889 results about "Analysis tools" patented technology

The invention discloses a multi-level energy management system based on multi-dimensional data, and relates to the technical field of energy intelligent management, and the system comprises a multi-source data collection module which collects power utilization, environment and equipment state data in real time; the data fusion processing module is used for processing abnormal values through an algorithm and fusing multi-scale data features; the energy state evaluation module is used for realizing equipment state evaluation and early warning by using a fusion algorithm and a prediction model; the multi-level energy scheduling module adopts an optimization algorithm to balance the energy cost, the production efficiency and the carbon emission, and dynamically adjusts the strategy; the energy performance analysis module is used for developing an analysis tool and an evaluation model; and the decision support module is used for configuring an expert knowledge base and developing a fault diagnosis system and a knowledge graph. Through multi-dimensional data acquisition and multi-level management, the energy data error is greatly reduced, the comprehensive energy cost and carbon emission are remarkably reduced, the cost of participating in enterprise operation is reduced, and the accuracy, safety and sustainability of energy management are improved.

The invention discloses a knowledge exploration method and system based on a generative thinking chain and a feedback mechanism, and the method comprises the steps: receiving a multi-modal query inputted by a user, and carrying out the intention analysis to recognize a core demand; preprocessing the multi-modal query, extracting key information, and decomposing a complex problem into a plurality of sub-problems or sub-tasks; dynamically constructing a hierarchical reasoning path based on the generative thinking chain, and marking a logic basis for each step of reasoning path; reasoning path priority is optimized through dynamic weight distribution, a reasoning strategy is dynamically adjusted on line in combination with a meta-reinforcement learning framework, and a reasoning path is optimized; verifying the reasoning path by using a causal reasoning analysis tool, and generating an anti-fact path to correct error nodes; and outputting a structured text and a visual reasoning process, and optimizing model parameters in real time based on multi-user feedback aggregation of the game theory. According to the method, the problems of intelligent query and reasoning in a complex scene can be effectively solved, and the accuracy, real-time performance and credibility of knowledge service are realized.

The invention relates to the technical field of computing power scheduling, and discloses a heterogeneous resource computing power intelligent scheduling method and system. According to the method, real-time state monitoring is conducted on heterogeneous computing resources, and resource state parameters such as the computing unit utilization rate and the memory occupancy rate are obtained; task attributes and user request parameters of the task queue are collected, historical task data are processed based on the genetic algorithm optimization model to execute task demand prediction, and predicted demand parameters are generated. A dependency graph containing resource unit nodes and communication link roadsides is constructed through a resource topology analysis tool, predicted demand parameters are input into a scheduling priority classifier trained by a graph neural network, and an actual scheduling priority is identified. And executing resource conflict prediction based on the priority, inputting task feature vectors into a conflict resolution module of a fuzzy logic decision maker, outputting actual conflict resolution parameters, and finally integrating to generate a scheduling scheme containing a resource allocation sequence and an execution time table.

The invention provides an intelligent data quality monitoring method and system. The method comprises the following steps: performing data processing on multi-source heterogeneous data by utilizing a unified data model; inputting historical data as a training set into the pre-training model for training optimization to obtain an optimized pre-training model, and performing threshold determination on the standardized distribution data stream by using the optimized pre-training model in combination with an adaptive threshold module; inputting the processed abnormal event list into a context awareness and dynamic priority management module to carry out de-duplication optimization transmission; carrying out root analysis on the abnormal event list after the de-weighting optimization by utilizing a root analysis tool; generating a repair strategy for the root analysis result by using the assistance of a repair suggestion engine; and inputting the repair strategy and the visual report into a cross-platform monitoring interface, and generating a repair suggestion and an early warning notification. According to the method, the data consanguinity map is constructed based on the graph database, minute-level traceability of abnormal events is achieved, and the troubleshooting time is shortened by 50%.

The invention provides an intelligent data production method and system based on a graph neural network and adaptive learning, and the method comprises the steps: collecting a text, a time sequence, an image and sensor data in real time, and converting unstructured data into a structured JSON format through a format analysis tool; extracting text entity features based on a large language model, modeling a cross-modal entity association relationship in combination with a graph neural network, and fusing multi-source heterogeneous data through a dynamic knowledge graph; the detection threshold is dynamically optimized by adopting a reinforcement learning algorithm, and the false alarm rate is reduced in combination with a double-track verification mechanism; and predicting a data trend by using the space-time diagram convolutional network, and outputting an interpretability analysis report through the generative large model. According to the invention, a multi-protocol adaptation engine and a distributed stream processing architecture are adopted, and a protocol analysis layer automatically identifies a plurality of industrial protocols, so that the manual adaptation time is reduced; and real-time synchronization of production line-level data is realized through edge node parallel acquisition and compression transmission.

The embodiment of the invention discloses a life omics research method and device based on artificial intelligence, and the method comprises the steps: carrying out the preprocessing of a project according to the intelligent interaction between a user and a system, and generating standardized data; and meanwhile, carrying out transfer learning on the large language model to construct a life science large language model. And according to the standardized data, decomposing a research target by adopting a life science big language model in cooperation with an intelligent agent, and generating an analysis plan. And based on the analysis plan, the intelligent agent is scheduled through the coordinator, and a hierarchical task is generated. And calling a multi-omics analysis tool by the intelligent agent to calculate and analyze the grading task, and outputting an analysis result. And based on data features, integrating analysis results through an integrated advanced model, and outputting a final report to complete the research of life omics. According to the embodiment of the invention, full-process automation and natural language interaction are realized, the standardization of data analysis and the repeatability of results are ensured, the research efficiency is remarkably improved, and the technical threshold is reduced.

The invention provides an intelligent project management system based on supervision integration, and the system employs a cloud platform for deployment, and comprises a project management module which is used for task management, progress tracking and document sharing, so as to achieve the planning, starting, execution, monitoring and ending of a whole life cycle of a project; the data acquisition and integration module is used for collecting project data in real time and performing integration processing; the risk assessment and early warning module is used for performing risk assessment on the project data and giving out early warning when the risk exceeds a preset threshold value; the intelligent decision support module is used for providing decision suggestions for a project manager based on the data analysis result; and the visual display module is used for visually displaying information such as a project state, progress, cost and quality in forms such as charts and reports. According to the system, through an integrated management platform and an intelligent analysis tool, real-time monitoring, risk assessment, resource optimization and decision support of the full life cycle of a project are realized, so that the project management efficiency is improved, and the project risk is reduced.

The invention discloses a logistics center intelligent optimization scheduling system based on data analysis, and relates to the technical field of intelligent optimization scheduling, during operation of the system, key data in logistics center operation is collected and processed, after preprocessing, the inventory state of a logistics center is managed and optimized, and based on real-time inventory data and order demand information, the logistics center intelligent optimization scheduling system based on data analysis is obtained. Based on real-time traffic data, order demands and vehicle positions, calculation is carried out to obtain a comprehensive optimization index SOF, according to the order demands, the cargo capacity, the vehicle capacity and the real-time positions, vehicles are intelligently distributed, the loading sequence and the loading rate are optimized, and through the Internet of Things technology and a data analysis tool, the real-time traffic data and the real-time traffic data are analyzed. And the vehicle position, the transportation state, the environment change and the order processing progress are monitored in real time, the scheduling optimization level of the logistics system is evaluated in real time by comparing the comprehensive optimization index SOF with a preset threshold value, and an abnormal early warning and feedback mechanism is provided.

Event-triggered forensics capture technologies balance security incident data availability against data processing and storage costs. A forensic correlation engine receives basic status data of a monitored computing system. A forensic computing system detects a trigger event in the basic status data, and starts capturing extended status data per a corresponding capture specification. Captured data is submitted to a forensic analysis tool. Different trigger events may cause different data captures. A query specifying which data to capture from a live stream or from virtual machines may operate as a capture trigger start event. Extended status data capture activity may be stopped by a change in the basic status data being received, by a timeout, or by forensic analysis that finds no vulnerability or threat based on captured data. Data transfers and storage may be restricted to comply with privacy regulations or policies.

The invention relates to the technical field of model quantification and compression, and discloses a low-computing-power environment-oriented model quantification and compression method, which comprises the following steps of: analyzing the demand of a large model in a specific task, and evaluating the hardware resource limitation of a low-computing-power environment by using a demand analysis tool; dynamically optimizing the quantization process of the neural network model by using a reinforcement learning method, and constructing a quantization perception training framework; performing structure optimization on the neural network model by applying a pruning technology and a rarefaction technology, and removing redundant parameters and connections; performing adversarial ability testing on the compressed neural network model by using an adversarial sample generation method, and evaluating the robustness of the model; converting the optimized neural network model into a low-computing-power environment format, and converting the model into a low-precision format by using a quantification tool; by monitoring operation feedback of the neural network model, a performance analysis tool is used to collect reasoning results and resource consumption data. The method has the advantage of improving the adaptability of the model in a low-computing-power environment.

According to the network security risk assessment method for the unmanned aerial vehicle system, real-time analysis of an AI algorithm is adopted, dynamic assessment and an automatic defense strategy are combined, real-time monitoring and dynamic adjustment of a risk assessment result can be carried out, it is ensured that effective risk assessment is carried out in a rapidly changing threat environment in the flight process of an unmanned aerial vehicle, and the risk assessment efficiency is improved. The use requirements are met; comprising the following steps: scanning source codes of safety key components of an unmanned aerial vehicle system by adopting a static code analysis tool, and identifying all potential attack points; the system state, network data and sensor information of the unmanned aerial vehicle are collected, threat intelligence is obtained, an AI algorithm is adopted to analyze data in real time, and abnormal behaviors are identified; quantifying the security threat of the current abnormal behavior, and evaluating the attack possibility and potential influence; dynamically adjusting a defense strategy according to a risk assessment result; all security events and response conditions are recorded, attack traceability analysis is formed, the AI model is updated regularly, and the recognition capability of novel attacks is enhanced.

A system and method are provided for dynamically modifying textual content by applying a text analysis tool and selectively using a large language model (LLM). The method includes applying a text analysis tool to text having been added to a document, identifying a portion of text in the document as violating a rule in a rule set associated with the document, and providing a first input to a large language model (LLM). The first input comprises at least one prompt requesting a revision to the portion of text in the document. The method also includes receiving, from the LLM, a response to the first input, the response comprising a suggested modification to the document related to the portion of text; and providing an option to apply the suggested modification to the document.

The invention relates to the technical field of enterprise data encryption, and discloses an enterprise data encryption method and system based on artificial intelligence, and the method comprises the steps: collecting internal and external heterogeneous data streams of an enterprise, carrying out the standardization processing, and recognizing potential risk features through combining time sequence alignment and vulnerability analysis; a risk probability analysis tool is utilized to carry out quantitative evaluation from two dimensions of data sensitivity and an influence range, an encryption parameter adjustment scheme is dynamically generated, calculation and storage resources are dynamically allocated according to a scene risk level, an encryption strategy is updated in real time, and hierarchical protection is carried out on sensitive information. According to the method, corresponding encryption strength can be adopted for data of different risk levels, resource utilization is optimized while safety is guaranteed, the data leakage risk is effectively dealt with, and the enterprise information safety protection capability is improved.

Systems are provided for implementing methods for generating and processing fix suggestions for alerts produced by code analysis tools. The methods involve receiving an alert, parsing the alert to identify relevant information, and assembling a prompt for a Large Language Model (LLM). The prompt includes a general description of the problem, a description of the alert message, a source-code location, and a request for a proposed fix to the alert. The prompt is sent to the LLM, and a response is obtained. The response includes a proposed fix comprising a set of edits to be applied to the source code and / or configuration files. The response is processed and validated, and a fix suggestion is assembled and provided based on the response. The fix suggestion includes a natural language explanation of the proposed fix, suggested source-code changes, and selectable options for accepting, rejecting, and editing the fix suggestion.

The invention discloses a network fault diagnosis and simulation method and device of a power grid system and a medium, belongs to the technical field of cloud computing networks and smart power grids, and aims to solve the technical problem of how to monitor, diagnose, effectively simulate and accurately predict various faults possibly occurring in the operation of a power grid in real time. In order to improve the self-healing capability, stability and reliability of a power grid and ensure the continuity and safety of power supply, the adopted technical scheme is as follows: analyzing fault scenes and types, identifying fault scenes possibly encountered by the power grid and classifying the fault scenes; deploying a real-time monitoring system, and uninterruptedly collecting power grid operation data; key features related to the fault type are extracted by adopting a feature extraction algorithm; a fault diagnosis model is constructed by using a machine learning or deep learning technology, and automatic identification of fault types is realized; and creating a power grid digital twinborn model, simulating physical characteristics and behaviors of the power grid, and developing a data analysis tool to perform statistics and trend analysis on a simulation result.

The invention relates to the technical field of data processing, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a data processing and analysis method, device, equipment and medium, and the method comprises the steps: obtaining a data source set, and registering a source label and field mapping relation; executing extraction, cleaning, conversion and loading in the data processing assembly line based on the data source set, the source label and the field mapping relation to generate a processing table set; performing quality verification and exception handling on the processing table set to generate a verified processing table set; generating an index cloud based on the verified processing table set and the configuration strategy; the index cloud is pushed to the analysis tool set through the interface transmission channel, and the analysis tool set generates analysis output according to the index cloud. According to the method, through unified data registration, standardized processing and quality verification, the index cloud is generated in combination with the configuration strategy and pushed to the analysis tool set, the analysis result is generated and transmitted, the data consistency is ensured, and the real-time performance is improved.

The invention provides a software code defect detection method and system based on program code feature fusion, and the method comprises the steps: carrying out the grammar and structure inspection of a code through a static analysis tool, and extracting defect features related to control flow chaos, exception handling and resource leakage; running a preset test case through a dynamic analysis tool, capturing an execution path and an abnormal behavior in a runtime state, and obtaining defect features which are not covered by static analysis; if the output result of the decision tree model is that defects exist, grouping comprehensive defect feature vectors by adopting a density-based clustering algorithm, determining the types and distribution ranges of the defects, and selecting the density clustering algorithm to process defect features in non-spherical distribution; and according to a clustering result, generating a defect repairing suggestion, combining code logic and execution path information, and determining a specific position and an influence range of the defect. According to the method, the stealth defect in the code can be effectively identified and positioned, the software quality and reliability are improved, and timely and accurate defect repairing guidance is provided for developers.

A system for personalized product recommendations in an e-commerce platform, comprising: a user data collection module configured to collect browsing history, purchase records, search queries, and demographic information; a data preprocessing module configured to apply cleaning, feature extraction, and natural language processing (NLP) techniques to structure raw data; a product information management module configured to extract, classify, and update product attributes using NLP and image recognition; a recommendation engine that uses machine learning models, including collaborative filtering, content-based filtering, and deep learning techniques, configured to generate personalized product recommendations; a contextual filtering module configured to dynamically refine recommendations based on real-time factors such as user location, device type, and market trends; a feedback learning module configured to continuously update recommendation models using user ratings, reviews, and engagement metrics; and an API integration module configured to enable seamless connectivity with third-party e-commerce applications, inventory management systems, and analytics tools.

The invention discloses a comprehensive performance evaluation device and method for domestic credit and creativity platform hardware, and the method comprises the implementation steps: constructing a hybrid load task, generating a hybrid load task description file adaptive to the characteristics of domestic hardware, analyzing and executing a dynamic test task, and collecting hardware performance data in real time. And carrying out quantitative scoring by adopting a reference comparison method, generating a radar map portrait, and carrying out multi-dimensional performance evaluation on a domestic processor, a memory, storage and network equipment. According to the method, based on customized instruction extension characteristics of a domestic credit and creativity platform, evaluation deviation caused by mismatching of a traditional evaluation architecture is eliminated through domestic hardware load adaptation; a dynamic hybrid load technology is introduced, so that response robustness and resource cooperation efficiency meeting dynamic load requirements in a high-complexity scene are realized; a hardware performance quantification algorithm and a visualization technology are combined, and an intuitive and practical performance analysis tool is provided.

Systems and methods perform predictive analytics using analytical tool(s) on an aggregation of user action history data. A cluster model classifies action(s) previously entered into by a user, including scoring the action(s) previously entered into by the user. A predicted action score predicted to be currently applied to at least one of the action(s) previously entered into by the user is programmatically generated. Predicted user reaction(s) of the user resulting from providing, via a digital platform, the user with recommended user action(s) are programmatically simulated, the simulating identifying a preferred recommended user action capable of being at least partially performed by the user across a network via the digital platform. Program instructions determine that the user is accessing, via a user interface of a user device, the digital platform to perform user action(s), and display, via the user interface, the preferred recommended user action.

The invention discloses a phishing document de-obfuscation and feature extraction method and an application thereof in attack detection. The de-obfuscation comprises the steps of obtaining an obfuscation macro code of a phishing document, constructing a prompt engineering template by utilizing a pre-training language model, analyzing an obfuscation logic structure and generating a de-obfuscation rule and a reduction strategy; the method comprises the following steps: structuring a confused macro code into an abstract syntax tree through an analysis tool, matching a typical confusion mode based on a regular expression, and performing simulation and cell reference analysis in combination with a function to realize structure preliminary reduction, control flow semantic reduction and operation path construction; according to the unmixing rule and the abstract syntax tree, the confusion structure is converted into a readable macro statement, a macro code instruction sequence with confusion semantics removed is generated, and a semantic sequence obtained after unmixing is output. The feature extraction comprises word feature extraction, Token feature extraction, abstract syntax tree feature extraction and relation feature extraction. According to the method, the bottleneck that traditional phishing attack detection and confusion documents are difficult to recognize is broken through, and the detection accuracy of phishing document attacks is improved.

The invention provides a method and system for improving the flutter derivative recognition precision of a large-span bridge, and relates to the technical field of road and bridge safety, and the method comprises the steps: obtaining basic data and section geometric parameters of a target bridge; according to the geometric parameters of the section, constructing a model by combining the bluff body streaming characteristics of the section of the bridge to obtain a virtual model; performing forced vibration simulation on the virtual model based on preset working condition parameters to obtain a simulated self-excitation force amplitude; obtaining a virtual model based on the simulated self-excitation force amplitude and the actually measured self-excitation force amplitude, and outputting a simulated phase difference; based on the simulation phase difference and the actually measured phase difference, phase compensation is carried out on the virtual model after force amplitude correction, and individual digital twin bodies are constructed and obtained; and performing global digital twinborn construction according to the individual digital twinborn to obtain a flutter derivative global prediction model. According to the method, the global prediction model is established, the flutter derivative recognition precision is remarkably improved, and an analysis tool considering reliability and high efficiency is provided for bridge wind resistance design.

The invention discloses an intelligent agent-based English composition scoring method and device, equipment and a storage medium. Comprising the following steps: inputting composition topics, composition contents and scoring standards into a scoring standard analysis agent; the scoring standard analysis agent selects a target analysis tool from a preset analysis tool group for analysis to obtain an analysis result; the analysis result comprises analysis of different scoring dimensions and corresponding suggested scores; and inputting the composition question, the composition content, the scoring standard and the analysis result into a scoring agent to obtain an English composition scoring result. Through a staged scoring mechanism, an analysis agent extracts key features to form an intermediate analysis result, and then a scoring agent performs secondary check and comprehensive judgment according to a scoring standard, so that the scoring accuracy and consistency are effectively improved. And meanwhile, the scoring standard is independently used as an external input parameter, so that the system can automatically adapt to different scoring systems, and the universality and the adaptability are enhanced.

The present invention provides an intelligent data quality monitoring method and system. The method includes: performing data processing on multi-source heterogeneous data using a unified data model; using historical data as a training set to input into a pre-trained model for training and optimization to obtain an optimized pre-trained model, and performing threshold determination on the standardized distributed data stream using the optimized pre-trained model in combination with an adaptive threshold module; inputting the processed list of abnormal events into a context awareness and dynamic priority management module for deduplication and optimized transmission; performing root cause analysis on the deduplicated and optimized list of abnormal events using a root cause analysis tool; using a repair suggestion engine to assist in generating a repair strategy based on the root cause analysis result; and inputting the repair strategy and a visualization report into a cross-platform monitoring interface to generate repair suggestions and warning notifications. The present invention constructs a data lineage graph based on a graph database, realizes minute-level traceability of abnormal events, and shortens the fault troubleshooting time by 50%.

The invention is suitable for the technical field of search enhancement generation, and provides a multi-intelligent-agent search enhancement generation system and a question answering method, equipment and product thereof. According to the question and answer method of the multi-agent search enhancement generation system provided by the embodiment of the invention, multiple agents such as the routing decision agent and the tool use agent are introduced, so that information can be obtained from an external database, a reasoning process can be autonomously planned based on domain knowledge, a search strategy is dynamically optimized, and the search efficiency is improved. And a specific analysis tool of a specific industry is flexibly called. Different from a static'retrieval-reading-generation 'mode of a retrieval enhancement generation system in the prior art, the multi-intelligent-body retrieval enhancement generation system adopts a multi-round interaction and intelligent optimization mechanism, and dynamically evaluates a retrieval result, adjusts a query mode and calls a professional tool in an answer generation process so as to ensure high-quality intelligent question and answer service.

The invention discloses a VASP-based metal surface adsorption energy differential charge analysis method and system, and relates to the technical field of metal surface adsorption energy differential charge analysis, and the method comprises the steps: carrying out the modeling and standardization processing of a metal surface-adsorbate system, and obtaining a standard data set; training the standard data set by adopting a deep neural network model to obtain a prediction model, inputting the standard data set into the prediction model, and outputting a prediction result; dynamically adjusting VASP calculation parameters by adopting an environmental effect correction method, and generating a VASP input file based on a prediction result and an actual operation condition; performing high-precision energy and electron density calculation on the input file by adopting a first principle calculation tool VASP to obtain electron density distribution; performing differential operation on the electron density distribution to obtain a differential charge density map; and performing three-dimensional rendering on the differential charge density map by adopting a visual analysis tool to obtain a visual analysis result.

Examples of file analytics systems are described that may obtain metadata data from a virtualized file server using a snapshot of the file virtualized file. The analytics system may mount a snapshot to retrieve metadata used in determining an overall size, structure, owner, permissions, and / or storage locations of parts of a file system managed by the virtualized file server. The analytics tool may communicate directly with each of file server virtual machines of the virtualized file server during the metadata collection process to retrieve the respective portions of the metadata. During the metadata collection process, the analytics system may add a checkpoint or marker (e.g., index) after every completed metadata transaction to indicate where it left off accessing the metadata snapshots. The checkpoint may allow the analytics system to return to the checkpoint to resume the process should the process be interrupted for some reason.

The invention relates to the field of petrochemical material physical property calculation, in particular to a physical information constrained petrochemical material hybrid modeling physical property calculation system and method, and the system comprises a data processing module which achieves the standardized cleaning and fusion of multi-source heterogeneous data; the multi-scale characteristic module realizes material characteristic characterization through quantum-mesoscopic-macroscopic cross-scale modeling; the physical constraint module is embedded into a thermodynamic constitutive equation and a phase equilibrium criterion to ensure the self-consistency of the model; the component interaction module constructs a non-ideal mixing effect prediction model based on a deep potential energy field theory, and establishes a component interaction knowledge graph; the uncertainty quantification module adopts a Bayesian deep learning method to assess and predict a confidence interval, a system integrates a physical mechanism and a data driving method, physical property prediction errors are reduced by 15%-20%, meanwhile, a molecular structure-physical property associated visual analysis tool is provided, process optimization and new product development decision are supported, and the reliability of the system is improved. And the core requirements of the petrochemical industry on high-precision and interpretable physical property prediction are met.

The invention relates to the technical field of automatic testing, in particular to an automatic software testing method and system based on multi-mode AI cooperation. According to the scheme, optical character recognition, voice recognition and natural language processing analysis tools are packaged into independent containers, and analysis results are input into a multi-mode semantic association model in real time; calculating the semantic similarity between the image description text and the corresponding voice transcription text; constructing a test demand complexity evaluation model; defining use case quality indexes, and establishing an error sample library to store manually labeled problem use cases and correction labels thereof; the intention demand is finally determined according to the screening result of the three-stage filter; and setting a test case template, optimizing a path according to the fusion quality, improving the case generation efficiency, and synchronously updating the optimized path to a knowledge base associated with the error sample library. According to the scheme, automatic software testing is achieved through multi-modal AI cooperation in combination with containerized deployment, model dynamic optimization and the like, and efficiency and recognition accuracy are improved.

The invention discloses an oiling machine anti-cheating monitoring system and method based on computer vision, particularly relates to the technical field of oiling machine supervision, and is used for solving the problem of supervision misjudgment caused by the fact that an existing monitoring system cannot distinguish compliance maintenance with similar visual features and illegal cheating operation. Tool use sequence features in the maintenance video stream are extracted in real time and are matched with the compliance operation knowledge base to generate a matching degree; when the matching degree is lower than a threshold value, extracting physical response characteristics of the equipment and calculating space-time offset between the physical response characteristics and the standard template; if the offset exceeds the limit, micro behavior similarity is generated; then micro material traces of a tool contact part are analyzed, and violation coefficient correction similarity is generated through thermodynamic entropy increase verification and space-time symmetry breaking detection; and finally, when the correction similarity exceeds a risk threshold value, an anti-cheating alarm is triggered, cheating behaviors such as chip welding and part replacement which take maintenance as disguise are effectively identified, and the supervision misjudgment rate is remarkably reduced.