178 results about "Scoring algorithm" patented technology

The invention discloses a precise numerical control machine tool operation state intelligent control method and system, and particularly relates to the technical field of numerical control machine tools. Multi-dimensional operation data, including working temperature, load, current, pressure, cutting force and tool wear data, of a machine tool are collected in real time, a Z-score algorithm is adopted for data preprocessing, abnormal values are extracted, a prediction model is constructed and trained based on a machine learning algorithm, abnormal data are analyzed in real time, and the abnormal values of the machine tool are analyzed in real time. And accurately identifying the operation state of the machine tool and evaluating the health condition. According to the health state evaluation result, an intelligent control strategy is automatically generated, and the feeding speed, the spindle rotating speed and the cutting depth of the machine tool are dynamically adjusted, so that the operation state of the machine tool is optimized, potential faults are found and dealt with in time, the machining quality is improved, the faults are reduced, the production efficiency and the equipment stability are improved, and the maintenance cost is reduced.

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 a deep learning-based historical urban style integrity evaluation method and system. The method comprises the following steps of: obtaining urban street view image data of a plurality of point locations; extracting a sky ratio and a green vision rate of an urban street scene image according to the urban street scene image data, respectively taking the sky ratio and the green vision rate as a street profile scale spatial feature and a natural element spatial feature, and calculating a signage color number and a color harmony degree of the urban street scene image as building color spatial features; calculating a traditional material proportion and material consistency of the urban street scene image as material mechanism spatial features; for the urban street view image data, obtaining an expert score obtained based on an ELO scoring algorithm; and constructing and training an evaluation model based on an artificial neural network based on the various spatial features and the small-scale expert scoring data set, and realizing historical urban style and appearance integrity evaluation by using the evaluation model.

Aspects of the invention provide a method, computer system, and computer program product for retrieval augmented generation. In one aspect, the method includes receiving a query. The method further includes classifying the query to a first domain within a multitude of domains. The method additionally includes retrieving an index of domain-specific vector embeddings corresponding to the domains. The method further includes prompting a large language model (LLM) with the query and the domain-specific vector embeddings. The method also includes receiving a query response from the LLM as grounded with the most relevant index results. The method further includes forwarding the query response.

The invention provides a cluster reliability test method based on fault simulation, which comprises the following steps: acquiring injection parameter information, and performing fault simulation in a cluster based on the injection parameter information and a fault transfer model to obtain a complex fault scene; key performance index records are obtained in real time based on the complex fault scene; executing a reliability detection step based on a preset reliability detection model and the key performance index record, and obtaining a data analysis report; and obtaining a reliability score, an influence analysis result and a risk prediction result based on a data analysis report, a weighted scoring algorithm, an anomaly detection algorithm and linear regression analysis, and completing reliability detection of the cluster. According to the cluster reliability test method and system based on fault simulation provided by the invention, the test efficiency and the reliability detection level are greatly improved, the fault simulation is carried out in the cluster and the reliability detection model is combined, so that the accurate quantification of the reliability detection result is realized, and the reliability detection efficiency and the detection capability are improved.

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 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 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 course recommendation method and system for intelligently building an education platform, and relates to the technical field of intelligent education informatization, and the method comprises the steps: building a knowledge graph, and completing the multi-level association of tasks and knowledge points; analyzing the assessment data, identifying weak knowledge points of the trainees, and setting score threshold fusion post weights; generating a course recommendation result on the basis of recognizing weak knowledge points of students, and optimizing recommendation logic in real time by adopting feedback; according to the method, a knowledge graph model associated with three layers of tasks, knowledge points and posts is constructed, student examination performance and post skill requirements are fused, and a course recommendation path construction mechanism driven by a multi-dimensional weight is realized; a dynamic feedback and real-time scoring algorithm is adopted, closed-loop logic of recommendation, assessment and re-recommendation is established, recommendation individuation and post matching degree are effectively improved, and the problems that knowledge points in an existing platform are not traceable, post adaptability is weak, and a recommendation strategy is single are solved.

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 provides an operation and maintenance index anomaly evaluation method, device and equipment. The method comprises the following steps: establishing probability distribution of an abnormal deviation sequence and an abnormal duration sequence of the operation and maintenance KPI according to a time sequence of the operation and maintenance KPI obtained in real time; respectively determining an abnormal deviation probability value and an abnormal duration probability value according to the probability distribution of the abnormal deviation sequence and the abnormal duration sequence of the operation and maintenance KPI; according to the abnormal deviation probability value and the abnormal duration probability value, determining an abnormal score based on a preset abnormal scoring algorithm; and determining an alarm level according to the abnormal score and a preset corresponding relationship between the abnormal score and the alarm level, and generating an alarm. According to the method, the overall trend on the time sequence of the operation and maintenance KPI can be reflected, the difference between the abnormal values on the time sequence of the operation and maintenance KPI can be reflected, the obtained abnormal score can better reflect the abnormal information of a real fault, and high-level false alarms are avoided or reduced.

The invention discloses a carbon emission accurate accounting system based on big data analysis, and relates to the technical field of carbon emission management, and the system comprises a data obtaining module which obtains carbon emission data in real time, and carries out the processing and fusion of the data based on a distributed big data storage technology; the carbon emission accounting engine module is used for processing the fused data based on a big data analysis algorithm, establishing a carbon emission calculation model and calculating the carbon emission; the intelligent prediction module is used for predicting future carbon emission by adopting a deep learning model; the carbon credit automatic evaluation and transaction module establishes a carbon credit scoring algorithm, dynamically evaluates the carbon emission reduction performance of the user based on the real-time carbon emission data and the carbon emission prediction data, and automatically generates the carbon credit. According to the invention, by integrating multi-source large-scale data and combining adaptive data weighting, a deep learning prediction model and a carbon credit automatic evaluation algorithm, accurate accounting, dynamic prediction and intelligent carbon credit evaluation of carbon emission can be realized.

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 discloses a building electromechanical system fault intelligent detection method and system, and relates to the technical field of building electromechanical system maintenance, and the method comprises the following steps: system data collection, health value evaluation, fault prediction, priority maintenance, and evaluation of the importance of electromechanical equipment needing maintenance through a priority scoring algorithm. And obtaining a maintenance priority score S, and notifying maintenance personnel to perform maintenance and data storage on the electromechanical equipment in sequence according to the maintenance priority score S. The method has the advantages that the equipment health value is quantized through an electromechanical system health evaluation algorithm, the equipment fault risk is predicted in combination with a fault probability prediction algorithm, and whether the equipment needs to be maintained or not is judged in advance; the system is prevented from being damaged by sudden faults, the downtime and the maintenance cost are reduced, the maintenance priority of the equipment is evaluated through a priority scoring algorithm, maintenance resources are reasonably distributed, the maintenance efficiency is improved, excessive maintenance is reduced, and stable operation of the electromechanical system is ensured.

The invention relates to a sweeping robot obstacle avoidance control method based on multi-sensor fusion, and the method comprises the steps: obtaining the data of a plurality of sensors through a multi-channel data collection module, and generating a time-synchronized multi-dimensional original data stream; performing noise reduction and feature extraction on the multi-dimensional original data stream through a distributed sensor preprocessing unit to generate a first multi-dimensional feature data set; a feature importance scoring algorithm is adopted to distribute weights to the second multi-dimensional feature data set, and a third multi-dimensional feature data set is generated; generating a second environment state vector based on the third multi-dimensional feature data set, and generating a first control instruction set through a parallel decision engine; evaluating environment complexity according to the second environment state vector, optimizing resource allocation of the first control instruction set, and generating a first optimized control instruction set; and comparing the multi-dimensional original data stream with the second environment state vector through a real-time feedback correction mechanism, generating a second control instruction set, and updating the second environment state vector.

The invention provides an information synchronization method, system and device for an embedded training simulator and a medium. The information synchronization method comprises the steps that task event information in the embedded training simulator is obtained; performing task classification on each piece of task event information to obtain each piece of task event information with attribute tags; according to the attribute tag of each task event information, performing task priority classification on each task event information by using an improved multi-factor priority scoring algorithm to obtain a task priority queue; according to the task priority queuing queue, generating a to-be-pushed data packet, and performing information synchronization on the to-be-pushed data packet; according to the invention, by introducing a task priority-based scheduling mechanism, the pushing sequence and the synchronization strategy of the task information are dynamically optimized, so that the scheduling rhythms and the transmission paths of the tasks with different priorities are intelligently distributed; and the dynamic adaptive capacity and the efficient task delivery capacity of the embedded training simulation in the training process can be improved.

The invention provides a metabolite qualitative annotation quality evaluation method based on high-resolution mass spectrum simulation. The metabolite qualitative annotation quality evaluation method comprises the following steps: collecting human serum sample mgf data measured by different instrument platforms; determining the distribution characteristic and the intensity mode of the mass-to-charge ratio; constructing a high-resolution mass spectrum data model based on mass spectrum characteristic quantity, adjacent m / z difference and intensity distribution; constructing a high-resolution mass spectrum simulation database of hundreds of thousands of metabolites with similarity and difference; constructing a strong interference strategy and a weak interference strategy, and simulating mass spectrum data change possibly encountered in an actual experiment; searching and evaluating an undetermined property spectrum under the interference model by adopting eight similarity evaluation methods; factor level analysis and range analysis are adopted to deeply discuss the internal relation between the scoring algorithm and the mass spectrum characteristics.

The invention relates to the field of data processing technologies and financial science and technology, and provides an HQL statement detection method which comprises the steps that an HQL statement to be detected is analyzed to generate an abstract syntax tree and optimized, and an execution plan is generated according to the optimized syntax tree; performing operation step decomposition on the execution plan according to task process stages, and generating a performance analysis syntax tree corresponding to the HQL statement by taking operation steps obtained after decomposition as syntax tree nodes; querying a database according to a query object contained in the operation steps in the performance analysis syntax tree to obtain a related database table of the HQL statement, and calculating data of the related database table and data of the operation steps of the performance analysis syntax tree to obtain numerical values of a plurality of preset data indexes of the HQL statement; and calculating the score of the HQL statement according to the numerical value of the preset data index and a preset data index scoring algorithm, and generating a detection report of the HQL statement. According to the HQL statement detection method, the quality of the HQL statement can be automatically evaluated according to a unified rule.

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.

Disclosed herein are system, method, and computer program product embodiments for training and deploying a machine learning model to generate an assessment of risks and mitigations in response to a novel initiative request. After generating labeled data from a corpus of prior risk assessments, a machine learning model may be trained to programmatically generate a risk assessment in response to a novel initiative request. A risk auditor may subsequently review the risk assessment generated using the machine learning model to provide various feedback reflecting the accuracy of the risks and mitigations. The machine learning model may then be retrained based on the feedback provided by the risk auditor to provide more accurate risks and mitigations in response to future initiatives.

The invention relates to a review system and method based on an adaptive scoring algorithm and multi-modal analysis, in particular to the field of college student innovation and entrepreneurship competition review, and the system specifically comprises a data input processing module, an automatic scoring calculation module, an expert review simulation module and an auxiliary decision support module. According to the technical scheme, the scoring weight and the standard can be dynamically adjusted according to the specific requirements and the standard of the contest, through the machine learning technology, historical review data are analyzed, key review factors are identified, the scoring model is automatically adjusted according to the characteristics and the target of the current contest, and the self-adaptive capability enables the review system to more accurately evaluate the competing works, so that the quality of the competing works is improved. And meanwhile, human prejudice is reduced.

The invention provides a Tai Chi motion detection scoring teaching system based on AI, and relates to the technical field of Tai Chi teaching, the Tai Chi motion detection scoring teaching system comprises a video acquisition module, a posture skeleton node detection module, a node calculation module, a motion standard degree scoring module and a feedback module, the video acquisition module is used for acquiring a motion video of a Tai Chi learner; human body postures in the video are detected in real time through a posture skeleton node detection module by using an AI deep learning algorithm, and a node calculation module is based on coordinate information of skeleton nodes; according to the invention, based on a real-time image processing algorithm and an AI scoring algorithm, core functions of real-time identification, real-time tracking, real-time detection, real-time scoring, big data statistical analysis output normative evaluation and guidance opinion correction of human body postures are developed; and therefore, real-time detection, accurate scoring and effective guidance of the actions of the shadowboxing learner can be realized, and the efficiency and action normalization of shadowboxing learning can be improved.

The invention relates to the technical field of intelligent metering of electric energy meters, and discloses a metering verification method and system for an electric energy meter group. The method comprises the steps of selecting a central node in a group through a dynamic scoring algorithm, generating a dynamic measurement reference based on environment information, collecting and detecting deviation data in real time, and carrying out calibration and data consistency processing on abnormal nodes. Compared with the prior art, the technical problem that high-precision automatic calibration cannot be realized by a traditional method in a manual regular calibration mode, especially under a complex power grid environment and a large-scale electric energy meter deployment condition, is solved. According to the invention, the optimal central node is automatically selected, and the metering reference value is dynamically adjusted based on the environmental condition, so that the real-time and adaptive metering calibration of the electric energy meters in the group is realized, the problems of low manual calibration efficiency and inconsistent data are avoided, and the metering efficiency of the electric energy meter group is remarkably 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.