748results about How to "Improve comprehensiveness" patented technology

The invention belongs to the technical field of artificial intelligence, and relates to a power document intelligent question and answer method and system based on a large language model. The method comprises the steps of obtaining document data according to a preset period; preprocessing the document data to obtain an adaptive document; constructing a vector retrieval library through an embedded model according to the adaptive document; obtaining a user query question, inputting the user query question into a vector retrieval library, performing retrieval based on a vector similarity retrieval method and a keyword retrieval method to obtain related text blocks, and screening out the most related text block from the related text blocks through a two-stage rearrangement technology; a user queries the question and the most relevant text block and inputs the question and the most relevant text block into the large language model to obtain preliminary intelligent answer information; and correcting the preliminary intelligent answer information based on a preset self-provincial inversion mechanism to obtain final intelligent answer information. Multiple rounds of questions and answers and context understanding are supported based on a correction mechanism, context consistency can be kept in continuous query of complex technical questions, and the accuracy of multiple rounds of questions and responses is ensured.

The invention discloses a thermal power plant equipment management method and system based on data fusion, and relates to the technical field of equipment management, and the method comprises the steps: collecting the real-time data of equipment through a sensor, and evaluating the health state of the equipment through a data fusion algorithm; based on the real-time operation data of the equipment, the intelligent rule engine dynamically adjusts the evaluation standard; predicting a potential fault and generating a maintenance priority through an intelligent fault prediction algorithm by using historical data and a health state evaluation result; and according to fault prediction and an equipment state, scheduling maintenance resources are optimized, and timely maintenance and repair of key equipment are ensured. According to the thermal power plant equipment management method based on data fusion provided by the invention, comprehensive monitoring of the equipment operation state is realized, and the accuracy and comprehensiveness of data are remarkably improved by combining a multi-stage data fusion technology based on dynamic weighted average and principal component analysis.

The invention relates to the technical field of quartz product measurement, and particularly provides a quartz product diameter measurer and a quartz product diameter measuring method, and the quartz product diameter measurer comprises a clamping mechanism, a moving table, a supporting guide mechanism and a laser outer diameter measuring mechanism. According to the invention, the clamping mechanism, the laser outer diameter measuring mechanism and the supporting and guiding mechanism cooperate with each other, so that the laser outer diameter measuring mechanism measures the diameter of the quartz rod in the circumferential direction in the process that the clamping mechanism drives the quartz rod to rotate axially; meanwhile, the mobile station drives the laser outer diameter measuring mechanism to move in the axial direction of the quartz rod through the supporting and guiding mechanism to measure the diameter, so that the diameter measurement of the quartz rod in the axial direction and the circumferential measurement of different axial positions are realized, the comprehensiveness of the diameter measurement of the quartz rod is improved, and the measurement result has representativeness; and the precision of diameter measurement of the quartz rod is improved.

The invention provides a digital twinborn model and method for a coal mine disaster scene based on multi-modal data. The method comprises the following steps: acquiring original data in a mine; performing cleaning, normalization and de-noising preprocessing on the original data; fusing different types of preprocessed original data by using a data fusion algorithm, and constructing a comprehensive digital twinborn model; establishing a virtual mirror image of the mine by using a digital twinning technology, and mapping the mine in the real world into a digital twinning model; real-time mapping and simulation analysis are carried out based on the established digital twinborn model, and the disaster development trend is predicted by monitoring the internal environment and the equipment state of the mine; and according to a model analysis result, optimizing prevention and control measures of coal mine disasters, establishing a real-time monitoring and early warning system of the disasters, and taking measures in time when abnormal conditions are found. The system comprises a data acquisition module, a model construction module and a model analysis module. The pertinence and effectiveness of coal mine disaster prevention and control can be improved.

The invention relates to the technical field of water source pollution investigation, and particularly discloses a soil groundwater pollution fine investigation method which comprises the steps of S1, pollution source investigation, S2, sampling design, S3, laboratory analysis, S4, ecological risk assessment, S5, restoration suggestion and S6, dynamic monitoring. According to the invention, through machine learning and big data analysis, historical pollution data, field sampling data and spatial geographic information can be effectively integrated to form an accurate pollution source distribution map, which not only facilitates identification of pollution hot spot areas, but also can predict the pollutant diffusion trend through a data-driven model to provide dynamic prediction of future pollution evolution; according to the method, high-precision positioning of a complex pollution area is realized, blind sampling and invalid investigation are reduced, the resource utilization efficiency is improved, the area where pollutants are possibly diffused in the future can be predicted, prevention and control measures are taken in advance, and further environmental damage is avoided.

The invention belongs to the technical field of fault prediction, and discloses a fault prediction method and system for an intelligent accessory monitoring platform. The method comprises the following steps: an intelligent accessory monitoring platform constructs a knowledge graph, a fault prediction model and a fault maintenance strategy generation model; performing knowledge mapping on the real-time monitoring log by using a knowledge graph to obtain a semantic enhanced real-time monitoring log; performing fault prediction by using a fault prediction model according to the real-time operation monitoring data and the semantic enhanced real-time monitoring log to obtain a real-time fault prediction result; generating a fault maintenance strategy by using a fault maintenance strategy generation model according to the real-time fault prediction result to obtain a real-time fault maintenance strategy; and visually displaying the real-time fault prediction result and the real-time fault maintenance strategy of the attachment. According to the invention, the problems of insufficient fault prediction accuracy, insufficient data processing capability and low intelligent degree in the prior art are solved.

The invention discloses a multi-modal emotion analysis method and system, and the method comprises the steps: carrying out the heart rate variability analysis of a collected vital sign signal, so as to extract a time domain feature, a frequency domain feature and a nonlinear feature corresponding to a heart rate variability signal obtained after the analysis, fusing the time domain feature, the frequency domain feature and the nonlinear feature to generate a heart rate variability feature corresponding to the vital sign signal; calculating a power spectrum and a power spectrum entropy corresponding to the collected electroencephalogram signals, and extracting electroencephalogram characteristics corresponding to the electroencephalogram signals according to the power spectrum and the power spectrum entropy; extracting skin electric characteristics corresponding to the collected skin electric signals; respectively inputting the heart rate variability feature, the electroencephalogram feature and the galvanic skin feature into a pre-trained emotion recognition model, and outputting a plurality of emotion analysis results; and acquiring an emotion regulation scheme matched with each emotion analysis result to output a corresponding emotion regulation strategy, thereby improving the accuracy of the emotion analysis and regulation strategy.

The invention discloses a sky-ground network integrated high-standard farmland intelligent monitoring management system, and belongs to the field of farmland monitoring management systems. The system is provided with a space monitoring data acquisition unit, an air monitoring data acquisition unit and a ground monitoring data acquisition unit; the system can be combined with satellite remote sensing equipment, unmanned aerial vehicle equipment and a plurality of sensor nodes in a multi-level manner to jointly form a sky-ground integrated data acquisition network for target farmland data acquisition, so that the comprehensiveness and accuracy of data acquisition are greatly improved; meanwhile, mapping layer three-dimensional modeling is carried out on data values acquired and converted by various sensors which are uniformly distributed at certain intervals, so that the display intuition, convenience and layering sense of different data on the target farmland are greatly improved; a user can more intuitively master the health states of crops at different positions of the target farmland and the advantages and disadvantages of the crop growth environment.

The invention belongs to the field of electrical performance test equipment, and particularly discloses insulation performance test equipment for a power distribution cabinet. Comprising a cabinet body; the double-head hydraulic cylinder is arranged in the cabinet body, and the double-head hydraulic cylinder is provided with an electric sliding rail; the electric sliding block is connected to an electric sliding rail of the double-end hydraulic cylinder in a sliding mode, an elastic telescopic rod is rotationally connected to the electric sliding block, and the telescopic end of the elastic telescopic rod is rotationally connected with a connecting frame; the circular plate is rotationally connected to the connecting frame, the connecting frame is rotationally connected with a rotating shell, sliding cylinders which are uniformly distributed in the circumferential direction are arranged on the rotating shell, and probes are slidably connected into the sliding cylinders. Through rotation of the double-head hydraulic cylinder and swinging of the elastic telescopic rod, in cooperation with stretching operation of the probe, the welding seam detection range is expanded, the integrity of welding seam detection of the power distribution cabinet is improved, meanwhile, the device can accurately and continuously detect a plurality of welding seams through the elastic telescopic rod, and the working continuity of the device is improved.

The invention provides a news manuscript sensitive word recognition method and system, electronic equipment and a storage medium, and relates to the field of sensitive word recognition. The method comprises the steps that internet vocabulary data is acquired and analyzed to obtain an initial sensitive word set, a sensitive word knowledge graph is obtained through knowledge graph construction and knowledge representation learning algorithm training, and a preliminary sensitive word recognition result is obtained through deep neural network integration of multi-level sensitive word recognition and an attention mechanism. And mapping the recognition result to a sensitive word knowledge graph, and performing semantic representation learning by using a graph neural network to obtain a semantic representation result. And performing association node reasoning based on the semantic representation result to obtain a knowledge enhanced recognition result. And finally, calculating the semantic similarity between the sensitive word and the context to obtain a similarity score, performing emotional tendency analysis to obtain an emotional score, and performing comprehensive evaluation in combination with a preset threshold to obtain a final sensitive word recognition result. Through the scheme, the comprehensiveness and accuracy of news manuscript sensitive word recognition are improved.

The invention relates to an automatic detection and fault early warning system for a self-service car washer and a method thereof. The automatic detection and fault early warning system comprises a data acquisition module, a diagnosis circuit module, a water quality characteristic acquisition module, a correlation analysis module and an alarm module, according to the method, the operation state and the water quality condition of the equipment are comprehensively known by collecting various parameters in real time, potential problems are accurately recognized and an alarm is given in advance through correlation analysis and a dynamic early warning mechanism, the situations of false alarm and missing alarm are reduced, the early warning accuracy is improved, and the early warning efficiency is improved through correlation analysis and the dynamic early warning mechanism. The method adapts to the dynamic change of the equipment operation state and the water quality condition, predicts and intervenes potential problems in advance, and has the effects of improving the comprehensiveness of fault monitoring of the self-service car washer, enhancing the early warning accuracy and improving the adaptability and reliability of the system.

The invention relates to the technical field of nondestructive testing, and particularly discloses a detection and positioning method and system for foaming defects of a sandwich foaming board, and the detection and positioning method comprises the following steps: S1, equipment preparation and environment control; s2, data acquisition: comprehensively scanning the sandwich panel by using an infrared thermal imager, and recording temperature distribution data; s3, performing normalization processing on the infrared thermal imaging and electromagnetic induction data, extracting key features such as temperature gradient from the infrared thermal imaging data, and extracting key features such as electromagnetic induction intensity change from the electromagnetic induction data; s4, establishing a sandwich foam board defect identification and classification model, and training a neural network model by using the data of the sandwich foam board marked with defects; and S5, inputting the preprocessed data into the trained sandwich foam board defect identification and classification model. The advanced thermal infrared imager is combined with the electromagnetic induction technology, and the deep learning model is used for intelligent identification and classification, so that the detection precision is improved, and good adaptability and real-time performance are achieved.

The invention discloses an intelligent route distribution system based on vehicle owner behavior prediction, and relates to the technical field of route distribution, and the distribution system comprises a data collection module which is used for obtaining the historical driving data of a vehicle owner; the behavior prediction module is used for analyzing the driving habit of the vehicle owner and predicting the behavior of the vehicle owner; the real-time traffic analysis module is used for acquiring real-time traffic data and analyzing real-time traffic conditions, weather information and road condition changes; the route optimization module is used for calculating an optimal route based on the prediction result and the real-time data and recommending the optimal route; and the user interaction module is used for displaying the recommended route and receiving user feedback and adjustment suggestions. According to the invention, by analyzing the historical driving data and driving habits of the vehicle owner, the system can provide route recommendation conforming to personal preferences, the user experience is improved, real-time traffic data and weather information are integrated, it is ensured that the recommended route adapts to the current traffic condition and road condition change, and a more accurate and efficient driving route can be provided.

The invention relates to the technical field of optical fiber networks, in particular to an optical fiber network failure judgment method and device based on a feedback decision mechanism, and the method comprises the steps: constructing an optical fiber network, setting an intelligent optical fiber distribution device at an intermediate node of the optical fiber network, and setting a service probe at a tail end node of the optical fiber network; feedback information is obtained; performing multi-source feature extraction on the obtained feedback information, wherein the multi-source feature extraction comprises optical power data feature extraction, OTDR data feature extraction and service probe data feature extraction; and analyzing the extracted multi-source features by adopting a rule-based and model-based dual failure judgment mechanism to obtain a judgment result of the current network state. Therefore, multi-source information fed back by the optical fiber network is comprehensively analyzed by a rule-based and model-based dual failure judgment mechanism, the comprehensiveness, accuracy and efficiency of failure judgment are improved, corresponding repair measures are given according to a fault judgment result of the decision tree model, the response time is greatly shortened, and the network availability is improved.

The invention relates to the technical field of ozone monitoring, in particular to an atmospheric ozone monitoring system and method. Comprising a monitoring box and a box door hinged to the monitoring box, a monitoring box is arranged in the monitoring box, the side wall of the monitoring box is communicated with an exhaust pipe, an ozone sensor is embedded in the top wall of the interior of the monitoring box, and during dynamic monitoring, an airflow conveying mechanism for conveying air into the monitoring box is arranged outside the monitoring box; air is discharged from the exhaust pipe through the ozone sensor. The device not only can carry out dynamic monitoring, but also can carry out static monitoring, the monitoring comprehensiveness is improved, when air in the air bag is released, the partition plate is burst open through airflow, the exhaust pipe, the air conveying box and the air bag are communicated, negative pressure is formed in the exhaust pipe, air in the monitoring box is sucked, and the monitoring accuracy is improved. And residual air in the air delivery pipe enters the monitoring box and is pumped away, so that the influence of the residual air in the air delivery pipe on static monitoring is reduced, and the monitoring accuracy is improved.

The invention relates to the technical field of cement-based composite cementing material preparation, in particular to a low-carbon environment-friendly cement-based composite cementing material and a preparation method thereof.The preparation method comprises the steps that an initial forming object is subjected to regional division according to position characteristics so as to obtain real-time monitoring results of all target monitoring regions, determining the actual state of each target monitoring area according to the real-time monitoring result; analyzing the actual state to determine a single mixing amount set of each target monitoring area; integrating the single mixing amount sets to obtain a target mixing amount set, and sequentially performing stirring and pouring molding according to the target mixing amount set to obtain a target molded object; in the stirring process, the initial stirring parameters are adjusted according to the actual stirring degree in combination with the overall analysis result, so that actual stirring parameters are determined; and in the pouring forming process, the initial pouring parameters are adjusted according to the bubble generation rate in combination with the overall analysis result, so that the actual pouring parameters are determined.

The invention provides an industrial Internet of Things safety monitoring method and system, and relates to the technical field of data processing. The method comprises the following steps: acquiring operation parameters of each industrial hardware device; determining a hardware exception score according to the operation parameters; acquiring operation data of each industrial application program; determining a software exception score according to the operation data; acquiring network flow data of each key network node; obtaining a network flow feature vector of the key network node; determining a network anomaly score according to the network traffic data and the network traffic feature vector; according to a result obtained by subtracting weighted summation of the hardware anomaly score, the software anomaly score and the network anomaly score from 1, determining an industrial Internet of Things safety monitoring score; and generating an industrial Internet of Things safety monitoring report according to the industrial Internet of Things safety monitoring score. According to the invention, the comprehensiveness, objectivity and accuracy of safety monitoring of the industrial Internet of Things can be improved through three aspects of hardware conditions, software conditions and network conditions.

The invention relates to a network abnormal behavior detection method based on dynamic data balance and generation, and the method comprises the steps: obtaining original network flow data, inputting the original network flow data into a dynamic abnormal data balance model, and obtaining an enhanced data set; the method comprises the steps of marking original network flow data based on an abnormal data balance model, performing selective undersampling on marked data, adjusting a ratio of normal data to abnormal data, obtaining a balance data set, generating new abnormal data according to the balance data set, and merging the new abnormal data with the balance data set to obtain an enhanced data set; and inputting the enhanced data set into a TimeDiT noise reduction model for denoising to obtain a target data set after noise reduction, inputting the target data set into an LSTM-Transform weighted training model for network abnormal behavior detection, and weighting abnormal data in the target data set in combination with a weighted loss function to obtain a detection result. According to the invention, the network traffic abnormal behavior detection efficiency and accuracy can be improved.

The invention discloses a big data hypergraph network modeling method and device, computer equipment and a product, aims to solve the problem that efficiency and accuracy are difficult to consider when large-scale data of intellectual property is processed in the prior art, and aims at the characteristics of high increment, high timeliness and multiple modes of the intellectual property data. The method comprises the following steps: analyzing and concluding passed intellectual property files, classifying according to corresponding professions, and establishing a corresponding intellectual property text library; constructing a text corpus based on intellectual property by using a BERT algorithm model, performing capsule graph neural network processing on the image information of the corresponding category, obtaining a correlation between image objects and text attributes thereof, and constructing an image database; capturing complex correlation characteristics in the text by utilizing the characteristics of the graph convolutional neural network; and constructing a hypergraph neural network model. The invention provides a big data hypergraph network modeling method capable of processing high-increment, high-timeliness and multi-modal data.

The invention provides a safety monitoring method and system based on artificial intelligence. The method comprises the steps that firstly, real-time video data and environment data are collected through a camera and a sensor which are installed at a preset position; secondly, splicing the preprocessed video data and environment data to form an input vector; then, the change amplitude of the preprocessed video data and the change amplitude of the normalized environment data in the time period T are calculated respectively; thirdly, outputting a danger degree value by adopting a first convolutional neural network or a second convolutional neural network or a third convolutional neural network; finally, the danger degree value output by the convolutional neural network is sent to a manager terminal to be displayed, and an alarm is pulled. According to the invention, different types of convolutional neural network algorithms are adopted to carry out danger degree calculation, so that the accuracy and efficiency of safety accident reminding are greatly improved.

The invention belongs to the field of software testing, and relates to an ROS 2 software package defect detection method based on a large language model and coverage rate guidance. According to the method, function information is extracted from a to-be-tested program, and a regular expression is used for analyzing function definition, parameter characteristics and other information; the method comprises the following steps: presetting verification standards related to a to-be-tested program, and extracting physical constraints and document constraints from the verification standards; integrating the extracted function information, physical constraint and document constraint into accurate information, filling a cue word template with the accurate information, inputting the cue word template into a large language model, and generating an initial test seed; a mutation strategy is introduced, and diversified mutation operation is performed on the generated seeds, so that test cases with higher diversity are generated; optimizing the test case by adopting a coverage rate guiding mechanism, and guiding to generate a new test case by monitoring the coverage rate in real time; through a defect verification mechanism, dominant defects such as program crash and the like can be detected, and hidden defects such as violation of physical constraints or violation of document constraints and the like can be identified.

The invention provides an electric energy meter comprehensive performance evaluation method and system, and relates to the technical field of electric energy meters, and the method comprises the steps: carrying out the application load analysis through obtaining the basic specification information of a to-be-evaluated electric energy meter, and generating a non-standard load set; carrying out introduction analysis and fusion on a harmonic component, a peak current, a nonlinear power factor and a dynamic load for a non-standard load set to generate a plurality of groups of fused nonlinear introduction variables; determining a test circuit for testing the to-be-evaluated electric energy meter; inputting the multiple groups of fused nonlinear introduced variables into a dynamic load simulator to carry out load simulation on the test circuit, and recording multiple groups of precision test results of the electric energy meter; and performing comprehensive performance evaluation according to the multiple groups of electric energy meter precision test results to generate a comprehensive evaluation result. According to the invention, the technical problem of insufficient accuracy of the evaluation result caused by lack of performance evaluation means for the electric energy meter under the dynamic change load in the electric energy meter test in the prior art is solved, and the accuracy and practicability of the performance evaluation of the electric energy meter are improved.

The invention relates to the technical field of air conditioners, in particular to a comfort level evaluation method, device and equipment of an air conditioner and a storage medium. And performing comfort evaluation on the current operation data and the user operation behavior information through a trained comfort evaluation model to obtain comfort evaluation data, and finally determining a current comfort evaluation result of the air conditioner according to a preset comfort value interval in combination with the comfort evaluation data. The technical problem that in the prior art, the comfort evaluation accuracy is not high when the user uses the air conditioner is solved, and the comprehensiveness of comfort evaluation is improved.

The invention relates to an antibody detection kit for neuroparatoma syndrome and a preparation method of the antibody detection kit. The antibody detection kit for neuroparatoma syndrome comprises an antibody detection strip, a cleaning solution, a buffer solution and an auxiliary agent, wherein the antibody detection strip is a nitrocellulose membrane which is coated with antigen proteins containing Amphiphysin, CV2, GAD65, Hu, PNMA2 (Ma2 / Ta), Recovery, Ri, SOX1, Titin, Tr (DNER), Yo and Zic4. The invention further discloses a detection method of the antibody detection strip. The antibody detection kit disclosed by the invention can be used for simultaneously detecting 12 autoantibodies related to the neuroparatoma syndrome. The design of simultaneous detection of multiple antibodies greatly improves the comprehensiveness of detection, so that multiple possible pathogenic factors can be covered in single detection, the kit can be used for differential diagnosis of suspected neuroparatoma syndrome patients in professional laboratories, and the problem of missed diagnosis caused by single antibody detection is avoided.

The invention discloses a scene generation method and device for a nuclear power unit simulator, equipment and a medium. The method comprises the steps that the number of target operation events in a target simulation scene and corresponding event screening conditions are acquired; wherein the number of the event screening conditions is the same as that of the target operation events, and different operation events correspond to different operation conditions of the nuclear power unit; according to the event screening condition, determining an event selection range of each target operation event in a pre-constructed operation event database, and determining a corresponding target operation event from candidate operation events corresponding to each event selection range; wherein the target operation events corresponding to different event selection ranges are different, and the event attribute information corresponding to each target operation event is different; and combining and generating the target simulation scene based on each target operation event. According to the technical scheme, automatic combination of diversified simulator scenes is realized, and the workload of simulator teachers is reduced.

The ecological slope protection structure for water and soil conservation for water conservancy construction comprises soil retaining plates fixedly supported on slopes on the two sides of a river, planting grooves for planting green plants are formed in the soil retaining plates, a water pumping pipe is fixedly connected, and the outer contour of the water pumping pipe is sleeved with a rectangular shell moving in a reciprocating mode; the rectangular shell is fixedly connected with a spray head and a drainage pipe which is in through connection with the spray head, in addition, a salvage mechanism and a liquid pumping mechanism are further arranged on the soil retaining plate and used for cleaning floating objects on the surface of river water and pumping the river water for irrigation, and the salvage mechanism achieves salvage of the floating objects through matched action of a cylindrical roller and a filter plate; according to the ecological slope protection structure, by means of an automatic irrigation system, the utilization efficiency of water resources is improved, the soil retention capacity is enhanced, plant growth is promoted, water and soil loss is effectively prevented, and the ecological slope protection structure can be applied to the field of ecological slope protection especially under the drought condition. The ecological environment is protected and recovered.

The invention discloses an ecological environment dynamic monitoring system based on machine learning, and relates to the technical field of artificial intelligence. The system comprises a sensor monitoring part, a feature extraction and mapping part, a support vector analysis and decision part and a prediction risk assessment part. The sensor monitoring part comprises a sample ecological region sensor group and a target ecological region sensor group; the sample ecological region sensor group comprises a plurality of same sample sensor groups; the target ecological region sensor group only comprises one target sensor group which is the same as the sample sensor group; the feature extraction and mapping part is used for obtaining mapping features; constructing a dynamic system state reconstruction equation; the support vector analysis and decision part is used for constructing an optimal support vector prediction controller; and the prediction risk assessment part is used for judging whether ecological environment early warning needs to be carried out or not according to the prediction judgment value. According to the invention, comprehensive monitoring, precise modeling and intelligent early warning of the ecological environment are realized.

The invention is suitable for the technical field of forest fire prevention and early warning, and provides an intelligent monitoring and early warning method and system for forest fire prevention. The method comprises the following steps: dividing a plurality of monitoring sub-regions, and calculating risk indexes of the plurality of sub-regions; performing hierarchical analysis, selecting a plurality of risk sub-regions, and determining corresponding risk levels; matching a plurality of monitoring densities and monitoring frequencies; performing unmanned aerial vehicle flight and monitoring shooting control in the plurality of monitoring sub-areas; and according to RGB three-color channels, calculation and comparison of fire judgment are carried out. A plurality of monitoring sub-regions can be divided for monitoring, risk indexes of the sub-regions are calculated, a plurality of risk sub-regions are selected, corresponding risk levels are determined, different monitoring densities and monitoring frequencies are matched for unmanned aerial vehicle flight and monitoring, and then calculation and comparison of fire judgment are carried out according to RGB three-color channels. The forest fire prevention monitoring blind area influence is effectively reduced, the monitoring comprehensiveness is improved, the accuracy is high, and the requirements of intelligence and low cost can be met.

The invention relates to an equalization circuit detection method and device, electronic equipment and a storage medium. The equalization circuit detection method comprises the steps of obtaining a first voltage of each battery before equalization and a second voltage of each battery after equalization; determining a first detection result of an internal equalization circuit corresponding to each battery based on the first voltage and the second voltage; and determining a second detection result of the external equalization circuit corresponding to any battery based on the first voltage of any battery, the second voltage of any battery and the first detection result of the adjacent battery of any battery. The detection result of the internal equalization circuit corresponding to each battery is determined according to the voltage before and after equalization of each battery in the battery pack, and then the detection result of the external equalization circuit corresponding to each battery is determined according to the voltage before and after equalization of each battery and the detection result of the internal equalization circuit corresponding to the adjacent battery of each battery. And the detection accuracy and comprehensiveness of the equalization circuit are improved.

The invention discloses a visual data construction and display method and system, electronic equipment and a medium, and relates to the technical field of data processing. The method comprises the steps of obtaining asset data of a target enterprise, and generating an asset hierarchical structure based on the asset data; combining the asset distribution layer, the asset association layer and the asset attribute layer to construct an annular visual interface of the target enterprise; in response to a display request of a user, a plurality of asset evaluation paths with different path widths are generated in the annular visual interface, and the path widths represent asset value proportions corresponding to the asset evaluation paths; obtaining the data distribution density of each asset evaluation path in the annular visual interface, and determining a display scale corresponding to each asset evaluation path according to each data distribution density; and displaying each asset evaluation path on an annular visual interface according to the corresponding display scale and path width. By implementing the technical scheme provided by the invention, the effect of improving the efficiency of constructing and displaying the visual data is achieved.