1546 results about "Evaluation system" patented technology

The invention provides an underwater tunnel shield construction excavation face stability evaluation method, system and equipment, and the method comprises the steps: determining an excavation face stability evaluation index based on a shield construction excavation face instability mechanism; dividing the stability of the excavation surface into a plurality of grades, establishing each grade space, and determining the quantitative interval of each evaluation index in each grade; calculating a combined weight of each stability evaluation index by adopting a combined weighting method, and taking the combined weight as a judgment basis of the influence of the evaluation indexes on an evaluation result; and constructing an ideal point evaluation function by adopting an ideal point method so as to represent the membership degree of the to-be-evaluated object to each grade, calculating the grade membership degree of the to-be-evaluated section in each grade of the evaluation system, and determining the stability grade of the excavation surface in the construction process of the evaluation section.

The invention relates to a metro field large language model evaluation method and system. The method comprises the following steps: step 1, constructing a data set; the step 1 comprises the following steps of: 1.1, collecting public professional data; step 1.2, collecting internal professional data; 1.3, collecting a general data set; 2, data processing; in the step 2, the following steps are executed: step 2.1, data formatting; step 2.2, data cleaning is carried out; step 2.3, performing data enhancement; 3, fine adjustment of the model; in the step 3, the following steps are executed: step 3.1: constructing a training data set; step 3.2, fine adjustment of the model; step 3.3, carrying out model evaluation; 4, constructing an evaluation system; in the step 4, the following steps are executed: step 4.1, determining evaluation indexes; step 4.2, constructing an evaluation system; step 4.3, constructing an intelligent agent; step 4.4, multi-agent cooperation is carried out; and 5, performing model evaluation.

The invention relates to the technical field of data processing, and discloses a financial compliance risk assessment method and system based on big data, and a storage medium. The method comprises the following steps: collecting and standardizing multi-source data to form a structured data set; constructing a three-level evaluation system comprising risk fields, subclasses and indexes; training the stacked fusion network to obtain an evaluation model; analyzing and generating a risk report in real time; identifying risk points and performing quantitative evaluation; and an early warning threshold value is set, and an early warning signal is generated and sent to a risk management department when the threshold value is exceeded. According to the invention, efficient identification, accurate evaluation and timely early warning of compliance risks are realized, and the automation level and scientific decision-making ability of compliance risk management of financial institutions are improved.

The invention relates to the technical field of power systems, in particular to a multi-dimensional network performance index evaluation system and method based on measured data of a new energy station. Comprising a data acquisition preprocessing unit; a multi-dimensional index construction unit; the weight adjustment unit dynamically adjusts the index weight according to the operation condition based on a fusion algorithm of an improved analytic hierarchy process and an entropy weight method; the performance evaluation model unit adopts a comprehensive evaluation model fused with a random forest algorithm to generate multi-dimensional performance scores and risk early warning; and the visual interaction terminal unit is used for displaying the evaluation result in real time and generating a trend analysis report. According to the invention, the three-dimensional evaluation system covering the static electrical parameters, the dynamic stability indexes and the harmonic pollution indexes is constructed, so that the limitation of single-dimensional evaluation in the prior art is broken through, and the extension of the network-related performance of the new energy station from local detection to full-dimensional coverage is realized; and the multi-scene evaluation requirements of the power grid on the steady-state operation, the transient response and the electric energy quality of the station are accurately matched.

The invention relates to the technical field of cloud computing and artificial intelligence, in particular to a disaster recovery drill and performance evaluation system and method for multi-cluster distributed training. In order to solve the problem of disaster recovery and performance evaluation in multi-cluster distributed training, the method comprises the steps of environment analysis and modeling, fault injection and drill control, cross-cluster scheduling and resource monitoring, performance data acquisition and index aggregation, training result evaluation and tuning closed loop and the like. Multi-cluster environment elements are identified, a fault and index mapping relation is constructed, faults are injected by using a fault injection tool, and task scheduling, resource monitoring and performance data acquisition and analysis are realized by means of a multi-cluster management and monitoring tool, so that an adjusting and optimizing strategy is formed, and closed-loop management is realized. According to the method, faults can be dynamically simulated in a multi-cluster environment, the training performance is monitored, the disaster recovery and recovery efficiency is evaluated, the training efficiency and robustness of the system are improved, and the requirements for high availability and high performance in the fields of finance, automatic driving and the like are met.

The invention discloses a large-model multi-scene antagonism dynamic evaluation system and method based on context perception strategy optimization, relates to the technical field of artificial intelligence safety evaluation, and aims to solve the problems of lack of multi-round context modeling, poor confrontation sample semantic consistency and lack of a feedback-driven optimization mechanism in the prior art. According to the method, a state space and an action space are constructed, an adversarial sample is dynamically generated by adopting a reinforcement learning strategy network, a high-quality sample set is expanded in combination with a semantic disturbance and screening mechanism, and a vulnerability knowledge base is constructed based on an interaction log to guide strategy optimization and security evaluation. The method can effectively improve the security evaluation coverage and vulnerability discovery capability of the model in a high-risk multi-round interaction scene, and is mainly used for security reinforcement and deployment support of large language models in the fields of medical treatment, customer service and the like.

The invention belongs to the technical field of plate spring performance analysis and evaluation, and discloses a high-strength two-stage gradual-change-stiffness plate spring performance analysis and evaluation system and method.The method comprises the steps that plate spring initial mechanical data collection and heterogeneous load response integrated processing are conducted on the basis of a multi-dimensional vehicle-mounted test experiment, and plate spring integrated mechanical response data are generated; performing double-rigidity gradient recognition analysis to generate rigidity transformation critical data; performing critical region stress field deconstruction analysis to generate stress field distribution characteristic data; carrying out local microstructure change characterization to generate structural deformation data; constructing a multi-scale stress transfer link, and generating critical region stress transfer path data; performing fatigue damage accumulative mapping to generate accumulative feature data; constructing a fatigue life prediction model, and generating life prediction data; carrying out comprehensive performance index quantitative evaluation to generate a performance evaluation result; executing plate spring optimization improvement scheme generation and verification; and a scientific basis is provided for performance optimization of the two-stage gradual-change-rigidity plate spring.

The invention belongs to the technical field of supply chain management, and discloses an intelligent purchasing system and method of a supply chain. Comprising the steps of collecting demand prediction data and supplier performance data; carrying out deep mining on the demand prediction data, and predicting future purchase demands; generating a capability portrait based on the performance data of the suppliers, and performing quantitative evaluation on performance scores of different suppliers according to a pre-constructed evaluation system; historical order data is acquired, a supplier relation network topology is constructed in combination with the capability portraits, and a synergistic effect between suppliers is identified; fusing the performance score, the future purchase demand and the synergistic effect, and recommending an optimal supplier combination; acquiring purchase management data, and intelligently formulating a purchase strategy in combination with the supplier performance data of the optimal supplier combination; according to the invention, the purchase efficiency can be improved, and the flexibility and toughness of the supply chain are enhanced, so that the overall response speed, cost control capability and operation quality of the supply chain are effectively improved.

The invention provides a road marking quality intelligent evaluation system based on image analysis, and relates to the technical field of road facility monitoring, and the system comprises a detection unit, a marking quality evaluation unit, a marking wear prediction unit, a GPS positioning unit, a vehicle driving information unit, a control unit and a remote central control unit. The marking quality evaluation unit is constructed based on a differential geometry theory and comprises a curvature flow edge detection module, a marking geometric characteristic manifold representation module and a multi-scale differential invariant evaluation module, the system regards a marking as a two-dimensional manifold, and the marking quality is evaluated by calculating differential geometric quantities such as a Gaussian curvature, an average curvature and a shape index. And constructing geodesic distance measurement on the feature manifold, and evaluating the completeness, visibility and reflective performance of the marked line. And the marking wear prediction unit predicts the service life of the marking based on the traffic flow information and the environment characteristic mapping relation model, and generates a maintenance suggestion.

The invention relates to a stamping die service life dynamic evaluation system and method based on multi-mode sensing. According to the system, a feature extraction module obtains stress distribution data and crack position information and inputs the data into a trained three-dimensional stress field model to obtain dynamic feature parameters of a stress concentration area under high-frequency cyclic loads; the crack prediction module judges the dynamic characteristic parameters based on a preset threshold value, and once the dynamic characteristic parameters exceed the preset threshold value, analysis is carried out according to the mapping relation between the stress level and the crack rate and the microstructure evolution rule at the same time, and a crack propagation prediction value is generated. And the service life evaluation module inputs the crack propagation prediction value into the trained health state evaluation model, and finally the service life evaluation value of the stamping die is obtained. The service life evaluation value of the stamping die under the existing working condition and the operation condition is accurately estimated, the production efficiency is effectively improved, the production cost is reduced, the potential crack risk of the die is perceived in advance, and accurate and dynamic die service life evaluation is achieved.

The subject technology includes an evaluation system for agentic applications. The evaluation system may use one or more evaluation applications to grade the performance of an agentic application based on one or more performance metrics. Scores determined for individual metrics may be combined using a set of weights to tailor the importance of each metric in the overall performance evaluation to a particular industry or application. An optimization engine may improve the performance of target agentic applications that are deficient in one or more metrics by training a portion of the agent application on a training dataset that includes example responses that score well for the one or more metrics where the target applications are deficient.

The invention relates to the field of building design, and discloses a building design project intelligent evaluation method and system based on multi-dimensional indexes, and the method comprises the following steps: S1, collecting building design data from multiple sources; s2, preprocessing the collected various data; s3, constructing a multi-dimensional evaluation system, and generating a comprehensive evaluation result; s4, adjusting the weight of each evaluation dimension; s5, generating a comprehensive evaluation report; the system comprises a data acquisition module, a data preprocessing module, an evaluation system construction module, a reinforcement learning adjustment module, a real-time feedback mechanism module, a comprehensive evaluation report generation module and a data fusion module. According to the method, data fusion is carried out through a multi-dimensional index evaluation system and in combination with a deep learning algorithm, comprehensive and accurate evaluation of the building design project is achieved, and compared with a technical scheme of general single-dimensional evaluation in the prior art, the problem that part of important factors are ignored during evaluation in the traditional technology is solved.

The invention relates to the technical field of safety simulation training drilling, in particular to an intelligent assessment and evaluation method for mine accident emergency risk avoiding, which comprises the following steps of: S1, acquiring multi-dimensional data in an emergency risk avoiding capability drilling process of a student; s2, performing data preprocessing on the multi-dimensional data, and constructing an evaluation data set; s3, constructing an emergency risk avoiding capability evaluation system, wherein the system comprises evaluation items and distribution weights of the evaluation items; s4, establishing a weight evaluation model, and dynamically adjusting the distribution weight of the evaluation item in combination with the environmental data; and S5, scoring is completed according to the emergency risk avoiding capability evaluation system, and a personalized evaluation report is generated. According to the invention, comprehensive, objective and real-time evaluation of the emergency risk avoiding capability of the miner is realized, and the influence of human factors on the evaluation result can be reduced; the method can flexibly adapt to different environments and scenes through dynamic adjustment of the evaluation model, provides personalized evaluation reports and improvement suggestions, and effectively assists students in improving the emergency risk avoiding capability.

A method and a Large Language Model (LLM) evaluation system provides an end-to-end evaluation of LLM, which includes evaluating both input prompts and output prompt responses, wherein the evaluation includes assessing a plurality of input and output characteristics that encompasses both quality and quantity. Each of the plurality of input characteristics are assigned with a corresponding normalized score by employing one or more statistical techniques to derive a composite health score for the input prompts. Evaluation further comprises evaluating output prompt responses in both absence and presence of the ground truth. Upon evaluating both input prompts and output prompt responses, a final aggregated health score for the LLM is computed by a scorer module employing threshold based statistical techniques that considers input prompt health and output prompt response health, wherein the aggregated health score is generated based on the granular scores of each characteristic.

The invention provides a large model autonomous evaluation method, device and equipment based on a multi-agent technology, and a storage medium. The method comprises the following steps: generating an evaluation task set covering multiple scenes and grading multiple difficulties by setting dimension weights of basic indexes and extension indexes; a multi-agent evaluation system of a questioner, an evaluator and a reference agent is constructed based on the task set, a parallel interaction mechanism is adopted to simultaneously initiate tasks to a to-be-evaluated model and the reference agent, the evaluator agent compares the to-be-evaluated model and the reference agent according to a multi-dimensional scoring standard and outputs the two agents, and task instances, output results and scores are constructed into a structured data set; the subsequent task set is dynamically optimized through analysis of the data set, comprehensive and iterative evaluation of the capacity of the large model is achieved, and the problem that an existing evaluation mode cannot comprehensively reflect the real capacity level of the large model is solved.

The invention discloses a self-correction structured bid evaluation guide method for public resource transaction, and relates to the field of resource transaction processing, the method is based on a bid evaluation system, and the method comprises a data preprocessing unit, a structured analysis unit, a rule unit, an intelligent evaluation unit, a workflow management unit, a report generation unit and a data filing unit. Historical similar project scoring data is analyzed based on a large language model through an intelligent review unit, a real-time scoring reference interval is generated, expert experience and data driving are combined, professional judgment is reserved, scoring randomness is reduced through data quantification, a workflow management unit adopts an LSTM neural network to construct a review behavior prediction model, and the review behavior prediction model is used for predicting the review behavior. According to the method, the degree of deviation between expert scores and predicted values is compared in real time, meanwhile, multi-party review cases are integrated based on federal learning to form a dynamic knowledge base, review after the event is changed into real-time monitoring and intelligent intervention, the objectivity of bid evaluation is enhanced, and subjective item scores tend to be standardized.

The invention discloses a power market large language model evaluation method and device based on dynamic scene perception, and the method comprises the steps: building a large model evaluation system covering the three dimensions of understanding, generation and safety according to a power market application scene; the method comprises the following steps: constructing a test question bank, evaluating a power market large language model according to various evaluation methods, endowing three dimensions with different weights based on a large model evaluation system in the evaluation process, constructing a test question-scene label fine tuning data set, and performing scene classification fine tuning on the model by using a fine tuning method. Evaluating scene classification performance of the fine-tuned model, and distributing corresponding initial evaluation dimension weights for different scenes; according to the requirements and differences of different scenes, the initial evaluation dimension weight is adjusted, the scene is judged to be matched with the corresponding evaluation dimension weight according to the input content, weighted summation is carried out from the three dimensions of understanding, generation and safety, and an evaluation result is obtained. According to the method, accurate evaluation of the performance and reliability of the large language model in the power field can be realized.

The invention belongs to the technical field of computer information processing, and relates to a scientific research performance information analysis and evaluation system and a processing method thereof.The scientific research performance information analysis and evaluation system comprises a data collection module, a weight distribution module, a real-time feedback module and an execution engine function module; weight distribution receives the first signal and analyzes a current strategic target priority identifier and a historical performance evaluation record contained in the first signal, a weight parameter space dynamically matched with a target priority is constructed through an online learning algorithm, and a second signal containing a real-time weight mapping relation is generated; real-time feedback is carried out, a multi-dimensional performance evaluation matrix is constructed based on the weight mapping relation in the second signal, and a third signal is generated; the execution engine receives the third signal, analyzes an abnormal positioning code of the third signal, and aggregates intervention records in a period to form a fourth signal; according to the method, the defects that traditional performance information analysis depends on static weight, the data dimension is single, and real-time dynamic adjustment is lacked can be overcome, and the analysis efficiency is improved.

The invention discloses an electronic product performance intelligent test system. The electronic product performance intelligent test system comprises an enhanced digital twinning and multi-physics field modeling system, a multi-modal perception fusion and intelligent test planning engine, a deep fault diagnosis and predictive health management feedback optimization module and a comprehensive optimization objective function framework. An intelligent and integrated test and evaluation system capable of comprehensively improving the test efficiency, the test coverage, the fault early warning capability and the product reliability can be realized.

The invention discloses an automatic driving performance optimization method, system and device, a storage medium and a product, and relates to the technical field of automatic driving, and the method comprises the steps: obtaining a driving scene parameter and an automatic driving algorithm; constructing a simulation platform by using the driving scene parameters and an automatic driving algorithm, and performing a simulation test on the automatic driving system through the simulation platform to obtain a simulation test result; and adjusting the weight and the evaluation key point of the simulation test result through a pre-constructed multi-dimensional evaluation system to obtain an evaluation result of the automatic driving performance. According to the scheme, by introducing a multi-dimensional evaluation system, more accurate and more comprehensive performance evaluation of the automatic driving system can be realized, and one-sided evaluation caused by a single index in the past is avoided. Besides, by dynamically adjusting the weight of the simulation test result and the evaluation key point, more accurate and more comprehensive performance evaluation of the automatic driving system is realized, and the reliability and safety of the automatic driving system in a complex and changeable real driving environment are improved.

The invention discloses an offshore pile foundation evaluation system construction method based on evolutionary algorithm optimization and PIML linkage. A standardized engineering data set is obtained; parameterizing the pile-soil coupling dynamics boundary condition, the pile-soil interaction specification criterion and the limit state and use state criterion, and storing the parameters as a physical constraint set; obtaining an initial performance evaluation model; generating an alpha evolutionary optimization performance evaluation model; outputting a bearing capacity-settlement relation index, a dynamic and static stiffness degradation curve index and a residual bearing capacity probability distribution index to form a structural performance index set; generating a credible structure performance index set containing confidence boundaries and extracting an influence degree sorting result; and automatically generating a structured evaluation report according to a verification result. The model output has physical consistency and engineering interpretation all the time, and the problem that a traditional pure data driving model is unreliable in result under data sparsity and environment sudden change is remarkably solved.

The invention relates to the technical field of photovoltaic performance evaluation, in particular to a photovoltaic performance evaluation system based on cloud computing, and the system comprises an electrical thermal response interpretation module, an abnormal aggregation distribution module, a rhythm deviation track module, a voltage accumulation deviation module and a risk early warning level judgment module. According to the method, multi-dimensional time sequence fusion is carried out on thermoelectric characteristics and voltage changes through cooperative comparison of a thermal response ratio and an electrical signal, the linkage anomaly identification capability of heterogeneous parameters is greatly improved, spatial structure distribution and number mapping are focused, hierarchical division of an anomaly aggregation phenomenon is realized, dynamic risk insight of a spatial dense area is enhanced, and the method is suitable for mass production. According to the method, a dynamic offset mode is constructed for running data across multiple days and minutes, hidden anomalies such as continuous offset and reverse turning in historical and real-time change trends are accurately captured, voltage trends and accumulated features are refined and filed, a foundation is laid for multi-node and multi-period risk positioning through classification and summarization, and a multi-source synchronous abnormal fragment alignment processing mode is achieved.

The invention provides a vocational ability assessment system based on a skill map, and relates to the field of human resource technology and knowledge map application. The evaluation main system comprises a skill star map generation module, an occupational trajectory simulation module, a skill challenge module, a skill authentication chain module, a future skill navigation module, a skill collaborative ecological module, a skill evolutionary tree module, an occupational story generation module, a skill energy pool module, an occupational time capsule module and a control flow management module; according to the invention, the dynamic skill star map is constructed and the 5G network is combined to transmit data in real time, so that the occupational experience, learning records and test data of the user can be collected in time, and the node weight is dynamically adjusted through the semantic analysis algorithm and the weighted scoring algorithm; the problems that traditional manual evaluation and online testing are high in subjectivity and low in efficiency, and the capability cannot be accurately quantified are effectively solved.

The invention relates to the technical field of compliance management, and particularly discloses a compliance management system dynamic evaluation system and method based on artificial intelligence, and the method comprises the steps: firstly carrying out the content segmentation of an internal compliance document of an enterprise, and obtaining all text units for describing the compliance practice of the enterprise; and performing semantic vectorization representation on each compliance text description unit and the external regulation change text segment by adopting a converter model finely adjusted by a domain adaptability model so as to capture context semantic information of the text, and further performing multi-dimensional semantic collaborative conjoint analysis on the compliance text description units and the external regulation change text segment. And the logic relationship between the two is deconstructed in a semantic space, so that the implication, contradiction or neutral relationship between the external regulation change and the current compliance system of the enterprise is intelligently identified. According to the method, the consistency of the enterprise compliance management system and the external regulation environment is efficiently and accurately evaluated, and effective decision support can be provided for adaptive iteration of the compliance system in the supervision environment.

The invention relates to the technical field of digital twinning, discloses a digital twinning-driven transformer substation state artificial intelligence evaluation system, and aims to solve the problems that the diagnosis precision is reduced and the model adaptability is insufficient when a traditional digital twinning model is inconsistent with a physical entity state. The system constructs a closed-loop architecture comprising a state perception and deviation quantification module, a systematic risk and cognitive focus distribution module, a focused causal diagnosis and introspection verification module and a cognitive twin adaptive evolution module. The method can actively sense the state deviation of a physical entity, intelligently distributes diagnosis resources based on the evaluation of systematic risks, and achieves the efficient diagnosis of a fault source. The system determines the real state of a physical entity through introspection verification, and performs closed-loop updating on a reference twin model by using the result, so that the reference twin model can be continuously evolved, and the accuracy, the automation level and the full life cycle adaptability of state monitoring and fault diagnosis of a complex industrial system are remarkably improved.

The present disclosure relates to a field of a fuel cell test, and in particular, to a method and an apparatus for optimizing design based on performance evaluation of a gas diffusion layer of a fuel cell. The method includes: determining an overall porosity of the gas diffusion layer of the fuel cell according to production requirements, and obtaining a plurality of porosity structures with the overall porosity; obtaining performance evaluation indexes of the gas diffusion layer of the fuel cell, and constructing a performance evaluation system for the gas diffusion layer of the fuel cell; calculating, with reference to evaluation functions and index weight ratios, performance comprehensive scores of the plurality of porosity structures in the performance evaluation system of the gas diffusion layer of the fuel cell; determining an optimal design scheme in the plurality of porosity structures according to the performance comprehensive scores.

The invention relates to the technical field of federated learning, and discloses a dynamic perception credibility evaluation system and method for cross-modal federated learning, and the method comprises the steps: obtaining local multi-modal features, constructing a time-varying condition dependence matrix, and generating shared statistical information; analyzing a cross-node dependency relationship, constructing a federal causal map, and outputting an inter-modal effect separation result; local model parameters are compressed, the minimum sufficient statistics are extracted, and model contribution characterization is output; calculating a time-varying weight of the node credibility, and outputting a dynamic node contribution degree; executing adaptive federal aggregation, generating global model credibility evaluation, and outputting a dynamically perceived global model; according to the invention, a more reliable and more accurate model evaluation and aggregation mechanism is provided for the distributed enterprise federated learning system, and the overall performance and stability of the system are effectively improved.

The invention belongs to the technical field of urban rail vehicle detection and maintenance, and discloses a full-process optimization method and system for polygonal wear of a metro vehicle wheel. The method comprises the following steps: firstly, constructing a digital twin-driven train rigid-flexible coupling dynamic model, carrying out global sensitivity analysis, establishing a sensitivity index model, screening key dynamic performance indexes, and carrying out batch simulation to construct a dynamic response database; feature extraction and classification model training are carried out on the index data, multi-layer wavelet packet decomposition is carried out on the one-dimensional vibration signals, and a multi-channel feature vector is constructed and input into a one-dimensional residual network model; inputting actually acquired data into the trained model, calculating a relative close degree to generate a comprehensive index and a grading result, and generating turning repair suggestions based on grading; meanwhile, multi-source monitoring data are collected, a long-short-term memory network is used for predicting the abrasion evolution trend, finally, turning repair suggestions and trends are integrated, an accounting model and an evaluation system are constructed, and an optimal maintenance decision is generated through a multi-target optimization algorithm.

The invention relates to safety compliance evaluation, in particular to a safety compliance evaluation system and method based on a multi-modal large model, and a functional module cluster, which are used for managing each functional module in the system, supporting cross-modal consistency detection and comparing answer consistency under different modals. The self-developed security system is responsible for execution of an assessment task, including resistance test enhancement and built-in multi-granularity data variation strategy, improves assessment robustness by simulating a real attack scene, realizes end-to-end automation from data generation, tested model test to risk assessment, supports configurable parameters and reproducible results, and has the advantages of high reliability and high reliability. The self-iteration and version management of the evaluation model are realized, and the self-iteration mechanism is deeply combined with a compliance label system, so that the risk identification capability under legal, ethical and industrial specifications can be continuously optimized; according to the technical scheme provided by the invention, the limitation that evaluation can be carried out only under a single mode and a single rule in the prior art can be effectively overcome.

The invention provides a train operation state estimation method and system based on a high-order extended Kalman filter considering noise correlation, and belongs to the technical field of rail transit train operation management. According to the method, correlation analysis between high-order terms is introduced, the filtering precision is remarkably improved, and particularly, excellent performance is shown under a strong nonlinear system; by establishing a multi-mode train state model, the dynamic characteristics of a train in operation modes such as traction, cruising, sliding and braking are comprehensively covered, and a more accurate input basis is provided for a filtering algorithm; by introducing a sliding window error criterion, the filter can quickly switch operation modes according to real-time errors, so that the filter is ensured to keep high adaptability in a complex and changeable operation environment; the comprehensive performance evaluation framework based on the error covariance matrix not only evaluates the mean square error and the mean absolute error, but also quantifies the contribution of the high-order correlation to the precision of the filter, and provides a more scientific and comprehensive evaluation system.