducted for abnormal evidence such as tampering, missing, and conflict, so as to accurately quantify the overall compliance quality of evidence; Step A5.3: Based on a preset threshold, complete the evidence grading judgment: The time-space coupled dynamic identification and collection module for audit evidence retrieves a unified preset compliance scoring threshold of 0.7 as the sole criterion for determining the validity of evidence, and applies this threshold to each individual piece of evidence. Perform hierarchical verification: when When the evidence is deemed to meet ISO audit standards in terms of temporal matching and scenario completeness, and has no serious anomalies or defects, it is defined as valid and compliant evidence; when If the evidence is found to have one or more defects such as excessive time discrepancy, missing scene dimension, or data tampering conflict, it is defined as invalid defective evidence. Step A5.4, Evidence Classification and Defect Source Tracing Record: All valid and compliant evidence determined in Step A5.3 is uniformly collected, duplicate and redundant data are removed, and a structured and standardized set of compliant evidence is generated. This set serves as the data input basis for the electronic seal permission adaptation of the electronic seal adaptive control module, which maps evidence compliance. For all invalid and defective evidence, the system automatically traces the cause of low scores: If The data is too low, indicating a timing defect (overdue data collection, premature data collection, or timing node deviation). The score is too low, indicating a deficiency in the spatial scene (missing review dimensions, one-sided framing); if If the value is too high, it is judged as a data anomaly defect (tampering, splicing, information conflict), and an evidence defect list with specific defect types and defect parameters is generated simultaneously; Step A5.5: Output standardized data: Finally, complete the accurate identification of all audit evidence. On the one hand, output a set of compliant evidence to support the dynamic adaptation of electronic signature permissions. On the other hand, retain a list of evidence defects to provide a precise target basis for the subsequent reverse drive of the electronic seal adaptive control module that maps the compliance of evidence, thereby realizing the data closed-loop linkage of the two creative modules.

[0010] Preferably, the weighting factor corresponding to the current review scenario in step A5.1 , , The specific acquisition logic is as follows: Step A5.1.1: Construct quantitative parameters for review scenario features: The time-series and space-coupled dynamic identification and collection module for review evidence automatically identifies the current review mode and extracts core feature parameters, defined as follows: To ensure strict adherence to the audit timeline, on-site audits must strictly adhere to the established timeframes, with no timeline deviations permitted. The value range is [value range missing]. Remote auditing offers higher fault tolerance in terms of timing, with a value range of [value range missing]. ; To assess the spatial integrity coefficient, values ​​are assigned based on the stringency of the corresponding ISO system standards. ISO 27001 Information Security System and ISO 14001 Environmental System have multiple verification dimensions and high spatial integrity requirements. ISO 9001 Standard Quality Management System And always satisfy the constraints. ; Step A5.1.2, Dynamically calculate temporal and spatial weight factors: Based on scene feature coefficients, solve in real time using a normalization adaptation algorithm. , The calculation formula is: ; ;in, This represents the number of time-constraint verification clauses in the current audit task. This represents the total number of clauses currently being reviewed. This refers to the number of spatial scenario constraint-related verification clauses in the current review task. This represents the total number of clauses currently being reviewed; the basic coefficient for each scenario is adjusted based on the proportion of clauses to ensure that the weighting factors fully align with the review focus of a single review task, and the calculation results are subject to mandatory normalization correction to strictly meet core constraints. This ensures that the original core scoring formula architecture remains unchanged and is compatible and adaptable. Step A5.1.3: Dynamically calculate the anomaly penalty factor: Based on the overall anomaly level of the evidence collected in this round, adaptively solve for the penalty factor. The calculation formula is: ;in, , The upper and lower limits of the penalty factor are preset for the temporal and spatially coupled dynamic identification and collection module of audit evidence to avoid the problem of weight extreme value failure. This represents the average abnormal deviation of all collected evidence in the current batch, with a range of values. The higher the overall abnormality of the evidence and the greater the average deviation, the larger the penalty factor and the stronger the deduction of scores for abnormal evidence, thus realizing an adaptive optimization mechanism of "the higher the abnormality, the stricter the screening". Step A5.1.4: Retrieve Pre-quantification Parameters: After completing the dynamic weight calculation, the temporal-spatial coupled dynamic identification and acquisition module for audit evidence uniformly retrieves the temporal weight coefficients of individual pieces of evidence that have been solved in the aforementioned steps. Spatial integrity weighting coefficient Abnormal bias of evidence All prerequisite parameters are matched and adapted to real-time dynamic weights, avoiding the technical defects of fixed weights not matching actual audit scenarios, and providing accurate input for subsequent compliance score calculation.

[0011] Preferably, the specific implementation steps for configuring the electronic seal adaptive control module for evidence compliance mapping are as follows: Step B1: Receive the compliance evidence set and quantify the overall compliance level: Receive the standardized compliance evidence set output by the time-space coupled audit evidence dynamic identification and collection module, and statistically analyze all valid evidence in the evidence set. The average evidence compliance traceability score is obtained by calculating the average value. Simultaneously, each piece of evidence is matched against the corresponding clauses of the current ISO audit, and the clause matching degree is calculated. Quantify the overall compliance quality of audit evidence; Step B2, Dynamically Adapt Electronic Seal Usage Permissions: [This will be done in the original text, but the translation is incomplete.] and Substitute the evidence compliance permission adaptation algorithm to calculate the real-time electronic seal permission level. The electronic seal adaptive control module, which maps evidence compliance, has three preset permission levels: Level 1 (full signing permission), Level 2 (limited scenario signing permission), and Level 3 (prohibited signing permission). The higher the evidence compliance, the higher the permission level, realizing dynamic linkage between permission and evidence quality, and abandoning the traditional static permission mode. Step B3, Compliance Signature Action Control and Log Retention: Execute corresponding signature operations according to the permission levels adapted in Step B2: Level 1 permissions can complete electronic signatures for audit reports and certification documents across all scenarios; Level 2 permissions can only sign documents corresponding to the compliance dimension; Level 3 permissions directly lock the electronic seal, prohibiting any signature actions; all signature operations, permission changes, and evidence verification records are retained in real time to the data storage module, forming a traceable signature log to prevent false signatures and unauthorized signatures. Step B4, Reverse-driven secondary optimization of evidence collection: After the signing in Step B3, the electronic seal adaptive control module, which maps the evidence compliance, pushes collection optimization instructions to the temporal and spatial coupled dynamic identification and collection module of audit evidence based on the evidence defect list. For evidence with missing spatial dimensions, temporal deviations, or low scores, it drives the temporal and spatial coupled dynamic identification and collection module of audit evidence to carry out targeted and precise secondary collection, supplement missing scenario evidence, replace invalid evidence, iteratively optimize the quality of evidence compliance, and form a closed-loop control.

[0012] Preferably, the specific steps for dynamically adapting the electronic seal usage rights in step B2 are as follows: Step B2.1: Retrieve the Permission Adaptation Algorithm Fixed Compliance Weight Parameters: The system automatically retrieves the preset compliance adaptation weight factors, including the evidence quality weight factor. Clause matching weight factor Satisfying the weight constraint conditions The weighting ratio prioritizes the overall authenticity and compliance quality of the evidence, while also considering the accuracy of the evidence's fit with ISO audit clauses. It fully adapts to the compliance control requirements of various ISO system audits, ensuring that the determination of authority aligns with the core audit principles. Step B2.2, Substitute into the formula to calculate the electronic seal permission level in real time: Average the compliance traceability score of the batch evidence received in Step B1. Matching degree of evidence review clauses Unified substitution of evidence compliance permission adaptation algorithm formula: ;in The electronic seal's real-time dynamic permission level, after normalization calculation, has a fixed value range of [value missing]. ; It contributes a score to the quality of evidence compliance, representing the comprehensive compliance level of the evidence in terms of timing, scope, and authenticity. The score contributes to the clause adaptation, which represents the validity of the evidence in supporting the ISO audit clauses. The superposition of the two-dimensional scores enables the precise quantification of the authority level. Step B2.3: Retrieve the standardized permission level threshold system: Retrieve the three-level permission judgment thresholds that are uniformly preset throughout the document. These thresholds are fixed standards adapted to ISO audit compliance requirements: The first-level full signature permission threshold is... The threshold for Level 2 restricted signature permissions is: The threshold for Level 3 prohibition of signature permissions is: By quantifying thresholds, the system achieves standardized and subjectively bias-free determination of permission levels. Step B2.4, Differentiated and Precise Adaptation and Control of Permission Levels: Based on the calculated... Numerical matching corresponds to the permission level, enabling dynamic and differentiated permission unlocking and locking: When determined to be Level 1 permission, it means that the overall compliance of the evidence in this round of review is high and the clause matching is accurate. The system fully unlocks the electronic seal, granting the reviewer legal signing permissions for all scenarios and all documents, and can complete the electronic signing of all review materials such as review reports, certification certificates, and rectification confirmation documents; When determined to be Level 2 permission, it means that there are slight dimension missing or clause matching deviations in the evidence. The system restricts the full-domain signing permissions, only opening the signing permissions for review documents corresponding to the dimensions of compliant evidence, and prohibiting the signing of core certification documents; When determined to be Level 3 permission, it means that the compliance quality of the evidence in this round of review does not meet the standards and has obvious defects. The system directly locks all permissions of the electronic seal, prohibiting all signing operations, eliminating the risk of unlicensed signing and illegal signing from the source; Step B2.5, Preparation for Archiving Permission Data and Module Linkage: Calculate the... Numerical values, matching permission levels, and permission open / closed status are uniformly archived to the data storage module; at the same time, the evidence defect list output by the time-space coupled dynamic identification and collection module for audit evidence is bound. If it is a level 2 or 3 permission, the reason for the permission restriction is automatically marked, providing permission dimension basis for the subsequent step B4 to drive the time-space coupled dynamic identification and collection module for audit evidence to collect more accurately for the second time, realizing a closed-loop linkage of evidence quality, permission level, and defect optimization.

[0013] Preferably, the evidence quality weighting factor in step 2.1 Matching weight factor with terms The specific steps to obtain it are as follows: Step B2.1.1: Extracting Algorithm Pre-Linkage Parameters: The electronic seal adaptive control module for evidence compliance mapping retrieves two types of core feature parameters for this round of review: one is the standard deviation of batch evidence compliance scoring. The first is used to characterize the quality stability of the entire batch of audit evidence; the second is the coefficient of the proportion of mandatory audit clauses. This is used to characterize the stringency of the terms in this ISO audit task; Step B2.1.2: Calculate the evidence quality stability coefficient: based on the compliance tracing score of all valid evidence in this round. With average score Solve for the standard deviation of evidence compliance The calculation formula is as follows: ;in The total number of valid evidence. The range of values ​​is ; The smaller the value, the more uniform and stable the compliance quality of all the evidence reviewed in this round is, and the stronger the overall credibility of the evidence. Therefore, the weight of evidence quality should be increased. A higher value indicates inconsistent evidence quality and weak credibility, necessitating a reduction in evidence weight and a strengthening of the weight for compliance verification of clauses; an evidence quality stability coefficient is constructed based on this characteristic. , Positively correlated with the overall credibility of the evidence; Step B2.1.3: Calculate the stringency coefficient of the audit clauses: Count the number of mandatory core clauses in this audit task. Total number of audited clauses Solve for the severity coefficient of the terms. : ,in, The range of values ​​is Highly stringent management systems such as ISO 27001 and ISO 14001 The value is relatively high, typical of ISO 9001 audit scenarios. The values ​​are moderate, accurately quantifying the stringent compliance requirements of different ISO systems. Step B2.1.4: Dynamically solve the dual-weighting factor: based on the evidence quality stability coefficient Strictness of Terms The solution is obtained in real time using a normalized coupling algorithm. , The calculation formula is: ; ;in As a dynamic evidence quality weighting factor, To match weighting factors for dynamic clauses, the two are naturally satisfied through normalization calculation. The constraints are such that there is no weight imbalance problem; Step B2.1.5, Weight Adaptive Logic Adaptation: When the quality and credibility of the evidence in this round are stable, Approaching 1, Automatically increase the priority given to evidence of authenticity and compliance quality; when the core mandatory clauses account for a high proportion and the compliance requirements are stringent in this audit. Approaching 1, Automatically increases and strengthens the matching and verification weight of evidence and audit terms, completely solving the technical defects of fixed weights that cannot adapt to different ISO audit scenarios, and ensuring that the permission determination is fully in line with the core compliance principles of this audit.

[0014] Compared with the prior art, the beneficial effects of the present invention are: This invention solves the specific problems of traditional ISO audit evidence collection, such as one-sidedness, chaotic timing, difficulty in distinguishing authenticity, and inability to quantify compliance, by using a time-space coupled dynamic identification and collection module for audit evidence. It achieves comprehensive verification of evidence in both time and space dimensions, accurately eliminates invalid, tampered, and one-sided evidence, and significantly improves the authenticity, completeness, and traceability of audit evidence, thereby avoiding audit disputes and certification failures caused by evidence defects from the source.

[0015] This invention breaks through the technical bottleneck of static permissions and process disconnect of traditional electronic seals by using an electronic seal adaptive control module that maps evidence compliance. It achieves dynamic linkage between electronic seal permissions and the compliance status of audit evidence, thereby eliminating compliance risks such as unlicensed signing, illegal signing, and false signing from a technical perspective. It complies with the Electronic Signature Law and ISO system audit compliance requirements, and avoids the risk of administrative penalties for institutions.

[0016] The temporal and spatially coupled dynamic identification and collection module for audit evidence and the adaptive electronic seal control module that maps evidence compliance form a tight closed-loop linkage mechanism. Based on evidence collection and signing, the module optimizes evidence collection by verifying the signature, thereby achieving adaptive optimization of the entire audit process. Compared with the existing independent and fragmented collection and signing systems, the logic is more rigorous and the adaptability is stronger, which greatly improves the level of digitalization and compliance control in ISO audits. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the overall system structure of the present invention; Figure 2 This is a schematic diagram of the workflow of the temporal-spatial coupled dynamic identification and collection module for audit evidence in this invention. Figure 3 This is a schematic diagram of the workflow of the electronic seal adaptive control module for evidence compliance mapping according to the present invention. Detailed Implementation

[0018] 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.

[0019] Please see Figure 1-3 This invention provides a technical solution: an audit evidence collection and electronic seal management system based on the ISO management system, including a data storage module, a process control module, a time-space coupled dynamic identification and collection module for audit evidence, and an electronic seal adaptive control module that maps evidence compliance. The data storage module stores ISO system audit standards and specifications, audit task information, collected audit evidence data, electronic seal permission parameters, and signature logs. The specific implementation logic of the data storage module is as follows: Step 1.1, Pre-storing of Standard Specifications and Audit Task Basic Data: The system pre-stores benchmark data for audit standards across all categories of ISO systems, covering the total number of standard audit scenario dimensions for systems such as ISO 9001, ISO 27001, and ISO 14001. Audit Standards Timeline Audit cycle benchmark Fixed benchmark parameters, such as core mandatory audit clauses and compliance verification thresholds, provide a unified standard for subsequent evidence quantification, weight calculation, and compliance determination. Simultaneously, during the audit task initialization phase, specific information for this audit task is received and stored in real time, including audit type (on-site / remote), audit system type, audit duration, and total number of verification clauses. Number of core clauses Task parameters are used to bind and store baseline standard data with dynamic task data, ensuring accurate parameter adaptation throughout the entire single audit process.

[0020] Step 1.2: Layered and Structured Storage of Multi-Source Audit Evidence: For multi-dimensional audit evidence collected by the time-space coupled dynamic identification and collection module, the evidence is layered and structured according to evidence type, compliance level, and defect type, avoiding the problems of traditional data fragmentation, disorganization, and lack of traceability. The stored content includes original evidence data such as offline on-site photos / videos, online ledgers and logs, and manually entered records, while simultaneously linking and storing quantitative parameters of the evidence after algorithm correction, including time-series weighting coefficients. Spatial integrity weighting coefficient Abnormal deviation values Single Evidence Compliance Tracing Score and distinguish storage Valid compliance evidence and The system identifies and retains defective and invalid evidence, while simultaneously recording defect labels such as time-series deviations, spatial missing data, and data anomalies, providing complete data support for evidence screening and secondary precise data collection.

[0021] Step 1.3: Real-time Archiving and Storage of Dynamic Algorithm Parameters: For the time-space coupled dynamic evidence identification and collection module and the electronic seal adaptive control module mapping evidence compliance, dynamic iterative parameters are used to achieve real-time calculation, real-time archiving, and end-to-end retention. For the time-space weighted tracing and identification algorithm, the dynamically calculated time-series stringency coefficients are stored. Spatial completeness coefficient Dynamic weighting factor , Adaptive penalty factor $ and average abnormal deviation of batch evidence For the evidence compliance permission adaptation algorithm, the standard deviation of the batch evidence compliance score is stored. Evidence quality stability coefficient Strictness of Terms Dynamically adapt weight factors , All dynamic algorithm parameters are updated and stored synchronously with each review and each secondary iteration, recording the parameter iteration trajectory to ensure that the algorithm operation is traceable and reviewable.

[0022] Step 1.4, Encrypted Retention of Electronic Seal Permission Parameters and Signing Logs: This step stores data on the permission adaptation and the entire signing process of the electronic seal adaptive control module, which maps evidence compliance. The core data includes the average compliance score of batch evidence. Clause matching degree Real-time electronic seal permission levels The system includes the corresponding Level 1 / Level 2 / Level 3 permission determination results. Simultaneously, all signature operation data is encrypted and permanently stored, including signature status, signed document type, operation timestamp, operator identity, permission change records, and evidence defect correlation information, forming a standardized and traceable signature log. All log data employs an anti-tampering encrypted storage mechanism, making it impossible to manually modify or delete, meeting ISO audit compliance and regulatory traceability requirements, and preventing the tampering of traces of false or unauthorized signatures.

[0023] Step 1.5, Two-way Data Call and Closed-Loop Iterative Update: The data storage module has two-way real-time data scheduling capabilities. On the one hand, it provides accurate historical data and real-time parameter call support for the algorithm calculation, compliance judgment, permission adaptation, and strategy generation of the time-space coupled dynamic identification and collection module for audit evidence and the electronic seal adaptive control module that maps evidence compliance. On the other hand, it receives the calculation results, defect list, secondary collection iteration data, and permission update data of the time-space coupled dynamic identification and collection module for audit evidence and the electronic seal adaptive control module that maps evidence compliance in real time, completing the dynamic iterative update of the database. At the same time, it retains the parameter correction records, defect rectification records, and permission iteration records of each secondary collection, forming a full-link data closed loop of "initial data - calculation data - defect data - optimization data - iterative data", continuously supporting the system's adaptive optimization and iteration, and ensuring the continuous improvement of the compliance of the entire audit process.

[0024] The process control module is used to coordinate the timeline nodes of the entire review process, enabling the coordinated scheduling of evidence collection, compliance verification, electronic signature, and log retention. The specific implementation logic is as follows: Step 2.1, Audit Task Initialization and Full-Process Timing Baseline Scheduling: At the initial stage of audit task creation, the process control module initiates initialization scheduling, linking with the data storage module to retrieve the corresponding standard audit timing nodes of the ISO system. Review period Standard scenario dimensions The entire process of rule initialization was completed using a full set of benchmark parameters, including a compliance score threshold of 0.7 and permission level thresholds. Simultaneously, the on-site / remote scenario attributes and the proportion of core clauses in this audit were considered. It pre-defines the execution sequence nodes and priorities of each step of the entire process, and defines the standard execution intervals for evidence collection, algorithm identification, permission calculation, signature execution, and iterative optimization to prevent problems such as process disorder and premature or delayed execution of steps.

[0025] Step 2.2: Full-process scheduling and control of the temporal-spatial coupled dynamic identification and collection module for audit evidence. The module precisely controls the core execution logic of the temporal-spatial coupled dynamic identification and collection module for audit evidence, proceeding in an orderly manner according to a fixed time sequence of "multi-source evidence collection - temporal weight correction - spatial integrity verification - anomaly deviation quantification - compliance scoring and screening," prohibiting step reversal or skipping. In each algorithm operation stage, the real-time scheduling data storage module retrieves baseline parameters and collected data, monitoring dynamic weight factors. , , The iterative calculation process, verification Weight constraints to ensure time-series weights Spatial weights Abnormal deviation values Compliance score The calculations are compliant and valid. At the same time, evidence screening rules are strictly enforced, and evidence is uniformly retained. The system collects defect evidence, compiles standardized compliance evidence sets and defect lists, and provides compliance data source assurance for cross-module collaboration.

[0026] Step 2.3, Cross-Module Data Flow and Interconnection Timing Control: The process control module precisely controls the timing and effectiveness of cross-module data transmission. After the dynamic identification and collection module for audit evidence, which is coupled in time and space, completes the identification of all evidence and outputs a compliant evidence set, it pushes the data to the electronic seal adaptive control module that maps the evidence compliance degree, thus preventing permission calculations and signing operations without compliant evidence. At the same time, it controls the timing of the reverse iteration link. Only after the electronic seal adaptive control module that maps the evidence compliance degree completes the signing action and log retention is the defect list parsing and secondary collection instruction pushed, ensuring that the closed-loop logic of "identification first, signing later, and optimization later" is not disrupted, and solving the problems of fragmented technology modules and disordered processes in traditional technology.

[0027] Step 2.4: Precise scheduling of permissions and signing processes in the electronic seal adaptive control module for evidence compliance mapping. The module manages the entire execution sequence of the electronic seal adaptive control module for evidence compliance mapping, sequentially scheduling the orderly operation of all stages: evidence compliance quantification, dynamic weight adaptive calculation, permission level determination, hierarchical signing control, and log retention. During the permission calculation phase, the dynamically adapted weights are monitored. , The normalization calculation process, verification Constraints, guarantees , , Precise parameter calculations; strict adherence to procedures during the signing and execution phase. , , The three-level threshold system schedules the unlocking and locking of differentiated signing permissions to prevent unauthorized or excessive signing behavior, while also requiring all signing actions to be synchronously retained in structured logs after completion.

[0028] Step 2.5, Second-stage iterative data collection closed-loop process control. The module manages the entire process, reverse-optimizing the iterative process and automatically determines the compliance termination conditions for review: when a single piece of evidence is met. Spatial integrity No evidence of exceeding the standard or abnormality. Overall permission level When all conditions are met, the process is deemed to be in compliance and closed loop, and the iteration is terminated; if any condition is not met, the time-space coupled dynamic identification and collection module for audit evidence is continuously scheduled to perform targeted secondary collection, evidence re-identification, and parameter recalculation, and iterates in a loop until the full-dimensional compliance standards are met, so as to achieve adaptive optimization and closed-loop control of the audit process. Step 2.6: Full-Process Anomaly Coverage and Timing Calibration. Throughout the entire review process, the module monitors the operational status and parameter validity of each stage in real time. It performs real-time corrections for issues such as algorithm calculation anomalies, missing data, process bottlenecks, and parameter out-of-bounds errors, while simultaneously linking with the data storage module to retain process anomaly logs. At the same time, it continuously calibrates the timing nodes of the entire process to ensure that all data collection, calculation, and signing actions are limited to the review cycle. Completed within the timeframe, this process completely eliminates compliance issues caused by pre-collection, expired data entry, and timeouts, ensuring standardized, orderly, and traceable operation throughout the entire process.

[0029] The temporal-spatial coupled dynamic verification and collection module for audit evidence is configured with a self-developed temporal-spatial weighted source tracing verification algorithm. This algorithm is used to perform temporal correction, spatial completion, and authenticity tracing verification on multi-dimensional audit evidence collected offline and remotely captured online, eliminating biased, tampered, and invalid evidence, and outputting a standardized and compliant evidence set. The specific implementation steps are as follows: Step A1: Synchronous Collection of Multi-Source Evidence: The time-space coupled dynamic identification and collection module for audit evidence synchronously collects evidence from the entire ISO audit scenario, including video / image evidence of equipment operation, work operations, and environmental conditions captured on-site, online ledger documents, system logs, and audit record data, and manually entered interview records and problem investigation notes. This achieves unified collection of evidence in multiple formats and scenarios, avoiding the problem of scenario omissions in traditional collection. Step A2, Time-Sequence Weight Correction Calculation: Retrieve the standard time-series nodes of the audit task. With review period Extract the collection timestamp of each piece of evidence. Substituting into the time series weight calculation formula, we get Evidence whose collection timeline deviates from the standard nodes is marked, and expired evidence added after the review task is completed and invalid evidence collected in advance are removed to resolve the problem of disordered evidence timeline; the specific implementation logic of this step is as follows: Step A2.1: Retrieve Audit Timing Baseline Parameters: Based on the currently initialized ISO audit task type, the system automatically matches and retrieves preset timing baseline parameters, including: the standard data collection timing nodes corresponding to each verification node of the current audit task. The total timeframe for a single review task .in, The standard execution time points for each stage of the ISO system audit specifications, such as on-site verification, document verification, and on-site interviews, are specified in the ISO system audit specifications. Different audit clauses correspond to independent standard time nodes. The total time for the entire process from the start of the audit task, on-site verification, document review to the end of the audit is uniformly expressed in hours (h), and serves as the benchmark range for quantifying time series deviations.

[0030] Step A2.2, Accurate Extraction and Preprocessing of Evidence Collection Timestamps: For all multi-source audit evidence collected in Step A1, extract the unique and authentic collection timestamp for each piece of evidence. The system utilizes the original timestamps from on-site image and video evidence retrieval equipment, the fixed timestamps generated by the online ledger and system log evidence retrieval platform, and the system timestamps submitted by operators for manually entered evidence binding. This prevents data distortion caused by manual alteration or false time entries, ensuring that all... The parameters are accurate and valid.

[0031] Step A2.3, Quantization Calculation of Time Series Weight Coefficients: Substitute the preprocessed time series parameters into the preset time series weight calculation formula: In the formula, The absolute deviation between the actual collection time and the standard timeline for a single piece of evidence is calculated using normalization to eliminate calculation errors caused by differences in the review cycle duration; the final solution is... The range of values ​​is The closer the value is to 1, the higher the time sequence of evidence collection is to the time sequence of ISO audit standards, and the better the time sequence compliance; the closer the value is to 0, the greater the time sequence deviation, and the worse the time sequence compliance of the evidence.

[0032] Step A2.4, Grading and Removal of Anomalous Evidence in Time Sequence: The system presets a time sequence deviation tolerance threshold and adaptively determines the evidence based on the differences in time sequence stringency between on-site and remote audits. For on-site ISO audits, the time sequence tolerance is low; a deviation exceeding 15% is considered anomalous evidence. For remote ISO audits, the time sequence tolerance is relatively high; a deviation exceeding 30% is considered anomalous evidence. Anomalous evidence is precisely removed in two categories: first, pre-collected evidence before the formal start of the audit task, which does not correspond to the real-time on-site status of the audit and has no audit validity; second, expired supplementary evidence after the end of the audit task cycle, which cannot accurately reflect the system's operational status during the audit period. Both are automatically removed and marked as time sequence defects.

[0033] Step A2.5, Time Series Parameter Archiving and Process Linkage: Standardize the time series weight coefficients corresponding to all valid evidence. Unified archiving and synchronous push to the subsequent dynamic adaptive weight calculation stage, serving as the time-series stringency coefficient. Time-series weighting factor The dynamic solution provides basic data support, enabling deep linkage between temporal dimension verification and algorithm weight optimization, fundamentally solving the problems of disordered temporal sequence of traditional audit evidence, inability to quantify temporal validity, and the mixing of invalid temporal evidence into the audit dataset.

[0034] Step A3, Spatial Integrity Completion Verification: Based on the corresponding ISO system standard, determine the number of standard scenario dimensions required for the current audit project. Count the number of effective scene dimensions actually collected. Calculate spatial integrity weights For evidence lacking spatial dimensions, an automatic list of collection defects is generated, marking uncovered review scenarios and providing precise guidance for secondary collection, thus solving the problems of one-sided perspectives and missing scenes in traditional collection methods; the specific implementation logic is as follows: Step A3.1: Retrieve ISO system standard space dimension baseline parameters: Based on the ISO system type corresponding to the currently initialized audit task, the system automatically retrieves the preset standard audit scenario dimension set to determine the total number of standard scenario dimensions that are mandatory for the current audit project. The general standard scenario dimensions cover four core dimensions: equipment on-site status, work process specifications, record keeping, and personnel operation qualifications. For specialized systems such as ISO 27001 and ISO 14001, it automatically expands to include dedicated verification dimensions such as safety management and environmental compliance. It adapts to different ISO system types to ensure that the benchmark dimensions are fully aligned with the audit standards.

[0035] Step A3.2: Screening and Statistics of Evidence Validity Dimensions: For the valid temporal evidence collected in Step A1 and screened through temporal verification in Step A2, spatial dimension validity is assessed. Each piece of evidence is evaluated to determine the corresponding review scenario dimension, eliminating invalid or one-sided evidence that is vaguely defined, partially cropped, or cannot correspond to the standard verification dimensions. The number of actual scenario dimensions that are completely and effectively covered in the current batch of review tasks is counted and denoted as [missing information]. ,make sure Only valid scenario dimensions that accurately correspond to ISO audit terms and fully demonstrate the on-site and document status are statistically analyzed, eliminating the interference of one-sided or invalid scenario data with calculation accuracy.

[0036] Step A3.3, Spatial Integrity Weight Coefficient Quantification Calculation: The screening statistics completed... and Substituting into the spatial integrity weight calculation formula of this invention: In the formula, The evidence space integrity weight coefficient has a value range of [value range missing]. .when When the value is 1, it means that the current evidence fully covers all standard review scenario dimensions, with no missing scenarios or one-sided views; the smaller the value, the more serious the lack of scenario coverage, the worse the compliance of the evidence space, and the more accurately the evidence completeness defects that traditional technologies cannot quantify.

[0037] Step A3.4, Spatial Dimension Defect Judgment and Defect List Generation: The system presets a spatial integrity benchmark threshold of 0.9, and performs a graded judgment based on the calculation results: If The current evidence space is deemed compliant and the scenario coverage is complete; if The system identifies spatial scene deficiencies. It automatically compares the standard dimension set with the actual collected dimensions to accurately locate the specific audit scenarios not covered and the corresponding ISO audit clauses for the missing dimensions. It automatically generates a structured list of spatial collection defects, clearly specifying the name of the missing scenario, the corresponding audit clause, and the defect type. This provides targeted guidance for subsequent accurate secondary collection and completely solves the problem of blindly capturing scenes and missing key audit scenarios during manual collection.

[0038] Step A3.5, Spatial Parameter Archiving and Full-Process Linkage: The standardized spatial integrity weight coefficients obtained from the solution are... The structured spatial defect list is uniformly archived to the data storage module. On the one hand, it will... The algorithm is pushed to step A5, the dynamic weight calculation and compliance scoring stage, to determine the spatial completeness coefficient. Spatial weighting factor Dynamic solution and final The scoring calculation provides core spatial dimension parameters; on the other hand, the spatial defect list is synchronously connected to the electronic seal adaptive control module that maps evidence compliance, serving as the core basis for the secondary supplementary collection of the dynamic identification and collection module of audit evidence that drives the temporal and spatial coupling after the subsequent signing is completed, realizing the closed-loop linkage of the entire process of temporal verification, spatial verification, compliance scoring, and reverse optimization.

[0039] Step A4, Quantification of Evidence Anomalies: Through image comparison, text deduplication, and data verification, detect whether the evidence contains anomalies including but not limited to tampering, splicing, and information conflicts, and quantify the anomaly deviation value. A higher deviation value indicates a more severe degree of abnormality in the evidence; the specific implementation steps are as follows: Step A4.1, Multimodal Evidence Classification and Adaptation Verification Mechanism: Based on the valid spatiotemporal evidence collected in Step A1 and filtered in Steps A2 and A3, three verification scenarios are divided according to evidence type, and differentiated anomaly detection algorithms are used to avoid missed or false detections caused by a single detection method: For image-type evidence such as on-site photos and video frames, pixel hash comparison and edge stitching anomaly detection algorithms are used; for text-type evidence such as ledger documents, audit records, and qualification documents, semantic deduplication and text tampering trace verification algorithms are used; for structured data-type evidence such as system logs, audit time-series data, and equipment parameter records, cross-field consistency and parameter rationality verification algorithms are used to achieve full coverage detection of anomalies for all types of evidence; Step A4.2, Multi-dimensional Anomaly Feature Extraction and Labeling: Anomaly feature screening is performed on each piece of evidence individually, defining three core anomaly features: 1) Image anomalies, including features such as partial splicing, pixel tampering, secondary cropping, and off-site synthesis; 2) Text anomalies, including features such as large-scale copying and plagiarism, alteration of key clauses, alteration of date parameters, and content inconsistent with ISO standards; 3) Data conflict anomalies, including features such as contradictions between evidence parameters and review timelines, conflicts between scene data and on-site regulations, and mutual exclusion of multi-source evidence information. The system independently labels each type of detected anomaly and calculates the total number of anomaly features and the severity level of each piece of evidence. Step A4.3, Quantitative Calculation of Abnormal Deviation Value: Using the formula for quantifying abnormal deviation of evidence, and combining the number of abnormalities, severity of abnormalities, and weighting of evidence, a normalization calculation is performed to accurately determine the abnormal deviation value of a single piece of evidence. The calculation formula is as follows: ,in For individual pieces of evidence with abnormal deviation values, the overall value range is strictly normalized to [value range missing]. The closer the value is to 1, the higher the degree of abnormal tampering of the evidence and the worse its validity. A value of 0 indicates that the evidence is not abnormal and is true and valid. The total number of actual abnormal features detected from a single piece of evidence; For the first The severity weights of the abnormal features are as follows: the weight for tampering with core key information is 0.8, and the weight for minor information defects is 0.3. This is the anomaly check coefficient, which takes a value of 1 when an anomaly is detected and a value of 0 when no anomaly is detected. The maximum number of detectable abnormal features for a single piece of evidence is preset to the system, with a fixed value of 10. This is used to standardize and normalize the deviation values, ensuring that the abnormal deviation values ​​of different types of evidence are comparable across different systems. Step A4.4, Batch Anomaly Mean Calculation and Parameter Linkage: Complete a Single Piece of Evidence After quantitative calculation, the system statistically analyzes the abnormal deviation values ​​of all evidence in the current review batch and calculates the average abnormal deviation value of the batch. The calculation formula is: ;in, This represents the total number of valid pieces of evidence in the current review batch. For the first The abnormal deviation value of each piece of evidence. This average value will be directly substituted into the dynamic penalty factor calculation formula in step A5.1.3; Step A4.5, Anomaly Parameter Archiving and Defect Pre-judgment: The system will archive individual pieces of evidence. Batch average The corresponding abnormal feature records are uniformly archived to the data storage module. An anomaly baseline threshold of 0.3 is preset; if a single piece of evidence... The preliminary assessment indicates that the evidence has serious flaws such as tampering and information conflicts, providing a preliminary basis for subsequent compliance score downgrading and invalid evidence removal.

[0040] Step A5, Compliance Scoring and Evidence Screening: Quantify the abnormal deviation values ​​obtained in Step A4. Substitute the time-series-space weighted source identification algorithm to calculate the compliance source identification score of a single piece of evidence. Evidence scoring above the system's preset compliance threshold is deemed valid and included in the standardized compliance evidence set; evidence scoring below the threshold is deemed invalid, automatically removed, and the reasons for the defects are recorded, thus achieving accurate evidence identification. The specific implementation steps are as follows: Step A5.1: Retrieve standardized algorithm parameters and pre-calculated data: The time-space coupled dynamic identification and collection module for audit evidence automatically retrieves the weight factors corresponding to the current audit scenario. , , Satisfying the weight constraint conditions Simultaneously retrieve the time-series weighting coefficients that have already been calculated for each piece of evidence. Spatial integrity weighting coefficient Abnormal bias of evidence This ensures that all input parameters are valid quantized data after the aforementioned time-series correction, spatial completion, and anomaly quantization, avoiding issues such as invalid parameters and time-series misalignment; among which, the weight factor corresponding to the current review scenario... , , The specific acquisition logic is as follows: Step A5.1.1: Construct quantitative parameters for review scenario features: The time-series and space-coupled dynamic identification and collection module for review evidence automatically identifies the current review mode and extracts core feature parameters, defined as follows: To ensure strict adherence to the audit timeline, on-site audits must strictly adhere to the established timeframes, with no timeline deviations permitted. The value range is [value range missing]. Remote auditing offers higher fault tolerance in terms of timing, with a value range of [value range missing]. ; To assess the spatial integrity coefficient, values ​​are assigned based on the stringency of the corresponding ISO system standards. ISO 27001 Information Security System and ISO 14001 Environmental System have multiple verification dimensions and high spatial integrity requirements. ISO 9001 Standard Quality Management System And always satisfy the constraints. ; Step A5.1.2, Dynamically calculate temporal and spatial weight factors: Based on scene feature coefficients, solve in real time using a normalization adaptation algorithm. , The calculation formula is: ; ;in, This represents the number of time-constraint verification clauses in the current audit task. This represents the total number of clauses currently under review. This refers to the number of spatial scenario constraint-related verification clauses in the current review task. This represents the total number of clauses currently being reviewed; the basic coefficient for each scenario is adjusted based on the proportion of clauses to ensure that the weighting factors fully align with the review focus of a single review task, and the calculation results are subject to mandatory normalization correction to strictly meet core constraints. This ensures that the original core scoring formula architecture remains unchanged and is compatible and adaptable. Step A5.1.3: Dynamically calculate the anomaly penalty factor: Based on the overall anomaly level of the evidence collected in this round, adaptively solve for the penalty factor. The calculation formula is: ;in, , The upper and lower limits of the penalty factor are preset for the temporal and spatially coupled dynamic identification and collection module of audit evidence to avoid the problem of weight extreme value failure. This represents the average abnormal deviation of all collected evidence in the current batch, with a range of values. The higher the overall abnormality of the evidence and the greater the average deviation, the larger the penalty factor and the stronger the deduction of scores for abnormal evidence, thus realizing an adaptive optimization mechanism of "the higher the abnormality, the stricter the screening". Step A5.1.4: Retrieve Pre-quantification Parameters: After completing the dynamic weight calculation, the temporal-spatial coupled dynamic identification and acquisition module for audit evidence uniformly retrieves the temporal weight coefficients of individual pieces of evidence that have been solved in the aforementioned steps. Spatial integrity weighting coefficient Abnormal bias of evidence All prerequisite parameters are matched and adapted to real-time dynamic weights, avoiding the technical defects of fixed weights not matching actual audit scenarios, and providing accurate input for subsequent compliance score calculation.

[0041] Step A5.2, Calculate the compliance tracing score for a single piece of evidence: Substitute the parameters retrieved in Step A5.1 into the time-series-spatial weighted tracing and identification algorithm formula: The compliance traceability score for a single piece of audit evidence is calculated. ;in Quantify the contribution of evidence to time-series compliance. Quantify the contribution of evidence to the completeness of the scene. As an abnormal penalty item, points are deducted for abnormal evidence such as tampering, missing, and conflict, so as to accurately quantify the overall compliance quality of evidence; Step A5.3: Based on a preset threshold, complete the evidence grading judgment: The time-space coupled dynamic identification and collection module for audit evidence retrieves a unified preset compliance scoring threshold of 0.7 as the sole criterion for determining the validity of evidence, and applies this threshold to each individual piece of evidence. Perform hierarchical verification: when When the evidence is deemed to meet ISO audit standards in terms of temporal matching and scenario completeness, and has no serious anomalies or defects, it is defined as valid and compliant evidence; when If the evidence is found to have one or more defects such as excessive time discrepancy, missing scene dimension, or data tampering conflict, it is defined as invalid defective evidence. Step A5.4, Evidence Classification and Defect Source Tracing Record: All valid and compliant evidence determined in Step A5.3 is uniformly collected, duplicate and redundant data are removed, and a structured and standardized set of compliant evidence is generated. This set serves as the data input basis for the electronic seal permission adaptation of the electronic seal adaptive control module, which maps evidence compliance. For all invalid and defective evidence, the system automatically traces the cause of low scores: If The data is too low, indicating a timing defect (overdue data collection, premature data collection, or timing node deviation). The score is too low, indicating a deficiency in the spatial scene (missing review dimensions, one-sided framing); if If the value is too high, it is judged as a data anomaly defect (tampering, splicing, information conflict), and an evidence defect list with specific defect types and defect parameters is generated simultaneously; Step A5.5: Output standardized data: Finally, complete the accurate identification of all audit evidence. On the one hand, output a set of compliant evidence to support the dynamic adaptation of electronic signature permissions. On the other hand, retain a list of evidence defects to provide a precise target basis for the subsequent reverse drive of the electronic seal adaptive control module that maps the compliance of evidence, thereby realizing the data closed-loop linkage of the two creative modules.

[0042] The electronic seal adaptive control module for evidence compliance mapping adopts an evidence compliance permission adaptation algorithm, which works in conjunction with the dynamic identification and collection module for audit evidence. It receives a standardized set of compliant evidence and calculates the evidence compliance level, dynamically matches and adjusts the electronic seal usage permissions of auditors, and triggers a valid signing action only when the evidence compliance level meets the standard. At the same time, it uses evidence defect information to drive the evidence collection module to complete a secondary accurate collection. The specific implementation steps are as follows: Step B1: Receive the compliance evidence set and quantify the overall compliance level: Receive the standardized compliance evidence set output by the time-space coupled audit evidence dynamic identification and collection module, and statistically analyze all valid evidence in the evidence set. The average evidence compliance traceability score is obtained by calculating the average value. Simultaneously, each piece of evidence is matched against the corresponding clauses of the current ISO audit, and the clause matching degree is calculated. Quantify the overall compliance quality of audit evidence; the specific implementation steps are as follows: Step B1.1, Receiving and Secondary Verification of Compliance Evidence Set: The system receives the standardized compliance evidence set output from step A5 of the time-space coupled dynamic identification and collection module for audit evidence in real time, and simultaneously performs preliminary verification to remove duplicate, redundant, and critically flawed data remaining in the evidence set. Strictly adhering to the previously preset compliance scoring threshold of 0.7, only retaining… Valid compliance evidence is required to ensure that all data sources for this quantitative calculation are valid audit data that are compliant in time series, complete in scenario, and free from serious anomalies, and to prevent invalid data from interfering with the accuracy of permission adaptation. Step B1.2, Calculation of Average Compliance Source Tracing Score for Batch Evidence: Calculate the total number of valid evidence in the current compliance evidence set. The compliance traceability score is calculated by retrieving a single valid piece of evidence and using a time-series-space weighted traceability identification algorithm. ( The evidence number, with a value of [value]. The overall compliance level of batch evidence is calculated using an arithmetic mean normalization algorithm, and the formula is as follows: ;in, This represents the average compliance traceability score of the evidence in this audit batch, with a range of values. The closer the value is to 1, the higher the overall quality of all the audit evidence collected in this round in terms of time sequence matching, scenario completeness, authenticity and compliance. It is the core indicator for quantifying the comprehensive compliance of evidence.

[0043] Step B1.3: Matching Evidence with ISO Audit Clauses Item by Item: The system retrieves the ISO system standard audit clause library bound to this audit task and accurately maps and matches each valid piece of evidence in the compliance evidence set with the audit clause: for scenario-based evidence, match on-site verification clauses; for ledger-based evidence, match document review clauses; and for personnel qualification evidence, match personnel management clauses, so as to achieve one-to-one binding of each piece of evidence with the audit clause and count the number of valid pieces of evidence that are completely matched.

[0044] Step B1.4, Review Clause Matching Measurement Calculation: Using a clause matching measurement formula, and combining the ratio of precisely matched evidence to the total number of evidence, along with the key weights of the clauses, a weighted adjustment is applied to solve the problem. The calculation formula is as follows: ;in, The range of values ​​represents the overall degree of conformity between the evidence and the ISO audit requirements. ; For the first The weight of the review clauses corresponding to each piece of evidence is as follows: the weight of core mandatory review clauses is 1.0, and the weight of general recommendation clauses is 0.5. To match the judgment coefficient, a value of 1 is used when the evidence fully complies with the corresponding clause, and a value of 0 is used when there is a slight deviation or the clause requirements cannot be fully verified, so as to achieve the effect of prioritizing the verification of key clauses and accurately quantifying the degree of matching.

[0045] Step B1.5, Compliance Parameter Archiving and Module Linkage Output: After completing the quantitative calculation, the system will... , The two core compliance parameters are uniformly archived to the data storage module and simultaneously pushed to the next permission adaptation calculation stage in real time. This provides accurate input for the evidence compliance permission adaptation algorithm, ensuring that the electronic seal permission level can truly and accurately adapt to the actual compliance quality of the evidence in this round of review, and achieving seamless closed-loop linkage between evidence collection and identification and signature permission control.

[0046] Step B2, Dynamically Adapt Electronic Seal Usage Permissions: [This will be done in the original text, but the translation is incomplete.] and Substitute the evidence compliance permission adaptation algorithm to calculate the real-time electronic seal permission level. The electronic seal adaptive control module, which maps evidence compliance, has three preset permission levels: Level 1 (full signing permission), Level 2 (limited scenario signing permission), and Level 3 (prohibited signing permission). The higher the evidence compliance, the higher the permission level, achieving dynamic linkage between permissions and evidence quality, abandoning the traditional static permission model. The specific steps are as follows: Step B2.1: Retrieve the Permission Adaptation Algorithm Fixed Compliance Weight Parameters: The system automatically retrieves the preset compliance adaptation weight factors, including the evidence quality weight factor. Clause matching weight factor Satisfying the weight constraint conditions The weighting of evidence prioritizes its overall authenticity and compliance quality, while also considering the accuracy of its alignment with ISO audit requirements. This ensures complete compatibility with the compliance control requirements of various ISO system audits, guaranteeing that authority determinations align with core audit principles. The evidence quality weighting factor is one such factor. Matching weight factor with terms The specific steps to obtain it are as follows: Step B2.1.1: Extracting Algorithm Pre-Linkage Parameters: The electronic seal adaptive control module for evidence compliance mapping retrieves two types of core feature parameters for this round of review: one is the standard deviation of batch evidence compliance scoring. The first is used to characterize the quality stability of the entire batch of audit evidence; the second is the coefficient of the proportion of mandatory audit clauses. This is used to characterize the stringency of the terms in this ISO audit task; Step B2.1.2: Calculate the evidence quality stability coefficient: based on the compliance tracing score of all valid evidence in this round. With average score Solve for the standard deviation of evidence compliance The calculation formula is as follows: ;in The total number of valid evidence. The range of values ​​is ; The smaller the value, the more uniform and stable the compliance quality of all the evidence reviewed in this round is, and the stronger the overall credibility of the evidence. Therefore, the weight of evidence quality should be increased. A higher value indicates inconsistent evidence quality and weak credibility, necessitating a reduction in evidence weight and a strengthening of the weight for compliance verification of clauses; an evidence quality stability coefficient is constructed based on this characteristic. , Positively correlated with the overall credibility of the evidence; Step B2.1.3: Calculate the stringency coefficient of the audit clauses: Count the number of mandatory core clauses in this audit task. Total number of audited clauses Solve for the severity coefficient of the terms. : ,in, The range of values ​​is Highly stringent management systems such as ISO 27001 and ISO 14001 The value is relatively high, typical of ISO 9001 audit scenarios. The values ​​are moderate, accurately quantifying the stringent compliance requirements of different ISO systems. Step B2.1.4: Dynamically solve the dual-weighting factor: based on the evidence quality stability coefficient Strictness of Terms The solution is obtained in real time using a normalized coupling algorithm. , The calculation formula is: ; ;in As a dynamic evidence quality weighting factor, To match weighting factors for dynamic clauses, the two are naturally satisfied through normalization calculation. The constraints are such that there is no weight imbalance problem; Step B2.1.5, Weight Adaptive Logic Adaptation: When the quality and credibility of the evidence in this round are stable, Approaching 1, Automatically increase the priority given to evidence of authenticity and compliance quality; when the core mandatory clauses account for a high proportion and the compliance requirements are stringent in this audit. Approaching 1, Automatically increase and strengthen the matching and verification weight of evidence and audit terms, completely solve the technical defects of fixed weights that cannot adapt to different ISO audit scenarios, and ensure that the permission determination is fully in line with the core compliance principles of this audit. Step B2.2, Substitute into the formula to calculate the electronic seal permission level in real time: Average the compliance traceability score of the batch evidence received in Step B1. Matching degree of evidence review clauses Unified substitution of evidence compliance permission adaptation algorithm formula: ;in The electronic seal's real-time dynamic permission level, after normalization calculation, has a fixed value range of [value missing]. ; It contributes a score to the quality of evidence compliance, representing the comprehensive compliance level of the evidence in terms of timing, scope, and authenticity. The score contributes to the clause adaptation, which represents the validity of the evidence in supporting the ISO audit clauses. The superposition of the two-dimensional scores enables the precise quantification of the authority level. Step B2.3: Retrieve the standardized permission level threshold system: Retrieve the three-level permission judgment thresholds that are uniformly preset throughout the document. These thresholds are fixed standards adapted to ISO audit compliance requirements: The first-level full signature permission threshold is... The threshold for Level 2 restricted signature permissions is: The threshold for Level 3 prohibition of signature permissions is: By quantifying thresholds, the system achieves standardized and subjectively unbiased determination of permission levels. Step B2.4, Differentiated and Precise Adaptation and Control of Permission Levels: Based on the calculated... Numerical matching corresponds to the permission level, enabling dynamic and differentiated permission unlocking and locking: When determined to be Level 1 permission, it means that the overall compliance of the evidence in this round of review is high and the clause matching is accurate. The system fully unlocks the electronic seal, granting the reviewer legal signing permissions for all scenarios and all documents, and can complete the electronic signing of all review materials such as review reports, certification certificates, and rectification confirmation documents; When determined to be Level 2 permission, it means that there are slight dimension missing or clause matching deviations in the evidence. The system restricts the full-domain signing permissions, only opening the signing permissions for review documents corresponding to the dimensions of compliant evidence, and prohibiting the signing of core certification documents; When determined to be Level 3 permission, it means that the compliance quality of the evidence in this round of review does not meet the standards and has obvious defects. The system directly locks all permissions of the electronic seal, prohibiting all signing operations, eliminating the risk of unlicensed signing and illegal signing from the source; Step B2.5, Preparation for Archiving Permission Data and Module Linkage: The calculated data... Numerical values, matching permission levels, and permission open / closed status are uniformly archived to the data storage module; at the same time, the evidence defect list output by the time-space coupled dynamic identification and collection module for audit evidence is bound. If it is a level 2 or 3 permission, the reason for the permission restriction is automatically marked, providing permission dimension basis for the subsequent step B4 to drive the time-space coupled dynamic identification and collection module for audit evidence to collect more accurately for the second time, realizing a closed-loop linkage of evidence quality, permission level, and defect optimization.

[0047] Step B3, Compliance Signature Action Control and Log Retention: Based on the permission levels adapted in Step B2, execute the corresponding signature operations: Level 1 permissions allow for full-scenario electronic signatures on audit reports and certified documents; Level 2 permissions only allow signatures on documents corresponding to compliance dimensions; Level 3 permissions directly lock the electronic seal, prohibiting any signature actions; all signature operations, permission changes, and evidence verification records are retained in real-time to the data storage module, forming a traceable signature log to prevent false signatures and unauthorized signatures; the specific implementation steps are as follows: Step B3.1, Secondary Verification of Permission Level and Signature Access Control: The system retrieves the real-time permission level value obtained in step B2. It reuses a unified, pre-defined three-level permission threshold to complete a second, precise review, avoiding misjudgments of permissions caused by algorithmic errors or data fluctuations, and locking in the final signature permission level corresponding to this review. Simultaneously, it links with the standardized compliance evidence set and defect list output by the time-space coupled dynamic evidence identification and collection module to verify the currently valid compliance evidence. Compliance benchmark conditions are established to confirm that the evidence required for signing and sealing is compliant, and to prevent unauthorized signing and sealing under the premise of no evidence or substandard evidence.

[0048] Step 3.2, Precise Execution of Differentiated Compliance Signature Actions: Based on the completed authorization level, the system adaptively unlocks or locks the electronic seal function, achieving precise matching between authorization level and signature scenario, and strictly implementing the control logic that "compliance level determines the scope of signature." when When the authorization level is determined to be Level 1 full signature authority, it represents the average compliance level of the evidence reviewed in this round. High, terms and conditions matching Excellent, with no obvious evidentiary defects, the system fully unlocks all functions of the electronic seal, supporting auditors to legally electronically sign all audit-related documents such as ISO audit reports, system certification certificates, rectification and acceptance documents, and filing materials in all scenarios, meeting the requirements for issuing certificates in a complete audit closed loop. when When the signature permission is determined to be a Level 2 restricted scenario, it means that there are minor non-fatal defects such as missing spatial dimensions or mismatched clauses in the evidence of this round of review. The system disables full-domain signature permission and, based on the review dimensions covered by the valid evidence and the matching ordinary review clauses, only grants signature permission for auxiliary review documents of the corresponding compliance dimensions. Signing operations of core compliance documents such as system certification certificates and official audit reports are strictly prohibited, so as to achieve differentiated control of "defects are limited and compliance is usable". when When the electronic seal is determined to be prohibited from signing at level three, it means that the overall compliance quality of the evidence in this round of review is substandard, with serious defects such as time sequence deviation, missing scenarios, and abnormal evidence. The system directly locks all signing permissions of the electronic seal and blocks all signing operation access points, thus completely eliminating the compliance risks of unlicensed signing, false signing, and unauthorized private signing from a technical perspective. Step B3.3, Dynamic Anti-Tampering Verification of the Signing Process: Throughout the entire signing process, the system dynamically verifies the data storage module in real time. On the one hand, it verifies the legality of the electronic seal private key and the validity of the signing identity and permissions to prevent attacks such as private key theft and identity forgery. On the other hand, it verifies the integrity of the document to be signed to prevent violations such as pasting signature images, tampering with signature content, and forging signature records, ensuring that every signing action is genuine, compliant, and valid, in accordance with the Electronic Signature Law and ISO system audit compliance standards.

[0049] Step B3.4, Structured Log Retention of Full-Dimensional Operational Data: The system retains all signature-related data for this review process in real time, automatically, and tamper-proofly, forming a standardized and traceable signature log. The core retention parameters and content of the log include: core quantitative parameters of this round of review ( , , Dynamic permission values, permission level determination results, signature execution status (success / prohibition / restriction), signature document name and type, signature timestamp, operator identity information, permission change records, evidence verification results, and existing evidence defect information enable end-to-end data traceability from evidence quantification and permission adaptation to signature execution.

[0050] Step B3.5, Log Archiving and Module Linkage Preparation: All signature log data is uniformly encrypted and archived to the data storage module, serving as the sole valid evidence for ISO audit compliance, regulatory verification, and accountability. Simultaneously, signature status, reasons for restricted access, and log defect data are pushed to the next stage, providing precise evidence for the targeted secondary precise collection by the time-space coupled audit evidence dynamic identification and collection module in Step B4, thus perfecting the closed-loop mechanism of the entire process: "evidence identification - access control adaptation - compliance signature - source tracing and retention - reverse optimization".

[0051] Step B4, Reverse-Driven Secondary Optimization of Evidence Collection: After the signing in Step B3, the electronic seal adaptive control module, which maps evidence compliance, pushes collection optimization instructions to the temporal-spatial coupled dynamic identification and collection module of audit evidence based on the evidence defect list. For evidence with missing spatial dimensions, temporal deviations, or low scores, it drives the temporal-spatial coupled dynamic identification and collection module of audit evidence to conduct targeted and precise secondary collection, supplementing missing scenario evidence, replacing invalid evidence, iteratively optimizing the quality of evidence compliance, and forming a closed-loop control system. The specific implementation steps are as follows: Step B4.1, Comprehensive Defect Data Collection and Precise Source Tracing and Classification: The system automatically collects defect data from the entire audit process. The data source includes two types of core compliance data to ensure comprehensive and complete defect identification: one is the single evidence defect record retained in the first creative module, including cases with low time-series weighting. Insufficient spatial integrity Abnormal deviation exceeds the standard Compliance score not up to standard The first part details various types of invalid evidence and their corresponding defect types; the second part details the access-restricted data retained in the signing process of the electronic seal adaptive control module, which maps evidence compliance. Level 2 restricted permissions The overall evidentiary shortcomings and clause mismatches corresponding to Level 3 prohibition. Global defects such as those that are too low are aggregated. The system classifies and grades all defects, distinguishing them into four major defect types: timing deviation, spatial missing, data anomaly, and clause mismatch, forming a standardized secondary data collection task list.

[0052] Step B4.2, Secondary Data Acquisition Strategy Adaptive Matching Generation: The system, based on the defect classification results, combined with the ISO audit scenario (on-site / remote), system rigor level, and standard audit dimensions,... Review timeline Based on baseline parameters, an adaptive matching and differentiated secondary optimization acquisition strategy is used to avoid blind full re-acquisition and achieve precise optimization at specific points: for evidence of time series defects, corresponding standard time series nodes are matched. To address the issue of pre-collection and overdue data entry, the system limits the time interval for secondary data collection, avoiding problems such as pre-collection and overdue data entry. To address the lack of spatial dimensions, it accurately retrieves uncovered standard scenario dimensions and specifically supplements missing verification scenarios such as equipment sites, work processes, ledger data, and personnel qualifications. For evidence with high anomalies or deviations, such as tampering or information conflicts, it marks it as invalid evidence to be replaced and requires full-dimensional re-collection. To address the issue of low clause matching, it specifically supplements supporting evidence corresponding to core mandatory review clauses.

[0053] Step B4.3: Push optimization instructions across modules and execute secondary data collection: The electronic seal adaptive management module, which maps evidence compliance, will push the generated targeted secondary data collection task list back to the time-space coupled dynamic identification and collection module for audit evidence, initiating the iterative data collection process. Upon receiving the optimization instructions, the time-space coupled dynamic identification and collection module for audit evidence will terminate the regular data collection logic and conduct targeted supplementary and replacement data collection according to a customized strategy: incremental supplementary data collection for missing scenarios to improve the actual number of data collection dimensions. Invalid evidence with excessive time deviation or abnormal alteration is batch-removed and replaced with new evidence to ensure that the second-collected evidence fully conforms to the standard time sequence and scenario specifications, thus correcting the defects of the original evidence from the source.

[0054] Step B4.4, Secondary Evidence Collection and Re-verification and Compliance Quantification: After the secondary collection is completed, the temporal and spatially coupled dynamic verification and collection module for audit evidence restarts the complete evidence verification algorithm process. It re-executes temporal weight correction, spatial integrity completion verification, and abnormal deviation quantification calculation on the newly generated batch of evidence, resulting in a completely new version. , , The parameters are then iterated in real time using a dynamic weighting algorithm. , , Weighting factors, substituted into the core formula The compliance traceability score of each piece of evidence is recalculated, and an updated standardized set of compliant evidence is selected, while defective evidence that still fails to meet the standards after secondary collection is removed.

[0055] Step B4.5, Evidence Compliance Iterative Update and Closed-Loop Termination Determination: The system will push the updated compliance evidence set back to the electronic seal adaptive control module mapped to the evidence compliance, and iteratively calculate the batch average compliance score. Clause matching degree and electronic seal access levels This completes a new round of compliance verification. The system's preset closed-loop termination condition is: when all valid evidence... Spatial integrity There is no evidence of any abnormality or exceeding of the limit, and the overall access level reaches Level 1. If the quality of the audited evidence is deemed to meet the compliance standards, the second iteration of the data collection process is terminated. If defects still exist, the above-mentioned optimized data collection process is repeated until all compliance requirements are met, forming a closed-loop control mechanism for sustainable iteration. Step B4.6, Iterative Data Archiving and Process Solidification: All secondary collection records, parameter iteration data, defect correction records, and permission update results are encrypted and archived to the data storage module. This serves as the foundational data for subsequent audit optimization, compliance tracing, and algorithm iteration, completely overcoming the technical shortcomings of traditional single-collection, lack of error correction, and lack of optimization, and continuously ensuring the authenticity, completeness, and signature compliance of ISO audit evidence.

[0056] This invention discloses an audit evidence collection and electronic seal management system based on the ISO management system, belonging to the field of ISO system audit digital technology. The invention features a time-space coupled dynamic identification and collection module for audit evidence and an electronic seal adaptive control module that maps evidence compliance. Through a self-developed time-space weighted traceability identification algorithm and an evidence compliance permission adaptation algorithm, it achieves comprehensive, traceable, and accurate collection of audit evidence, as well as dynamic linkage control of electronic seal permissions, signing actions, and evidence compliance status. The time-space coupled dynamic identification and collection module for audit evidence and the electronic seal adaptive control module that maps evidence compliance form a closed-loop logic of "compliant collection first, linked signing later, and reverse error correction and optimization," significantly improving the authenticity and completeness of ISO audit evidence, completely eliminating the risk of unauthorized use of electronic seals, and ensuring the legality and authority of ISO system audit results. The technical solution of this invention is logically rigorous and highly adaptable, applicable to online and offline digital audit control scenarios for all categories of ISO management systems.

[0057] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A management system for audit evidence collection and electronic seal based on the ISO management system, characterized in that, It includes a data storage module, a process control module, a time-space coupled dynamic identification and collection module for audit evidence, and an electronic seal adaptive control module that maps evidence compliance. The data storage module is used to store ISO system audit standards and specifications, audit task information, collected audit evidence data, electronic seal permission parameters, and signature logs. The process control module is used to coordinate the time nodes of the entire review process and realize the coordinated scheduling of evidence collection, compliance verification, electronic signature, and log retention. The time-space coupled dynamic identification and collection module for audit evidence is configured with a self-developed time-space weighted source identification algorithm, which is used to perform time-series correction, spatial completion, and authenticity source identification on multi-dimensional audit evidence collected offline and remotely captured online, eliminating one-sided, tampered, and invalid evidence, and outputting a standardized and compliant evidence set. The electronic seal adaptive control module for evidence compliance mapping adopts an evidence compliance permission adaptation algorithm, which works in conjunction with the dynamic identification and collection module for audit evidence. It receives a standardized set of compliant evidence and calculates the evidence compliance, dynamically matches and adjusts the electronic seal usage permissions of the auditors, and triggers a valid signing action only when the evidence compliance meets the standard. At the same time, it drives the evidence collection module to complete a second precise collection based on the evidence defect information.

2. The audit evidence collection and electronic seal management system based on the ISO management system as described in claim 1, characterized in that: The specific implementation steps of the time-space coupled dynamic identification and collection module for audit evidence are as follows: Step A1: Synchronous Collection of Multi-Source Evidence: The time-space coupled dynamic identification and collection module for audit evidence synchronously collects evidence from the entire ISO audit scenario, including video / image evidence of equipment operation, work operations, and environmental conditions captured on-site, online ledger documents, system logs, audit record data, and manually entered interview records and problem investigation notes, to achieve unified collection of evidence in multiple formats and scenarios. Step A2, Time-Sequence Weight Correction Calculation: Retrieve the standard time-series nodes of the audit task. With review period Extract the collection timestamp of each piece of evidence. Substituting into the time series weight calculation formula, we get Evidence whose collection timeline deviates from the standard nodes is marked, and expired evidence collected after the review task is completed or invalid evidence collected in advance is removed. Step A3, Spatial Integrity Completion Verification: Based on the corresponding ISO system standard, determine the number of standard scenario dimensions required for the current audit project. Count the number of effective scene dimensions actually collected. Calculate spatial integrity weights For evidence lacking spatial dimensions, an automatic list of collection defects is generated, marking the uncovered review scenarios; Step A4, Quantification of Evidence Anomalies: Through image comparison, text deduplication, and data verification, detect whether the evidence contains anomalies including but not limited to tampering, splicing, and information conflicts, and quantify the anomaly deviation value. ; Step A5, Compliance Scoring and Evidence Screening: Quantify the abnormal deviation values ​​obtained in Step A4. Substitute the time-series-space weighted source identification algorithm to calculate the compliance source identification score of a single piece of evidence. Evidence scoring above the system's preset compliance threshold is deemed valid and included in the standardized compliance evidence set; evidence scoring below the threshold is deemed invalid, automatically removed, and the reasons for the defects are recorded, thus completing the accurate identification of evidence.

3. The audit evidence collection and electronic seal management system based on the ISO management system as described in claim 2, characterized in that: The specific implementation steps for compliance scoring and evidence screening in step A5 are as follows: Step A5.1: Retrieve standardized algorithm parameters and pre-calculated data: The time-space coupled dynamic identification and collection module for audit evidence automatically retrieves the weight factors corresponding to the current audit scenario. , , Satisfying the weight constraint conditions Simultaneously retrieve the time-series weighting coefficients that have already been calculated for each piece of evidence. Spatial integrity weighting coefficient Abnormal bias of evidence ; Step A5.2, Calculate the compliance tracing score for a single piece of evidence: Substitute the parameters retrieved in Step A5.1 into the time-series-spatial weighted tracing and identification algorithm formula: The compliance traceability score for a single piece of audit evidence is calculated. ;in Quantify the contribution of evidence to time-series compliance. Quantify the contribution of evidence to the completeness of the scene. As an abnormal penalty item, points are deducted for abnormal evidence such as tampering, missing, and conflict, so as to accurately quantify the overall compliance quality of evidence; Step A5.3: Based on a preset threshold, complete the evidence grading judgment: The time-space coupled dynamic identification and collection module for audit evidence retrieves a unified preset compliance scoring threshold of 0.7 as the sole criterion for determining the validity of evidence, and applies this threshold to each individual piece of evidence. Perform hierarchical verification: when When the evidence is deemed to meet ISO audit standards in terms of temporal matching and scenario completeness, and has no serious anomalies or defects, it is defined as valid and compliant evidence; when If the evidence is found to have one or more defects such as excessive time discrepancy, missing scene dimension, or data tampering conflict, it is defined as invalid defective evidence. Step A5.4, Evidence Classification and Collection and Defect Tracing Record: Collect all valid and compliant evidence determined in Step A5.3, remove duplicate and redundant data, and organize them into a structured and standardized set of compliant evidence. This set will serve as the data input basis for the electronic seal permission adaptation of the electronic seal adaptive control module for evidence compliance mapping.

4. The audit evidence collection and electronic seal management system based on the ISO management system as described in claim 3, characterized in that: The weighting factor corresponding to the current review scenario in step A5.1 , , The specific acquisition logic is as follows: Step A5.1.1: Construct quantitative parameters for review scenario features: The time-series and space-coupled dynamic identification and collection module for review evidence automatically identifies the current review mode and extracts core feature parameters, defined as follows: To ensure strict adherence to the audit timeline, on-site audits must strictly adhere to the established timeframes, with no timeline deviations permitted. The value range is [value range missing]. Remote auditing offers higher fault tolerance in terms of timing, with a value range of [value range missing]. ; To assess the spatial integrity coefficient, the value is determined based on the stringency of the corresponding ISO system standard's scenario verification. ISO 27001 Information Security System and ISO 14001 Environmental System have multiple verification dimensions and high spatial integrity requirements. ISO 9001 Standard Quality Management System And always satisfy the constraints. ; Step A5.1.2, Dynamically calculate temporal and spatial weight factors: Based on scene feature coefficients, solve in real time using a normalization adaptation algorithm. , The calculation formula is: ; ;in, This represents the number of time-constraint verification clauses in the current audit task. This represents the total number of clauses currently being reviewed. This refers to the number of spatial scenario constraint-related verification clauses in the current review task. This represents the total number of clauses currently being reviewed. Step A5.1.3: Dynamically calculate the anomaly penalty factor: Based on the overall anomaly level of the evidence collected in this round, adaptively solve for the penalty factor. The calculation formula is: ;in, , The upper and lower limits of the penalty factor are preset for the temporal and spatially coupled dynamic identification and collection module for audit evidence; This represents the average abnormal deviation of all collected evidence in the current batch, with a range of values. .

5. The audit evidence collection and electronic seal management system based on the ISO management system according to claim 1, characterized in that: The specific implementation steps for configuring the electronic seal adaptive control module for evidence compliance mapping are as follows: Step B1: Receive the compliance evidence set and quantify the overall compliance level: Receive the standardized compliance evidence set output by the time-space coupled audit evidence dynamic identification and collection module, and statistically analyze all valid evidence in the evidence set. The average evidence compliance traceability score is obtained by calculating the average value. Simultaneously, each piece of evidence is matched against the corresponding clauses of the current ISO audit, and the clause matching degree is calculated. Quantify the overall compliance quality of audit evidence; Step B2, Dynamically Adapt Electronic Seal Usage Permissions: [This will be done in the original text, but the translation is incomplete.] and Substitute the evidence compliance permission adaptation algorithm to calculate the real-time electronic seal permission level. ; The electronic seal adaptive control module, which maps evidence compliance, has three preset permission levels: Level 1, Level 2, and Level 3. Step B3, Compliance Signature Action Control and Log Retention: Perform corresponding signature operations according to the permission levels adapted in Step B2: Level 1 permissions can complete full-scenario electronic signatures for audit reports and certification documents; Level 2 permissions can only sign documents corresponding to the compliance dimension; Level 3 permissions directly lock the electronic seal and prohibit any signature actions; All signature operations, permission changes, and evidence verification records are retained in real time to the data storage module to form a traceable signature log; Step B4, Reverse-driven secondary optimization of evidence collection: After the signing in Step B3, the electronic seal adaptive control module, which maps the evidence compliance, pushes collection optimization instructions to the temporal and spatial coupled dynamic identification and collection module of audit evidence based on the evidence defect list. For evidence with missing spatial dimensions, temporal deviations, or low scores, it drives the temporal and spatial coupled dynamic identification and collection module of audit evidence to carry out targeted and precise secondary collection, supplement missing scenario evidence, replace invalid evidence, iteratively optimize the quality of evidence compliance, and form a closed-loop control.

6. The audit evidence collection and electronic seal management system based on the ISO management system as described in claim 5, characterized in that: The specific steps for dynamically adapting electronic seal usage permissions in step B2 are as follows: Step B2.1: Retrieve the Permission Adaptation Algorithm Fixed Compliance Weight Parameters: The system automatically retrieves the preset compliance adaptation weight factors, including the evidence quality weight factor. Clause matching weight factor Satisfying the weight constraint conditions ; Step B2.2, Substitute into the formula to calculate the electronic seal permission level in real time: Average the compliance traceability score of the batch evidence received in Step B1. Matching degree of evidence review clauses Unified formula for adapting evidence compliance permissions: ;in The electronic seal's real-time dynamic permission level, after normalization calculation, has a fixed value range of [value missing]. ; It contributes a score to the quality of evidence compliance, representing the comprehensive compliance level of the evidence in terms of timing, scope, and authenticity. The score contributes to the clause adaptation, which represents the validity of the evidence in supporting the ISO audit clauses. The superposition of the two-dimensional scores enables the precise quantification of the authority level. Step B2.3: Retrieve the standardized permission level threshold system: Retrieve the three-level permission judgment thresholds that are uniformly preset throughout the document. These thresholds are fixed standards adapted to ISO audit compliance requirements: The first-level full signature permission threshold is... The threshold for Level 2 restricted signature permissions is: The threshold for Level 3 prohibition of signature permissions is: ; Step B2.4, Differentiated and Precise Adaptation and Control of Permission Levels: Based on the calculated... Numerical matching corresponds to the permission level, enabling dynamic and differentiated permission unlocking and locking: When it is determined to be a level 1 permission, it means that the overall compliance of the evidence in this round of review is high and the clause matching is accurate. The system fully unlocks the electronic seal and grants the reviewer legal signing permissions for all scenarios and all documents; When the system determines that the level is Level 2, it means that there are minor missing dimensions or mismatches in the evidence. The system restricts the signing permissions across the entire domain and only allows signing permissions for audit documents corresponding to the dimensions of compliant evidence, prohibiting the signing of core certification documents. When the system determines that the level is Level 3, it means that the compliance quality of the evidence in this round of audit is substandard and there are obvious defects. The system directly locks all permissions of the electronic seal and prohibits all signing operations. Step B2.5, Preparation for Archiving Permission Data and Module Linkage: Calculate the... Numerical values, matching permission levels, and permission open / closed status are uniformly archived to the data storage module; at the same time, the evidence defect list output by the time-space coupled dynamic identification and collection module for audit evidence is bound. If it is a level 2 or 3 permission, the reason for the permission restriction is automatically marked, providing permission dimension basis for the subsequent step B4 to reverse drive the time-space coupled dynamic identification and collection module for audit evidence to collect more accurately.

7. The audit evidence collection and electronic seal management system based on the ISO management system as described in claim 6, characterized in that: The evidence quality weighting factor in step 2.1 Matching weight factor with terms The specific steps to obtain it are as follows: Step B2.1.1: Extracting Algorithm Pre-Linkage Parameters: The electronic seal adaptive control module for evidence compliance mapping retrieves two types of core feature parameters for this round of review: one is the standard deviation of batch evidence compliance scoring. This is used to characterize the quality stability of the entire batch of audit evidence; Second, the percentage of mandatory review clauses. This is used to characterize the stringency of the terms in this ISO audit task; Step B2.1.2: Calculate the evidence quality stability coefficient: based on the compliance tracing score of all valid evidence in this round. With average score Calculate the standard deviation of evidence compliance. The calculation formula is as follows: ;in The total number of valid evidence. The range of values ​​is ; The smaller the value, the more uniform and stable the compliance quality of all the evidence reviewed in this round is, and the stronger the overall credibility of the evidence. Therefore, the weight of evidence quality should be increased. The larger the value, the more inconsistent the quality of the evidence and the weaker its credibility. Therefore, it is necessary to reduce the weight of the evidence and strengthen the weight of compliance verification of the clauses. Based on this feature, an evidence quality stability coefficient is constructed. , Positively correlated with the overall credibility of the evidence; Step B2.1.3: Calculate the stringency coefficient of the audit clauses: Count the number of mandatory core clauses in this audit task. Total number of audited clauses Solve for the severity coefficient of the terms. : ,in, The range of values ​​is ; Step B2.1.4: Dynamically solve the dual-weighting factor: based on the evidence quality stability coefficient Strictness of Terms The solution is obtained in real time using a normalized coupling algorithm. , The calculation formula is: ; ; Step B2.1.5, Weight Adaptive Logic Adaptation: When the quality and credibility of the evidence in this round are stable, Approaching 1, Automatically increase the priority given to evidence of authenticity and compliance quality; when the core mandatory clauses account for a high proportion and the compliance requirements are stringent in this audit. Approaching 1, It increases automatically.

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