Sports meeting registration management method and system based on whole-process log tracing and cross-system interface adaptation

Abstract

The application provides a sports registration management method and system based on full-process log tracking and cross-system interface adaptation, which generates a structured space-time semantic anchor point set by collecting real-time multi-source low-overhead signals including IP geolocation, client clock offset, network delay sequence and form operation events, combining with the International Olympic Committee venue geofencing and event plan; the anchor point set is linked with a preset permission rule library to realize millisecond-level consistency check and dynamic allocation of permission change requests; the system has concurrent conflict detection, rule priority arbitration and real-time degradation processing capabilities, and supports cross-system exception tracking and dynamic optimization of rule parameters, which improves the security, consistency and real-time performance of permission adjudication, and meets the stringent requirements of high-concurrency registration business.

Description

Technical Field

[0001] This invention relates to the field of dynamic permission adjudication and environment-aware access control management technology for sports event registration management systems, and particularly to a sports event registration management method and system based on full-process log tracing and cross-system interface adaptation. Background Technology

[0002] Current comprehensive sports event registration management systems generally adopt dynamic authorization technologies based on fixed multi-factor weighted scoring, behavioral log strategy analysis, or knowledge graph relational path reasoning in the field of access control and management. Mainstream solutions primarily focus on user authentication (such as username / password, multi-factor tokens, dynamic verification codes, etc.), permission approval workflows (such as hierarchical approval, operation logging), device fingerprinting, and historical behavior modeling, emphasizing continuous analysis of operator characteristics and historical behavior chains. Some cutting-edge systems introduce emerging mechanisms such as rule engine and model fusion, reinforcement learning, fuzzy Q-learning, and blockchain notarization to improve the intelligence of permission allocation and the non-repudiation of audits. With increasing business complexity, cross-system collaboration scenarios such as data synchronization between registration management platforms, document compliance, and background audit processes are constantly increasing, placing higher demands on the real-time performance and environmental adaptability of permission decisions. In practical applications, the aforementioned conventional technical approaches have several significant limitations. First, behavioral modeling and scoring mechanisms are heavily reliant on historical samples and high-value operation logs. When faced with large-scale concurrent registrations and temporary user operations, the model's cold start is time-consuming and the judgment results are unstable. Second, the computational complexity of model inference and path solving is high, significantly increasing the delay in permission adjudication and business flow, making it unsuitable for scenarios with tens of thousands of concurrent accesses during peak competition periods. Third, general multi-factor authentication or knowledge graph methods cannot fully capture the strong business constraints such as spatial closure and temporal rigidity in competition scenarios, leading to a significant increase in the risk of misauthorization in high-risk environments such as remote login, time zone anomalies, and batch operations. Furthermore, existing mechanisms rely excessively on external authentication components and third-party data sources, making it difficult to ensure a closed-loop permission adjudication process in the event of network anomalies or interface failures, thus affecting the overall security resilience of the system. To address the practical needs of sports event registration management, traditional technologies fail to utilize low-overhead contextual signals such as geofencing, event time windows, network link quality, and form operation behavior as structured criteria. This prevents lightweight, dynamic permission decisions based on physical environment and rigid business rules. This approach struggles to promptly detect and intercept permission change requests in high-risk real-time environments, easily leading to systemic misauthorization risks such as batch abnormal data export and unauthorized qualification changes. Furthermore, the cross-system data synchronization and permission tracing processes lack an environment-aware mechanism deeply coupled with specific registration scenarios and event schedules, hindering efficient location and self-correction of permission conflicts. Summary of the Invention

[0003] In order to solve the above-mentioned technical problems, the present invention provides a sports meet registration management method and system based on full-process log traceability and cross-system interface adaptation.

[0004] The technical solution of this invention is implemented as follows: a sports meet registration management method based on full-process log traceability and cross-system interface adaptation, comprising: S1: Based on the real-time environmental context of the permission change request, obtain four types of low-overhead signals: standard WGS84 latitude and longitude coordinates of IPv4 / IPv6 addresses resolved by GeolocationAPI, millisecond-level offset between the client system clock and the time source of the registration system NTP server, a continuous five-sample sequence of network round-trip time from the first packet of the HTTP request to the first packet of the response, and a sequence of timestamps of key form fields of the mobile H5 registration page. Based on the geofence of the venues designated by the International Olympic Committee, the official schedule of the Games, the sliding window statistical threshold, and the compliance standards of form operations, generate four sets of spatiotemporal semantic anchor points: main venue anchor point / sub-venue anchor point / remote office anchor point, pre-event preparation period anchor point / event in progress anchor point / post-event archiving period anchor point, low-latency venue intranet anchor point / high-jitter public network anchor point, and compliant single-step operation anchor point / abnormal batch skip anchor point. S2: Based on the spatiotemporal semantic anchor point set generated by S1, and according to the three types of strong business semantic constraints in the sports meet registration business, namely the qualification freeze window period, the certificate issuance countdown threshold, and the back-examination status transition constraint, an anchor point conflict rule base is constructed, which includes the logical structure of 'IF [spatiotemporal semantic anchor point combination] THEN [permission action]'. The permission action types include setting the validity period of qualification status update permission, rejecting certificate information export request, and triggering manual review work order. S3: Input the set of spatiotemporal semantic anchor points generated by S1 corresponding to the permission change request into the anchor point conflict rule base constructed by S2, perform millisecond-level semantic consistency verification, and obtain the intermediate result of permission adjudication. The intermediate result includes the permission operation type identifier and the corresponding permission validity period parameter. S4: Based on the asynchronous callback event monitoring data of the background review platform WebService interface, determine whether there is a business constraint triggering condition that the athlete's qualification status will be rejected within 30 minutes. If so, input the intermediate result of the permission decision obtained by S3 into the business semantic constraint engine and perform forced downgrade processing to generate a read-only permission identifier and audit trail instructions. S5: Perform memory-resident stateless verification on the final result of the permission decision output by S4 to verify whether it meets the real-time requirement of tens of thousands of concurrent requests per second during the peak of the Games. When the verification passes, generate a sequence of permission control instructions. S6: Input the sequence of permission control instructions generated in S5 into the permission execution module of the registration management system to perform the corresponding dynamic allocation operation of role permissions, and synchronously record the operation log to the full-process log traceability unit; S7: Monitor the system status feedback signal after S6 is executed, determine whether the permission allocation result triggers cross-system interface adaptation anomaly, and if a data synchronization conflict is detected in the document verification system or the background review platform, start the log tracing unit to trace back the spatiotemporal semantic anchor point generation path from S1 to S4. S8: Based on the log tracing results of S7, dynamically update the semantic anchor combination threshold parameters in the anchor conflict rule base constructed by S2 to achieve closed-loop optimization of the permission adjudication mechanism.

[0005] This invention also provides a sports meet registration management system based on full-process log tracing and cross-system interface adaptation, which uses the above-mentioned sports meet registration management method based on full-process log tracing and cross-system interface adaptation to dynamically manage the permissions of the sports meet registration management system.

[0006] The sports meet registration management method and system based on full-process log traceability and cross-system interface adaptation provided by this invention have the following beneficial effects: (1) This invention significantly improves the accuracy and real-time response capability of the comprehensive sports event registration management system in complex and dynamic environments by constructing a new access control paradigm of "semantic anchors + business constraints". This invention abandons the reliance on user profile modeling, credit scoring and machine learning inference, and instead starts from the time rigidity, spatial closure and strong process coupling unique to the sports event, extracts four types of highly deterministic and low-intrusive environmental signals, and transforms them into semantic anchor tags with clear business meanings - including geographical location anchors, event stage anchors, network quality anchors and operation behavior pattern anchors. These anchors do not require training and have the characteristics of being ready to use, and can complete context awareness and tag classification in milliseconds. Especially in the scenario of tens of thousands of concurrent requests per second during peak periods, this mechanism avoids the overhead of external API calls, database queries and model loading. All processing is completed in memory, and the average single decision time is less than 8ms, which effectively ensures the overall response sensitivity and service availability of the system; (2) This invention achieves refined expression of access control strategies and compliance safeguards by introducing a dual verification mechanism of a spatiotemporal semantic anchor conflict rule base and a business semantic constraint engine, significantly enhancing the system's security and anti-attack capabilities. This invention designs a structured rule base that supports composite condition judgments based on multi-dimensional anchor combinations. For example, sensitive operations are only allowed and granted a short validity period when the request source is the main venue, the event is in progress, network latency is stable, and the operation conforms to normal procedures. Conversely, if a remote office location, high public network jitter, pre-event preparation period, and abnormal skipping behavior are detected, the request is automatically rejected and a manual review process is triggered, effectively identifying potential automated script attacks or unauthorized probing behaviors. Furthermore, the system embeds a sports meet-specific business semantic constraint engine as a compliance gate for the final decision—for example, when the background check platform is about to reject an athlete's qualification, the relevant permissions are forcibly downgraded to read-only and audited to prevent the risk of state tampering. This layered architecture of "real-time rule matching + asynchronous business fallback" not only ensures high-performance online adjudication but also guarantees a secure closed loop for critical business logic, fully demonstrating the advantages of business-driven security design. Attached Figure Description

[0007] Figure 1 This is a flowchart of the sports meet registration management method based on full-process log traceability and cross-system interface adaptation of the present invention; Figure 2 This is a sub-flowchart of the sports meet registration management method based on full-process log traceability and cross-system interface adaptation of the present invention; Figure 3 This is another sub-flowchart of the sports meet registration management method based on full-process log traceability and cross-system interface adaptation of the present invention. Detailed Implementation

[0008] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0009] The following disclosure provides many different embodiments or examples for implementing different structures of the invention. To simplify the disclosure, specific examples of components and arrangements are described below. Of course, these are merely examples and are not intended to limit the invention. Furthermore, reference numerals and / or letters may be repeated in different examples; such repetition is for simplification and clarity and does not in itself indicate a relationship between the various embodiments and / or arrangements discussed.

[0010] like Figure 1As shown, this invention provides a sports meet registration management method based on full-process log traceability and cross-system interface adaptation, specifically including: S1: Based on the real-time environmental context of the permission change request, obtain four types of low-overhead signals: standard WGS84 latitude and longitude coordinates of IPv4 / IPv6 addresses resolved by GeolocationAPI, millisecond-level offset between the client system clock and the time source of the registration system NTP server, a continuous five-sample sequence of network round-trip time from the first packet of the HTTP request to the first packet of the response, and a sequence of timestamps of key form fields of the mobile H5 registration page. Based on the geofence of the venues designated by the International Olympic Committee, the official schedule of the Games, the sliding window statistical threshold, and the compliance standards of form operations, generate four sets of spatiotemporal semantic anchor points: main venue anchor point / sub-venue anchor point / remote office anchor point, pre-event preparation period anchor point / event in progress anchor point / post-event archiving period anchor point, low-latency venue intranet anchor point / high-jitter public network anchor point, and compliant single-step operation anchor point / abnormal batch skip anchor point. S2: Based on the spatiotemporal semantic anchor point set generated by S1, and according to the three types of strong business semantic constraints in the sports meet registration business, namely the qualification freeze window period, the certificate issuance countdown threshold, and the back-examination status transition constraint, an anchor point conflict rule base is constructed, which includes the logical structure of 'IF [spatiotemporal semantic anchor point combination] THEN [permission action]'. The permission action types include setting the validity period of qualification status update permission, rejecting certificate information export request, and triggering manual review work order. S3: Input the set of spatiotemporal semantic anchor points generated by S1 corresponding to the permission change request into the anchor point conflict rule base constructed by S2, perform millisecond-level semantic consistency verification, and obtain the intermediate result of permission adjudication. The intermediate result includes the permission operation type identifier and the corresponding permission validity period parameter. S4: Based on the asynchronous callback event monitoring data of the background review platform WebService interface, determine whether there is a business constraint triggering condition that the athlete's qualification status will be rejected within 30 minutes. If so, input the intermediate result of the permission decision obtained by S3 into the business semantic constraint engine and perform forced downgrade processing to generate a read-only permission identifier and audit trail instructions. S5: Perform memory-resident stateless verification on the final result of the permission decision output by S4 to verify whether it meets the real-time requirement of tens of thousands of concurrent requests per second during the peak of the Games. When the verification passes, generate a sequence of permission control instructions. S6: Input the sequence of permission control instructions generated in S5 into the permission execution module of the registration management system to perform the corresponding dynamic allocation operation of role permissions, and synchronously record the operation log to the full-process log traceability unit; S7: Monitor the system status feedback signal after S6 is executed, determine whether the permission allocation result triggers cross-system interface adaptation anomaly, and if a data synchronization conflict is detected in the document verification system or the background review platform, start the log tracing unit to trace back the spatiotemporal semantic anchor point generation path from S1 to S4. S8: Based on the log tracing results of S7, dynamically update the semantic anchor combination threshold parameters in the anchor conflict rule base constructed by S2 to achieve closed-loop optimization of the permission adjudication mechanism.

[0011] Step S1: Based on the real-time environmental context of the permission change request, obtain four types of low-overhead signals: standard WGS84 latitude and longitude coordinates of IPv4 / IPv6 addresses resolved by the Geolocation API, millisecond-level offset between the client system clock and the time source of the registration system's NTP server, a continuous five-sample sequence of network round-trip time from the first packet of the HTTP request to the first packet of the response, and a sequence of timestamps for key form fields on the mobile H5 registration page. Based on the geofencing of venues designated by the International Olympic Committee, the official schedule of the Games, sliding window statistical thresholds, and form operation compliance standards, generate four sets of spatiotemporal semantic anchor points: main venue anchor point / sub-venue anchor point / remote office anchor point, pre-Games preparation period anchor point / event in progress anchor point / post-Games archiving period anchor point, low-latency venue intranet anchor point / high-jitter public network anchor point, and compliant single-step operation anchor point / abnormal batch skip anchor point. Specifically, this includes: S1.1: Perform geocoding parsing on the IPv4 or IPv6 network address data carried in the permission change request, use the Geolocation API interface to convert the IP address into standard WGS84 latitude and longitude coordinate data, and perform spatial inclusion relationship comparison calculation with the geofence boundary data of the venues designated by the International Olympic Committee to generate spatial location anchor point labels that identify the user's physical location attributes; The IPv4 or IPv6 network address field in the permission change request data packet is parsed using a geocoding method (parameters: Geolocation API server interface address, IOC venue geofence metadata) to map the network address to a unique physical coordinate point; Furthermore, by calling the Geolocation API interface (parameters: IP address, positioning accuracy options), the IP address is converted to latitude and longitude in the standard WGS84 coordinate system, and the numerical pairs containing latitude and longitude are obtained as the raw positioning data; Furthermore, through a spatial inclusion relationship judgment algorithm (parameters: the coordinate set of the venue polygon boundary specified by the International Olympic Committee, and the converted WGS84 coordinate points), the spatial location attribution of the user's physical location and the venue fence range is determined, and a Boolean relationship identifier value is output; Furthermore, a polygon point inclusion test algorithm (parameters: venue boundary vertex sequence, coordinate point latitude and longitude) is adopted to accurately determine whether the coordinate point falls inside the boundary of any venue and generate location attribution type label data; Furthermore, the spatial inclusion judgment result is transformed into semantic spatial location anchor tags by the tag generation rules (parameters: location attribution boolean value, venue number mapping table), indicating the user's location attribute category, including main venue anchor, sub-venue anchor or remote office anchor. By using geocoding parsing and spatial inclusion relationship comparison processing, the WGS84 coordinate data generated in the previous step is transformed into spatial location anchor tags that can be directly called by the permission adjudication rule base, thereby realizing the semantic structured representation of physical location attributes in permission adjudication. For example, in a multi-sport event registration management scenario, the IPv4 address in the permission change request data packet is "203.0.113.56". When calling the Geolocation API interface, the positioning accuracy option is set to high-precision mode, resulting in WGS84 coordinates of latitude 39.992 and longitude 116.315. Using a spatial inclusion relationship judgment algorithm, these coordinates are compared with the geofence data of the main venue designated by the International Olympic Committee. The fence boundary forms a closed polygon with 12 vertices. The coordinate point is determined to fall inside the polygon, with a Boolean judgment value of true. Through label generation rules, the true value is mapped to the label "Main Venue Anchor Point". In the spatial inclusion calculation, if the fence polygon is... and coordinate points The ray casting method is used to determine the user's location as follows: A horizontal ray R is constructed and extends eastward from point C. The number of intersections between the ray and the polygon boundary is counted; if the number is odd, the user is considered to be inside the polygon. In this scenario, the number of intersections is 3, determining the user's location as the main venue. The final output spatial location anchor label will be referenced in the subsequent S1.2 time-cycle anchor generation step, providing the spatial dimension as the basic input for dynamic permission adjudication. S1.2: Based on the physical area range determined by the spatial location anchor point labels generated in S1.1, obtain the difference data between the local clock time of the client system and the time source time of the NTP server of the registration system, calculate the millisecond-level clock offset value, and perform interval mapping matching between the clock offset value and the key time nodes in the official schedule of the Games to generate time period anchor point labels that identify the business stage attributes. Based on the physical area defined by the spatial location anchor tags generated in S1.1, the input data includes the client system's local clock time and the time source time of the registration system's NTP server. A bidirectional time difference calculation method (parameters: local clock timestamp, standard time source timestamp) is used to quantify the millisecond-level difference between the client and server times. Furthermore, a drift compensation algorithm (parameters: timing difference, network transmission delay) is used to correct the network delay of the clock difference and obtain the corrected clock offset. Furthermore, a normalized mapping method (parameters: corrected offset, key node timestamps of the competition schedule) is adopted to map millisecond-level offsets to business stage intervals and generate interval index results; Furthermore, through the interval index matching algorithm (parameter: the schedule segment mapping rule base), semantic matching between the offset and the anchor points during the pre-match preparation period, the during-match period, and the post-match archiving period is achieved, and the business stage attribute labels are output. The millisecond clock offset is calculated using the following formula. : in, The client system's local clock time. The standard time source for the registration system; Furthermore, the network latency compensation formula is used to calculate the corrected offset. : in, This refers to the network round-trip time. Mapping intervals to key time points in the official sports meet schedule, using logical judgment formulas: Implement the generation of attribute tags for business stages; By using clock offset normalization mapping and semantic matching, the time difference result of the previous step is transformed into a time period anchor label, so as to directly reflect the time status of the physical area into the spatiotemporal semantic set of the permission adjudication. For example, within the venue of a comprehensive sports event registration management system, the local clock time of the client device is 1627545601000 milliseconds, the time source time of the registration system's NTP server is 1627545600000 milliseconds, and the network round-trip time is 20 milliseconds. The millisecond-level clock offset can be calculated using this difference. The corrected offset is calculated using the delay compensation formula. Milliseconds. Map this value to the competition schedule, matching and outputting the "competition in progress" anchor label if the current time in the schedule falls within that interval. For devices used during the pre-competition preparation period, if... If the time falls within the task preparation window in milliseconds, a pre-competition preparation period anchor tag is generated. After performing this step, the system can accurately map the current device time state to the business stage attribute without relying on historical behavior, providing accurate spatiotemporal semantic input for subsequent permission conflict rule matching, effectively improving the real-time performance and accuracy of the adjudication; S1.3: For HTTP protocol transmission links, continuously collect network round-trip time series data from the sending of the first request packet to the completion of the reception of the first response packet, construct a network delay sliding window dataset containing five consecutive sample values, perform variance and mean statistical algorithms on the network delay sliding window dataset to quantify network jitter characteristics, classify and judge the statistical results according to the preset delay threshold standard, and generate network environment anchor tags that identify network connection quality attributes; S1.4: Listen to the focus state change event stream of key form field controls in the mobile H5 registration page, extract the timestamp sequence data of focus acquisition and focus loss, calculate the time interval between adjacent timestamps to form an operation rhythm sequence, logically compare the operation rhythm sequence with the single-step operation time threshold in the form operation compliance standard, identify abnormal rapid combo or batch skip behavior patterns, and generate operation behavior anchor tags that identify user interaction behavior attributes; S1.5: Aggregate the spatial location anchor tags generated in S1.1, the time period anchor tags generated in S1.2, the network environment anchor tags generated in S1.3, and the operation behavior anchor tags generated in S1.4. Perform structured encapsulation and semantic alignment processing of multi-source heterogeneous tags, and construct a spatiotemporal semantic anchor set containing four sets of classification tags as the final output for subsequent anchor conflict rule base consistency verification.

[0012] Step S2: Based on the spatiotemporal semantic anchor point set generated in S1, and according to three types of strong business semantic constraints in the sports meet registration business—the qualification freeze window period, the certificate issuance countdown threshold, and the back-examination status transition constraint—an anchor point conflict rule base is constructed, containing the logical structure 'IF [spatiotemporal semantic anchor point combination] THEN [permission action]'. The permission action types include setting the validity period of qualification status update permissions, rejecting certificate information export requests, and triggering manual review work orders. Specifically, this includes: S2.1: Logically combine and process the spatial semantic tags such as the main venue anchor point, sub-venue anchor point and remote office anchor point generated by S1. Based on the geofence data of the venues designated by the International Olympic Committee, generate spatial constraint primitives with physical location exclusivity to ensure that subsequent rule matching can accurately identify the credible area range where user operations occur. S2.2: Based on the generated spatial constraint primitives, and combined with the time semantic tags such as the pre-competition preparation period anchor, the event in progress anchor, and the post-competition archiving period anchor in the official schedule of the Games, spatiotemporal coupling calculations are performed. Using the sliding window statistical threshold and the form operation compliance standard as the judgment criteria, a spatiotemporal context feature vector covering both time and space dimensions is generated as the core input object for the construction of the rule base. S2.3: Utilize the generated spatiotemporal context feature vector to perform logical mapping processing on three types of strong business semantic constraints: qualification freeze window period, certificate issuance countdown threshold, and back-examination status transition constraint. Using the IF condition triggering THEN action execution as the structural paradigm, generate atomic permission adjudication rule entries of various types, including qualification status update permission validity period setting, certificate information export request rejection, and manual review work order triggering. Based on the spatiotemporal context feature vector generated by S2.2, a business semantic mapping algorithm (parameters: qualification freeze window period, certificate issuance countdown threshold, and background check status transition constraint) is adopted to quantify and associate the dual-dimensional spatiotemporal context features with the three types of strong business semantic constraints of the sports meet registration business. Furthermore, through the multi-condition logical decomposition method (parameter: IF condition trigger THEN action execution paradigm), the mapping path of each type of business semantic constraint condition and the four anchor elements of space, time, network and behavior in the feature vector is parsed, and a preliminary condition-action pairing set is obtained; Furthermore, an atomic rule construction algorithm (parameter: set of permission action types) is adopted to generate independent and indivisible permission adjudication rule units based on the condition-action pairing set, and to generate atomic permission adjudication rule entries of various types, including setting the validity period of qualification status update permissions, rejecting document information export request, and triggering manual review work order. Furthermore, by using a condition combination normalization processing method (parameter: anchor point logical combination priority matrix), the precondition part of the atomic permission adjudication rule entry is logically normalized and encoded to ensure the consistency of the judgment of the rule execution precondition, and output a structured and matchable rule encoding sequence. By using a rule entry indexing algorithm (parameter: rule unique identifier UUID generator), the result of the previous step is transformed into atomic permission adjudication rule data with a unique index and explicit execution follow-up actions, thus achieving the expected technical effect of the initial construction of the rule base; For example, in the access control scenario of a comprehensive sports event registration management system, the qualification freeze window is set to 1800 seconds, the certificate issuance countdown threshold is set to 600 seconds, and the back-review status transition constraint is "no updates to qualification information are allowed before the rejection status takes effect". When the spatiotemporal context feature vector output by S2.2 contains a combination of [main venue anchor point, event in progress anchor point, low-latency venue intranet anchor point, compliance single-step operation anchor point], a business semantic mapping algorithm is used to generate a preliminary pairing rule: IF(main venue anchor point ∧ event in progress anchor point ∧ low-latency venue intranet anchor point ∧ compliance single-step operation anchor point) THEN(qualification status update permission validity period = 90 seconds). For this rule, the validity period parameter is calculated using the formula: in, This is the final validity period in seconds. Here, Δ is the NTP clock offset in seconds, and 0.5 is the empirical weighting coefficient. In this scenario, Δ = 4 seconds, and the calculated result is T = 92 seconds. Verification results show that under high-concurrency permission change request conditions, the execution of this rule entry can significantly improve the security of business-sensitive operations. While maintaining millisecond-level adjudication speed, it introduces dynamic decision-making capabilities for business constraints. The output rule base entry is: UUID = "rule-001", condition code = "LOC01-TIM02-NET01-BEH01", action type = "qualification status update, validity period 92 seconds", possessing the characteristic of being directly loaded into the memory rule engine for execution. S2.4: Perform conflict detection and priority sorting on the generated atomic permission adjudication rule entries. Using business security strength level and operational urgency as weighting factors, a static priority coverage strategy is adopted to eliminate logical contradictions between rules and generate an anchor conflict rule base index table with deterministic execution paths to ensure the uniqueness and consistency of permission adjudication results under concurrent requests. S2.5: Based on the generated anchor conflict rule base index table, it is loaded into the memory-resident rule engine for pre-compilation optimization to eliminate redundant logical branches and solidify jump addresses, generating a millisecond-level response anchor conflict rule base instance that can be directly called by step S3, realizing the final transformation from business semantic constraints to system executable instructions.

[0013] like Figure 2 As shown, step S3 involves inputting the spatiotemporal semantic anchor set generated in S1 corresponding to the permission change request into the anchor conflict rule base constructed in S2, performing millisecond-level semantic consistency verification, and obtaining an intermediate result of the permission adjudication. This intermediate result includes the permission operation type identifier and the corresponding permission validity period parameter. Specifically, it includes: S3.1: The spatiotemporal semantic anchor set generated by S1 is standardized and serialized and encapsulated. Based on the memory mapping protocol, discrete semantic tags such as main venue anchor, pre-match preparation period anchor, low-latency venue intranet anchor and compliant single-step operation anchor are converted into fixed-length binary feature vectors to generate a data packet of anchors to be verified with a unified data structure, which serves as the standardized input object for subsequent rule matching. S3.2: Based on the semantic tag combination in the anchor data packet to be verified, perform parallel hash index retrieval operation in the anchor conflict rule base, and use the Bloom filter algorithm to quickly filter out all candidate IF-THEN logical rule entries that have an intersection with the current spatiotemporal semantic anchor set, so as to generate a rule candidate list containing address pointers of potentially applicable rules, and narrow the search range for subsequent precise matching. For the anchor data packets to be verified generated by the standardized serialization and encapsulation process in step S3.1, a parallel hash index retrieval method (parameters: number of hash buckets, memory address where the index table resides) is adopted to realize multi-threaded parallel positioning of binary feature vectors in the rule base index space. Furthermore, by using the Bloom filter algorithm (parameters: number of hash function groups, bit array length, false positive rate threshold), a fast existence detection between the rule base antecedent condition set and the current spatiotemporal semantic anchor combination is achieved, and a Boolean matching flag matrix is ​​obtained. Furthermore, by using the association lookup method that matches the mapping relationship between the flag matrix and the index table address (parameters: address mapping table, rule index offset), we can extract all candidate IF-THEN logical rule entries that have an intersection with the set of anchor points to be verified, and generate a list of candidate rule addresses. Furthermore, by using the deduplication and sorting processing method of the candidate address list (parameters: address deduplication strategy, sorting basis field), duplicate candidate rules caused by multiple hash mappings are eliminated and a candidate rule list arranged in access order is obtained. By combining the Bloom filter with the parallel hash index retrieval method, the fixed-length binary feature vector from the previous step is transformed into a list of rule candidates containing pointers to potentially applicable rule addresses, thereby reducing the search range and latency in the subsequent precise matching stage. For example, in the access control module of a registration management system for a certain comprehensive sports event, for binary feature vectors (128 bits in length) containing anchor points for the main venue, anchor points during the event, anchor points for low-latency venue intranets, and anchor points for compliant single-step operations, the number of hash buckets for parallel hash index retrieval is set to 1024, and the index table resides in a high-speed memory area; the Bloom filter uses 3 hash function groups, a bit array length of 2^20, and a false positive rate threshold set to 0.01. When performing existence detection, the Bloom filter scatters the hash values ​​of the feature vectors and maps them into the bit arrays, and calculates the false positive rate using the following formula: in, The number of hash function groups. The number of elements to be inserted. The length of the bit array. The false positive rate is calculated to be stable within the threshold. The association search method maps the index table address corresponding to the matching flag to rule entry numbers, filtering out a set of rule entry numbers containing the combination of "main venue ∧ event in progress ∧ low-latency venue intranet ∧ compliant single-step operation anchor point," forming a candidate rule address list of length 4, which is then sorted for use in sub-step S3.3. In this embodiment, the candidate rule list generation takes approximately 2.6ms, significantly improving the performance of the rule matching stage and ensuring real-time response capability of permission adjudication under peak concurrency conditions. S3.3: Perform Boolean logic truth value judgment processing on each candidate IF-THEN logical rule entry in the rule candidate list, and perform bit-by-bit comparison operation between the specific semantic tag value in the anchor data packet to be verified and the spatiotemporal semantic anchor combination constraint conditions in the rule antecedent, so as to identify the truth value matching rule set that fully satisfies all antecedent conditions and eliminate the interference of false positive rules caused by partial condition mismatch. When performing consistency determination on the input data of the rule candidate list, a Boolean logic truth value determination algorithm (parameters: rule antecedent condition set, feature vector of anchor data packet to be verified) is used to realize the bit-by-bit detection function of anchor value matching status; Furthermore, by using a bitwise mask matching processing method (parameters: bitwise mask matrix, anchor feature bits), the bitwise comparison operation between the rule condition bits and the anchor feature bits is realized, and a Boolean sequence result of the condition-satisfied bits is obtained. Furthermore, a condition satisfaction calculation method (parameters: Boolean sequence result length, total number of conditions) is adopted to achieve a quantitative evaluation of the overall satisfaction of the rule's antecedent conditions and generate a satisfaction scale value to indicate the degree of matching of the rule with the current anchor point combination; Furthermore, by calculating the matching flag using the satisfaction truth value judgment formula, the rule filtering function that fully satisfies all conditions is realized. The formula is as follows: in, To meet the scale value, To ensure the condition is met for counting, The total number of rule conditions; Furthermore, by using the matching flag to determine the match, false positive rule entries with a satisfaction level of less than 1 are eliminated, thereby filtering out the candidate rule set that does not match precisely. By combining the Boolean logic truth value judgment algorithm with the satisfaction calculation formula, the rule candidate list selected in the previous step is transformed into a truth value matching rule set, so as to achieve accurate identification of rule conditions that match the full match and ensure the accuracy of subsequent permission action generation. For example, in the permission adjudication process of the comprehensive sports event registration management system, the anchor data packet to be verified contains four tags: main venue anchor, pre-event preparation period anchor, low-latency venue intranet anchor, and compliant single-step operation anchor. A rule in the rule candidate list has four preconditions: main venue anchor, pre-event preparation period anchor, low-latency venue intranet anchor, and compliant single-step operation anchor. When using Boolean logic for judgment, the condition satisfaction counts. The total number of rule conditions is 4. The value is 4, so we substitute it into the formula. Obtain satisfaction scale value At this point, the flag is set to true, and the rule enters the truth-matching rule set. For another rule, its antecedent condition lacks a low-latency venue intranet anchor point; the condition is satisfied with a count of 3, and the total is 4. The formula is used to calculate... The result was 0.75, which did not meet the full condition matching requirement. Therefore, the judgment flag was set to false, and the rule was discarded. Performance verification shows that in high-concurrency permission adjudication scenarios, this method can significantly improve the accuracy of the matching rule set and reduce the false judgment rate, thereby improving the stability of the adjudication results. S3.4: Based on the multiple concurrent matching rules that may exist in the truth value matching rule set, priority weight arbitration and conflict resolution are performed. The THEN follow-up actions of the conflicting rules are sorted and optimized according to the preset business security level sequence table to generate a unique target permission action instruction and its associated validity period parameter, thereby eliminating decision ambiguity caused by the simultaneous triggering of multiple rules. S3.5: The unique target permission action instruction generated after conflict resolution is structurally encapsulated, and the permission operation type identifier, the dynamically calculated permission validity period parameter, and the spatiotemporal semantic anchor source tag hit by this ruling are packaged and combined to generate a permission ruling intermediate result object containing a complete decision context, which is used by the subsequent business semantic constraint engine for final compliance verification. Using the unique target permission action instruction after conflict resolution as the initial input, a structured encapsulation method (parameters: permission operation type identifier, permission validity period parameter, spatiotemporal semantic anchor traceability label) is adopted to achieve unified data formatting processing of permission adjudication content; Furthermore, by using a fixed-length field mapping algorithm (parameters: operation type encoding system, validity period in milliseconds, anchor tag index table), the standardized encoding of the permission operation type identifier is achieved, and integer encoded data that can be used for subsequent memory mapping processing is obtained; Furthermore, a dynamic validity period calculation method (parameters: rule matching result, time offset, business security level) is adopted to achieve precise assignment of the permission validity period parameter. The validity period value is calculated based on the time difference between the preset time window in the matching rule and the current system time; the formula is: in, The validity period is dynamic. The basic validity period determined by rule matching, This represents the offset between the current system time and the business time node. Furthermore, by using the anchor point source tag encapsulation algorithm (parameters: four types of spatiotemporal semantic anchor point sets, tag index mapping table), the indexed compression processing of the hit anchor point set is realized, and an anchor point tag sequence with sequential index characteristics is generated to facilitate the rapid backtracking of the permission adjudication results. By using structured encapsulation, fixed-length encoding, dynamic validity period calculation, and anchor-point traceability tag indexing, the unique target permission action instruction from the previous step is transformed into an intermediate result object for permission adjudication, thereby achieving complete decision context data that meets the input requirements of the subsequent business semantic constraint engine. For example, in a comprehensive sports event registration management system, for a qualification status update request from a sub-venue, the target permission action instruction output after the conflict resolution step is "Qualification Status Update," with a basic validity period of 90 seconds. The set of hit anchor points includes sub-venue anchor points, event in progress anchor points, low-latency venue intranet anchor points, and compliant single-step operation anchor points. During implementation, the permission operation type identifier is mapped to the integer value 101 through the coding system, with a basic validity period of... = Milliseconds, a discrepancy was detected between the current system time and the event stage. = The dynamic validity period is calculated using a formula in milliseconds. = Milliseconds. The anchor traceability tag is compressed into a sequence [12, 21, 33, 47] using an index mapping table. The final generated intermediate result object for permission adjudication contains: permission operation code 101, validity period 85000ms, and anchor index sequence [12, 21, 33, 47]. This object is recognized as valid input by the subsequent business semantic constraint engine, which can significantly improve the processing efficiency of subsequent verification and execution while maintaining semantic integrity.

[0014] like Figure 3 As shown, step S4 involves monitoring asynchronous callback events from the WebService interface of the background check platform to determine if there is a business constraint triggering condition that would cause an athlete's eligibility status to be rejected within 30 minutes. If so, the intermediate permission decision result obtained in S3 is input into the business semantic constraint engine to perform forced downgrade processing and generate a read-only permission identifier and audit trail instructions. Specifically, this includes: S4.1: Parse and process the asynchronous callback event messages returned by the WebService interface of the background check platform, and extract the structured event data containing the athlete's unique identity code, qualification status change type code and status effective timestamp, so as to obtain the original information set of qualification status change to be verified; For asynchronous callback event messages returned by the WebService interface of the background review platform, a message parsing method based on multi-level syntax rules (parameters: message structure pattern definition, namespace mapping table) is adopted to separate fields and extract tag values ​​from the original XML or JSON format callback content. Furthermore, the unique identification code of the athlete in the message is accurately located by using a field mapping algorithm (parameters: identity field regular expression, status code lookup table, timestamp parsing template), and the code value is standardized and converted with the unified identity identifier format used in the registration management system to obtain identity code data that can be used for subsequent retrieval. Furthermore, the qualification status change type code in the message is decoded and the business semantic tagging is performed through the status type parsing method (parameters: qualification status change code list, status category mapping rule), and the corresponding status category enumeration value is obtained. Furthermore, through a timestamp interpretation algorithm (parameters: UTC time format template, system time zone offset), the status effective timestamp contained in the message is converted into a unified time format corresponding to the system standard clock source, and a numerical time object that can be used for time difference calculation is generated; By encapsulating and processing field associations, identity code data, status category enumeration values, and effective time values ​​are combined into a structured set of original information on qualification status changes, thereby realizing the transformation from asynchronous callback original messages to standardized business data objects. For example, in a background check platform interface callback, the message contains the fields: athleteId with the value "CN202400356", statusCode with the value "Q_REVOKE", and effectiveTime with the value "2024-08-15T14:05:00Z". During parsing, the field mapping algorithm uses the regular expression CN[0-9]{9} to match the identity code and maps it to the unified identifier format of the registration management system, resulting in "CN202400356". The status code is mapped to the enumeration value "qualification cancelled" through a lookup table. The timestamp is interpreted by the timestamp algorithm, combined with the system's East 8 time zone offset, converting the UTC time to Beijing time "2024-08-15 22:05:00", and then to seconds based on UNIX time. The final encapsulated output set of original information regarding the qualification status change includes: identity code "CN202400356", status category "qualification cancelled", and effective time in seconds. This result can be used in step S4.2 to calculate the remaining valid time, thus preparing preliminary data for determining high-risk eligibility status. S4.2: Based on the difference between the effective timestamp of the status in the original information set of the qualification status change and the current standard clock source of the system, calculate the remaining valid duration of the qualification status, and compare the value with the preset 30-minute business threshold to output a high-risk qualification status judgment flag that meets the time window constraint. S4.3: Using the high-risk qualification status judgment flag that meets the time window constraint as the triggering factor, retrieve the pre-set business semantic constraint rule library, match the corresponding forced degradation strategy template, and generate a set of business constraint execution parameters containing read-only access control level identifier and full-link audit trail activation instruction; In the input conditions, the object to be processed is the high-risk qualification status judgment flag output by the previous sub-step S4.2. This flag reflects the business risk attribute that the remaining valid time of the current qualification status is less than the critical threshold of thirty minutes. A rule-based retrieval method (parameters: judgment flag value, rule base index table reference) is used to locate the downgrade strategy template that matches the risk flag from the pre-set business semantic constraint rule base; Furthermore, through a template matching algorithm (parameters: trigger factor = high-risk qualification status judgment flag, rule base condition key-value pair), the precise matching of policy entries is achieved, and the corresponding read-only access control level configuration data is obtained; Furthermore, by using the instruction combination generation method (parameter: the set of audit trail triggering conditions in the policy template), the audit trail activation instruction and the read-only access control level identifier are combined to obtain the set of business constraint operation instructions to be executed; Furthermore, a parameter set encapsulation algorithm (parameters: access control level identifier, audit logging instruction) is adopted to encapsulate the above data into a structured business constraint execution parameter set, so that it can be input into the subsequent business semantic constraint engine for permission overriding processing; By using rule retrieval and template matching, the results of the previous step are transformed into a set of business constraint execution parameters containing read-only permission identifiers and full-link audit trigger instructions, thereby achieving the expected technical effects of automatic permission downgrade in high-risk qualification states and full-link monitoring. For example, in a comprehensive sports event registration management system, when the calculated remaining validity period of an athlete's eligibility status is... When the timeout period is set to 1 hour (where n represents the number of hours) and the judgment flag is true, the system rule base retrieves a downgrade policy template number of ACR-READ-07, corresponding to a read-only access control level configuration of READ_LEVEL_3. Audit logging trigger conditions include two types of events: field changes and status transitions. The instruction combination generation method merges READ_LEVEL_3 and AUDIT_TRIGGER_ALL into an operation instruction set {permission level: READ_LEVEL_3, audit: ALL}. The parameter set encapsulation algorithm encapsulates this instruction set into a standard JSON control package with a fixed length of 512 bytes to ensure low-latency performance when the subsequent business semantic constraint engine executes in memory. Execution results show that in high-risk qualification status judgment scenarios, all permissions involving qualification status updates are automatically downgraded to READ_LEVEL_3, and full-link auditing is simultaneously initiated. The number of entries recorded in the system monitoring log is significantly increased, and the risk of mis-authorization is significantly reduced. S4.4: Input the intermediate permission adjudication results generated in the previous steps and the business constraint execution parameter set into the logical gating unit of the business semantic constraint engine, perform the permission action overriding operation, replace the original writable permission action with the restricted read-only permission action and attach an audit mark to generate the final permission adjudication result data after business compliance correction. S4.5: Perform integrity verification and format encapsulation on the generated final permission decision result data after business compliance correction, and construct a standardized control instruction package containing read-only permission identifiers and audit trace metadata, so as to serve as the direct input object for the subsequent memory-resident stateless verification module; The input conditions include the final permission decision result data after business compliance correction. This data contains read-only permission identifiers and audit logging instructions, and has completed the replacement of write permission actions and the attachment of audit metadata in the preceding logic unit. A hash digest generation algorithm (parameter: SHA-256) is used to perform field-level digest operations on the final authorization decision data to realize the data content integrity verification function, and a fixed-length digest value is obtained as the integrity verification benchmark. Furthermore, by using the field perturbation redundancy coding method (parameters: Reed-Solomon coding length 32 bytes, redundancy coefficient 2), fault-tolerant encapsulation of the digest value is achieved, and an integrity verification extension code with error correction capability is generated to ensure the stability of data transmission in a high-concurrency communication environment; Furthermore, by using a binary fixed-length structured conversion algorithm (parameters: 8-byte length for the permission identifier field, 64-byte length for the audit metadata field, and 32-byte length for the extension code field), the format standardization of the final permission decision result is achieved, mapping multiple heterogeneous data fields into a unified binary arrangement order, and generating a standardized permission control data block with fixed-length characteristics; Furthermore, a field position index table construction algorithm (parameters: starting offset, field length sequence) is used to build a metadata positioning index for the data block, enabling rapid positioning and parsing of subsequent data reading, and obtaining the field index table as additional information for format encapsulation; By using a standardized control instruction packet generation algorithm (parameters: protocol version 1.0, control instruction type "read-only permission execution", metadata tag "audit trace"), fixed-length permission control data blocks and field index tables are combined and transformed into control instruction packets that conform to the interface specification of the memory-resident stateless verification module, thereby achieving direct availability and zero-copy transmission of permission adjudication results in the high real-time verification pipeline. For example, regarding the final permission decision result data from the business semantic constraint engine, the read-only permission identifier is encoded as 0x1F45A3B2, and the audit trail metadata includes event ID 0x00FF1122 and a timestamp. Trigger rule ID 0x09AB. The hash value is obtained after executing the SHA-256 hash algorithm. (Total 64 bytes) The extended code is generated using Reed-Solomon encoding parameters (length 32 bytes, redundancy factor 2). The permission identifier is mapped to an 8-byte field, the audit metadata to a 64-byte field, and the extended code to a 32-byte field. These are then concatenated in a predefined order to generate a fixed-length binary data block, totaling 104 bytes. When constructing the field index table, the starting offsets are 0 (permission identifier), 8 (audit metadata), and 72 (extended code), with field lengths of 8, 64, and 32 bytes respectively. The final standardized control instruction package conforms to protocol version 1.0, with the control instruction type being "read-only execution" and the attached metadata tag being "audit trace." It is directly injected and executed in a memory-resident stateless verification module, significantly reducing latency and meeting real-time requirements during peak periods.

[0015] Step S5: Perform a memory-resident stateless verification on the final permission decision result output by S4 to verify whether it meets the real-time requirement of tens of thousands of concurrent requests per second during the peak of the Games. When the verification passes, generate a sequence of permission control instructions. Specifically, this includes: S5.1: Perform data structure standardization processing on the final permission decision result output by S4, which includes read-only permission identifier and audit trail instructions. Use memory-mapped file technology to convert unstructured decision data into fixed-length binary permission decision data blocks to eliminate data serialization overhead and obtain standardized permission decision data blocks with fixed-length characteristics. S5.2: Based on the generated standardized permission decision data block, the concurrent request arrival rate characteristics under the current system load state are extracted using the sliding time window algorithm. Combined with the peak traffic model of the sports meet registration business, a virtual concurrent stress test vector is constructed to simulate the scenario of tens of thousands of concurrent requests per second during peak periods and obtain a virtual concurrent stress test vector containing load intensity factors. Based on the standardized permission decision data block generated by S5.1, a sliding time window algorithm is adopted (parameters: window length L, step size). Extract concurrent request arrival rate features from high-frequency permission decision request sequences; Furthermore, by statistically analyzing the ratio of the number of requests within the window to the window length, an instantaneous concurrent arrival rate vector is obtained, and these vectors are arranged in a time series to form an arrival rate curve. Furthermore, an exponentially weighted moving average method is employed (parameter: decay coefficient). The arrival rate curve is smoothed to eliminate the impact of sudden jitter in a short period of time on the stability of the feature and to generate a smooth arrival rate feature vector. Furthermore, by comparing the relative load intensity coefficient with the theoretical arrival rate curve in the peak traffic model of the sports meet registration business, a point-by-point difference determination is performed. The formula is as follows: in, Let i be the observed attainment rate of the i-th sampling point. To predict arrival rates for peak flow models, This represents the total number of sampling points; Furthermore, by combining the calculated relative load intensity coefficient, a virtual concurrent stress test vector is constructed. Using a vectorized structure encapsulation, the three types of data—arrival rate characteristics, load intensity coefficient, and peak model curve—are combined into a load intensity factor matrix according to a fixed field sequence. An algorithm is constructed using virtual concurrent stress (parameter: stress amplification factor). Number of iterations The load intensity factor matrix is ​​mapped to a data vector with simulated peak concurrency characteristics, forming a virtual concurrency stress test vector containing the load intensity factor for use by S5.3 parallel injection processing; By combining sliding time window extraction and stress construction algorithms, the standardized permission adjudication data block results from the previous step are transformed into virtual stress data that can directly drive peak concurrency simulation verification, achieving the expected technical effect of simulating tens of thousands of concurrent scenarios per second during the peak of the Games. For example, in a stress test of a registration system for a large-scale comprehensive sports event, the sliding time window length L was set to 100 milliseconds, and the step size was... With a timeout of 20 milliseconds, concurrent arrival rate statistics were performed on a sequence of adjudication requests within a 10-second window, yielding an average arrival rate of 8500 requests / second. An attenuation coefficient was used. After smoothing with an exponentially weighted moving average of 0.3, the fluctuation range of the arrival rate curve was significantly reduced. The peak flow model predicts a peak flow rate of 10,000 times / second, and the relative load intensity coefficient is calculated using the formula. for This indicates that the current load is lower than the theoretical peak. The arrival rate feature vector, load intensity coefficient, and model curve are encapsulated into a load intensity factor matrix, using a pressure amplification factor. A virtual stress test was constructed with a time factor of 1.2 and an iteration count of k of 5. After the virtual concurrent stress test vector was generated and tested in the simulation injection verification module, the latency stabilized at around 7.5 milliseconds, which met the real-time requirements during peak periods. S5.3: Parallel injection processing is performed on the generated virtual concurrent stress test vector containing load strength factor and standardized permission decision data block. A lock-free ring buffer mechanism is used to execute memory-resident stateless verification logic. A high-precision timer is used to collect the end-to-end time consumption from instruction injection to verification completion to obtain a deterministic latency metric for a single permission decision. Parallel injection processing is performed on virtual concurrent stress test vectors containing load intensity factors and standardized permission adjudication data blocks. A multi-threaded memory mapping mechanism (parameters: thread pool size = 64, mapping page size = 4KB) is adopted to achieve interleaved writing of data blocks and test vectors on the same memory page, so as to reduce the probability of cache invalidation and ensure stable memory access latency. Furthermore, a lock-free circular buffer mechanism (parameter: buffer capacity = 1024 instructions, write pointer and read pointer use atomic addition operation) is used to achieve high-concurrency streaming injection of standardized permission adjudication data blocks and virtual concurrent stress test vectors, keeping the data stream continuously circulating in the buffer without triggering thread blocking, and obtaining multiplexed buffer injection state data; Furthermore, a high-precision timer acquisition function (parameters: resolution = nanosecond level, calibration source is CPU TSC register) is used to realize end-to-end delay measurement from the injection of the first data block into the buffer to the completion of processing by the stateless verification logic, and generate a delay time series; Furthermore, by using statistical analysis methods for delayed time series (parameters: mean, variance, standard deviation), the stability of single-time authorization decision delay is quantified, and a deterministic delay metric is generated. By using a lock-free circular buffer mechanism and a high-precision timer measurement method, the concurrent injection result of the previous step is transformed into latency measurement data, enabling a quantifiable assessment of the time consumption of a single permission decision. For example, in the comprehensive sports meet registration management system, the thread pool size is configured to 64, the mapped page size to 4KB, and the buffer capacity to 1024 permission instructions. Write pointers and read pointers are used to achieve concurrency safety through atomic addition. In the virtual concurrent stress test vector, the load intensity factor is set to 9500 req / s, and the standardized permission adjudication data block size is 256 bytes. During parallel injection processing, a multi-threaded memory mapping mechanism is used to interleave data blocks and test vectors into the same memory page, reducing CPU cache misses. A lock-free circular buffer maintains non-blocking pointer operations during high-concurrency streaming injection, ensuring continuous data circulation. A high-precision timer uses the CPU TSC as the timing source, with a resolution down to the nanosecond level, collecting the end-to-end time from the data block entering the buffer to the end of stateless verification. The average and variance are calculated using the delay time series, as shown in the following formula: in, Indicates the timestamp of stateless verification completion. Indicates the timestamp of the injected data block. This indicates the number of samples taken. The latency metric stabilized at 7.6 microseconds, significantly improving the real-time response capability for permission decisions during peak periods. S5.4: Based on the deterministic delay metric value of the obtained single permission decision, use the preset real-time threshold judgment rule to perform compliance comparison analysis, and determine whether the delay metric value is less than the maximum response time threshold allowed during the peak period of the Games, so as to generate a Boolean real-time verification pass flag bit representing the real-time verification pass; S5.5: The generated Boolean real-time verification is processed by logic gating through flag bits. When the flag bit is true, the standardized permission adjudication data block in S5.1 is encapsulated into an opcode sequence that conforms to the permission execution module interface protocol to generate a permission control instruction sequence that can directly drive the dynamic allocation of role permissions.

[0016] Step S6: Input the permission control instruction sequence generated in S5 into the permission execution module of the registration management system to perform the corresponding role permission dynamic allocation operation, and simultaneously record the operation log to the full-process log traceability unit. Specifically, this includes: S6.1: Perform protocol parsing on the permission control instruction sequence output by S5, extract the target user identity, dynamic permission validity period parameter and operation type flag, and generate a standardized permission allocation request data packet as the direct input object for subsequent execution modules; S6.2: Based on the generated permission allocation request data packet, the memory-resident access control list update algorithm is used to perform an atomic write operation on the role permission mapping table in the current session context to complete the injection of the dynamic permission validity period parameter corresponding to the target user's identity and obtain the access control state vector updated in real time. The generated permission allocation request data packet is processed by an in-memory resident access control list update algorithm (parameters: access control list memory mapping base address, target user identity identifier, dynamic permission validity period parameter, operation type flag) to locate and load the role permission mapping table in the current session context; Furthermore, by using a hash index retrieval algorithm (parameters: target user identity hash value, session context index table length), fast addressing of the corresponding user record in the access control list is achieved, and the binary data structure of the current permission mapping entry is obtained; Furthermore, an atomic write operation mechanism (parameters: CAS comparison and exchange instruction, memory barrier control flag) is adopted to achieve secure injection of the dynamic permission validity period field in the located permission mapping entry, and generate a new version data block containing the updated permission field to ensure data consistency in a concurrent environment; Furthermore, the access control state vector is updated in real time through a session context state refresh algorithm (parameters: update timestamp, operation type flag, permission validity period parameter), and an access control state vector containing the latest permission state value is generated. By using a memory-resident access control list update algorithm, the permission allocation request data packet result from the previous step is transformed into a real-time updated access control state vector, thereby achieving immediate effect and secure control of permission mapping. For example, when a sports meet registration management system performs permission allocation operations under peak conditions, the memory-mapped base address of the access control list is set to 0x7fffc000, the index table length is 1024 records, and the target user's identity hash value is... The dynamic permission validity period parameter is Seconds. Based on this configuration, the hash index retrieval algorithm is first used to locate the number in the access control list. The record position is used to obtain the binary data structure of the current permission mapping entry. Then, an atomic write operation is performed using a CAS comparison and swap instruction with the memory barrier control flag, changing the expiration field from its original position. Updated in seconds The process involves counting seconds and generating a new version of the data block while ensuring thread safety. Finally, the session context state refresh algorithm is invoked to update the timestamp. (Unix clock value), operation type marked as "qualification status update", permission validity period parameter is Within 1ms, an access control state vector containing the latest permission status values ​​is generated. In this embodiment, the permission allocation operation takes effect immediately after the update, and the system completes the entire update process within 1ms. Even under peak periods with tens of thousands of concurrent requests per second, it maintains a stable response, achieving a significant improvement in the security and real-time performance of dynamic permission validity period injection. S6.3: Perform consistency verification logic on the access control state vector after real-time update, compare the operation type flag with the system's preset business rule constraint set to confirm the legality of the dynamic allocation of permissions, and generate a permission execution confirmation signal with a verification pass flag; For the access control state vector updated in real time, a rule matching and verification algorithm (parameters: access control state vector, system preset business rule constraint set) is used to verify the correspondence between the values ​​of each field in the state vector and the business rule constraints one by one, so as to identify potential risks of permission allocation violations. Furthermore, by using a field logic comparison algorithm (parameters: operation type flag, list of allowed operation types in the business rule constraint set), the legality of the target operation type is detected, and the legality Boolean judgment result data is obtained; Furthermore, a constraint consistency verification method (parameters: legality judgment result data, dependency relationship matrix between access control state vector fields) is adopted to realize cross-field dependency matching verification and generate dependency matching integrity index; Furthermore, by using a comprehensive judgment generation method (parameters: legality Boolean judgment result data, dependency matching integrity index), multi-index fusion calculation is achieved to output a globally consistent judgment result. The calculation formula is as follows: in, The Boolean value for determining the legality of the operation. For the value of the match integrity determination, This is the value used for global consistency determination; By using the permission execution confirmation signal generation method (parameters: global consistency judgment value, permission execution confirmation signal format template), the result of the previous step is transformed into a permission execution confirmation signal with a verification pass flag, thereby realizing the legality confirmation of dynamic permission allocation and triggering subsequent log writing. For example, in a comprehensive sports event registration management system, the access control state vector is configured to include a user identity field, a permission validity period field, an operation type field, and a status timestamp field. The system's preset business rule constraint set allows the following operation types: [qualification status update, document information export]. The dependency matrix stipulates that the qualification status update operation must be performed during the event and the network latency must be less than 50ms. During a permission allocation execution process, the operation type in the access control state vector is marked as qualification status update, the event stage mapped to the status timestamp is "event in progress," and the network latency is measured to be 35ms. The rule matching verification algorithm verifies that the operation type marker exists in the allowed operation type list, and the field logic comparison algorithm outputs a legality judgment result I=true, and a dependency matching integrity index C=true. The comprehensive judgment generation method is based on the formula... The global consistency judgment value R=true is calculated, and the final permission execution confirmation signal generation method embeds R=true into the confirmation signal template. The output confirmation signal includes user identity identifier, permission validity period parameter, operation type flag and verification pass flag, realizing the confirmation of the legality of dynamic permission allocation, ensuring that the log traceability unit can record legal execution events and maintain the security closed loop of permission control. S6.4: Based on the generated permission execution confirmation signal, call the asynchronous write interface of the full-process log traceability unit to encapsulate the target user identity, dynamic permission validity period parameter, operation type marker and the set of spatiotemporal semantic anchors that triggered this allocation into a structured audit event to generate an immutable operation log record entry. S6.5: Perform persistent storage operation on the generated operation log record entries, write them to the specified partition of the distributed log storage cluster, and send a status feedback signal to the preceding step S7 to complete the closed loop of the entire execution of the access control instruction sequence from parsing to implementation.

[0017] Step S7: Monitor the system status feedback signal after S6 execution, determine whether the permission allocation result triggers a cross-system interface adaptation anomaly, and if a data synchronization conflict is detected in the document verification system or the background review platform, activate the log tracing unit to trace back the spatiotemporal semantic anchor point generation path from S1 to S4. Specifically, this includes: S7.1: Real-time acquisition of the execution results of the access control instruction sequence output by S6, obtaining system status feedback signals including interface response status codes, data synchronization delay duration and transaction rollback flags, and eliminating noise interference caused by instantaneous network jitter through a sliding window filtering algorithm to generate a standardized cross-system interaction status vector; S7.2: Based on the generated cross-system interaction state vector, semantic parsing is performed using a pre-set interface protocol feature matching rule base to extract identity verification conflict identifiers returned by the document verification system or qualification status transition anomaly identifiers returned by the background review platform, and to construct an interface adaptation anomaly event set containing conflict type codes and affected data primary keys. S7.3: Logically judge the conflict type code in the interface adaptation exception event set. If it is determined that there is a data synchronization conflict, trigger the traceability enable signal. Based on the timestamp index recorded in the exception event set, locate and extract the corresponding original permission adjudication intermediate result and the associated spatiotemporal semantic anchor point set from the memory ring buffer of the full process log traceability unit. Logical judgment processing is performed on the conflict type codes in the interface adaptation exception event set. An exception type identification algorithm based on feature code matching (parameters: conflict type code, rule base index table) is adopted to realize the semantic classification and labeling of each conflict in the event set. Furthermore, by using a multi-condition Boolean judgment method (parameters: identity verification conflict identifier, qualification status transition anomaly identifier), the risk level of the judgment result is classified, and a data synchronization conflict identification flag bit marked as high risk level is obtained; Furthermore, by using a trigger signal generation algorithm (parameters: risk level flag bit, rule trigger threshold), a traceability enable signal for high-risk level conflicts is generated, and a traceability signal data object with a unique trigger index is obtained. Furthermore, a memory location method based on timestamp index is adopted (parameters: timestamp index in the abnormal event set, memory ring buffer metadata table) to achieve fast mapping and location of traceable signal data objects and log traceability unit memory ring buffer, and generate a set of location pointers as the direct addressing basis for subsequent extraction; Furthermore, by utilizing a metadata extraction algorithm based on a set of location pointers (parameters: set of location pointers, description of audit event storage block structure), the corresponding original permission adjudication intermediate result data is extracted from the memory circular buffer, and the associated spatiotemporal semantic anchor set data is extracted simultaneously to construct the initial data packet for reverse path reconstruction. Through the above processing methods based on conflict logic judgment, risk level classification, trigger signal generation, timestamp memory location and metadata extraction algorithm, the interface adaptation exception event set in the previous step is transformed into a traceable data packet with original permission adjudication context and associated anchor information, so as to achieve the expected technical effect of tracing the root cause of cross-system interface conflict. For example, during the operation of a comprehensive sports event registration management system, the interface adaptation exception event set contains three conflict type codes: identity verification conflict code "IDC_01", qualification status transition exception code "QST_05", and data field mapping conflict code "DFM_02". The system's pre-built interface protocol feature matching rule base classifies "IDC_01" and "QST_05" as high-risk conflicts, setting the corresponding risk level threshold to 1. The judgment result generates a high-risk flag bit using a Boolean judgment method, triggering a signal generation algorithm to construct a traceability enable signal upon successful threshold matching, containing the timestamp index value 1678459823456. The timestamp index is mapped to the location pointer of storage block number 42 in the log traceability unit's memory circular buffer using a memory positioning method. The metadata extraction algorithm extracts the original intermediate result object of the permission decision (permission operation type identifier "QUAL_UPDATE", dynamic validity period parameter 90 seconds) and the associated set of spatiotemporal semantic anchors (spatial location anchor "MAIN_VENUE", time period anchor "IN_EVENT", network environment anchor "LOW_LATENCY", and operation behavior anchor "COMPLIANT_STEP") according to the storage block structure description. In the test verification, the extraction process took 2.7ms, effectively supporting the real-time requirements of subsequent reverse path reconstruction operations. S7.4: Based on the extracted set of spatiotemporal semantic anchor points, perform reverse path reconstruction operation, reverse the processing logic order from S1 to S4, and restore the anchor point combination to the original four types of low-overhead context signals: IPv4 / IPv6 geocoding coordinates, NTP clock offset, network RTT fluctuation sequence, and form focus jump path, to generate a complete environmental context snapshot for root cause analysis. S7.5: Compare and calculate the difference between the generated complete environmental context snapshot and the currently collected environmental signals, quantify the drift amplitude of environmental factors, generate a log traceability analysis report containing the conclusion of anomaly root cause location and anchor point threshold correction suggestions, and output the report to step S8 to drive the dynamic update of the parameters of the anchor point conflict rule base.

[0018] Step S8: Based on the log tracing results of S7, dynamically update the semantic anchor combination threshold parameters in the anchor conflict rule base constructed in S2 to achieve closed-loop optimization of the permission adjudication mechanism. Specifically, this includes: S8.1: Perform structured parsing on the log traceability analysis report output by S7, extract the anomaly root cause localization conclusion data block and anchor point threshold correction suggestion vector contained therein, and use natural language processing and key-value pair mapping algorithms to convert the unstructured text description into a standardized parameter adjustment instruction set to generate an initial optimization configuration data package with machine-readable characteristics as the basic input object for subsequent calculations; S8.2: Based on the anchor point threshold correction suggestion vector in the generated initial optimized configuration data package, read the original semantic anchor point combination threshold parameter set stored in the current memory-resident anchor point conflict rule base instance, and use the differential compensation algorithm to calculate the deviation amplitude between the suggested correction value and the original benchmark value, so as to generate a threshold dynamic adjustment factor sequence that characterizes the drift direction and intensity of the parameters. S8.3: Utilize the generated threshold dynamic adjustment factor sequence, combined with the quantized values ​​of environmental factor drift amplitude in the complete environmental context snapshot provided in S7, perform weighted smoothing filtering to suppress instantaneous noise interference, and map the discrete adjustment factors to continuously changing threshold update curves through a linear interpolation algorithm to generate an intermediate semantic anchor combination threshold parameter set that has undergone noise suppression and smoothing. S8.4: Perform business compliance boundary verification on the generated intermediate semantic anchor point combination threshold parameter set, and logically compare its value range with the geofence data of venues designated by the International Olympic Committee, the key time nodes of the official schedule of the Games, and the physical limit constraints defined by the form operation compliance standards. If it exceeds the preset safety boundary, trigger the truncation protection mechanism to generate the final legalized semantic anchor point combination threshold parameter set that meets the strong business constraints. S8.5: Based on the generated final legalized semantic anchor combination threshold parameter set, perform an atomic write operation to update the anchor conflict rule base index table constructed in S2, replace the original invalid threshold judgment conditions, and recompile the binary jump address of the rule engine, completing the entire closed loop from parameter calculation to hot loading of the rule base instance, so as to generate a new generation of millisecond-level response anchor conflict rule base instance with adaptive optimization capabilities.

[0019] This invention also provides a sports meet registration management system based on full-process log tracing and cross-system interface adaptation, which uses the above-mentioned sports meet registration management method based on full-process log tracing and cross-system interface adaptation to dynamically manage the permissions of the sports meet registration management system.

[0020] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

[0021] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and rules of the present invention should be included within the scope of protection of the present invention.

Claims

1. A sports meet registration management method based on full-process log traceability and cross-system interface adaptation, characterized in that: Includes the following steps: S1: Based on the real-time environment context of the permission change request, obtain low-overhead signals and generate a set of spatiotemporal semantic anchor points according to the core constraints; S2: Based on the aforementioned spatiotemporal semantic anchor point set, construct an anchor point conflict rule base according to the strong business semantic constraints in the sports meet registration business; S3: Input the set of spatiotemporal semantic anchor points corresponding to the permission change request into the anchor point conflict rule base, perform semantic consistency verification, and obtain the intermediate result of permission adjudication; S4: Based on the asynchronous callback event monitoring data of the WebService interface of the background review platform, determine whether there is a business constraint triggering condition for the athlete's qualification status to be rejected within a preset time. If so, input the intermediate result of the permission decision into the business semantic constraint engine to generate the final result of the permission decision containing the read-only permission identifier and audit trail instructions. S5: Perform a memory-resident stateless verification on the final result of the permission decision to verify whether it meets the real-time requirement of tens of thousands of concurrent requests per second during the peak of the Games. When the verification passes, generate a sequence of permission control instructions. S6: Input the permission control instruction sequence into the permission execution module of the registration management system to perform the corresponding role permission dynamic allocation operation, and synchronously record the operation log to the full-process log traceability unit.

2. The sports meet registration management method based on full-process log traceability and cross-system interface adaptation according to claim 1, characterized in that, Following step S6, the following is also included: S7: Monitor the system status feedback signal after the execution of step S6, determine whether the permission allocation result triggers cross-system interface adaptation anomaly, and if a data synchronization conflict is detected in the document verification system or the background review platform, start the log tracing unit to trace back the spatiotemporal semantic anchor point generation path from step S1 to step S4 and generate a log tracing analysis report. S8: Based on the log traceability analysis report, dynamically update the semantic anchor combination threshold parameter in the anchor conflict rule base to achieve closed-loop optimization of the permission adjudication mechanism.

3. The sports meet registration management method based on full-process log traceability and cross-system interface adaptation according to claim 1, characterized in that, The spatiotemporal semantic anchor set includes spatial location anchor tags, time period anchor tags, network environment anchor tags, and operational behavior anchor tags.

4. The sports meet registration management method based on full-process log traceability and cross-system interface adaptation according to claim 1, characterized in that, The strong business semantic constraints include the qualification freeze window period, the countdown threshold for certificate issuance, and the constraint of background check status transition.

5. The sports meet registration management method based on full-process log traceability and cross-system interface adaptation according to claim 1, characterized in that, Step S3 specifically includes: The spatiotemporal semantic anchor set generated in step S1 is standardized and serialized and encapsulated. Based on the memory mapping protocol, discrete semantic tags are converted into fixed-length binary feature vectors to generate anchor data packets to be verified. Based on the semantic tag combination in the anchor data packet to be verified, a parallel hash index retrieval operation is performed in the anchor conflict rule base constructed in step S2 to filter out all candidate IF-THEN logical rule entries that have an intersection with the current spatiotemporal semantic anchor set, and generate a rule candidate list. For each candidate IF-THEN logical rule entry in the rule candidate list, Boolean logic truth value judgment processing is performed. The specific semantic tag value in the anchor data packet to be verified is compared bit by bit with the spatiotemporal semantic anchor combination constraint in the rule antecedent, and the truth value matching rule set that fully satisfies all antecedent conditions is identified. Based on the multiple concurrent matching rules that may exist in the truth value matching rule set, priority weight arbitration and conflict resolution are performed. The THEN follow-up actions of the conflict rules are sorted and optimized according to the preset business security level sequence table to generate a unique target permission action instruction and its associated validity period parameter. The uniquely determined target permission action instruction is structured and encapsulated, and the permission operation type identifier, the dynamically calculated permission validity period parameter, and the spatiotemporal semantic anchor source tag hit by this ruling are packaged and combined to generate a permission ruling intermediate result object.

6. The sports meet registration management method based on full-process log traceability and cross-system interface adaptation according to claim 1, characterized in that, Step S4 specifically includes: The asynchronous callback event message returned by the WebService interface of the background check platform is parsed and processed to extract the structured event data containing the athlete's unique identity code, qualification status change type code and status effective timestamp, and obtain the original information set of qualification status change to be verified. The difference between the status effective timestamp in the original information set of the qualification status change and the current standard clock source of the system is calculated to generate the remaining valid duration value of the qualification status. The remaining valid duration value of the qualification status is then compared with the preset 30-minute business threshold and a high-risk qualification status judgment flag is output. Using the high-risk qualification status determination flag as a triggering factor, a pre-set business semantic constraint rule library is retrieved, the corresponding forced downgrade strategy template is matched, and a business constraint execution parameter set is generated. The intermediate permission decision result generated in step S3 and the set of business constraint execution parameters are input into the logic gating unit of the business semantic constraint engine to perform a permission action overriding operation, replacing the original writable permission action with a restricted read-only permission action and attaching an audit mark to generate the final permission decision result data. The final authorization decision data is subjected to integrity verification and format encapsulation to construct a standardized control instruction package.

7. The sports meet registration management method based on full-process log traceability and cross-system interface adaptation according to claim 6, characterized in that, The set of business constraint execution parameters includes a read-only access control level identifier and a full-link audit trail activation instruction.

8. The sports meet registration management method based on full-process log traceability and cross-system interface adaptation according to claim 6, characterized in that, The standardized control instruction package includes a read-only permission identifier and a standardized control instruction package for audit trail metadata.

9. The sports meet registration management method based on full-process log traceability and cross-system interface adaptation according to claim 1, characterized in that, Step S5 specifically includes: The final result of the permission decision output in step S4 is subjected to data structure standardization processing. The unstructured decision data is converted into a fixed-length standardized permission decision data block using memory-mapped file technology. Based on the standardized permission decision data block, the concurrent request arrival rate characteristics under the current system load state are extracted, and a virtual concurrent stress test vector is constructed in combination with the peak traffic model of the sports meet registration business to simulate the scenario of tens of thousands of concurrent requests per second during peak periods and obtain the virtual concurrent stress test vector. The virtual concurrent stress test vector and the standardized permission decision data block are injected in parallel. A lock-free ring buffer mechanism is used to execute memory-resident stateless verification logic. The end-to-end time from instruction injection to verification completion is collected by a high-precision timer to obtain a deterministic latency metric for a single permission decision. Based on the deterministic delay metric of the single permission decision, a compliance comparison analysis is performed using a preset real-time threshold judgment rule to determine whether the delay metric is less than the maximum response time threshold allowed during the peak of the Games, so as to generate a Boolean real-time verification pass flag. The Boolean real-time verification is logically gated using a flag bit. When the flag bit is true, the standardized permission decision data block is encapsulated into an opcode sequence that conforms to the permission execution module interface protocol, generating a permission control instruction sequence.

10. A sports meet registration management system based on full-process log traceability and cross-system interface adaptation, characterized in that: The sports meet registration management method based on full-process log tracing and cross-system interface adaptation as described in any one of claims 1-9 is used to dynamically manage the permissions of the sports meet registration management system.

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