272 results about "Change analysis" patented technology

The invention relates to the field of transformer parameter regulation and control, in particular to a parameter optimization method and device for a coupling type transformer. The method comprises the following steps: acquiring multi-dimensional real-time working monitoring parameters and real-time working environment state parameters of the coupling type transformer; performing nonlinear dynamic analysis and global state sensing modeling on the multi-dimensional real-time working monitoring parameters so as to construct a transformer global state sensing model; performing multi-parameter combination definition on the multi-dimensional real-time working monitoring parameters, performing dynamic operation simulation based on a transformer global state sensing model, and collecting operation simulation response data of a plurality of schemes; performing multi-target dynamic balance analysis on the operation simulation response data of the plurality of schemes to obtain an optimal transformer working parameter combination; and carrying out environment dynamic parameter change analysis on the real-time working environment state parameters, and constructing a real-time environment condition distribution field. According to the invention, efficient and self-adaptive adjustment transformer parameter optimization is realized.

The invention discloses a multi-physical field coupling simulation analysis method and system for power business, and relates to the technical field of power business environment change analysis, and the method comprises the following steps: 1, obtaining physical field data of a power business scene, the physical field data comprising a temperature field, a stress field, a displacement field, a magnetic field and an electric field of power equipment; and step 2, carrying out coupling calculation on the physical field data by using the multi-physical field simulation model, according to the multi-physical field coupling simulation analysis method and system for the power business, through coupling and building of different models in the multi-physical field simulation model, coupling simulation of power equipment in a complex multi-physical field environment can be realized, and the coupling simulation efficiency is improved. The simulation model can accurately predict dynamic changes of multiple physical fields such as electromagnetism, heat effect and mechanical stress of the equipment, help to optimize design and operation strategies of the power equipment, and improve reliability and safety of the equipment.

The invention relates to the technical field of information traceability, in particular to a digital information traceability management system. The system comprises an information fingerprint extraction module, an origin node consensus module, a circulation path mapping module, an information integrity verification module and a behavior consensus feedback module, and can be used for acquiring original information; performing text semantic feature extraction on the original information to obtain semantic features; performing information fingerprint extraction on the semantic features to obtain semantic fingerprints; performing consensus processing on the semantic fingerprint based on an origin node to obtain a consensus result; generating a source point voucher according to the consensus result to obtain the source point voucher; performing circulation behavior triggering according to the source point voucher to obtain a to-be-recorded behavior; and performing content change analysis according to the to-be-recorded behavior to obtain a changed semantic fingerprint. According to the method, through fusion of semantic hash and behavior consensus, the source tracing robustness, safety, efficiency and applicability are improved.

The invention discloses a hardware fault real-time detection method and system based on cooperation of a CPU (Central Processing Unit) and a BMC (Baseboard Management Controller), and the method comprises the following steps: respectively collecting high-frequency state data and tendency indexes by establishing a communication mechanism between the CPU and the BMC; the processor uses a CUSUM algorithm to carry out abrupt change analysis on the periodically collected operation state to generate an abrupt change event; and the management controller uses an EWMA algorithm to model the trend data, and extracts abnormal changes. And the system performs fusion analysis on the mutation event and the trend anomaly to form a fusion anomaly vector. The risk assessment module calculates a risk score based on a preset rule, determines a fault level, positions a target component, and outputs a processing strategy. And triggering a response action according to the strategy and recording an execution state. And the fusion data and the response record are input into the adaptive module together for dynamically adjusting CUSUM and EWMA parameters, so that adaptive updating of the algorithm is realized, and finally a fault detection signal is generated.

The invention provides an early-stage automatic identification method and system for acute kidney injury related to cardiac surgery, and relates to the technical field of physiological parameter detection. The method comprises the following steps: acquiring average arterial pressure, cardiac blood discharge volume, central venous pressure and blood oxygen saturation, regularly recording and extracting data of adjacent time points, calculating a variation range, screening a mutation region, judging whether a reference range is exceeded or not, analyzing a multi-parameter synchronous variation condition, and marking a hemodynamics abnormal state. According to the method, abnormal changes of hemodynamics and urine metabolism are accurately recognized through real-time monitoring and multi-parameter analysis, the urine flow rate and the metabolite excretion rate are combined, misjudgment of a single index is avoided, the fineness of renal function change analysis is improved, the high-risk time period is accurately positioned through time cross comparison, the diagnosis accuracy is improved, and the diagnosis accuracy is improved. Trend analysis and risk grade division enable the change of the illness state to be more visual, and a real-time early warning system effectively monitors the fluctuation trend, optimizes clinical management and improves the timeliness of medical response.

The invention discloses a source code change analysis method, system and equipment. The method comprises the following steps: acquiring a project source code and identifiers corresponding to the project source code in at least two versions; comparing differences of project source codes between different versions based on the identifiers, and determining code change content; filtering the code change content according to a preset filtering rule, and determining a change method name; performing code analysis on a method calling relation in the project source code, and determining a method calling link diagram; identifying the method calling link diagram by referring to a change method name, and determining a change influence link diagram; analyzing according to the entry of the change influence link diagram, and determining the range of the entry and a corresponding calling link; according to the method and the device, case design is performed according to the range of the code change influence entry and the corresponding calling link, and the evaluation of the test coverage is completed, so that the efficiency of test work is improved, the possibility of human errors is reduced, the test result is more accurate and reliable, and the evaluation of the test coverage is realized.

The invention belongs to the technical field of power file rule extraction and change comparison, and particularly relates to a policy document intelligent rule extraction and change comparison method for electricity charge checking, which comprises the following steps: analyzing, reconstructing and optimizing an electricity charge related policy document, and retaining original context and logic level information; designing a core statement recognition Prompt template and a rule extraction and conversion Prompt template, constructing a policy and rule extraction agent, guiding a large language model to recognize core statements, extracting rule element information and forming standardized policy and rule description; according to the method, similar policy rules are retrieved from a historical policy rule description knowledge base, a policy rule comparison agent is constructed, and deep semantic comparison and change analysis are performed on current standardized policy rule description and historical similar policy rule description, so that automatic identification of newly added and modified contents of current rules and historical rules is realized; and a key technical support is provided for automation and intelligentization of electricity charge checking business rule generation.

The invention belongs to the technical field of inspection equipment control systems, and discloses an inspection equipment control system, method and device, and the system comprises a difficulty recognition module, a path density adjustment module, a state monitoring module, a boundary compensation module and a control linkage module. According to the method, a trend included angle is obtained through numerical value change analysis of a track section structure, an angle matching ratio is introduced to realize dynamic adjustment of a path section interval, a three-period sequence is constructed in cooperation with gyroscope data to extract main direction change, disturbance rhythm change is accurately captured, disturbance amplitude is extracted through a minimum interval distance trend, and the disturbance accuracy is improved. A time axis turning position is reconstructed by combining data of a path trigger, a boundary offset reference is positioned, linkage control of the distance between path points and an average path is realized, a processing logic establishes a multi-dimensional linkage relation of path adjustment, disturbance identification and boundary compensation, and path response flexibility and equipment operation stability under environment change are enhanced. And the inspection precision and adaptability in a high-disturbance scene are improved.

The invention discloses a dynamic environment monitoring system data change analysis early warning method integrating an AI large model. According to the embodiment of the invention, the AI large model is used to analyze the environmental data collected by the plurality of sensors, and the preset alarm threshold is adjusted according to the analysis result. Therefore, when it is determined that one or more pieces of environmental data exceed the adjusted threshold according to the analysis result, the risk continuity is further determined according to the change trend of the environmental data generated by the AI large model, and the current risk is further determined according to the risk continuity, so that alarm information is generated. Therefore, according to the early warning method provided by the embodiment of the invention, the trend of the environmental data can be analyzed and predicted in advance, the accuracy and timeliness of warning are improved, the preventive maintenance capability of the system is enhanced, and the warning strategy can be continuously optimized through the self-learning function of the AI model to adapt to the change of the environment.

The invention belongs to the technical field of computer-aided project management, and discloses an electric power engineering design project management system and method, and the system comprises a data integration and processing module, a multilayer dynamic knowledge graph data storage library, a graph-based automatic compliance check engine, and a graph neural network-based predictive risk engine. And an active design change impact analysis module. According to the method, a unified and intelligent project data core is constructed, heterogeneous data sources such as design files and law and regulation texts are integrated in a multi-layer dynamic knowledge graph, automatic compliance review is realized by utilizing graph query, data-driven risk prediction is realized by utilizing a graph neural network GNN, and the risk prediction efficiency is improved. And the linkage influence of design change can be simulated and quantitatively evaluated. According to the method, the problems of data islanding, passive compliance examination, subjective risk management, difficulty in change analysis and the like in the prior art are solved, and the management efficiency, accuracy and initiative of the electric power engineering design project can be remarkably improved.

The invention discloses a ground subsidence monitoring system under the condition of ultra-large water level drawdown, and relates to the technical field of foundation pit dewatering, a seepage field change analysis module arranged on the system can analyze the movement process of underground water in a soil body of a foundation pit wall according to water body state data monitored in real time and in combination with the saturation state and permeability of the soil body, and the seepage field change analysis module can analyze the movement process of underground water in the soil body of the foundation pit wall through the module. The system can automatically perform hierarchical division on the soil body in the foundation pit wall, obtain the spatial and temporal distribution of the water head of each soil layer in the precipitation process, make the subsequent stress analysis and settlement evaluation more accurate, and accurately calculate the effective stress change of the soil body in the precipitation process based on the spatial and temporal distribution of the water head of each soil layer. And the additional stress influence of the settlement of the upper layer soil layer on the lower layer soil layer is analyzed, so that the settlement volume of each layer of soil body is obtained, and the stress and settlement monitoring based on stratified analysis enables the system to carry out detailed evaluation on differential settlement and interlayer stress change, so that the risk of structural instability caused by settlement difference is effectively prevented.

The invention relates to the technical field of cold and hot impact box monitoring, in particular to an intelligent monitoring method and system for a cold and hot impact box. The method comprises the following steps: carrying out real-time operation state monitoring on a cold and hot impact box, carrying out time point transient state feature mining one by one, and generating a transient state feature of each time node; performing transient abrupt change analysis on the transient state characteristic of each time node, and performing cold and hot alternating temperature fluctuation analysis so as to generate a cold state temperature fluctuation parameter and a hot state temperature fluctuation parameter; state alternating transition analysis is conducted on the cold state temperature fluctuation parameters and the hot state temperature fluctuation parameters, dynamic transition trend evolution is conducted, and therefore a cooling and heating alternating state trend evolution graph is generated; and obtaining a hot and cold impact box operation log. According to the invention, efficient and accurate fault analysis and prediction of the cold and hot impact box are realized.

The invention discloses a method for analyzing software code similarity and an analysis system, and relates to the field of software engineering and machine learning. The method comprises the steps of establishing a code snippet database, extracting multi-dimensional features of code snippets, executing similarity analysis of the code snippets, and establishing a similarity analysis result; performing context backtracking analysis on the code snippets in the database by using an adaptive analysis network, and establishing a context similarity result; performing static and dynamic fusion analysis on the database, establishing a joint function similarity, and establishing a function similarity result; code version change analysis is executed, and a version similarity result is established; and performing code similarity retrieval management according to the four analysis results. The technical problem that the code similarity analysis efficiency and precision are low due to the fact that the code snippets are high in complexity in multi-dimensional feature analysis is solved, and the code similarity analysis accuracy and efficiency are improved in the mode that multi-dimensional analysis and self-adaptive backtracking analysis are combined.

The invention discloses a health trend analysis early warning method based on big data, and relates to the technical field of data processing, and the method comprises the steps: analyzing female physiological cycle data based on big data, and building a health trend analysis model; the method comprises the steps of obtaining a physiological cycle type of a target user, collecting a sleep state, a physiological cycle and physical sign data of the target user, generating standard health monitoring data, initializing an analysis model according to basic information of the user, performing sleep quality change analysis, evaluating a health trend of the user according to an analysis result in combination with physiological cycle characteristics, and providing personalized sleep improvement suggestions. The technical problem that traditional health management in the prior art neglects the influence of the female physiological cycle on the health state and the sleep quality and lacks pertinence and individuation is solved, and individualized health management is provided by combining the female physiological cycle data and the sleep state data. And the sleep quality of women in different physiological cycle stages is improved.

The invention relates to the technical field of flood prediction, in particular to a non-consistent regional flood frequency analysis method based on the Bayesian theory. The method comprises the following steps: acquiring flood sequence data, meteorological data and drainage basin characteristic data of hydrological stations in a drainage basin; performing regional flood dynamic change analysis on the flood sequence data and the meteorological data to generate regional flood time sequence dynamic change data; on the basis of the regional flood time sequence dynamic change data, hydrological station flood characteristic parameters in the drainage basin are extracted from the drainage basin characteristic data, and hydrological station flood characteristic parameters in the region are obtained; and performing flood distribution parameter regression coefficient prior information derivation on the flood characteristic parameters of the hydrological stations in the region to obtain a maximum likelihood estimation value of a single hydrological station distribution parameter. According to the method, the Bayesian theory, the spatial correlation analysis and the spectral analysis are introduced, so that the accuracy and the applicability of flood frequency analysis based on the Bayesian theory are improved.

The invention discloses a tobacco appearance quality control method and related equipment based on microenvironment intelligent maintenance, a tobacco appearance quality control model is constructed, an appearance feature extraction network is utilized, feature extraction is performed on appearance time sequence data of tobacco based on aligned timestamps, and features reflecting appearance changes of the tobacco are obtained; and performing feature extraction on the time sequence data of the environment where the tobacco leaves are located based on the aligned timestamps through an environment feature extraction network to obtain features of environment change. The appearance change characteristics and the environment change characteristics are mapped into a control network for appearance quality change analysis, the influence of the appearance change characteristics and the environment change characteristics on appearance quality change is found through the change relation between the appearance change characteristics and the environment change characteristics after alignment and time stamping, and the environment parameters are adjusted based on the appearance quality change analysis. And the tobacco appearance quality change speed is accurately regulated and controlled, so that the tobacco is in the best appearance state during use.

Solutions for integration and ongoing management of enterprise applications operating in a networked environment are disclosed. An AI transformer extracts, transforms, and manages integration point information from various sources, including documents, UMLs, and other system diagrams. Instead of relying on traditional documentation, the invention utilizes blockchain-based NFTs (non-fungible tokens) to store and manage integration point information. NFTs are created using smart contracts, which define the rules and conditions for the NFT. A metadata engine prepares metadata for generating the Integration Point NFTs. An NFT manager creates the integration point NFTs. A change analyzer determines the impact of changes to integration points on different applications and functionalities. A release compliance component is integrated with DevOps and is responsible for ensuring that integration point changes are incorporated into all impacted applications according to the release defined in the NFT.

The embodiment of the invention provides a geological disaster recognition method and system combined with a remote sensing technology, and the method comprises the steps: firstly obtaining a multi-time-sequence remote sensing image data set of a target region, which comprises remote sensing images collected at different times; then, performing topographic feature extraction processing on the data set to obtain a topographic feature set, covering earth surface coverage features, slope features and elevation change features, then, performing multi-time-sequence change analysis on the topographic feature set to generate a dynamic change feature set, and performing abnormal region identification on the dynamic change feature set by using a geological disaster identification model to obtain an abnormal region identification result; according to the method and the device, the abnormal region set with the geological disaster risk is determined, and finally, the disaster early warning signal is generated according to the abnormal region set and is sent to the disaster monitoring terminal, so that comprehensive and dynamic monitoring and accurate early warning of the geological disaster risk region are realized, and the accuracy and the efficiency of geological disaster monitoring and early warning are improved.

The invention discloses a map database-based land type map spot storage and analysis method and system. The method comprises the following steps of: acquiring and processing data; storing data; monitoring pattern spot type distribution analysis; performing annual change investigation pattern spot and land type change analysis; and reading and displaying data. According to the method, spatial analysis is carried out on land type change monitoring pattern spots and annual change investigation pattern spots, and a method based on a pattern database is designed to store analysis results; the incidence relation and important attributes of the change monitoring pattern spots and the annual change pattern spots can be expressed on a visual platform, and the monitoring type distribution condition and the annual change survey of the change conditions of different land types in a certain space range are analyzed; according to the storage and analysis method, the internal relation between the change monitoring pattern spots and the annual change pattern spots is visually presented, the effectiveness of change monitoring pattern spot analysis is improved, the data value of the monitoring pattern spots is deeply mined, natural resource data analysis and application are facilitated, and planning and decision making of related departments of the natural resource industry are supported.

The invention relates to an intervertebral disc degenerative change analysis system based on multi-dimensional information. The system comprises a data processing module, a modifiable factor processing module, a circular RNA data processing module, a biomarker processing module, an image processing module, a treatment information processing module, a diagnostic feature anchoring module, a model generation module and a change analysis module. Anchoring is carried out on circular RNA, biomarkers, treatment information and modifiable factors through image information to form an anchoring network, and a reliable and effective basis can be provided for disease course analysis through big data and a large number of research results, so that each doctor has a targeted expectation and understanding on the disease course of a patient in the diagnosis stage; the method has the advantages that an interventional therapy scheme can be arranged in advance or the condition of a patient can be observed in real time, research results can be collected and fed back in time through big data, the information utilization rate is increased, and an information dimension integration scheme for degenerative disease recognition is created, and the accuracy of recognition results is improved.

The invention relates to an intelligent old-age care remote state monitoring method and device combined with a user portrait, and relates to the technical field of data processing, and the method comprises the steps: communicating a positioning patch array of a first old-age care user, and receiving a positioning time sequence matrix of a first time zone; processing a first pension user portrait and the positioning time sequence matrix through a health sample N-level adjacency searcher to obtain an adjacency sample state; aggregating the adjacent sample states to generate state baseline information; the communication state monitor is used for collecting state monitoring information of the first time zone; and when the state monitoring information does not belong to the state baseline information, performing abnormal state identification on the state monitoring information. The technical problem that in the prior art, due to the fact that flexible change analysis of the health value interval cannot be achieved, the monitoring accuracy of abnormal states of old people is insufficient is solved.

The invention discloses a systematic monitoring method for the electrical safety performance of a cable, and relates to the field of electrical safety, and the method comprises the steps: collecting sensor data to an edge calculation node, preprocessing the data, and outputting the data as real-time calculation temperature data, local discharge point data, resistance and capacitance data and vibration data; the data are sent to a hidden danger classification model and a fault positioning model in the cloud, and the models output early warning information to an operation and maintenance project for processing; the sensor data sending process is judged according to the data frequency, when it is judged that the frequency is larger than 1 kHz, a 1D-CNN electric arc detection model is loaded, when it is judged that the frequency is smaller than 1 kHz, a Light GBM feature classification model is loaded, and 24-hour sensor data smaller than 1 kHz are periodically loaded into an LSTM-Attention gradient analysis model; and related data of partial discharge generated by judgment of the 1D-CNN arc detection model is sent to an LSTM-Attention gradient analysis model for evaluating the partial discharge trend of the global cable. Classification model output is used as prior knowledge of a positioning model, and the positioning accuracy can be improved.

The invention relates to the technical field of medical information, in particular to an anomaly recognition system for trend characteristics of chronic disease monitoring data. The system comprises a chronic disease monitoring data integration module, a chronic disease feature change analysis module, a chronic disease composite feature analysis module and a chronic disease trend feature anomaly recognition module, and can perform chronic disease classification and associated chronic disease integration according to real-time chronic disease monitoring data. Generating integrated associated chronic disease data; performing tissue structure mapping reconstruction of the chronic disease patient and disease mutation characteristic factor analysis of the chronic disease patient according to the integrated and associated chronic disease data, and generating disease mutation characteristic factor data of the chronic disease patient; according to the disease mutation characteristic factor data of the chronic disease patient, performing disease composite characteristic analysis on the chronic disease patient, and performing chronic disease trend abnormal characteristic analysis to generate chronic disease trend abnormal characteristic data. According to the invention, accurate identification of chronic disease trend abnormity is realized.

The invention relates to the technical field of engineering management, in particular to an AI-based engineering quantity list automatic compiling method and system, and the method comprises the following steps: constructing a model through analyzing a construction task dependency relationship, tracking the progress in real time, and predicting changes through a neural network to generate change analysis; and optimizing delay task adjustment time and resource configuration in combination with constraint conditions, evaluating a weather risk level and dynamically adjusting a construction period, and finally dynamically updating a project quantity list based on resource and risk results. According to the method, a task network structure with a quantifiable scoring mechanism is constructed by analyzing the dependency relationship between the tasks, so that the logic association between the tasks not only stays on the static hierarchy presentation, but also introduces the difference weight of time and resource dimensions to realize dynamic modeling on the dependency of key paths; and depth support is provided for subsequent prediction and adjustment. Tracking of task progress changes does not depend on a static progress comparison mode any more, but deep extraction of actual construction data is carried out.

The invention relates to the technical field of equipment fault prediction, in particular to an on-line monitoring analysis and fault pre-judgment method and system for a switch cabinet of a switch cabinet, and the method comprises the following steps: collecting a voltage sequence, recognizing a main peak, recording the main peak, extracting a rear section of the main peak, analyzing amplitude change, judging discontinuous features, constructing a track trend sequence, recognizing continuous offset, and carrying out fault prediction. According to the method, through extraction and serialization processing of voltage main peak characteristics in the contact conduction period and combination of comparison of change amplitudes of sampling sections after main peaks, accurate identification of transient anomalies is achieved, and through dip angle change analysis of main peak tracks in the continuous periods, accurate identification of the transient anomalies is achieved, so that the accuracy of the transient anomalies is improved, and the accuracy of the transient anomalies is improved. According to the method, the deviation behavior of the running trend is recognized, abnormal sections which are consistent in direction and are distributed in a concentrated mode are positioned through cross comparison of abnormal periods and track deviation, the deviation trend is comprehensively judged based on the period number and direction characteristics, and the timeliness and accuracy of fault pre-judgment are effectively improved.

The invention relates to a fault prediction method for a solid state disk, which relates to the field of hard disk life prediction, and comprises the following steps of: acquiring a redistribution sector count to correct a plurality of erasure equalization parameters, and performing erasure equalization change analysis to obtain erasure equalization change parameters; fault time prediction is carried out according to the erasure equilibrium change parameters and the latest erasure feature map, and fault remaining time is obtained; according to the multiple redistribution amplification coefficients, the multiple write amplification coefficients and the multiple common write coefficients, an amplified write wear coefficient is obtained through calculation, the fault remaining time is corrected, and the corrected fault remaining time is obtained and serves as a fault prediction result. According to the method, the technical problems that the dynamic use condition and the abrasion condition of the solid state disk are not comprehensively considered in a traditional solid state disk service life prediction method, and the service life prediction accuracy and reliability are insufficient can be solved; precision, accuracy and reliability of hard disk residual life prediction can be significantly improved, and potential fault risks can be identified in time.

The invention relates to the technical field of sensing data processing, and discloses an old people home safety decision service system fusing multi-mode sensing data, which comprises a data interaction analysis unit, a data change analysis unit, a data calculation unit, a safety analysis unit and a safety decision unit. The hidden association of the multi-modal indexes is accurately captured, the early degradation of the collaboration level can be perceived through the graph condensation degree and the graph equilibrium value, the collaboration degradation track can be used for analyzing the collaboration state, and the finally generated safety decision instruction can perform early warning in advance. According to the scheme, single-mode threshold dependence is broken through, hidden dangers hidden in data correlation evolution are recognized in advance, and the lagging of home safety decision of old people is solved.

The invention provides a change analysis method and device based on cross-temporal frequency domain information interaction and a readable storage medium thereof. The method comprises the following steps: acquiring a dual-temporal remote sensing image and partitioning the dual-temporal remote sensing image; extracting spatial features through a shared encoder; fourier transform is carried out on the double-tense features in a feature domain, high and low frequency components are divided according to a step length r, low frequency information is exchanged, and after inverse transform, splicing is carried out to obtain a fusion feature F; performing same frequency domain interaction on an original image in a data domain, and modeling through a video encoder to obtain a feature E; and finally fusing F and E, and decoding and outputting a change prediction map. According to the method, the high and low frequency information proportion is dynamically regulated and controlled through the frequency domain step length, dual interaction of the data domain and the feature domain is combined, structural difference perception is enhanced, pseudo change interference is suppressed, and change detection precision and robustness are remarkably improved. Experiments show that the method is superior to the prior art in F1 score, intersection-union ratio and other indexes, and is suitable for the fields of smart cities, disaster monitoring and the like.

The invention discloses an embedded system integration test case multiplexing method and system, and the method is characterized in that the multiplexing of a test case is carried out based on the existing software assets: firstly, based on the existing software requirements and the test case, an existing requirement-test case tracking chain is constructed; performing demand change analysis on the new demand activity graph information; performing change influence range analysis based on a change analysis result in combination with a demand-test case tracking chain, and determining a test case reuse and generation scheme; and finally, executing the test case multiplexing and generating scheme. According to the method, the tracking relation between existing requirements and test case assets is established, meanwhile, the execution cost of downstream test tasks caused by requirement change is reduced through multiplexing, and the test efficiency of integration testing is improved.

The invention relates to the technical field of machine learning, and discloses an outsourcing service quality evaluation method based on deep learning, which comprises the following steps: capturing operational micro habit dynamics of a service subject through lightweight representation data, establishing a self-adaptively updated reference normal state model, monitoring deviation in real time, generating an early disturbance signal, and determining the outsourcing service quality according to the early disturbance signal; and performing correlation analysis on the multiple signals to identify a predefined disturbance mode, and finally dynamically evaluating the service quality based on mode matching. According to the method, early-stage risk early warning which cannot be captured by a traditional macroscopic index is realized through fine-grained perception and modeled association of micro habits, and meanwhile, a judgment threshold is dynamically calibrated in combination with a situation beacon, so that the early-warning precision and timeliness are remarkably improved, and the early-warning effect is improved. And the multi-dimensional monitoring capability from individual anomaly to collective out-of-order is expanded by means of group behavior entropy change analysis.