997 results about "Risk rating" patented technology

The invention relates to the technical field of risk assessment, in particular to an AI enterprise financial risk dynamic assessment method and system. The method comprises the following steps: obtaining enterprise multi-dimensional financial data, operation activity data, industry environment data and macroeconomic index data, carrying out cleaning, standardization and time sequence alignment processing, and integrating the data into a financial risk assessment multi-dimensional feature matrix; constructing a dynamic weight distribution model based on the financial risk assessment multi-dimensional feature matrix, endowing differentiated weights, and generating a time sequence weighted feature vector; risk level mapping and comparative analysis are carried out on the time sequence weighted feature vectors, risk points exceeding a threshold value are identified, and feature sources of the risk points are traced; and constructing a risk conduction path map based on the risk points and feature sources thereof, simulating risk evolution trends under different intervention measures, and evaluating and generating a dynamic evaluation report containing risk early warning levels, key influence factors and intervention measure suggestions. According to the invention, the enterprise financial risk assessment efficiency can be improved.

The invention provides an intelligent inspection risk assessment method and system based on multi-sensor fusion, and the method comprises the steps: obtaining original multi-source data of a transformer substation, the original multi-source data comprises a binocular vision image, infrared thermal image data and environment sensor data, and carrying out the time-space calibration and preprocessing of the original multi-source data, obtaining a multi-source sensor data stream; carrying out feature extraction on the multi-source sensor data stream to obtain a multi-modal feature set, generating a refined semantic mask based on the multi-modal feature set, constructing an initial scene relation graph, calculating a risk level based on a multi-layer perceptron classifier, and generating a risk level evaluation result and a risk distribution graph; outputting a safety distance violation warning and a risk area identifier; and generating comprehensive risk early warning information based on the risk level assessment result, the risk distribution diagram, the safety distance violation warning and the risk area identifier.

The invention relates to artificial intelligence generation content AIGC, in particular to an AIGC test case self-adaptive generation system based on risk feedback, which comprises a semantic variation generation module for selecting a variation operator from a variation operator library according to a self-adaptive strategy, performing variation processing on a seed case provided by a basic test case library by using the variation operator, and generating a semantic variation result; generating a new test case; the AIGC model takes the test case as model input and performs model output to the risk assessment and quantification module through an AIGC model interface; the risk assessment and quantification module is used for performing multi-dimensional risk assessment on a model output result by utilizing the risk detection model library, calculating a risk level and generating a test report; the risk feedback processing module is used for storing the high-risk test case, performing feature extraction and converting a risk assessment result into an operable risk feedback signal according to extracted features; according to the technical scheme provided by the invention, the defects of low efficiency and difficulty in automatically adapting to environment change can be overcome.

The invention relates to the technical field of smart home, discloses a full-life-cycle fault prediction and management method and system for smart home equipment, and aims to solve the problems of high fault rate and low operation and maintenance efficiency of the smart home equipment in the prior art. The method comprises the steps that multi-source heterogeneous data are collected and preprocessed; time domain and frequency domain features are extracted and fused to generate a comprehensive feature vector; predicting the fault probability and type by using a deep transfer learning model; a dynamic risk score is calculated through a weighted risk assessment model, and risk grades are divided; and triggering a differential response strategy according to the risk level. The system comprises a multi-source heterogeneous data sensing module, a dynamic feature extraction and fusion module, an adaptive fault prediction module, a quantitative risk assessment module, an intelligent response and decision module and a full life cycle data management module. According to the technical scheme, comprehensive and accurate monitoring, efficient fault prediction and intelligent management of the smart home equipment can be achieved, the fault rate is remarkably reduced, and the overall performance of the system and the user satisfaction degree are improved.

The invention discloses a power system data access management and control system based on risk dynamic assessment, and particularly relates to the technical field of power system information security and access control. Comprising an access request interception module, a multi-dimensional risk data acquisition module, a dynamic risk assessment engine module, a self-adaptive access control decision module and a security audit and learning module. Multi-dimensional dynamic context information such as an access subject, an object, an environment and a behavior sequence is collected in real time, a rule engine and machine learning hybrid model is combined to carry out real-time risk calculation, a quantitative comprehensive risk value is output, and an access control decision is dynamically generated according to a risk level threshold. The system has self-learning and self-adaptive optimization capabilities, continuously optimizes the model through an audit log, improves the security and flexibility of data access of the power system, and balances service availability and security protection requirements.

Aspects of the subject disclosure may include, for example, generating network configuration data for a network device according to device information of a vendor; providing the network configuration data to an Artificial Intelligence (AI) analysis platform that applies an AI model to the network configuration data based at least in part on topology data resulting in a configuration analysis, wherein the configuration analysis evaluates the network configuration data and generates at least one of rating information, adjustment information or a combination thereof. The rating information can include a security rating, a risk rating and / or an efficiency rating. The adjustment information can include a recommendation for changing at least a portion of the network configuration data for the network device.

A security platform employs a variety techniques and mechanisms to detect security related anomalies and threats in a computer network environment. The security platform is “big data” driven and employs machine learning to perform security analytics. The security platform performs user / entity behavioral analytics (UEBA) to detect the security related anomalies and threats, regardless of whether such anomalies / threats were previously known. The security platform can include both real-time and batch paths / modes for detecting anomalies and threats. By visually presenting analytical results scored with risk ratings and supporting evidence, the security platform enables network security administrators to respond to a detected anomaly or threat, and to take action promptly.

The invention provides an ultrahigh bridge tower safety regulation and control method and system based on a concrete time-varying effect, and relates to the technical field of bridge tower regulation and control systems. Aiming at the problems that in the prior art, the prediction accuracy of long-term internal force redistribution caused by concrete shrinkage and creep is insufficient, the coupling effect of multiple risk factors and bridge tower concrete shrinkage and creep is insufficiently considered, so that the prediction accuracy of the state of a bridge tower is low, and a maintenance regulation and control guidance basis is lacked, multi-source heterogeneous data are fused, and the prediction accuracy of the state of the bridge tower is improved. Concrete shrinkage and creep, dynamic ice impact, static ice pressure, concrete freeze thawing and ice wedging influence indexes are calculated respectively, a comprehensive risk assessment model based on machine learning is adopted for fusion, and a comprehensive risk report containing risk levels and regulation and control suggestions is generated; according to the method, the accuracy of single risk factor assessment is remarkably improved, the comprehensive prediction precision of complex coupling risks is improved, and the decision robustness and maintenance efficiency of the system in a multi-risk concurrent complex scene are enhanced.

The invention discloses a building outer wall falling risk detection method and system based on deep learning, and the method comprises the steps: obtaining an original surface image, extracting vertical face geometric features, constructing a homography matrix based on vanishing points, carrying out the geometric correction, and reconstructing an orthographic projection image; performing enhancement processing on the orthographic projection image, extracting an effective detection area and intercepting a detection image; inputting the detection image into a deep learning segmentation model into which edge weight constraint is introduced, and performing semantic segmentation, binaryzation and optimization to obtain a falling region; a mapping relation is established according to the actual physical size of the building outer wall, and pixel information of the falling area is converted into multi-dimensional parameters such as the actual physical area, the elevation, the width and the height; and combining the defect intensity input quantification risk assessment model to calculate a comprehensive risk index, determining a risk level and outputting a result. Automatic identification and quantitative evaluation can be realized, distortion and background interference are eliminated, accurate conversion from pixels to a multi-dimensional physical space is realized, and a scientific basis is provided for safety investigation.

The invention discloses a document auditing method and device based on cooperation of a large model and a rule engine. The method comprises the following steps: analyzing an original document to form an intermediate representation file; extracting entities from the intermediate representation file to form an entity candidate set; inputting the thinking chain cue word into the large model to obtain a first triple, a first confidence coefficient and a first risk level, and calculating a first traceable score of the large model according to the reasoning path node; performing deterministic judgment by using the rule engine to obtain a second triple, a second confidence coefficient, a risk level and a second traceable score; calculating two weighted voting values and obtaining a conflict difference value; taking a conclusion corresponding to the high-weighted voting value as a final conclusion when the conflict difference value is smaller than a preset judgment threshold value; otherwise, starting an artificial rechecking process; and generating audit reports in various formats. According to the method, the advantages of relatively high language understanding ability of a large model and relatively high certainty of a rule engine are exerted, and the problem of missed checking or excessive marking is avoided.

The invention discloses a dynamic data storage method, system and equipment based on risk levels and a medium, and belongs to the field of electric power systems.The method comprises the steps that feature extraction is conducted on to-be-stored data to obtain an initial feature set, semantic analysis and context association analysis are conducted on the to-be-stored data to obtain a risk feature set, and the risk feature set is stored; combining to obtain a target feature; performing risk assessment according to the target feature to obtain a risk level, and determining an encryption level according to the risk level and a preset encryption mapping table; according to the method and the device, the to-be-stored data is automatically routed to the target storage node matched with the encryption grade, and the corresponding encryption storage operation is executed on the target storage node to complete dynamic data storage, so that the optimal balance of safety and efficiency in data storage can be realized by implementing the method and the device.

The invention discloses a data transaction security assessment method and system, and relates to the technical field of data security and intelligent assessment. The method comprises the steps of performing feature analysis on a data field, determining a sensitivity weight and generating a sensitivity vector; the fields are graded according to the sensitivity vectors, and dynamic adjustment is carried out in combination with transaction related information to obtain a grading result; calculating a data content risk, a field association risk, and a transaction environment risk based on the sensitivity feature set; determining a weight according to a transaction scene, and weighting various risks to obtain a comprehensive score; and judging the risk level according to the comprehensive score and a threshold value dynamically determined based on historical data. According to the invention, through hierarchical sensitivity modeling and dynamic risk calculation, fine-grained security assessment of different types and sensitivity data is realized, and the risk result can be adaptively adjusted along with changes of data contents, transmission paths and transaction environments, so that the accuracy, real-time performance and scene adaptability of assessment are improved, and the risk assessment efficiency is improved. The defects of a static evaluation method in the prior art are overcome.

The invention discloses a remote warning and monitoring system and method for an insulin pump, and relates to the technical field of medical equipment monitoring, and the system comprises a data collection module which is used for obtaining multi-modal physiological and equipment data from user side equipment in real time; the risk assessment engine is used for performing fusion analysis based on the multi-modal physiological and equipment data to generate a comprehensive risk assessment result; the dynamic early warning module is used for dynamically triggering warning information of different risk levels according to the comprehensive risk assessment result; the remote communication module is used for simultaneously pushing the warning information to at least one remote monitoring terminal and user side equipment; and the remote intervention interface is integrated on the remote monitoring terminal and is used for receiving and displaying the warning information and enabling a remote guardian to initiate at least one intervention instruction. Alarm omission caused by the fact that a patient is alone or the response ability is limited is effectively avoided; the accuracy and reliability of risk assessment are improved, and false alarms are reduced; the risk response time is shortened, and the safety of patients is guaranteed.

The invention provides a risk monitoring method for a coal mine goaf, and the method comprises the steps: obtaining risk association information provided by a plurality of information collection devices in the coal mine goaf in real time according to a preset encryption communication mode; based on all the risk association information and a pre-trained risk assessment model, determining a current risk index value of the coal mine goaf, the risk assessment model being used for reflecting an association relationship between the risk index value of the coal mine goaf and all the risk association information; determining the current risk level of the coal mine goaf based on the current risk index value of the coal mine goaf and the index weight of the risk index value; and executing a risk early warning strategy corresponding to the current risk level of the coal mine goaf. According to the technical scheme, reliable technical guarantee is provided for safe production of the coal mine goaf.

The invention discloses a multi-source information fusion safety production risk intelligent early warning and disposal platform, which comprises a multi-source information acquisition module, an information preprocessing module, a multi-source information fusion module, an intelligent risk identification and early warning module, a disposal scheduling module and a data storage and interaction module, heterogeneous data such as sensor monitoring, video monitoring, equipment logs and manual reporting are collected and cleaned and standardized, and then a weighted fusion algorithm and a cross-modal deep learning model are adopted to realize multi-source data fusion; based on a preset risk assessment index system and the trained risk identification model, mapping risk levels by quantizing comprehensive risk scores, generating graded early warning information and pushing the graded early warning information in multiple channels; meanwhile, a disposal scheme is automatically matched, an instruction is issued, a closed loop is tracked, and data security is guaranteed in combination with distributed and block chain storage. Comprehensive perception, accurate identification, intelligent early warning and efficient disposal of safety production risks are achieved, and the intelligent and refined level of safety production management is remarkably improved.

The invention provides a method for monitoring cracks in a high-pressure vibration state of a wind tunnel in real time, and belongs to the technical field of wind tunnels. A piezoelectric ceramic sensor array is arranged on the wall surface of the wind tunnel, a vibration fatigue matrix and a structure resonance matrix are established, and a self-adaptive filtering algorithm is adopted to preprocess sensor signals; establishing a vibration coupling equation set to extract crack characteristic parameters, constructing a dynamic threshold discrimination matrix and a risk assessment matrix, utilizing a multi-sensor data fusion algorithm to accurately position crack positions and sizes, calculating risk levels according to a crack risk assessment index equation, and performing three-level safety discrimination. A complete crack real-time monitoring and risk assessment system in the high-pressure vibration environment is formed, and the technical problems that in the prior art, crack monitoring precision is low in the high-pressure vibration environment, and real-time risk assessment cannot be achieved are solved.

The embodiment of the invention provides a vehicle risk avoiding method, electronic equipment and a vehicle, and the method comprises the steps: determining a control abnormality index value and a risk score threshold value of the vehicle according to vehicle driving information and driver control habit information; determining a real-time risk score of the vehicle according to the driving environment information, the vehicle driving information and the control abnormity index value; and determining a risk level of the vehicle according to the real-time risk score and a risk score threshold, and controlling the vehicle to execute risk avoiding operation corresponding to the risk level. According to the method, the dynamic risk score threshold is obtained by considering the vehicle driving information and the driver control habit information, the control abnormal index value is introduced, and the real-time risk score is obtained by combining the vehicle driving information, the driving environment information and the control abnormal index value, so that the risk level is accurately evaluated, and the risk assessment efficiency is improved. And the rationality and effectiveness of risk avoiding decision making are ensured.

The invention provides a granary safety intelligent early warning method and device, relates to the field of data processing and early warning, and solves the technical problems that the existing granary operation risk detection is not accurate enough, and the conditions of incomplete identification, false alarm or missing alarm are easy to occur. The method comprises the following steps: collecting multi-source time sequence data in a granary, and constructing environmental safety time sequence characteristics based on oxygen concentration time sequence data, toxic gas concentration time sequence data and smoke state time sequence data; based on the temperature and humidity time sequence data and the grain pile temperature time sequence data, risk trend time sequence characteristics are extracted; constructing a risk time sequence based on the environmental safety time sequence characteristics, the risk trend time sequence characteristics and the personnel access information time sequence data; carrying out fusion modeling on the risk sequential sequence through Transform, and identifying a risk level; and when the risk level reaches a preset threshold value, the system controls the bin gate access control to execute opening or closing operation, and synchronously triggers linkage early warning treatment. The method is used in the granary operation safety early warning process.

The invention relates to the technical field of financial transactions, in particular to a financial transaction real-time anti-fraud monitoring system which comprises a data processing module, a graph construction module, a graph traversal module, a risk estimation module, a risk level determination module and a transaction management and control module. Performing feature quantization on the historical transaction sequence to obtain an intermediate state feature vector; taking the transaction subject identifier and the transaction counterparty information as nodes, taking the transaction behavior as an edge, and constructing a transaction association graph; on the basis of a preset fraud mode rule set, performing graph traversal on the transaction association graph, and identifying abnormal transaction sub-graphs; generating a comprehensive risk index according to the deviation degree of the abnormal transaction sub-graph and the intermediate state feature vector; performing grade judgment on the abnormal transaction sub-graph to obtain a risk disposal grade, and managing and controlling a corresponding transaction behavior of the transaction platform; according to the invention, the real-time performance of real-time anti-fraud monitoring of financial transactions can be improved.

The invention relates to the technical field of information, and discloses a comprehensive operation supervision system based on an intelligent supply chain. The system comprises a real-time data acquisition module, a multi-modal feature extraction module, a dynamic risk assessment module, a self-adaptive decision generation module and an execution feedback regulation and control module. By constructing a real-time data acquisition and multi-modal feature extraction module, efficient fusion and deep feature extraction of internal and external multi-source heterogeneous data of the supply chain are realized, the sensing sensitivity and accuracy of the system to market fluctuation and emergencies are remarkably improved, and the prediction deviation is reduced from the data source. The dynamic risk assessment module adopts a dual anomaly detection mechanism, various potential risks can be quickly and comprehensively identified, risk level quantification is carried out in combination with a time sequence mode, and accurate and timely risk situation assessment is provided for subsequent decision making.

The invention discloses a fixed source carbon emission data quality comprehensive evaluation method and system, and relates to the technical field of carbon emission monitoring, and the method comprises the steps: S1, collecting dual-source monitoring and production data, carrying out the invalid recognition, emission amount accounting aggregation, working condition classification and correlation coefficient construction, and building a score working condition basic data set through variable coefficient detection; s2, calculating a correlation coefficient of the to-be-inspected valid data, and obtaining a valid data risk comprehensive score through multi-dimensional inspection scoring and in combination with an industry weight; s3, calculating an invalid data proportion as an invalid data risk score; and S4, performing two-dimensional risk rating on the valid data and the invalid data, and outputting a four-dimensional result. The system realizes the method through four layers of modules of data input, processing, risk evaluation and result output, and solves the technical problems that the existing data quality evaluation only pays attention to valid data, the inspection method is single, and a complete dual-source collaborative quality control and full-dimensional quality control scheme is lacked.

The invention belongs to the field of risk prediction, and relates to a wet and slippery road risk assessment method based on vehicle trajectory identification by an unmanned aerial vehicle, which comprises the following steps: extracting video data of a vehicle driving trajectory of a dangerous road section by adopting a mode of carrying a holder on a high-resolution unmanned aerial vehicle, and mapping the video data to obtain a vehicle ground trajectory sequence; calculating parameter indexes of the normal sample and the wet and slippery road according to the reference trajectory curve, comparing the wet and slippery road with the normal sample, and outputting a distribution difference; self-adaptively adjusting a threshold index under the wet and slippery road based on the meteorological coefficient, and calculating a weighted threshold proportion and duration exceeding 95% quantile of normal weather; and judging the adjacency consistency and the intra-segment space coherence, and finally forming a segment-level risk score and a risk level. The accuracy, the real-time performance and the interpretability of risk early warning are remarkably improved, and the false alarm rate caused by environment misjudgment or equipment errors is effectively reduced.

The invention discloses a steel structure construction safety risk dynamic assessment method and system based on artificial intelligence. The method comprises the steps of obtaining a multi-dimensional data set in real time; based on the multi-dimensional data set, a deep learning algorithm is adopted to fuse an image and a sensor signal, a structure defect is accurately identified, and a defect feature vector is extracted; when the defect feature vector exceeds a preset threshold value, a machine learning classifier is introduced to construct a risk assessment model, classification quantification of defects is realized, and an initial risk level is output; for the initial risk level, analyzing the space-time fusion data by using a probability state transition model, simulating the dynamic evolution trend of the risk and judging the stability of the risk; a mapping model is constructed based on a virtual simulation technology, dynamic simulation deduction is carried out on risk changes, and a visual visualization report and a graded early warning signal level are generated. According to the invention, full-chain intelligent closed loop from defect perception to risk prospective early warning is realized, and the safety management and control level and accident prevention capability of a construction site are greatly improved.

The invention relates to an intelligent driving assistance method and system based on driver state grading and a medium. The method comprises the steps that behavior characteristic data of a driver is collected through a driver monitoring system; based on the behavior characteristic data, calculating and generating a risk score and a confidence coefficient of a driver state through multi-evidence fusion; obtaining a driving scene risk factor, and carrying out coupling calculation on the risk score, the confidence coefficient and the driving scene risk factor to generate a comprehensive risk index; determining a target intervention level based on the comprehensive risk index; generating an auxiliary driving system control parameter set corresponding to the target intervention level according to the target intervention level; and issuing the control parameter set to a corresponding controller of the auxiliary driving system so as to realize self-adaptive adjustment of the auxiliary driving function. According to the method, the response strategy of the auxiliary driving function can be adaptively adjusted according to the real-time state of the driver, so that gradient safety guarantee measures matched with the driver are provided under different risk levels.

The invention discloses an intelligent auditing method based on knowledge graph constraint, and relates to the technical field of natural language processing, and the method comprises the steps: constructing an auditing rule knowledge graph, carrying out the constraint training of an initial large language model, obtaining an auditing fact generation model, receiving a to-be-audited work document, inputting the to-be-audited work document into a pre-trained auditing fact generation model, and obtaining a to-be-audited work document. Generating a candidate fact triple path; calling a pre-constructed auditing hook knowledge graph, and calculating a hook matching degree; determining a target auditing event and a target auditing hook; based on the associated target question keyword, executing deep information verification through an auditing fact generation model, and generating a verification fact triple; and determining a risk type and a risk level through the auditing rule knowledge graph to generate an auditing conclusion report. The big language model is specially trained through the auditing rule knowledge graph, so that the internal logic and the output result of the model are guided by the domain rule in a structured manner when the auditing facts are generated by the model.

The invention discloses a risk assessment method and system for access of new energy photovoltaic power generation to a power distribution network, and belongs to the technical field of operation safety of the power distribution network, and the method comprises the steps: collecting illumination resource data and load data of the power distribution network, and generating a basic data set covering typical and extreme scenes through empirical mode decomposition, Copula function and improved Latin hypercube sampling; a coupling evaluation model fusing the graph neural network and the risk propagation theory is constructed, an improved CRITIC-entropy weight method is adopted to dynamically empowerly weight the multi-dimensional risk indexes, and a time-varying comprehensive risk level matrix is output; analyzing the risk characteristics, matching a pre-constructed measure risk knowledge graph, and generating a dynamic self-adaptive coping scheme through conflict verification and priority ranking; verifying the scheme in the digital twin power distribution network simulation model, and updating model parameters and a basic data set by incremental data feedback when the scheme does not reach the standard; according to the invention, the comprehensiveness and timeliness of risk assessment are improved.

The invention is suitable for the technical field of data processing, and particularly relates to a financial data processing and classification method, system and equipment based on verifiable computing, and the method comprises the steps: obtaining on-chain transaction data verified by a block chain consensus node storage and risk-related index data verified by an under-chain oracle machine; the problems that the authenticity of traditional financial data is difficult to verify and the data is split are solved; calling a risk assessment model through an intelligent contract deployed on a block chain, and placing an assessment process in a decentralized block chain network; performing verifiable calculation through an intelligent contract to determine a total performance score and a risk index, and realizing whole-course tracing and verification of a calculation process by utilizing the characteristics of verifiable calculation of a block chain; the total performance score and the risk index are stored in the block chain as non-tampering records, and the risk rating groups are automatically divided according to the fixed rating rule on the chain through the smart contract, so that the authenticity, objectivity, verifiability and fairness of financial data processing are guaranteed.

The invention discloses a community fire risk dynamic assessment method and system based on AI prediction, and belongs to the technical field of fire prevention and control, and the method comprises the steps: S1, collecting and preprocessing multi-source data of a community, and constructing a comprehensive risk factor database; s2, constructing a risk prediction model, taking the preprocessed multi-source data as input, identifying a key risk factor, and outputting a fire risk probability; s3, calculating the dynamic weight of each index by fusing the static expert experience and the real-time risk contribution degree; s4, based on the dynamic weight and the risk probability, calculating a comprehensive risk value and dividing risk levels; s5, constructing a prevention and control strategy library based on the risk level and the key risk factor; according to the community fire risk dynamic assessment method and system based on AI prediction, the change rule of the fire risk factors in the community can be captured in real time, potential fire hazards can be predicted in advance, and a targeted prevention and control strategy can be automatically generated.

The invention relates to the technical field of risk management, and discloses a financial AI large model-oriented multi-dimensional test intelligent risk management and control method, which comprises the steps of integrating channel test, data test, AI test and AI large model test by constructing a financial AI large model test special database; traditional financial sensitive fields and AI test specific sensitive fields are accurately identified based on data consanguinity analysis, and dynamic desensitization is realized in combination with a lightweight desensitization engine; calling a preset risk scene library, defining a risk index system of each scene, dynamically calculating a risk level through a weighted scoring algorithm, and carrying out modeling by using a graph database to identify hidden linkage risks; triggering a grading response strategy according to the risk grade, and dynamically optimizing resource allocation; and after the test is finished, a multi-dimensional risk thermodynamic diagram and a root cause analysis report are generated, and processing experience is fed back to the scene library optimization strategy. According to the invention, the efficiency and safety of risk management and control can be improved.

The invention discloses a dynamic credit assessment risk control method and system based on tendency score matching, and relates to the technical field of credit assessment, and the method comprises the steps: S1, periodically collecting credit behavior data, and carrying out the preprocessing of the collected data; s2, constructing a field priority ranking table, and quantifying the joint fluctuation degree of the transaction behavior and the repayment behavior to obtain a credit behavior evaluation value; s3, generating an effective difference value sequence, executing a sliding window aggregation operation on the effective difference value sequence, and identifying an unstable window sequence; and S4, reading the instability window sequence to construct an instability fragment feature sequence, evaluating the credit fluctuation risk level of each instability fragment, executing credit risk grade judgment, and generating a grading risk report. The problem that a dynamic credit assessment result is prone to generating false risk fluctuation due to the fact that an existing credit risk assessment technology cannot identify a credit behavior instability fragment through time sequence joint fluctuation of a transaction behavior and a repayment behavior is solved.