404 results about "System evaluation" patented technology

A computerized system for dynamic cybersecurity policy using AI-based contextual adaptive learning includes an AI system that evaluates business contexts, risk tolerance, and productivity impact to generate threat intelligence assessments. The system includes a Contextual Adaptive Learning module that dynamically adjusts cybersecurity policies based on threat assessments to create security workflows. A Cybersecurity Mesh Development module that integrates policies across security frameworks. A Dynamic Scenario Catalog module that updates policy adjustments based on threat intelligence. An Automated Workflow Orchestration module that creates and refines security workflows for optimal efficiency. A Policy Recommendation and Automation module that generates prioritized security recommendations and automates policy changes based on organizational risk profiles and current security controls. This system harmonizes security policies while considering business context, risk, and productivity impacts.

The system (100-2) and method for identification of high ranked neighbor cells in a telecommunications network provide an efficient and accurate approach to selecting neighboring cells with superior performance for handover purposes. The system (100-2) leverages performance metrics, algorithms, and dynamic adaptation techniques to determine the suitability and ranking of potential neighbor cells. By considering factors such as signal strength, signal quality, interference levels, load balancing requirements, and operator-defined policies, the system (100-2) evaluates the performance of neighbor cells and assigns them scores or rankings to identify the higher ranked neighbor cells. The system's dynamic adaptation ensures that the rankings remain up to date and responsive to changing network conditions. The present system (100-2) and method optimize handover decisions, enhance network performance, and provide users with seamless connectivity and improved quality of service.

The invention provides an intelligent automobile test case automatic generation and evaluation system, device and product, and relates to the technical field of intelligent automobile test.According to the system, domain knowledge support is provided through a test knowledge base, and a demand understanding module is combined with a fine-tuning large model to deeply analyze demands; function points are extracted, and hidden scenes such as sensor faults and multi-system cooperation are identified; the test case generation module is based on a special prompt template and few-sample learning, a complete case containing elements such as purposes, steps and data is generated by fusing a vehicle model configuration library, and the specialty and the performability are ensured; and the evaluation module realizes objective quality evaluation through multi-dimensional quantitative scoring of demand coverage rate, scene integrity, risk coverage degree, normalization and the like. All the modules cooperate to realize intelligent generation and system evaluation of test cases, and the automation level, coverage range and reliability of intelligent automobile testing are improved.

The invention discloses an avionics system evaluation index system construction method based on a large language model agent. The avionics system evaluation index system construction method comprises the steps that a knowledge extraction module performs knowledge extraction on a design document of an avionics system by using the large language model agent and preliminarily constructs a knowledge graph; the reflection verification module automatically checks and corrects entities and relationships in the knowledge graph based on a large language model agent in combination with a learning normal form, and finally outputs an optimized knowledge graph; and the index generation module automatically generates an evaluation index for each entity node in the optimized knowledge graph based on a large language model agent to form an avionics system evaluation index system. According to the method, extraction, induction and system construction of avionics system evaluation indexes are automatically completed, and the method has the advantages that manual decomposition is not needed, the process is traceable, the coverage is wide, and the efficiency is high.

The invention discloses an ecological flow guarantee method based on runoff prediction and supply. Meteorological, hydrological and other data of the Tongkeng river basin are collected, an SWAT model is constructed and verified to simulate runoff, and extended rainfall is analyzed based on an R / S method and a wavelet method. And constructing a surface water and underground water coupling model, and predicting underground water volume, water level and water quality. And ecological flow safeguard measures are formulated after water resource supply and demand balance analysis, wherein the ecological flow safeguard measures comprise optimal allocation of water resources, enhanced management, ecological water supplementation, water environment treatment, ecological restoration project development and the like. The method has the advantages that measure implementation subjects and progresses are defined, monitoring system evaluation effects are established, adjustment and optimization are performed according to results, ecological flow is guaranteed, sustainable utilization of drainage basin water resources and ecological environment protection are realized, remarkable social, economic and environmental benefits are achieved, and a scientific and effective method is provided for water resource management.

The invention discloses an unmanned aerial vehicle random patrol strategy system evaluation method in a confrontation environment. The method comprises the steps of setting an unmanned aerial vehicle intelligent patrol attack and defense confrontation system and a target steady-state distribution model of the unmanned aerial vehicle intelligent patrol attack and defense confrontation system; determining the average strategy entropy of the patrol person; determining conditional probability differences of the patrol persons; determining a transfer strategy dispersion degree of the patrol person; a patrol strategy comprehensive structure difference index is determined; determining a single-time-point key state average relative error; determining the average relative error of the time average key state; and determining a time-weighted key state average relative error. According to the method, the comprehensive structure difference index of the difference between the single-state transition matrix and different matrixes is quantified, and a balanced framework is provided to evaluate the overall unpredictability of the strategy. A multi-dimensional evaluation system based on key state and time weighting is designed, and time importance is directly fused into error calculation through a key state screening mechanism.

The invention relates to an intelligent diagnosis system for echelon utilization and recovery of a power battery, and aims to evaluate the health state of the battery with high precision and assist the echelon utilization and recovery of the battery. The system integrates five modules: the battery health state monitoring module acquires parameters such as voltage, current and temperature in real time through a high-precision sensor; the battery health state evaluation module accurately evaluates the battery state by applying algorithms such as ICA, DVA and LSTM neural networks in combination with a transfer learning technology; the echelon utilization recovery decision-making module outputs an optimal recovery scheme based on a three-level decision-making model; the intelligent disassembling and safety protection module adopts an advanced process and a monitoring system to guarantee safe and efficient disassembling; and the data storage and cloud prediction feedback module realizes system dynamic optimization by using big data analysis and machine learning. The evaluation accuracy of the system exceeds 95%, the single diagnosis time is shorter than 5 minutes, multiple types of batteries are supported, the self-learning iteration period is shorter than 24 hours, the detection cost can be remarkably reduced, the resource utilization rate is increased, carbon emission is reduced, and key technical support is provided for sustainable development of the new energy automobile industry.

The invention discloses an intelligent item selection recommendation system of a live broadcast room based on AI digital people, relates to the technical field of intelligent item selection recommendation, and solves the technical problems of generating a dynamic and high-confidence matching scheme based on highly matched commodities and adjusting a combination strategy in combination with a real-time scene and user feedback. Through the multi-dimensional weighted calculation of the feature matching score, the crowd matching score and the scene association score, the hidden integrating degree of the commodity and the user demand is quantified, the highly matched commodity is screened out, the invalid recommendation is reduced, the comprehensive confidence degree of the commodity combination is calculated based on the association rule algorithm, the personalized matching scheme conforming to the real-time scene is generated, and the user experience is improved. A high-potential scheme is screened through a self-adaptive threshold value, the matching rationality and transformation potential are improved, a comprehensive feedback processing module is introduced, a user is supported to customize combined commodities, and optimization suggestions are provided through system evaluation; and for user change feedback, generating hidden collocation based on a user portrait, and realizing real-time iteration of a recommendation scheme.

The invention discloses a power supply service intelligent management system, and relates to the technical field of intelligent power grids, and the system comprises a power grid holographic sensing module which obtains multi-dimensional unstructured data of a power system, extracts core features through cross-domain fusion analysis, and generates a power grid real-time operation state map; the equipment health twinning module is used for constructing an equipment health model in combination with a digital twinning technology and outputting an equipment fault early warning signal and a residual life prediction value; the customer energy efficiency service module is used for analyzing the association relationship and then generating a personalized energy efficiency optimization scheme and a power utilization suggestion; and the dynamic scheduling decision module is used for generating a power grid optimal scheduling strategy and an emergency response scheme. The power grid holographic sensing, digital twinning technology, dynamic scheduling, block chain and encryption technology are utilized, the power system is monitored in real time, equipment health is evaluated, faults are predicted, power utilization, energy conservation and emission reduction are optimized, data safety and stable operation of the system are ensured, and the cooperation efficiency of power enterprises and users is improved.

The invention discloses a CKM-CNN-based power system transient stability assessment method, system, equipment and medium, and relates to the technical field of power system assessment, and the method comprises the steps: collecting time sequence electrical quantity data after power system fault disturbance, constructing a transient sample set, carrying out the standardization processing and marking of a sample, and carrying out the calculation of the transient stability of the power system; a transient sample set is divided through a clustering algorithm, based on a sample cluster division result, an adaptive oversampling technology is adopted to carry out directional enhancement on an unstable sample, an equalized sample space is generated, a multilayer convolutional neural network is utilized to carry out hierarchical feature extraction on the equalized sample space, a high-dimensional feature vector is generated, and according to the high-dimensional feature vector, a high-dimensional feature vector is generated. And outputting a transient stable state probability evaluation result through the classification decision-making layer. According to the method, a generalization sample set is constructed through standardization and labeling, the scarce instability sample identification capability is improved by combining two-stage clustering and ADASYN oversampling, and the system dynamic state is accurately captured by utilizing 1D-CNN hierarchical feature extraction, so that efficient and robust evaluation of the transient stability of the power system is realized.

The invention relates to an RAG system evaluation data set automatic synthesis method and device based on reinforcement learning, and the method comprises the steps: obtaining knowledge base data of an RAG system, and extracting keywords and related documents of a target vertical field from the knowledge base data; according to the keyword and the document, synthesizing an initial question-answer pair by using a preset cue word template; constructing a reward of the initial question-answer pair; determining training data of the initial strategy model according to the keyword, the initial question and answer pair, the reward and parameters of the initial strategy model; training the initial strategy model based on the training data to obtain a target strategy model; and generating the target question-answer pairs meeting the reward in batches based on the keywords and the documents by utilizing the target strategy model, thereby solving the problems of high cost, low efficiency and influence on the effectiveness of the assessment RAG system in the assessment data set in related technologies, and being capable of reducing the synthesis cost and improving the synthesis efficiency.

The invention belongs to the field of energy storage system evaluation, and particularly provides a compressed air energy storage system state evaluation method based on digital twinning, in the method, a multi-physics field coupling digital twinning model is constructed based on a compressed air energy storage system, and a time sequence modeling module based on an LSTM-Transformer hybrid architecture is built in the digital twinning model; based on the calibrated digital twinborn model, state evaluation is carried out on the compressed air energy storage system in combination with a preset evaluation dimension, simulation deduction data of the digital twinborn model and real-time operation data of a physical system are fused in the evaluation process, and an evaluation result is obtained and output; and based on an evaluation result, triggering an uncertainty quantification result based on an LSTM-Transform architecture to carry out graded state early warning. According to the method, the multi-physics field coupling digital twinborn model is constructed and dynamically calibrated, simulation deduction, real-time data evaluation and graded early warning based on uncertainty quantification are fused, and the compressed air energy storage system state evaluation precision, real-time performance and early warning credibility are improved.

The invention discloses a cultivated land ecological safety comprehensive evaluation method and system based on multi-model coupling. The method comprises the steps that a future multi-source data set of cultivated land to be evaluated in a future time period is set; performing grid division on the cultivated land to be evaluated by adopting a grid calculator in ArcGIS software to obtain a plurality of grid regions, and performing grid division on the future multi-source data set based on the plurality of grid regions to obtain a future multi-source sub-data set of each grid region; and predicting the future multi-source sub-data set of each grid region by adopting the constructed multi-ecological safety comprehensive evaluation function model, the spatial classification method and the constructed ecological safety comprehensive evaluation index calculation formula to obtain an ecological safety comprehensive evaluation index corresponding to each grid region. According to the method, the ecological function of the to-be-evaluated cultivated land in the future scene is simulated by coupling the multi-ecological safety comprehensive evaluation function quantity model, so that systematic evaluation on the cultivated land ecological function of the to-be-evaluated cultivated land in the future scene is realized.

The invention discloses an unmanned aerial vehicle cluster survivability evaluation method based on a killing chain, and relates to the technical field of unmanned combat system evaluation, and the method comprises the following steps: S1, summarizing the types and number of our unmanned aerial vehicles, forming a our unmanned aerial vehicle equipment list, and collecting enemy target information, and forming an enemy target list; s2, according to the communication relationship between the unmanned aerial vehicle clusters, analyzing the network topology structure of the unmanned aerial vehicle cluster combat, eliminating invalid network communication modes and enemy target nodes, and forming a directed unmanned aerial vehicle cluster survivability analysis network graph D = lt; e, Vgt; . According to the unmanned aerial vehicle cluster survivability evaluation method based on the killing chain, different unmanned aerial vehicle nodes are destroyed, the comprehensive survivability evaluation value change of the whole unmanned aerial vehicle cluster is calculated, a survivability change trend chart is drawn, and key equipment nodes and weak links which have the largest influence on network survivability are judged.

The invention provides an intelligent driving system evaluation method and device, electronic equipment and a storage medium, and relates to the technical field of intelligent driving, detection data of an intelligent driving system is collected, and the detection data comprises environment data and driving behavior data of each vehicle; performing causal reasoning according to the environment data and the driving behavior data in combination with a preset confrontation scene, and updating a scene key factor of each driving scene; performing user portrait processing according to the environment data and the driving behavior data, and updating the weight of the evaluation dimension decision model; and according to the scene key factors and the weights, combining with the environment data and the driving behavior data, applying an evaluation dimension decision model to carry out personalized scoring on each driving scene, and obtaining a personalized scoring result for optimizing the intelligent driving system. Accurate, dynamic and demand-fitting adaptive evaluation of the intelligent driving system is realized, so that the intelligent driving system is optimized.

The invention belongs to the field of power system evaluation, and discloses an integrated energy system multi-criterion energy efficiency evaluation method based on scene adaptive dynamic weighting, which comprises the following steps: acquiring an energy efficiency evaluation index model; actual operation data of the integrated energy system are collected and extracted to serve as initial index data; inputting the initial index data into the energy efficiency evaluation index model to obtain energy efficiency evaluation index data; and calculating the energy efficiency evaluation index data through a method of combining a grey correlation method and an approximate ideal solution sorting method to obtain a comprehensive evaluation index. And the comprehensiveness of comprehensive energy efficiency index evaluation is improved.

The technology relates to program monitoring, which can involve using metadata as part of span information evaluated by the system in order to identify a location of interest in user (source) code. This can be used to pinpoint the exact code location that participated in a trace being debugged. The span information may be enriched with the code location from which it originated. The technology is also able to avoid unnecessary execution of instrumented functions by creating an alternate code execution path. This alternate path gets the code path to a variation of the instrumented function, in which the variation has a different set of operations. Upon determination that an execution call path has been previously followed, the alternate path would be automatically called instead of the path with the original instrumented function.

The invention discloses a collaborative improvement method for a horizontal wind speed algorithm in an urban block network model, and the method comprises the steps: enabling a high-resolution CFD wind field simulation to serve as a quasi-truth value, carrying out the comparison verification of wind speeds at different positions of the urban block network model and a quasi-truth value wind field, introducing local roughness and displacement height parameters which are combined with building features, and carrying out the calculation of a horizontal wind speed algorithm. The system evaluates the influence of the combination of the local roughness and the displacement height under different scales and the local friction speed inverted by the CFD on the wind speed simulation, selects the optimal parameter combination to replace the regional average roughness and the displacement height parameter according to the evaluation result, and optimizes the wind speed simulation capability of the urban block network model under the urban non-uniform underlying surface condition. The method has the advantages that the characteristic that the height of the underlying surface of the center of the city is not uniform can be more accurately represented by combining the local friction resistance speed, the local roughness and the displacement height parameters which are inversed by CFD simulation, so that the dragging effect of the city building is accurately simulated, and the horizontal wind speed closer to the reality can be obtained.

The invention relates to a drug interaction prediction method and system based on multi-view comparative learning, and belongs to the technical field of natural language processing. According to the method, two channels of a drug molecular map and a biomedical knowledge map are constructed in parallel, structural and semantic features are extracted by using a pre-trained heterogeneous map neural network, and multi-view comparative learning guided by information gain is introduced for joint optimization, so that the generalization ability and robustness of the model to unknown drug pairs are enhanced. According to the method, system evaluation is carried out on the performance of the system in two types of prediction tasks (multi-type and multi-label) and three prediction scenes. Experimental results show that the method has excellent performance in all tasks and scenes. Further case analysis also verifies the effectiveness of the system in predicting the interaction type of the unseen drug pair.

The invention provides a self-adaptive planning aviation fuel cell extended range power system evaluation method and system, and belongs to the technical field of power system evaluation, and the method comprises the steps: constructing a state space model of an aviation fuel cell extended range power system, building a multi-stage decision model, solving a control strategy of each stage through a self-adaptive dynamic planning algorithm, and calculating the state space model of the aviation fuel cell extended range power system. Performing difference mining on each state difference matrix, and determining an incidence relation graph of state variables and control variables in the corresponding flight stage; acquiring current state data of the aviation fuel cell extended range power system in real time, adjusting optimization parameters of a control strategy in a corresponding stage to obtain an optimal strategy in combination with a state space model and an incidence relation graph, and dynamically adjusting output power of a fuel cell and a super capacitor in combination with a power distribution strategy; and when the power output margin is lower than a safety preset threshold value, an alarm signal is triggered. And the requirements of efficient and safe operation of the aviation fuel cell range extending system are met.

A map quality assessment system that assesses an error associated with primary map data, the map quality assessment system comprising one or more central computers that execute instructions to determine an error associated with the primary map data, an error associated with the primary map data is compared to a range of values defined by the one or more quality metric values, and in response to determining that the error associated with the primary map data falls within the range of values defined by the one or more quality metric values, a template for selecting the primary map data is retained. In response to determining that an error associated with the primary map data falls outside of a range defined by the one or more quality metric values, the one or more central computers evaluate the primary map data for real anomalies within the one or more roads represented by the primary map data.

The invention relates to a system evaluation method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the steps of obtaining a knowledge document data set, and performing segmentation processing on the knowledge document data set to obtain knowledge document data blocks; performing feature extraction on each knowledge document data block based on the generator model to obtain key features of the knowledge document data blocks, and constructing an index relationship of the key features according to position information of the key features and the position information of the knowledge document data blocks; generating a question according to the generator model and the key features, and constructing an initial question and answer pair based on the question, the key features corresponding to the question and an index relationship of the key features; based on each initial question and answer pair, constructing an evaluation data set; the evaluation data set is used to evaluate the retrieval enhancement generation system. By adopting the method, the efficiency of the system evaluation method can be improved.

The invention discloses a multi-system adaptive switching radio direction finding method and system. The method comprises the following steps: acquiring signal modulation characteristic parameters and electromagnetic environment interference characteristic data to construct a system evaluation criterion, identifying a system mismatch sensitive area, and generating an interference weight distribution diagram and a system coverage margin; performing system competitive advantage decline analysis on the two to generate a switching urgency level and construct a hierarchical switching index set; obtaining a degradation offset coefficient through performance degradation trend tracking, and performing threshold correction on the real-time switching index to generate an adaptive switching index and a switching cooperation parameter; and tracking a system performance evolution trajectory based on the switching cooperation parameters and the interference weight distribution diagram, identifying an optimal system migration trigger point, completing optimal direction finding system selection and outputting an incoming wave direction measurement result. According to the invention, adaptive sensing of electromagnetic environment change and dynamic optimization of system switching are realized, and the accuracy and continuity of wave direction measurement in a complex interference environment are improved.

A system generates domain-expert agents for a particular domain. The system generates a hierarchy of topics for the domain by forming structured query inputs and requesting a machine learning-based language model to produce topics and associated sets of search terms. A knowledge store is built for the domain by identifying, for each topic, facts represented symbolically and / or in natural language. For at least one topic, programs comprising instructions for executing domain-specific procedures are generated and stored as symbolic and / or natural language programs. The knowledge store is evaluated to determine an expertise level for a domain-expert agent. If the knowledge store meets a threshold of knowledge, a deployment package is created comprising the knowledge store, and the domain-expert agent is deployed. The deployed agent utilizes the knowledge store together with a machine learning language model to address and solve problems specific to the domain.

The invention provides a radar system integrity evaluation parameter adaptive optimization method based on dynamic iteration, and relates to the technical field of radar system evaluation, and the method comprises the steps: collecting the operation data of a radar system in real time, executing the dimension reduction processing, and extracting a key evaluation parameter and a performance reference value; the performance deviation is used as a state variable, and a parameter value range is used as an action variable to optimize an evaluation parameter; a sliding time window is initialized, confidence is calculated and clustered, time sequence change characteristics are extracted, evaluation parameters are dynamically corrected, self-adaptive optimization of the evaluation parameters is achieved, and accuracy and timeliness of integrity evaluation of the radar system are improved.

The invention provides a virtual reality body rehabilitation system. The virtual reality body rehabilitation system comprises a virtual domain system, a body feeling stimulation system, a body feeling evaluation system, an equipment domain system and a central control module. The virtual domain system specifically induces the sense of ownership, the sense of autonomy and the sense of position by creating a virtual environment of a virtual avatar, real-time action mapping and adaptation rehabilitation tasks; the body feeling stimulation system takes visual stimulation as a core and is matched with linkage of various kinds of sensory stimulation, so that the authenticity of body experience is enhanced; the body feeling evaluation system evaluates real-time body feeling and provides reliable data support for regulation and control. On the basis of the physical cognition theory, the virtual reality technology, the multi-mode sensing technology, the neuroscience and the rehabilitation medicine principle are fused, multi-channel feeling collaborative stimulation such as vision, body feeling and touch feeling is achieved by constructing the immersive virtual environment, a patient is induced to generate physical experience, abnormal body representation is remodeled, nerve plasticity and function recovery are promoted, and the rehabilitation effect of the patient is improved. The patient is helped to return to normal life and society.

The invention discloses a dynamic evaluation system and method for adhesiveness of natural gravel asphalt, and relates to the field of road engineering analysis. The simulation chamber is integrated with a temperature control system which is used for changing the internal temperature of the simulation chamber; the solution system comprises a conveying unit for multiple kinds of liquid, the conveying unit can convey one kind of liquid into the simulation cavity at a time, and liquid circulation is formed between the conveying unit and the simulation cavity; the dynamic loading system is used for loading a sample through a loading head and can carry out dynamic rigidity measurement; the image acquisition and analysis system is used for acquiring a sample image and obtaining the adhesion area ratio of asphalt according to the sample image; and the evaluation system is used for evaluating the adhesiveness according to the combination of the dynamic stiffness measurement result and the adhesion area ratio. By adopting the scheme, the dynamic coupling effect of complex environmental factors of an actual road surface can be simulated, so that the real-time quantitative evaluation of the adhesion decay process is realized.

The invention belongs to the field of degraded grassland restoration evaluation research, and discloses a degraded grassland restoration evaluation research method based on multivariate remote sensing data, and the method comprises the steps: carrying out the observation through employing a FragMap unmanned aerial vehicle aerial photography analysis system, and carrying out the preprocessing through employing Pixel Base Management Classifier software; collecting grassland above-ground biomass data; acquiring and preprocessing remote sensing data; constructing a grassland coverage and aboveground biomass remote sensing estimation model, and performing precision verification; analyzing grassland coverage and biomass space difference before and after restoration of the degraded grassland; and analyzing space-time dynamic changes of grassland coverage and biomass. According to the method, a high-temporal-spatial-resolution NDVI data set of a research area is constructed by combining a high-temporal-spatial-resolution data fusion algorithm, a grassland coverage and aboveground biomass remote sensing estimation model is constructed by combining ground survey and unmanned aerial vehicle aerial photography, and the grassland coverage and aboveground biomass change conditions before and after a degraded grassland restoration measure is implemented are systematically evaluated. And the restoration effects of different restoration measures on the degraded grassland are evaluated.

The invention discloses an undercarriage reliability verification method based on key fault factor active injection. The method comprises the following steps: constructing an undercarriage multi-body dynamics simulation model, simulating an undercarriage folding and unfolding motion process, analyzing motion characteristic parameters, and determining key components according to a stress state; reliability and sensitivity evaluation is carried out based on the simulation model, and key fault factors influencing the undercarriage time-varying failure probability are identified; carrying out a component-level fault simulation test aiming at the identified key fault factors, and generating a fault state for simulating actual wear; and actively injecting the fault state into a system-level test environment, and verifying the extension and retraction function of the undercarriage through a single system-level test to replace a full-cycle system-level test. According to the method, the reliability of the undercarriage movement mechanism is systematically evaluated through the way of combining simulation analysis and a local test scheme, the number of tests of the whole system is reduced, the test cost is reduced, and the test development efficiency is improved.

The invention discloses a multi-dimensional dynamic trust evaluation method and system for a new energy power terminal, and the method comprises the steps: extracting the universal features of the terminal based on terminal log interaction data, extracting special knowledge evidence according to an equipment level perception layer and a network layer, and converting all features into 0-1 standardized values; when a terminal accesses for the first time, the static attribute of the terminal is complementarily collected, so that an initial trust baseline is established and an initial trust value is given; firstly, an IoT device interaction diagram is constructed, nodes are terminal devices, attributes are knowledge evidence vectors, edges are device direct interaction relations, and weights of the edges are defined by historical interaction quality; fusing the equipment knowledge evidence and the interaction relationship through a node2vec algorithm to generate a low-dimensional embedded vector; and finally, calculating the cosine similarity of the device vectors, multiplying the cosine similarity by the interaction frequency proportion to obtain the direct trust degree of the 0-1 interval, and quantifying the direct trust relationship between the devices. Performing trust evaluation on the message flow dimension; and historical interaction behavior trust evaluation and timeliness optimization.