77results about How to "Realization risk" patented technology

The invention relates to the technical field of visual health management, in particular to a ciliary muscle adjustment training system and method based on eye movement tracking, and the method comprises the steps: collecting eye movement data of a fixation point track, an adjustment amplitude change rate and pupil response time in real time, and combining personalized parameters such as the age of a user, diopter and a training target; inputting a ciliary muscle training dynamic planning algorithm module, dynamically generating a stage scheme containing a training action type, a single-group duration, a stimulation interval and a focal length adjustment strategy, and adaptively adjusting the strategy according to real-time adjustment stability and a historical ability curve; through multi-dimensional matching analysis of a pre-stored standard eye movement template, a training specification score is calculated from the track goodness of fit, speed uniformity, amplitude standard-reaching rate and binocular coordination, visual fatigue parameters are obtained based on continuous training duration, pupil fluctuation and retina reflex change, a scoring result is dynamically corrected, and a visual fatigue evaluation result is obtained. Personalized, self-adaptive and quantitative safety control of the training process is realized.

The invention belongs to the technical field of e-commerce logistics management, and particularly relates to an e-commerce product storage full-process monitoring management method, which comprises the steps of synchronously acquiring multi-source heterogeneous monitoring data in real time through a sensing equipment network; performing space-time alignment preprocessing on the original data, and converting heterogeneous data into a uniform data format; semantic fusion is carried out on the standardized data, and a job behavior knowledge graph with a complete spatio-temporal context is constructed; inputting the atlas into a pre-training hybrid model, and detecting an inventory abnormal event; and generating a strategy instruction in combination with the real-time order and resource data, carrying out joint analysis on an operator, and outputting a compliance score and a specific violation operation type. Through adoption of the technical scheme, complete reconstruction of the context of the operation behavior can be realized, the anomaly detection accuracy is remarkably improved, the abnormal event response delay is shortened, and the inventory loss rate caused by human errors is reduced.

The application provides a positive plate preparation method based on electrolyte moisture control and a battery. The core rule that "moisture of solid-state electrolyte wet powder is a key factor affecting the moisture of the positive plate" is first determined, and the long-standing error cognition in the industry that "controlling the total moisture of the slurry is enough" is corrected, providing a scientific theoretical basis for moisture control. By detecting three indexes before the slurry is used, the moisture of the positive plate can be accurately predicted whether it will exceed the standard, and the risk of scrapping the whole batch of positive plates is fundamentally avoided. By strictly controlling the moisture of the positive plate, the side reactions in the battery are effectively reduced, the capacity retention rate, rate performance and cycle life of the battery are improved, and the safety hazards such as swelling and thermal runaway caused by excessive moisture are reduced.

The invention discloses a wire tape production equipment remote monitoring and fault early warning system based on the Internet of Things, and relates to the technical field of equipment fault early warning, and the system comprises the steps: obtaining the operation state data of an industrial equipment part, the operation state data comprises vibration amplitude data and temperature change data, processing the running state data by adopting a time sequence analysis method so as to identify a performance degradation starting point and generate a performance degradation starting point identifier; acquiring sensor position information and determining a signal monitoring area through the performance degradation starting point identifier, and generating a coverage map identifier; according to the boundary of the signal monitoring area, extracting temperature change data and vibration amplitude data from the operation state data, and constructing a performance evolution law model by adopting clustering analysis and interpolation fitting; according to the invention, by constructing a spatial coverage modeling and performance evolution analysis mechanism based on multi-source state data, performance degradation identification, coverage area perception and fault risk early warning in the remote monitoring process of industrial equipment are realized.

The invention relates to the technical field of power grid operation, and provides a power grid operation safety distance accurate sensing device and method in a non-fixed scene, and the device comprises a hardware device and terminal software which is in communication connection with the hardware device. The hardware device is integrated and deployed on a movable support or a holder, and comprises a laser radar sensor which is used for rapidly scanning a power grid operation scene and obtaining three-dimensional point cloud data including three-dimensional coordinates, laser intensity and echo information; the high-definition visible light camera is approximately parallel to the optical axis of the laser radar sensor and is used for collecting real-time video images of the operation scene; an AI visual analysis module and a point cloud processing unit are arranged in the embedded processor; and the terminal software is deployed on the edge computing equipment or the background server and is used for receiving and processing the data from the hardware device. According to the invention, the safe distance can be accurately sensed.

The invention provides a battery system state evaluation and early warning method based on multi-source information fusion, and the method comprises the steps: synchronously collecting the multi-source state data of a battery system, which at least comprises a real-time synchronization parameter group for cooperatively evaluating the mechanical integrity and environment interaction risk, the real-time synchronization parameter group comprises vibration acceleration data, organic volatile matter concentration data, humidity data and corrosion rate data; processing the data of the real-time synchronization parameter group, extracting combined feature parameters having an association relationship with each other to form a feature vector, the combined feature parameters at least comprising: a structure risk feature, a sealing risk feature, and a connection risk feature; and inputting the feature vectors into a preset multi-parameter fusion analysis model to analyze a linkage mode among the combined feature parameters, and synchronously generating and outputting a safety risk report based on an analysis result of the linkage mode, the safety risk report at least comprising a mechanical integrity risk index and an environment interaction risk index.

The invention provides a vehicle door control method, a vehicle and a medium, and relates to the technical field of vehicle safety. According to the method, when a target vehicle door opening instruction is received, first surrounding obstacle information of a target vehicle door is obtained, first preset opening condition judgment of a time sequence dimension is executed in combination with the opening time of the target vehicle door, so that the time sequence conflict risk caused by obstacle approaching is accurately avoided, and then when the first preset opening condition is met, the target vehicle door is opened. And according to the first surrounding obstacle information and the movement envelope data of the target vehicle door in the full-stroke movement, second preset opening condition judgment of the spatial dimension is executed so as to accurately judge whether the vehicle door movement range and the spatial margin of the obstacles are sufficient or not, and the target vehicle door is controlled to be opened only when the two conditions are both met. Dynamic perception of a complex space environment is achieved, the risk of collision between the target vehicle door and surrounding obstacles when the target vehicle door is opened is accurately recognized and avoided, the use safety and reliability of the target vehicle door are improved, and the use experience of a user is optimized.

The invention discloses a shield tunnel segment intelligent cooperation method and system based on dynamic damage prediction and self-repairing. The method comprises the steps that digital twin bodies synchronously mapped with a physical tunnel entity are constructed; acquiring strain and temperature data streams, performing data cleaning and feature extraction, and inputting the data streams to the digital twinborn body; real-time simulation is carried out to diagnose the damage state of the physical segment, and a first-level decision node is triggered: a critical state predictor is called, the remaining time T from damage to the critical state is dynamically calculated, a second-level decision node is triggered, and a repair task identifier is generated; the repair strategy optimizer decides an optimal repair scheme and generates a control instruction set, and the mobile repair robot is driven to execute automatic repair operation; and evaluating the repairing efficiency and carrying out self-learning updating. According to the invention, by fusing the real-time sensing data and the multi-factor damage model, accurate diagnosis and dynamic risk prediction of the structural damage are realized, the early warning capability is greatly improved, and dynamic decision and continuous optimization are realized.

The invention discloses a supply chain node toughness dynamic evaluation method based on information entropy, and belongs to the field of supply chain network toughness research. The method comprises the steps of calculating efficiency and redundancy of enterprise nodes in supply and demand directions based on an information entropy theory; according to the roles of the enterprise nodes in the supply chain network, performing weighted fusion to obtain the total efficiency and the total redundancy of the enterprise nodes; by comprehensively considering efficiency and redundancy, dynamic quantification of enterprise node toughness is realized. In addition, node toughness driving mechanism diagnosis based on an internal structure is further introduced, and internal structure factors for driving node toughness fluctuation are accurately recognized through exponential decomposition and sensitivity analysis. According to the method disclosed by the invention, dynamic quantification and attribution analysis of the fine-grained toughness of the enterprise node level based on the supply and demand bidirectional dependency relationship are realized, and powerful decision support is provided for accurately positioning the fragile link of the supply chain, optimizing the structure of the supply chain and improving the overall anti-risk capability of the supply chain.

The invention relates to the technical field of accounting document automatic auditing, and discloses an accounting document automatic auditing and error correction system based on image recognition, which comprises a recognition feature acquisition unit for acquiring a financial field candidate value set and a corresponding first confidence coefficient; the business logic mapping unit translates the financial articulation rule into a logic constraint factor; the credit portrait analysis unit calculates a credit weight factor based on the historical correction characteristics; the global logic verification unit constructs a business logic topological graph, and determines a target solution according to the residual error and confidence coefficient distribution so as to correct the original identification value; according to the method, the logic fitting bottleneck caused by identification bias is solved by coupling the credit portrait and an active detection mechanism, and the auditing identification capability of the management system on the substantial compliance risk under the incomplete information working condition is improved.

The application discloses a biological age evaluation method and system based on multi-modal clinical data, wherein the evaluation method comprises the following steps: step 1, data acquisition and multi-modal construction; step 2, data preprocessing and feature expression; step 3, reference population construction and feature screening; step 4, biological age prediction model training and verification; step 5, biological age acceleration index calculation; and step 6, standardization and risk assessment output. By fusing heart ultrasound examination data and blood biomarker data, the application realizes comprehensive characterization of the aging state of an individual, further calculates a biological age acceleration index, and uses the biological age acceleration index for cardiovascular disease risk assessment, so as to solve the problems that a single-modal biological age model in the prior art cannot comprehensively depict the aging process and cannot simultaneously reflect changes in organ function and changes in systemic physiological state.

The invention discloses a construction method and application of a safety evaluation risk prediction model after child medication marketing, and the method comprises the steps: constructing a three-level index system, and determining the weight of each level of index through an analytic hierarchy process; based on the three-level index system, determining a risk feature set, constructing a three-level model architecture, and performing model training by adopting a machine learning algorithm; collecting multi-source real world data, and establishing a standardized data pool; the performance of the model is improved through hyper-parameter optimization and integrated learning, the performance of the model is verified and evaluated by adopting internal cross validation and an external independent data set, and the transparency of the model is enhanced through an interpretability technology; and establishing a dynamic updating mechanism, deploying the model as a cloud API service, and realizing risk prediction and early warning functions. According to the invention, multi-source real world data can be effectively integrated, the children medication safety risk can be accurately identified, the method is used for safety evaluation after children medication is listed, and the method is of great significance to supplement clinical test insufficiency before children medication is listed, reduce medication risk and guarantee children medication safety.

The invention relates to a power transmission and transformation reduction verification method and system based on a knowledge graph, and belongs to the technical field of power transmission and transformation project review. The method comprises the following steps: acquiring review file data of a power transmission and transformation station, extracting text content according to a natural language processing technology to construct a knowledge graph sub-graph, and generating a design review standard file list; performing text word segmentation based on the design review standard file list to obtain a structured review template; inputting the review file data of the power transmission and transformation station into a model, and verifying the review key points of the structured power transmission and transformation project in combination with the file integrity and the structured review template; and taking the review key points as cue words, writing the cue words into an intelligent review verification model, and generating a power transmission and transformation review report.

The invention belongs to the field of dewatering well construction, and particularly relates to a novel dewatering well structure for hydraulic engineering and a construction method.The construction method specifically comprises the following steps that firstly, in the preparation stage, a detachable filter pipe with a cleaning function is prefabricated, and a multi-dimensional sensing system with a water quality and flow monitoring function is arranged; step 2, pore-forming and mounting: performing pore-forming operation on the precipitation well, reserving a precipitation space at the pore-forming depth, and then assembling detachable filter tubes in sections and lowering the detachable filter tubes into the pores; step 3, filling a filter material, namely filling the filter material between the filter tube and the hole wall by using a material guide device according to a predetermined grading layer by layer, and taking an anti-overhead measure in the filling process to ensure the compactness of the filter material; 4, sealing and connecting: constructing a sealing structure above the filter material layer; 5, operation and maintenance are carried out, wherein pilot operation is carried out on the dewatering well system; and step 6, operation and maintenance: formal precipitation is carried out, and the physical state of the filter tube is diagnosed in real time according to vacuum degree, water level, flow and turbidity data fed back by the sensing system.

This application relates to a method for cross-border digital certificate mutual recognition based on dynamic trust anchors, comprising: collecting and preprocessing multi-dimensional trust assessment data of cross-border certificate authorities; collaboratively training a global model using federated XGBoost combined with a scenario-based attention mechanism to output a real-time trust score; constructing a hierarchical dynamic trust chain based on the score and writing the evidence data into the blockchain; generating proofs based on the Groth16 algorithm to complete certificate verification under privacy protection; deploying smart contracts to execute trust warnings, dispute arbitration, and rule updates; monitoring score changes, adaptively adjusting, and triggering an abnormal circuit breaker mechanism. This invention achieves real-time updating of trust weights and dynamic reconstruction of the trust chain, improving the efficiency and security of cross-border mutual recognition while ensuring data privacy compliance.

The application discloses a kind of sepsis multiple complications prediction method, system, storage medium and equipment, belong to intelligent medical technical field, method includes the following steps: S1. acquisition sepsis patient multidimensional time series physiological data;S2. medical perception feature vector is constructed to the time series physiological data;S3. sepsis multiple complications prediction model is constructed and introduced physiological logic space-time composite mask matrix;S4. the output multiple complications prediction probability using hierarchical expert hybrid network H-MoE;S5. the model is trained using dynamic weighted loss function based on task uncertainty;S6. risk output and feature contribution degree explanation;The model of the application is introduced by physiological logic mask and H-MoE architecture, realizes the accurate, synchronous prediction of sepsis complex complications, can effectively filter redundant features, and reveal the complex correlation therebetween, provide quantitative decision support for clinician, to realize early intervention and individualized treatment.

The present application relates to the technical field of edge computing, in particular to a groundwater pollution monitoring method and system based on edge computing, comprising the following steps: acquiring groundwater sequence data and calculating unit time fluctuation intensity parameters, comparing threshold interval to generate sampling frequency, collecting physicochemical indexes to generate deviation value set, extracting emergency weight parameters, combining edge node residual computing power cycle to generate task scheduling strategy, collecting environmental indexes to write into deviation cumulative matrix and accumulate row by row, comparing with the set risk threshold to generate pollution risk grade. In the present application, by adaptively matching the sampling frequency according to the fluctuation intensity state, the fixed interval blind area is effectively discarded and the mutation signal is captured, the scheduling strategy is constructed by combining the multi-dimensional deviation characteristics and the node computing power to generate the scheduling strategy, the dynamic allocation and computing power balance of the task are completed on the edge side, the link congestion caused by redundant baseline transparent transmission is avoided, the pollution trend is tracked based on the cumulative matrix, and the prepositioned research and judgment are realized.

This invention discloses an early diagnostic method for heatstroke that combines pattern classification and clustering, relating to the fields of medical health and bioinformatics. The method involves real-time acquisition of temperature, humidity, and radiation data in high-temperature environments via sensor networks, combined with heart rate, skin temperature, and sweat secretion indicators monitored by individual wearable devices, to form a comprehensive environmental heat intensity data and an individual physiological response dataset, yielding preliminary heat stress indicators. This early diagnostic method for heatstroke that combines pattern classification and clustering can significantly improve the scientific rigor and effectiveness of early diagnosis of heatstroke, providing reliable protection for the health of people in high-temperature environments.

The application discloses a cyanobacterial community succession prediction method and device fusing ecological mechanism and causal enhancement. The method comprises the following steps: collecting multi-source high-frequency monitoring data of a target water area, and constructing an environmental driving factor characteristic matrix; constructing a causal weight matrix through causal relationship aggregation analysis, and outputting a causal enhancement characteristic matrix; constructing a species ecological strategy embedding matrix; performing multi-scale decomposition on algal density time series data; constructing a multi-species interaction prediction model based on a multi-head self-attention mechanism, fusing environmental characteristics, causal enhancement characteristics, ecological strategy embedding and multi-scale time series characteristics, and predicting the biomass and relative abundance of each dominant cyanobacterial species; and outputting a cyanobacterial community succession process and a water bloom risk grade. Through deep fusion of causal enhancement, ecological strategy embedding and multi-head attention mechanism, the precision, stability and ecological interpretability of cyanobacterial community succession prediction are significantly improved, and the application is suitable for water bloom early warning and ecological management of lakes, reservoirs and rivers.

This invention discloses a method and system for energy consumption analysis of highway power equipment based on smart gateways. The method includes: acquiring power equipment operation data collected by smart gateways deployed at multiple nodes along the highway; identifying operational energy consumption correlation characteristics corresponding to each smart gateway based on a preset correlation model; predicting the gateway area risk corresponding to each smart gateway based on the operational energy consumption correlation characteristics using a prediction model; and performing energy consumption structure analysis on the highway and identifying abnormal energy consumption data based on the gateway area risk, thereby outputting operational management suggestions for the power equipment. Therefore, this invention can achieve digital monitoring and risk warning of the operational status of highway power infrastructure, thus providing scientific decision-making suggestions and data support for the precise operation and management of power equipment.

This invention discloses a method for evaluating the stability of steep slopes in open-pit mines under multiple working conditions, relating to the field of geotechnical engineering stability analysis. The method includes: collecting geological and mechanical data of steep slopes in open-pit mines and constructing a three-dimensional geomechanical model; based on the three-dimensional geomechanical model, obtaining various working conditions of the steep slopes in open-pit mines; defining a time-series-related dynamic loading path for multiple working conditions according to the logical sequence of open-pit mining operations and natural events; applying loads of various working conditions of the steep slopes in open-pit mines to the three-dimensional geomechanical model in numerical simulation according to the time-series-related dynamic loading path, and coupling the simulation to depict the progressive failure process of the steep slopes in open-pit mines; and discretizing the mining plan into a continuous time series. This invention, through multi-working-condition time-series loading and dynamic update simulation, overcomes the limitations of traditional analysis methods that isolate working conditions and fail to reflect dynamic coupling effects, achieving accurate evaluation and risk warning of slope stability.

The invention provides a rolling equipment control method and device based on multi-target detection, and relates to the technical field of target detection, and the method comprises the steps: obtaining image data collected by camera equipment for rolling equipment; multi-target detection is carried out on the image data through a pre-trained target detection model to obtain a target detection result corresponding to the image data, and the target detection result comprises one or more of target edge contours corresponding to the living body of the operator, the hand region of the operator and the region of gloves worn by the operator; determining a current state instance corresponding to the target detection result, wherein the current state instance represents a physical monitoring area in which a target edge contour contained in the target detection result falls; and determining a target control strategy according to the equipment state of the rolling equipment and the current state instance so as to execute a control action corresponding to the target control strategy on the rolling equipment. The method can effectively prevent and intervene high-risk conditions in the operation of the rolling equipment in real time.

The invention is applicable to the technical field of circuit breaker management, and provides a circuit breaker management system capable of quickly switching between grid connection and grid disconnection, which comprises an acquisition module, an analysis module, a correction processor module and a fixed instruction module, and is characterized in that the acquisition module is used for acquiring basic power grid parameters and load power data in real time; and an initial switching demand index is generated according to the obtained power grid parameters. According to the method, multi-dimensional core features of real-time load power are extracted, real-time feature vectors are constructed instead of single parameters, a typical disturbance template library is constructed in combination with historical load curves and feature logs, the real-time features and historical templates are quantitatively matched through a weighted cosine similarity formula, benign disturbance, fault disturbance and instantaneous disturbance are accurately distinguished, and the real-time disturbance of the real-time load power is obtained. According to the method, multivariable collaborative judgment logic is formed, the one-sidedness of a single threshold value is thoroughly eliminated, and unnecessary off-grid operation is effectively avoided.

The invention relates to the technical field of wind power generation blade production equipment, and discloses a mold heating area temperature monitoring method, system and equipment and a storage medium. A heating area is arranged on one side of a mold, and a covering layer for heat preservation is arranged on the other side of the mold; the method comprises the following steps: acquiring real-time measurement temperature values of a plurality of temperature measurement probes in the heating area; the absolute value of the difference value between the real-time measured temperature values of any two temperature measuring probes is calculated, whether the absolute value of the difference value exceeds a preset temperature difference threshold value or not is judged, and a corresponding alarm state is triggered according to the judgment result; and determining a representative temperature value of the mold heating area based on the real-time measurement temperature values of all the temperature measurement probes, and controlling start and stop of a heating device according to the representative temperature value and a preset target temperature value. The problem that in the prior art, due to the fact that single-point temperature measurement cannot reflect overall temperature distribution of a heating area, the temperature abnormal risk caused by uneven heat preservation coverage cannot be recognized can be solved.

The application provides a fine-grained access control method and system of a dynamic policy decision mechanism, comprising: receiving a data access request sent by an access subject; calculating an access risk value according to a risk identification vector; generating a dynamic authentication requirement corresponding to the current access request through risk level mapping of the access risk value, and generating zero-knowledge authentication challenge information; the client generates a zero-knowledge proof response based on the zero-knowledge authentication challenge information; performing dynamic zero-knowledge authentication based on the authentication result of the zero-knowledge proof response and the identity state of the access subject; and generating a fine-grained access authorization and an access token according to the authentication result; performing validity verification on the access token, and returning restricted data to the access subject according to the fine-grained access authorization when the verification is passed, so that trusted authentication and dynamic risk control of data resource access behavior can be realized without leaking sensitive identity information and authentication factors of the access subject, and the security of data access is improved.

The invention discloses a method for evaluating wave-induced seabed liquefaction susceptibility in an extreme environment, and particularly relates to the technical field of ocean geotechnical engineering. The method comprises the following steps: acquiring a multi-period wave energy spectrum sequence, identifying abnormal energy abrupt change points, constructing a disturbance-driven shear stress disturbance field, generating a two-dimensional permeability response matrix in combination with seabed microstructure parameters, coupling to form a three-dimensional shear stress-permeability tensor, and further solving a disturbance-induced pore pressure response function; then, constructing a liquefaction triggering model by adopting fractional Fourier transform, and outputting liquefaction potential density distribution; a liquefaction master control path is identified in combination with the distribution, a liquefaction susceptibility comprehensive index is constructed based on the alignment degree of the path, the geologic horizon and the load direction, and liquefaction high-risk area boundary demarcation is achieved; according to the method, disturbance characteristics, multi-scale structure response and fractional domain modeling technologies are fused, and the method has the advantages of high resolution, real response mechanism, accurate risk identification and the like, and is suitable for seabed liquefaction risk assessment and engineering safety design under extreme sea conditions.

The application relates to the technical field of accounting document automatic auditing, and discloses an accounting document automatic auditing and error correction system based on image recognition, which comprises the following units: an identification feature acquisition unit that acquires a financial field candidate value set and a corresponding first confidence degree; a business logic mapping unit that translates financial auditing rules into logical constraint factors; a credit image analysis unit that calculates a credit weight factor based on historical error correction features; a global logic checking unit that constructs a business logic topology graph and determines a target solution according to residual error and confidence degree distribution to correct original identification values; and a disturbance test module that detects logical fraud risks by applying numerical offset to the target solution. The application solves the logical fitting bottleneck caused by identification bias by coupling a credit image and an active detection mechanism, and improves the auditing identification ability of a management system to substantial compliance risks under non-perfect information working conditions.

This invention relates to the field of vehicle driving safety monitoring technology, and more particularly to a driver behavior monitoring device and electronic equipment based on multimodal visual recognition. The device includes a physical device and a control device. The control device is integrated into an embedded computing platform and includes a data acquisition module that acquires vehicle dynamic parameters through a vehicle bus interface and controls a multispectral imaging unit to acquire video streams; a feature extraction module that fuses spatial features and temporal motion features from the video stream to generate a spatiotemporal feature tensor; a behavior analysis module that generates standard behavioral features through a generative adversarial network and calculates statistical deviations; a risk decision module that uses a dynamic weighted algorithm to output a comprehensive risk level and confidence assessment; and a control output module that executes early warning operations and sends feedback data to the behavior analysis module for model optimization. This invention reduces the false positive rate and improves the timeliness of early warnings through multimodal data fusion and closed-loop optimization mechanisms.

The invention provides a building economic risk monitoring method, electronic equipment and a storage medium, and the method comprises the steps: determining a first target subject, a second target subject and a third target subject according to the feature data integrity of subject feature data corresponding to each target subject included in a target building; determining a subject negative impact value of the first target subject according to the plurality of subject feature data corresponding to the first target subject; determining a subject negative impact value of the second target subject according to the impact type of the feature field of the subject feature data of the second target subject; according to the similarity between the main body feature data of the same type of main body and the main body feature data of a third target main body, determining a main body negative impact value corresponding to the third target main body; and weighting the subject negative impact value of the target subject and the building negative impact value to obtain a building economic risk value, so as to reflect a real risk condition between the target building and the target subject through the building economic risk value.

The application discloses a semantic-driven archive compliance and anomaly detection method and system, and belongs to the field of intelligent archive management. In view of the problems of rigid rules, insufficient semantic understanding, poor regulation adaptability, difficult identification of complex anomalies and lack of explainability in traditional methods, the application constructs a "integrity-semantic consistency" two-dimensional evaluation system, generates clauses-level fragments through dynamic division of legal semantics, constructs an archive heterogeneous knowledge graph integrating time and space, business and regulations, and completes knowledge-enhanced feature representation; through multi-scale space-time graph convolution modeling of space-time features, relying on a multi-regulation adaptive model pool, compliance dynamic detection is realized, and combined with semantic consistency reasoning, anomaly discrimination, risk tracing and explainable report generation are completed. The application has high detection precision, fast regulation adaptation, can accurately identify cross-entity and cross-temporal complex anomalies, and is suitable for intelligent supervision and risk prevention and control of the whole life cycle of archives.