97 results about "Scoring algorithm" patented technology

The invention discloses an intelligent operation abnormity monitoring and positioning method for a polypropylene cable, and relates to the technical field of intelligent operation and maintenance of a power system, and the method comprises the steps: collecting multi-source physical signals in the operation process of the cable, and constructing a multi-dimensional feature matrix fusing multiple physical quantities through multi-scale time window division and space mapping processing; extracting space-time coupling characteristics among nodes by using a graph attention embedding network, and training a running state recognition model in combination with a label perception contrast learning mechanism; constructing a cable topological graph based on an identification result, introducing an improved Bayesian space reasoning network, and calculating abnormal probability distribution of each node; a weighted abnormal heat map is further generated, an abnormal propagation path is extracted through an abnormal state flow model and a directional propagation scoring algorithm, and abnormal node positioning and trend evolution prediction are achieved; the method has the advantages of high spatial resolution, high identification precision and good online adaptability, and is suitable for intelligent state perception and abnormity early warning of the polypropylene cable in a complex operation environment.

The invention relates to the field of computer systems, in particular to an operation stability testing method and system oriented to a heterogeneous computing system, and the method comprises the steps: constructing a system overall operation logic model comprising a resource topological structure, a task scheduling rule and a communication link matrix; identifying a key communication path according to the communication link matrix, analyzing a resource competition hotspot in combination with a task scheduling rule, and establishing an interference model library; calling a specific model instance in the interference model library according to a test target, configuring injection parameters, and dynamically applying interference in a system operation process; key performance indexes in the system operation process are collected in real time; based on the real-time monitoring data, a multi-index weighted scoring algorithm is adopted to calculate an overall stability coefficient, a robustness grade and a self-healing capability index of the system, and a stability evaluation result is generated; and generating a visual report according to the topological structure of the operation logic model, and supporting comparison and analysis of historical versions. Unified centralized management is realized, and test efficiency and evaluation accuracy are improved.

The invention provides a contaminated site remediation technology scoring system which comprises a multi-dimensional evaluation index system construction module, a situational weight dynamic adjustment module, a remediation technology applicability scoring module, an uncertainty analysis and sensitivity test module, a scoring result visualization module, a technical scheme sorting and screening unit and the like. Through four innovations of a dynamic weight adaptation mechanism, a multi-model fusion scoring algorithm, a technology combination scheme evaluation module and uncertainty robust analysis, the problems of single dimension, weight solidification, difficulty in quantification of a synergistic effect and the like in pollution remediation technology evaluation are systematically solved; and an objective, efficient and transparent intelligent decision-making tool is provided for repairing a complex polluted site.

The invention discloses a multi-mode mental health risk intelligent early identification system and method. On the basis of awareness and voluntary, the system can collect 9 types of data sources in an anonymization mode, a quintuple privacy protection architecture is constructed, and the quintuple privacy protection architecture specifically comprises irreversible code ID generation, national cryptographic algorithm and quantum key dual encryption, federal learning localization processing, block chain evidence storage and dynamic desensitization erasing. The innovation point is that based on a multi-modal fusion model and a dynamic scoring algorithm, a dynamic psychological scale is used for scoring, and graded early warning of psychological health risks is realized. By means of a federated learning framework, continuous optimization and cooperation of parameters of the cross-calibration scale can be realized. On the premise of strictly protecting privacy, early recognition and grading suggestion of student mental health risks can be achieved, medical diagnosis is not involved, and the requirements of GDPR and other data protection laws and regulations are met.

The invention relates to the technical field of information security, in particular to a user information protection method and device based on a dynamic desensitization strategy, and the method comprises the steps: determining sensitive data and a sensitivity score thereof through a named entity recognition model and a sensitivity scoring algorithm, extracting scene parameters, and determining a scene risk score through a risk quantification algorithm; and then a dynamic desensitization strategy generation algorithm is called to generate a target desensitization strategy, and finally, the strategy is used for desensitization and a reversible desensitization data identifier is generated. Through the series of steps, the desensitization strategy is dynamically generated according to different use scenes, the problem of scene adaptability deficiency caused by static desensitization strategy stiffness is solved, meanwhile, the reversible desensitization reduces the security risk caused by reverse cracking of rules, excessive desensitization is avoided, and the data availability is improved.

The invention discloses an LSTM electric power Internet of Things ring main unit optimization method, system, device and medium, and belongs to the technical field of electric power Internet of Things management and data analysis, and the method comprises the steps: collecting communication state and network environment data in real time, and generating a multi-dimensional feature matrix through dynamic standard score standardization and derivative feature calculation; dynamically adjusting an LSTM forgetting gate weight based on the bandwidth fluctuation index, and outputting an equipment performance score; constructing a dynamic scoring algorithm and triggering a differentiated maintenance instruction in linkage with a multi-threshold scene strategy; a closed-loop feedback mechanism is adopted to screen high-deviation data, new scene training is enhanced through a dynamic loss function, and minute-level hot updating of the model is achieved; the method solves the problems of static threshold misjudgment, poor environment adaptability of the prediction model and single decision-making mechanism, remarkably improves the feature quality, the equipment evaluation precision and the resource allocation rationality, and strengthens the intelligent operation and maintenance capability of the system.

The invention discloses a cold chain temperature and humidity dynamic early warning method and system based on neural network prediction. The method comprises the following steps: collecting state data and environmental parameter data of equipment operation in real time; inputting the data into the trained multi-task prediction model, and outputting a future prediction value; evaluating the credibility of the predicted value, if the credibility is greater than a threshold value, performing prediction after correcting the multi-task prediction model, and if the credibility is less than or equal to the threshold value, performing the next step; and comparing the prediction result with a current actual threshold interval through a dynamic risk scoring algorithm to generate a dynamic risk score, and if the risk score exceeds a dynamic threshold, pushing a grading early warning to a management platform or a user. Compared with a traditional method, the method has the advantages that more time is brought forward to find potential overrun risks, sufficient time is reserved for manual intervention, data sequences are collected regularly based on sensor data collection, and meanwhile, the working conditions of equipment and external public opinion information are comprehensively analyzed to give intelligent early warning information.

The invention relates to the technical field of risk management, and discloses a financial AI large model-oriented multi-dimensional test intelligent risk management and control method, which comprises the steps of integrating channel test, data test, AI test and AI large model test by constructing a financial AI large model test special database; traditional financial sensitive fields and AI test specific sensitive fields are accurately identified based on data consanguinity analysis, and dynamic desensitization is realized in combination with a lightweight desensitization engine; calling a preset risk scene library, defining a risk index system of each scene, dynamically calculating a risk level through a weighted scoring algorithm, and carrying out modeling by using a graph database to identify hidden linkage risks; triggering a grading response strategy according to the risk grade, and dynamically optimizing resource allocation; and after the test is finished, a multi-dimensional risk thermodynamic diagram and a root cause analysis report are generated, and processing experience is fed back to the scene library optimization strategy. According to the invention, the efficiency and safety of risk management and control can be improved.

The invention discloses a regional wind-solar combined power interval prediction method and system based on multi-task learning and a hybrid neural network, and relates to the technical field of combined prediction. Meteorological data and wind-solar historical power data of a to-be-researched region are collected, and a historical data set is constructed; preprocessing the historical data set, and performing feature selection by adopting a predictive ability scoring algorithm to obtain screened meteorological data and historical power data; constructing a joint power interval prediction model; and performing combined power generation prediction based on the trained combined power interval prediction model. According to the method, the space-time correlation and the complementary relation of wind energy and photovoltaic energy are excavated, the joint power interval prediction model is constructed, and the power prediction precision is improved.

The invention provides a user portrait method and device for driving training learning, electronic equipment and a medium, and belongs to the technical field of information recommendation, and the method comprises the steps: collecting multi-source heterogeneous data of a student, carrying out the data preprocessing, and obtaining a student data file, the multi-source heterogeneous data comprises learning stage data, capability evaluation data and basic attribute data; based on the trainee data file, extracting two-dimensional features of trainees, including obtaining learning stage features according to division of passing states and learning progress completion rates of driving test subjects, and quantifying the mastering degree of subdivided test points under weak subjects based on a weighted scoring algorithm to obtain weak subject features; and fusing the two-dimensional features and the basic attribute data, and constructing a student portrait including a learning stage label and a weak subject label. According to the invention, the accuracy of the user portrait is improved.

The invention relates to the technical field of conversation type artificial intelligence, in particular to a multi-round conversation robot intention hit optimization method. The method comprises the following steps: after a user utterance is received, a system generates candidate intentions through a double-path mechanism; then, each candidate intention is evaluated through a multi-factor scoring algorithm, and the algorithm fuses four key signals: a graph transition probability, semantic correlation, a context coherence score calculated through a cross attention mechanism, and an out-of-domain penalty; and finally, making a decision according to the final scores of the candidate intentions: if the confidence coefficient of the candidate intention with the highest score is high enough and the candidate intention is obviously distinguished from other candidates, confirming the intention; otherwise, a targeted clarification problem is generated to solve ambiguity. According to the method, the accuracy and robustness of intention detection are remarkably improved through collaborative integration of dialogue streams, semantic matching and deep context analysis, and meanwhile, the user experience is improved through intelligent ambiguity processing.

The invention discloses a remote monitoring and scheduling management and control platform for signal shielding equipment, which comprises a general control center and four linkage sub-modules, wherein the four linkage sub-modules realize data interaction with the general control center through an encryption special communication protocol; the four linkage sub-modules are respectively a secret-involved subject permission shielding linkage module, a multi-scene shielding mode adaptation module, a precise anti-secret-stealing shielding module and a full-link monitoring traceability module; the general control center is internally provided with a multi-dimensional confidential risk decision-making engine, the decision-making engine is integrated with a weighted scoring algorithm, and accurate binding of confidential subject qualification and a shielding strategy is achieved. Based on a double-biological-characteristic cross verification mechanism and carrier circulation management and control logic, personnel confidentiality qualification, carrier confidentiality-related state and shielding permission are deeply associated, confidentiality-related risks caused by access of non-qualified personnel are completely eradicated, meanwhile, shielding strategy automatic switching of the whole carrier access process is achieved, the problem that platform personnel permission is disjointed from a protection strategy is solved, and the safety of the platform is improved. And the basic protection safety of a secret-related area and a carrier is greatly improved.

The invention provides a dynamic reactive compensation ad hoc network control system and method for a power transmission line. The control method comprises the steps that a self-repairing network is constructed through a scoring algorithm after compensation nodes are self-initialized; acquiring data such as voltage and current in real time, and preprocessing and transmitting; the central control unit generates a dynamic compensation strategy based on an NSGA-II multi-objective optimization algorithm in combination with voltage stabilization, a power factor, active loss, load balancing and renewable energy consumption targets, and edge calculation realizes local reactive rapid adjustment through fuzzy logic; faults are monitored in real time and rapidly isolated, network topology is reconstructed based on residual nodes, a compensation strategy is recalculated, and system self-healing is achieved. The communication reliability and flexibility are improved through a distributed ad hoc network architecture, the single compensation defect is solved through multi-target collaborative optimization, the method is suitable for a complex power grid containing a distributed power supply, the voltage stability is remarkably improved, the line loss is reduced, and an efficient and reliable reactive compensation solution is provided for an intelligent power grid.

The invention relates to a data analysis method based on large model and rule collaboration and a related device. The method comprises the following steps: receiving a natural language query input by a user; calling a large language model to carry out context induction and rewriting on the natural language query and the historical dialogue to obtain a standardized query statement; matching the standardized query statement by utilizing a similarity comprehensive scoring algorithm, and identifying to obtain a corresponding target index; based on a preset time rule library and an organization library, labeling time entities and organization entities in the standardized query statements to obtain labeling results; according to a target index, the labeling result and the corresponding index metadata information, generating an executable structured query statement; and submitting the structured query statement to a database for execution, and returning a query result. The method has the advantages that the accuracy of query generation, the performance of system response and the controllability of an output result are ensured while the semantic understanding capability of the large language model is fully utilized.

The invention discloses a green financial transformation field judgment system based on a keyword library and a scoring algorithm. The green financial transformation field judgment system is characterized by comprising a keyword library construction module, a data uploading module, a scoring module, a core phrase extraction module and a field judgment module, the keyword library construction module is used for analyzing and arranging the content of the transition finance support economic activity catalog to form a keyword library; the data uploading module is used for receiving enterprise data; the core phrase extraction module is used for extracting core phrases from enterprise data; the scoring module is used for matching and scoring the extracted core phrases according to the importance weight of each keyword in the keyword library, and calculating to obtain a comprehensive score of each field; and the field judgment module is used for comparing the comprehensive scores of the fields calculated by the scoring module, and selecting the field with the highest comprehensive score as the green financial transformation field to which the target enterprise belongs. The method has the advantages that the green financial transformation field to which the enterprise belongs can be automatically, efficiently and accurately judged.

The invention provides a security authentication data management method and system, and relates to the technical field of data management, and the method comprises the steps: obtaining authentication data of a plurality of platforms; performing data cleaning on the authentication data through a three-layer processing model; performing automatic matching and mapping of similar fields on the authentication data after data cleaning through a pre-constructed intelligent field matching mechanism to obtain standardized authentication data; calculating the similarity between the standardized authentication data and each classification category in a predefined authentication category map through a VSM model and cosine similarity measurement, and screening out high-correlation authentication data; classifying the high-correlation authentication data into corresponding classification categories; obtaining a query keyword of a user; according to the query keyword, obtaining a query result corresponding to the query keyword through a correlation scoring algorithm; and displaying the query result to the user according to a sorting and paging mode.

The invention discloses an AI-based schizophrenia clinical thinking training simulation system, which relates to the technical field of artificial intelligence, and is characterized by comprising the steps of constructing a dynamic knowledge base, receiving and preprocessing multi-modal input, performing standardized mapping by extracting keywords, and then performing judgment of a rule layer and a semantic layer. A student question method and a standard answer method in the dynamic knowledge base are sent into a model for response generation, then four-dimensional quantitative scoring and clinical operation multi-dimensional scoring are carried out, error codes and positioning sentences thereof are output, and finally personalized training courses are recommended to students; and hot-updating the dynamic knowledge base continuous optimization model. According to the method, four dimensions of the ICD-10 diagnostic standard are converted into a quantitative scoring algorithm capable of being automatically executed, quantitative assessment of clinical operation specifications is integrated, a diagnosis and operation double-track evaluation system is constructed, the problems that a traditional method is lack of evaluation standards and high in subjectivity are solved, and the evaluation efficiency is improved. Therefore, the evaluation of the clinical thinking ability of the medical students becomes objective, comprehensive and evidence-based.

Disclosed herein are systems and methods for generating an Al work score for tasks assisted by artificial intelligence. The score is determined by collecting and processing user data using scoring algorithms that consider at least one of the following: Al integration, task complexity, baseline metrics, and time calibration. The system and methods also generate composite scores across users, tasks, groups or industries and classifications across various metrics, enabling correlation reports. These scores can assist in evaluating Al integration, optimizing task assignments and improving Al usage in supervised environments.

The application relates to a kind of audio and video acquisition terminal position auxiliary installation method and system, wherein the audio and video acquisition terminal position auxiliary installation method includes: audio and video acquisition terminal receives the scoring algorithm program that platform server issues and matches target application scene;In the program is run locally in audio and video acquisition terminal and real-time analysis is carried out to self live video stream, and objective position visual angle score is generated;The score is sent to the client to guide the client user to adjust position.By the application, the problem that the result is not accurate and the feedback is not timely caused by model difference and network delay in cloud side evaluation is solved, and the pain point that evaluation standard is not unified in artificial subjective installation is solved, real-time, accurate and quantitative installation process is realized, so as to improve installation efficiency, quality and recognition accuracy of back-end AI algorithm.

The application discloses an LSTM power internet of things looped netowrk cabinet optimization method, system, device and medium, belongs to the technical field of power internet of things management and data analysis, including real-time collection of communication state and network environment data, generating a multi-dimensional feature matrix through dynamic standard score standardization and derivative feature calculation; dynamically adjusting the LSTM forgetting gate weight based on the bandwidth fluctuation index, outputting the device performance score; constructing a dynamic scoring algorithm and triggering differentiated maintenance instructions in conjunction with multi-threshold scene strategies; screening high-bias data using a closed-loop feedback mechanism, strengthening new scene training through a dynamic loss function, and realizing minute-level hot updating of the model; The application solves the problems of static threshold misjudgment, poor environmental adaptability of the prediction model, and single decision mechanism, significantly improves feature quality, equipment evaluation accuracy, and resource allocation rationality, and strengthens the intelligent operation and maintenance capability of the system.

A method is disclosed. The method includes generating a graph with nodes and edges, inputting the node data for the nodes into a first scoring algorithm to obtain node scores, and inputting the edge data into a second scoring algorithm to obtain edge scores. The method also includes forming one or more subgraphs from the graph. Each subgraph is formed by: determining an initial node with a node score that exceeds a threshold, determining an initial set of edges connected to the initial node, determining a set of nodes connected to the initial set of edges, and determining additional edges and additional nodes extending from the set of nodes, until a predetermined number of the additional nodes and edges have edge scores and node scores that do not meet the threshold, thereby forming the subgraph.

The embodiment of the invention discloses an agent knowledge structure determination method and device, equipment, a medium and a product. The method comprises the steps that scene data and task data of a target agent in a current operation task scene are acquired; inputting the scene data and the task data into an agent knowledge structure recommendation model to obtain at least one recommendation knowledge structure corresponding to the target agent; the recommended knowledge structure is a knowledge structure defined in a preset knowledge structure library; determining a scoring result corresponding to the recommended knowledge structure by using a preset scoring algorithm; and according to the scoring result, determining a target knowledge structure adopted by the target agent in the current operation task scene from the recommended knowledge structure. According to the technical scheme, the skills of the intelligent agent are accurately improved, and the ability of the intelligent agent to adapt to different industries and scenes is improved.

The invention discloses a grabbing method, device and equipment based on a motion intention and concentration degree and a medium, and the method comprises the steps: obtaining a training sample set of an intention level and a grabbing power level, and training a support vector machine model through the training sample set; performing standardization processing on the first energy ratio feature sequence, the first original feature sequence and the first intrinsic mode feature sequence by adopting a Z-Score algorithm to obtain a second energy ratio feature sequence, a second original feature sequence and a plurality of second intrinsic mode feature sequences; and performing classification decision by using the target support vector machine model to obtain an intention level, simultaneously obtaining a user concentration average value, and determining a target output holding power according to the user concentration average value and the intention level. And the stress state of the grabbed target is detected in real time, and PID operation is carried out. Fine hierarchical control of the grabbing force is achieved, the recognition precision of the grabbing intention is improved, and the problem that traditional brain control equipment grabs but cannot stably hold is solved by conducting anti-falling PID operation in real time.

The present application provides a professional ability evaluation system based on a skill map, relates to the technical field of human resources and the application field of knowledge maps, and comprises an evaluation main system, which comprises a skill star map generation module, a professional track simulation module, a skill challenge module, a skill certification chain module, a future skill navigation module, a skill coordination ecological module, a skill evolution tree module, a professional story generation module, a skill energy pool module, a professional time capsule module and a control flow management module. The present application can collect user professional experience, learning records and test data in time by constructing a dynamic skill star map and combining 5G network real-time data transmission, and can effectively overcome the problems of strong subjectivity, low efficiency and inability to accurately quantify ability of traditional manual evaluation and online testing by dynamically adjusting node weights through a semantic analysis algorithm and a weighted scoring algorithm.

The invention relates to the technical field of electric power sample detection, and discloses an electric power detection sample intelligent scheduling system and method based on multi-modal perception, and the system comprises a multi-modal perception collection module which is used for carrying out the real-time monitoring of the identity binding of a sample, the three-dimensional space positioning and the state of detection equipment through a UHF RFID tag, a UWB positioning base station and equipment PLC protocol analysis; the data co-processing module is used for performing millisecond-level synchronization and structured processing on the multi-source sensing data through timestamp alignment and feature fusion; an intelligent scheduling decision module; an automatic execution control module; and a whole-process tracing management module. In the invention, the task type is matched with the preset weight parameter, the comprehensive scheduling score of the candidate equipment is calculated based on the priority scoring algorithm, the hierarchical decision strategy is triggered according to the idle time threshold of the equipment, and the scheduling instruction set containing the coordinate of the target equipment and the transfer instruction is output, so that the resource utilization rate of balancing the task type and the path is improved.

The invention relates to the field of higher vocational practice teaching quality evaluation, in particular to a higher vocational practice teaching quality evaluation method based on multi-source data fusion. The method comprises the following steps: firstly, carrying out standardization processing on collected original scoring data to obtain standardized scoring data; based on the standardized scoring data, generating a fusion score of each student by using a multi-subject fusion scoring algorithm; then, based on the fusion score of each student, a comprehensive teaching quality index is calculated through a comprehensive teaching quality index calculation algorithm; and finally, based on the comprehensive teaching quality index, carrying out grading evaluation on the teaching quality. The technical problems that fairness and consistency of multi-subject evaluation are insufficient, diversity and complexity of evaluation indexes are difficult to integrate, nonlinear growth of students is difficult to quantify, influence of a group learning environment on individual performance is ignored, and adaptability and dynamism of teaching quality grading evaluation are insufficient are solved.

The invention belongs to the technical field of code generation, and particularly relates to a business system code automatic generation method and system.The method comprises the steps that a natural language requirement document is analyzed through a pre-trained natural language processing model, a structured requirement case is generated, and a domain model is constructed; dynamically calculating an optimal system framework through a framework selection scoring algorithm to obtain a target framework; converting the domain model into a plurality of executable code modules of the target framework; calculating a module cohesion degree for the executable code module, and maintaining a current module structure when the module cohesion degree is greater than or equal to a set threshold value; otherwise, automatically splitting the executable code module, generating a distributed code module according to the coupling degree of the service capability unit, and recalculating the cohesion degree of the modules until the cohesion degrees of all the modules are greater than or equal to a set threshold value; and optimizing the generated code by adopting static code analysis in combination with reinforcement learning. The high cohesion and low coupling of the code are ensured, and the quality and maintainability of the code are improved.

This invention relates to a method and system for evaluating the integrity of historical urban landscape based on deep learning. The method includes: acquiring urban street view image data from multiple locations; extracting the sky ratio and green view rate of the urban street view images as street-scale spatial features and natural element spatial features, respectively; calculating the number of shop sign colors and color harmony as architectural color spatial features; and calculating the proportion and consistency of traditional materials as material mechanism spatial features; obtaining expert scores based on the ELO scoring algorithm for the urban street view image data; constructing and training an evaluation model based on an artificial neural network based on the various spatial features and a small-scale expert score dataset; and using the evaluation model to evaluate the integrity of the historical urban landscape.

The invention discloses a data analysis processing method and system based on hydraulic power plant intelligent early warning, and relates to the technical field of hydraulic power plant intelligent early warning. The method comprises the following steps: firstly, collecting water flow, equipment operation and power generation data of whole plant equipment such as a water regimen reservoir and a 500kV system, and then carrying out data cleaning and special processing through a sliding window Z-score algorithm and the like; then, generating real-time early warning data through feature pre-calculation, recognizing an abnormal unit by combining four types of analysis and a dynamic threshold value, and obtaining an event classification result through linkage analysis and a multi-classification model; and finally, event levels are divided and faults are diagnosed. According to the invention, the problem of depending on manual screen monitoring and fixed threshold alarm is solved, equipment abnormity can be found in advance, faults can be accurately positioned, and the monitoring efficiency is improved.

The invention relates to a scientific and technological paper competitiveness quantitative evaluation method fusing multi-dimensional academic indexes. Belongs to the field of academic evaluation and scientific research management. In order to solve the problem that a traditional evaluation method excessively depends on a single index and cannot comprehensively reflect the quality of scientific research achievements, a comprehensive evaluation framework including four dimensions of production, quality, influence and cooperation is provided. Through a dynamic weighted scoring algorithm, the method can achieve the flexible adjustment of the weight of each dimension according to the real-time data, and improves the accuracy and timeliness of evaluation. According to the method, by means of a logic growth function, a quality index model, a social influence score and the like, multi-dimensional quantitative evaluation is carried out on each thesis, and finally a comprehensive score is generated. In addition, an evaluation result is displayed by adopting a data visualization technology, so that a user can intuitively understand and apply the information. A scientific and objective evaluation tool is provided for scientific research personnel, academic institutions and review institutions, and effective development and innovation of scientific research work are promoted.