161results about How to "Avoid one-sidedness" patented technology

The invention relates to the field of communication, and discloses an intelligent traffic scheduling optimization method, device and equipment and a storage medium, and the method is used for 5G network intelligent traffic scheduling optimization. The method comprises the following steps: receiving a service request, analyzing the service request to obtain a 5G standard service type, an application scene and a 5G network slice identifier, then obtaining an SLA parameter corresponding to the service request and a candidate link required by service transmission, and converting the SLA parameter into an SLA preference vector; acquiring global data of the candidate link, and predicting by using the network state prediction model to obtain a predicted network state of the candidate link; the SLA preference vector is used as optimization target guidance, the predicted network state is used as a constraint condition, and a global optimization routing strategy is calculated through a multi-target optimization algorithm; and generating a control message packet based on the global optimization routing strategy, issuing the control message packet to the network forwarding equipment, and adjusting parameters of the network state prediction model and the multi-objective optimization algorithm based on the actual performance data after execution.

The invention provides a tumor medical image classification method of zero sample evolution NAS based on double-index collaborative evaluation, which comprises the following steps: collecting tumor medical image data, and constructing a data set containing a tumor focus image and a normal tissue image; constructing an extended search space based on a cell structure, wherein the search space comprises a basic operation set, the cell structure and a network overall structure; steady-state evolution algorithm parameters are set, and steady-state evolution initialization is carried out to obtain an initial candidate architecture; taking a double-index comprehensive score calculated by the information rate and the FIM stability as a comprehensive index for evaluating the current candidate architecture, updating the candidate architecture through a steady-state evolutionary algorithm based on a dynamic optimization mechanism until the maximum evolutionary algebra is reached, and obtaining an optimal architecture; and training the optimal architecture by using the data set, and carrying out tumor medical image classification by using the trained optimal architecture. According to the method, the dependence on the annotated data is reduced, the consumption of computing resources is reduced, and meanwhile, the diagnosis efficiency and accuracy are improved.

The invention belongs to the crossing field of network space security, software engineering and artificial intelligence, and discloses a dynamic and static cooperative vulnerability full-link intelligent analysis and verification method based on a large language model and an MCP protocol, and the method comprises the steps: S1, task initialization and context construction; s2, target code deep analysis and static taint preanalysis are carried out; s3, carrying out Fuzzing Harness directional generation and iterative optimization on the basis of the stain paths; s4, performing dynamic and static collaborative sandbox execution and dynamic stain tracking; and S5, vulnerability accessibility judgment and report output of multi-source data fusion are carried out. According to the method, a dynamic and static combination mechanism is adopted, vulnerability analysis and verification can be automatically completed for software target codes, and accurate technical support is provided for vulnerability risk grading and repairing priority ranking.

The invention discloses a wireless network resource scheduling method and a wireless network resource scheduling system, relates to the technical field of wireless network scheduling, and aims to identify an area with abnormal and prominent resource requirements by performing standardization processing on the resource requirements so as to provide a clear quantitative basis for subsequent strategy adjustment. Compared with the existing empirical or fixed threshold scheduling mechanism, the method has dynamic adaptability and cross-regional comparability, and is convenient to realize a partition and hierarchical scheduling strategy. Through construction of scheduling strategy adjustment amount, three key control dimensions in scheduling logic, namely bandwidth channel priority, transmission time slot allocation and cache scheduling sequence, are broken through, so that each control module can perform linkage adjustment based on a unified scheduling urgency degree signal. And common problems of unbalanced resource configuration and the like are avoided. Through a standardized scheduling gradient calculation mechanism, the resource urgency degrees of different regions are quantitatively compared, and the problem of wrong resource investment caused by inflexible threshold setting and incomparable regions in a traditional scheduling system is solved.

The application provides a multi-dimension evaluation method for an interference scene, which solves the problems of evaluation hysteresis, improves the consistency of evaluation, solves the single dimension of evaluation and the lack of consideration of terrain in the evaluation of the interference scene; the position accessibility analysis of terrain perception of a propagation path between a signal simulation device and a target device is carried out based on a digital elevation model; the device behavior compliance analysis is carried out in four dimensions of time domain, space domain, frequency domain and energy domain; if there is a moving route, the route coverage and signal continuity analysis are carried out in combination with the analysis results of the former two; finally, the analysis results of the three dimensions are weighted and fused to calculate the scene comprehensive effectiveness score and map it to high, medium and low three levels of confidence. Through the construction of the multi-dimension collaborative pre-evaluation system, the scientific and quantitative pre-evaluation of the training scene planning scheme is realized, the planning quality and efficiency are effectively improved, and the risk of waste of training resources is reduced.

The invention provides a historic building damage prediction method, a model, equipment, a medium and a program. The method comprises the following steps: acquiring multi-source time sequence data of key components of a historic building; preprocessing the multi-source time sequence data; extracting features from the Raman spectrum data by adopting three-layer 3 * 3 convolution, extracting features from the infrared spectrum data by adopting multi-scale convolution, and obtaining spectral features by self-adaptive pooling unified length; converting the structure attribute data and the environment data into low-dimensional features matched with spectral feature dimensions through a two-layer full-connection network; splicing the spectral features and the low-dimensional features, unifying the number of channels through 1 * 1 convolution, screening key features through a gating module, and outputting a global feature vector Y1; inputting the global feature vector Y1 into a three-layer full-connection network, and outputting an original output vector Y2 of the current damage level; and calculating the confidence of each grade of building damage through a Softmax function. The method does not need to contact key components, and secondary damage caused by damage prediction is avoided.

The present application relates to the technical field of data processing and business intelligence, and discloses a multi-dimensional data driven self-brand growth value prediction method, which extracts a main feature vector of a previous time step, calculates a dynamic weight for a same-product white label control group to generate a dynamic benchmark, and accordingly peels off a pure brand-driven incremental index, and combines a repeat purchase decay rate and a search index to construct a three-dimensional state vector; through eigenvalue decomposition on a state covariance matrix, a current main feature vector and a maximum eigenvalue are obtained, and the main feature vector is fed back to a weight calculation link to form a closed loop; finally, the decomposition result and a historical state sequence are respectively input into a pre-trained machine learning model to output a brand life cycle stage and a future brand asset valuation. The present application accurately anchors a reference system through a dynamic feedback mechanism, and realizes comprehensive and accurate diagnosis and prediction of brand value.

The invention discloses a liver tissue regeneration regulation and control method based on epigenetic state regulation and control, and relates to the technical field of biology. Comprising the following steps: sampling the liver tissue at a plurality of preset time points after the liver tissue is damaged or partially resected, selecting a target gene related to liver tissue regeneration regulation, and detecting an epigenetic state of a promoter region of the target gene; comparing and analyzing the change of the epigenetic state of the target gene promoter region on the basis of detection results obtained at different time points, and determining epigenetic modification characteristics in the liver tissue regeneration process; according to an analysis result, intervening the expression level of a regulatory factor related to DNA methylation or DNA hydroxymethylation so as to regulate the epigenetic state of the target gene promoter region; after intervention is completed, the transcription expression condition of a target gene and the activity change of a cell signal channel related to liver tissue regeneration are detected, and the liver tissue regeneration process can be effectively regulated and controlled through the method.

The embodiment of the invention relates to the technical field of federated learning and information security, in particular to a federated multi-target scheduling method and system for industrial control crowdsourcing testing, which is characterized in that sensitive data such as ability, experience and the like of a tester are only reserved at a local terminal by constructing a framework combining local feature coding and federated model training, so that the test efficiency is improved, and the test efficiency is improved. Outward transmission is not carried out; only the model update quantity after the privacy enhancement processing such as gradient cutting and differential privacy noise injection is uploaded to the federated matching learning server, and only the model update quantity after the privacy enhancement processing is uploaded to the federated learning server, so that the leakage risk of concentrated data storage is avoided from the source, the requirements of privacy regulations such as GDPR and the like are completely met, and the reliability of the system is improved. Compared with traditional privacy technologies such as homomorphic encryption, the method has the advantages that the calculation and communication overhead is greatly reduced, the design of local matching degree calculation is matched, the task matching response speed is greatly increased, and the real-time requirement of industrial control system testing is met.

The invention belongs to an intelligent highway pavement structure based on a self-sensing asphalt material and a monitoring system of the intelligent highway pavement structure, and discloses the intelligent highway pavement structure based on the self-sensing asphalt material and the monitoring system of the intelligent highway pavement structure. A pavement structural surface layer is made of a self-sensing asphalt mixture containing an amorphous silicon-based composite functional phase, the functional phase is prepared through a specific process, a nano bowl-shaped array is arranged on the surface of the functional phase, tungsten carbide nano crystal nucleuses are coated on the surface of the functional phase, and the functional phase and asphaltene form a non-equilibrium electron cloud overlapping system. The monitoring system is based on a non-contact microwave resonant cavity coupling principle, analyzes reflected signals, extracts characteristic vectors, and inverts real-time stress, strain, damage factors and temperature state parameters in combination with an inverse problem solver. A traditional sensor pre-embedding mode is abandoned, the pavement body and the sensing function are fused, the problems of implantation damage, life mismatching, low spatial resolution and the like are solved, and a new normal form is provided for intelligent road safety early warning and full-life management.

This invention belongs to the field of power load forecasting technology, and discloses a method, system, equipment, and medium for hierarchical analysis and interpretable modeling of multi-source driving factors in power systems, to solve the problems of insufficient forecasting accuracy, poor model interpretability, and fragmented analysis processes in existing technologies. The method of this invention includes the following steps: S1, fusion and preprocessing of multi-source heterogeneous data; S2, analysis of the inherent laws of power load based on time-series autocorrelation; S3, quantification of the multi-factor correlation strength based on Spearman correlation coefficient; S4, analysis of the nonlinear influence contribution based on XGBoost and SHAP values; S5, comprehensive importance fusion assessment based on two-dimensional weighted geometric average; S6, multi-dimensional sensitivity and marginal effect analysis based on interaction terms. This invention achieves high-precision, interpretable, and closed-loop quantification of the contribution of driving factors in power load analysis.

This invention discloses an AI-based physical education exam scoring method, belonging to the field of exam scoring methods, including the following steps: (1) activating the cameras deployed in the exam room to collect motion images and facial expression images of the examinee during pull-ups; (2) analyzing the collected facial expression images using AI image recognition technology to divide the exam process into three stages; (3) for each stage, analyzing the examinee's motion images using AI technology to determine whether each pull-up motion is a qualified motion; (4) counting the number of qualified motions in each stage and scoring them separately, adding the scores of the three stages to obtain the examinee's final exam score. This invention can divide the exam into three stages based on facial expression recognition, perform differentiated analysis on the motion characteristics of each stage, better fit the examinee's physical fitness change patterns, and make the evaluation results more targeted.

The embodiment of the invention provides a knowledge question and answer method and device. The method comprises the following steps: checking a target question and answer text in a question and answer knowledge base by using a knowledge base checking agent to obtain a checking result, and sending the checking result to a maintainer, so that the maintainer corrects the content of the question and answer knowledge base; analyzing a question and answer text in the question and answer system by using a question and answer case learning agent, and generating a question and answer case library according to an analysis result; wherein the question and answer case library comprises question and answer error cases and question and answer correct cases; when a question text input by a user is received, searching a target question and answer case from the question and answer case library, wherein the similarity between the target question and answer case and the question text is higher than a similarity threshold value; and optimizing the question-answering system according to the system optimization item parameters in the target question-answering case, and generating an answer corresponding to the question text according to the question-answering knowledge base and the target question-answering case after optimization.

The invention relates to the technical field of molecular immunology and biology, in particular to a sicca syndrome model mouse B cell immune repertoire sequencing and subpopulation analysis method. The method comprises the following steps: S1, sample preparation: preparing a single-cell suspension from a target autoimmune disease animal model and a preset tissue of a control animal, and carrying out B cell enrichment treatment on the single-cell suspension to obtain a B cell sample; s2, multi-dimensional data parallel analysis: analyzing the B cell sample, and performing parallel construction of the multi-dimensional data; and S3, integrated correlation analysis: carrying out correlation analysis on the immune group library data, the cell subset data and the signal channel data obtained in the step S2 to establish a correlation relationship among specific B cell cloning characteristics, specific B cell subset changes and specific signal channel activity, and carrying out multilevel analysis and generating a B cell immune response mechanism of the autoimmune disease. The efficiency and accuracy of disease mechanism research and drug action mechanism evaluation are remarkably improved.

The application discloses a kind of based on neighborhood relative entropy multiscale subspace electric load anomaly detection method, including obtaining electric load data, constructs original data matrix and pre-processes;Using principal component analysis to the data matrix is extracted and is reconstructed, constructs multiscale subspace and generates reconstructed sample set;For each scale's subspace, calculate reconstruction error and outlier score;For reconstructed sample set, construct subspace neighborhood information system, and define neighborhood and neighborhood relationship in system;Calculate neighborhood relative entropy outlier score;Reconstruction error and neighborhood relative entropy outlier score are weighted and fused, obtain final fusion outlier score, and whether abnormal is determined according to threshold value.The application removes noise and redundant information in data effectively by multiscale principal component analysis, enhances the capture ability to global anomaly pattern, uses subspace neighborhood information system to mine the uncertainty of local neighborhood, improves the precision and robustness of anomaly detection under complex environment.

The application discloses a kind of thermal power plant rotary air preheater comprehensive health evaluation method and system, determine multiple characteristic parameters, and based on the weight coefficient and deviation degree of each characteristic parameter, comprehensive calculation is carried out, the one-sidedness caused by the evaluation of only single parameter is avoided, and the evaluation result can more truly reflect the overall operation condition of equipment, the deviation degree range of each characteristic parameter used for air preheater comprehensive health evaluation is given in calculation, and the evaluation result of different degrees of influence on comprehensive health index is obtained in later period, the method provides definite maintenance basis for field operation, and ensures the stable operation of thermal power plant rotary air preheater.

This invention provides a method, apparatus, electronic device, and storage medium for identifying critical nodes. The method includes: determining multiple indicator values ​​for any node based on multiple indicators, whereby the multiple indicators are used to evaluate the importance of the node; determining the comprehensive weight corresponding to any indicator using the analytic hierarchy process (AHP) and entropy weight method; determining the node's importance ranking value based on the multiple indicator values ​​and the comprehensive weights corresponding to the multiple indicators for any node; and identifying critical nodes based on the node importance ranking values ​​of all nodes. This invention solves the problem of the one-sidedness of existing critical node identification methods in related technologies.

The present application relates to the technical field of acoustic logging, and particularly relates to a high-stability wide-frequency distributed optical fiber acoustic wave sensing logging method and system. The method comprises the following steps: acquiring logging requirements and downhole geological structure characteristic data, simultaneously collecting corresponding acoustic wave signals, temperature field distribution signals and wellbore structure stress signals, and performing preliminary denoising and synchronization processing and wide-frequency expansion and distortion compensation processing, to generate downhole wide-frequency enhanced acoustic wave signals; based on the downhole wide-frequency enhanced acoustic wave signals, performing downhole propagation stability evaluation and stable adaptive optimization adjustment, to generate corresponding high-stability wide-frequency acoustic wave sensing signals; based on the high-stability wide-frequency acoustic wave sensing signals, performing distributed optical fiber acoustic wave sensing logging analysis, to generate corresponding geological structure and reservoir characteristic logging information. The present application can solve the problems of bandwidth limitation, signal distortion and poor stability of traditional optical fiber acoustic logging in a complex downhole environment, so as to realize high-precision detection of oil and gas reservoirs.

The application provides a power transmission and transformation engineering reinforced concrete corrosion evaluation method and system, and relates to the technical field of civil engineering durability evaluation. The application collects temperature data of the engineering site, total volume of reinforced concrete, water content and pore volume, and constructs a freeze-thaw damage index formula. According to the measured sulfate concentration, the product type is distinguished, and different sulfate corrosion index formulas are constructed by combining related data. The magnesium ion concentration is considered alone in the marine environment. The chloride salt corrosion index formula is constructed according to the chloride ion concentration. The carbonation corrosion index formula is constructed by extracting data from the research report. Then, the weight of each corrosion type is given to establish a comprehensive corrosion evaluation index formula, and the corrosion degree can be determined according to the result. Through this method, various corrosion factors can be considered comprehensively, and the corrosion degree can be effectively determined according to the evaluation result, which provides key basis and scientific guidance for the durability protection, protection measure development and maintenance decision of the reinforced concrete structure of the power transmission and transformation engineering.

The application discloses a detection method of Baizhu San standard decoction fingerprint combined with liquid chromatography-mass spectrometry, the fingerprint and application thereof, and belongs to the field of traditional Chinese medicine quality standard research and application. Test sample solution and single control sample solution are injected into a high performance liquid chromatograph to obtain respective chromatograms; mass spectrometry analysis is conducted on the test sample solution to obtain mass spectrometry results; common peaks existing in the chromatogram of the test sample solution are taken as common peaks, chemical components in the Baizhu San standard decoction are identified in combination with the chromatogram of the single control sample solution and the mass spectrometry results of the test sample solution, and the Baizhu San standard decoction fingerprint is obtained. The fingerprint is constructed through liquid chromatography-mass spectrometry, 10 intersecting components are identified in combination with the mass spectrometry information of the control sample, so that the quality control of the Baizhu San standard decoction is realized, the method is simple, easy to operate, high in precision, good in stability and reproducibility, and can effectively solve the technical problem that the quality of the Baizhu San cannot be effectively evaluated.

The invention relates to the technical field of constructional engineering, and provides a personnel post matching method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring project dynamic information, personnel description information of to-be-matched personnel and post description information of to-be-matched posts; determining a personnel feature vector of a to-be-matched personnel according to the project dynamic information, the personnel description information and a plurality of pre-constructed evaluation dimensions, and determining a post feature vector of a to-be-matched post according to the project dynamic information, the post description information and the plurality of evaluation dimensions; and according to the personnel feature vector and the post feature vector, determining a matching result between the to-be-matched personnel and the to-be-matched post. According to the method, the weight of the dimension parameter can be dynamically adjusted according to the dynamic information and actual requirements of different projects, so that the matching result better fits an actual engineering scene. The one-sidedness of judgment purely depending on experience is avoided, and the accuracy and rationality of personnel post matching are effectively improved.

The application relates to the technical field of production quality control, in particular to a cable production quality control method based on multi-source data; the method comprises the following steps: collecting multi-source production data on a cable production line; based on environment state data and process parameter data, calculating an environment comprehensive cooling coefficient at each collection time; based on the environment comprehensive cooling coefficient and the process parameter data, calculating a dynamic cooling rate factor at each collection time; calculating the proportion of the difference between the dynamic cooling rate factor and a preset optimal dynamic cooling rate factor in the optimal dynamic cooling rate factor, and obtaining a non-uniform crystallization risk index at each collection time; inputting the non-uniform crystallization risk index and the multi-source production data into a pre-trained dynamic Bayesian network model to obtain a predicted value of a cable quality parameter at the next collection time, and controlling the cable production quality; the problem that the cable production quality control effect is poor is solved.

The application discloses an airport bird risk assessment method based on bird air situation, and belongs to the technical field of aviation safety. The flight position and speed of a flying bird, the three-dimensional coordinate information of the starting point and the ending point of the center line of a fixed runway of an airport, and the flight path, real-time position and speed of an aircraft target are acquired; the spatial proximity probability is calculated based on the center position and the flight path of a flying bird group; the speed consistency probability is calculated based on the speed vector and the relative position of the flying bird group center position and the aircraft; the height matching probability is calculated based on the flying bird group center position and the aircraft position; the spatial area risk probability is calculated based on the horizontal distance between the flying bird group center position and the runway center line; and the cluster dispersion probability is calculated based on the number of the flying bird group and the dispersion characteristics of the three-dimensional space of the flying bird group. The comprehensive collision probability is obtained by weighting and summing each partial probability according to the weight. The application provides strong technical support for the stable operation of an airport.

The application relates to the technical field of detection of electrified railway contact network systems, and discloses a contact network system fluorescent fingerprint intelligent analysis system, which comprises a visual unit, an emitting unit, a deflection unit, a receiving unit, a detection unit and a main control unit. The visual unit calculates relative motion parameters of insulators, drives the deflection unit to perform reverse compensation motion, so that a laser landing point remains stationary relative to a target surface within an integration time. The detection unit respectively collects parallel and vertical polarization components of Rayleigh scattering light and Stokes luminescence after spectral separation. The main control unit calculates a surface roughness factor by using the Rayleigh scattering component, carries out numerical correction on a polarization anisotropy parameter of the Stokes luminescence, eliminates the influence of geometric depolarization, and extracts fluorescent lifetime and rotation-related time characteristics. The application solves the fluorescent detection distortion problem caused by rough surfaces in a dynamic environment, and realizes multi-dimensional diagnosis of the contamination type and the substrate aging degree of the insulator surface.

The present application provides a kind of methanol hydrogen energy risk assessment method and device based on fusion strategy for ship, it is related to system safety engineering and risk management technical field, the risk assessment method of the present application includes: through system level hazard analysis, the unsafe control behavior of power system is identified, and combined with component level failure analysis, a hazard causal chain from bottom physical failure mode to top system level hazard is constructed;Based on hazard causal chain, a hierarchical causal hazard and operability knowledge graph is constructed;Through multi-modal perception to the running state of power system, unstructured multi-modal data stream is converted into a structured semantic event sequence in real time;Structured semantic event and causal hazard and operability knowledge graph are matched and cognitively reasoned to output the diagnosis report to the current risk state of power system, so as to complete risk assessment.

The invention relates to the technical field of financial risk control, in particular to a financial transaction abnormal data real-time monitoring method and system based on deep learning. Comprising the steps of collecting external macroscopic variables in real time; identifying the macroscopic state of the current market; generating a macroscopic state control signal; transmitting the macroscopic state control signal to a preset dynamic baseline management unit; retrieving a situation baseline model matched with the macroscopic state from a preset situation baseline model library; calling a situation baseline parameter corresponding to the situation baseline model; coding the financial transaction flow data by using a deep neural network; generating a microcosmic behavior feature vector; calculating a semantic distance between the microscopic behavior feature vector and a normal feature space; comparing the semantic distance with a preset discrimination threshold value; and generating an abnormal risk alarm in response to the semantic distance being greater than the discrimination threshold. According to the invention, the false alarm rate when the market fluctuates violently and the missing report rate under the stable market are obviously reduced, and the monitoring accuracy is improved.

The invention discloses a power grounding state monitoring method and system, which is applied to the field of power equipment state monitoring, and comprises the following steps: synchronously obtaining a time sequence mechanical state signal and a time sequence electrical state signal of a grounding terminal; extracting and constructing a multi-dimensional feature vector containing static and dynamic feature components; performing differentiation evaluation and integration on the feature components by using a pre-constructed hybrid analysis model to generate an initial monitoring result; and introducing environmental parameters to dynamically calibrate the initial result to obtain a calibrated grounding state monitoring result. According to the electric power grounding state monitoring method provided by the invention, the mechanical tightness and the electrical connectivity information are fused, and the intelligent model is adopted to carry out collaborative analysis and environment adaptive calibration, so that the comprehensive and accurate sensing of the connection state of the grounding terminal is realized.

This invention relates to an intelligent milling tool wear state monitoring method. It pre-constructs a milling tool type sub-matching model and a set of milling signal curves generated during the milling of workpieces of different materials. This allows for the acquisition of benchmark values ​​for the wear state of the milling tool when milling workpieces of different materials under different wear conditions. Furthermore, it pre-constructs a set of workpiece surface feature parameters and a set of milling waste chip feature parameters when milling workpieces of different materials under different wear conditions. Then, it constructs separate milling tool wear state judgment sub-models for milling workpieces of different materials using milling tools of the same material. The workpiece material, the current milling tool material type, the current set of milling signal curves, the current workpiece surface feature parameters, and the current milling waste chip feature parameters are input into the milling tool wear state judgment sub-models to obtain the monitored wear state of the current milling tool.

The invention relates to the technical field of computer vision and system reliability engineering, in particular to a visual system reliability early warning method, a visual system reliability early warning system, visual system reliability early warning equipment and a storage medium. The multi-modal time series data comprises input data features, output data features, environmental parameters and performance degradation indexes; carrying out fusion processing on the multi-modal time sequence data based on a multi-modal fusion model to generate a system state representation vector; inputting the system state representation vector into a time sequence probability model, updating parameters of the time sequence probability model through an online learning mechanism, and calculating a fault probability of the visual system in a preset future time window; dynamically calculating an early warning threshold according to the environmental parameters and the performance degradation index at the current moment; and when the predicted fault probability exceeds an early warning threshold, triggering an early warning signal. By adopting the scheme, the early warning accuracy and the system reliability can be improved.

The invention relates to the technical field of power system frequency modulation auxiliary service markets, in particular to a unit frequency modulation auxiliary service market transaction settlement method and system. The method comprises the following steps: acquiring real-time operation data, quotation data and market node electricity price data of a unit from a power grid dispatching center and an electric power spot market system; based on the processed data, adjusting frequency, response time and adjusting precision are extracted to serve as evaluation parameters, and a subjective and objective combination weighting method is adopted to calculate comprehensive performance indexes; sorting the units from low to high according to the sorting prices, and determining bid-winning units and capacities; and a unified weighted average settlement price is calculated, and final settlement is carried out in combination with opportunity cost compensation. According to the method, the problems of insufficient fairness, low efficiency and lack of transparency in existing frequency modulation market transaction settlement are solved, a high-quality and high-price fair market environment is realized through comprehensive performance indexes and a block chain technology, participation of a high-performance unit is stimulated, and the settlement efficiency is improved.