139 results about "Secondary analysis" patented technology

The invention relates to the technical field of intelligent traffic, in particular to a highway traffic incident recognition and early warning method and system, and the method comprises the steps: collecting traffic data in real time through sensing equipment arranged along a highway; performing preliminary identification and classification of conventional traffic events on the preprocessed traffic data through edge computing nodes; inputting a preliminary analysis result and unclassified traffic data into a cloud end, and carrying out secondary analysis and semantic understanding; and merging the logic relationship and the events of the same target to obtain a final event identification result. According to the traffic event attribute and the event identification result, generating early warning information; and inputting feedback data after each event processing into the recognition model for self-learning and optimization, and dynamically optimizing model parameters, event classification standards and early warning rules. According to the invention, the problems of false report and missing report in traffic event identification are effectively solved, the identification precision is improved, the repeated report is reduced, and the method can dynamically adapt to the problems of complex scenes and non-uniform standards.

The invention discloses an equipment preventive maintenance and diagnosis method based on multi-dimensional data verification, relates to the technical field of equipment preventive diagnosis, and solves the technical problems that fault reasons are difficult to comprehensively and deeply analyze, and maintenance measures are lack of pertinence due to the fact that the fault reasons are often misjudged or cannot be positioned. According to the method, the fault diagnosis model is constructed, the adaptation algorithm is adopted for different types of data, the diagnosis accuracy of the model to equipment faults and the adaptability of the model to complex working conditions are improved, the equipment fault type and the abnormal state can be rapidly and accurately recognized, and the fault diagnosis accuracy is improved through the methods of fault phenomenon matching, parameter correlation analysis, historical data deep backtracking and the like. According to the method, fault reasons can be accurately determined, the performance degradation trend and potential risks of the equipment can be effectively identified in combination with a hybrid similarity measurement method, correlation among data is further mined through a secondary analysis mechanism, the potential risks are accurately pre-judged in combination with historical data, and real preventive maintenance is achieved.

The invention belongs to the technical field of strain clamp detection control, and discloses a strain clamp intelligent detection control method, system and device and a medium, and the method comprises the steps: dynamically regulating and controlling the detection data collection frequency of a strain clamp according to an installation environment and a use state; determining a risk judgment threshold value of the strain clamp and outputting a fault early warning information standard based on the detection data acquisition frequency; acquiring use process data of the strain clamp according to the detection data acquisition frequency, and processing the use process data based on the risk judgment threshold and the strain clamp state analysis model to obtain a state judgment index of the strain clamp and output fault early warning information; and performing secondary analysis on the use process data of the strain clamp based on the state judgment index and the output fault early warning information, and determining fault information of the strain clamp according to an analysis result. According to the invention, the effectiveness of data acquisition is ensured, system resources are saved, and the response speed and resource consumption of the system are effectively balanced.

The invention relates to an anti-electricity-stealing monitoring method and device based on an intelligent universal meter, equipment and a medium. The method comprises the following steps: acquiring real-time electric sampling data of a universal meter, and preprocessing the data to obtain electric characteristic data; carrying out electricity stealing feature extraction on the electricity feature data by utilizing an edge electricity stealing detection model, and carrying out preliminary judgment on an electricity utilization type to obtain a preliminary judgment result; if the preliminary judgment result is at least one of an electricity larceny event and reverse feed, using a cloud electricity larceny detection model to perform secondary analysis on the electricity characteristic data to obtain an abnormal probability; and if the abnormal probability exceeds a preset threshold, generating a corresponding alarm work order, and pushing the alarm work order to a supervision platform. By adopting the method, the field acquisition capability of the intelligent multimeter can be fully combined, the local and cloud computing characteristics are combined, and the recognition and monitoring of electricity stealing behaviors such as low-current electricity stealing and reverse feed are effectively enhanced through a multi-source feature extraction and judgment mechanism.

The invention discloses a waistband type falling protection early warning device for old people living alone, and belongs to the technical field of intelligent wearable equipment. The problems that an existing device is single in early warning mode and cannot effectively guarantee the safety of old people living alone are solved, corresponding action results are matched for various data of the old people through the convolutional neural network and the lightweight decision tree model, and if a preliminary judgment result conforms to a normal mode, a judgment result is directly output; if not, triggering a secondary analysis mechanism to further judge whether the old people fall down or not; the GPS positioner is also used for analyzing whether falling behaviors occur at the same position for many times, and if a high-risk position is found, when the old people approach the high-risk position, the warning lamp and the alarm are used for reminding and paying attention to action behaviors; the fall detection accuracy is improved, the possibility of false alarm and missing alarm is reduced, the old people can be actively prevented from falling again in a high-risk area, and a more comprehensive safety guarantee is provided for the old people living alone.

The invention discloses a target behavior detection method and device in video monitoring, equipment and a storage medium. The method comprises the following steps: acquiring video stream data of a monitoring area; detecting an image in the video stream data based on one or more preset behavior detection models to obtain a detection result image of the preset behavior detection model; and filtering the detection result image by using a trained multi-modal large language model to obtain a monitoring target image. According to the technical scheme provided by the invention, the multi-mode large language model is introduced as a false alarm filtering mechanism for secondary analysis, different application scenes can be easily adapted, and the detection of the specified behavior is realized in combination with the required behavior detection model, so that diversified requirements can be flexibly adapted, false alarms are effectively reduced, and the detection efficiency is improved. The detection accuracy and reliability of the whole scheme are improved, high accuracy can be kept even under the complex background or illumination condition, and the user experience is improved.

The invention relates to the field of farmland soil fertility improvement, and discloses a farmland soil fertility improvement method, which comprises the steps of data acquisition, data screening, data primary analysis, soil nutrient regulation, data secondary analysis and evaluation, pH value detection and monitoring feedback. A farmland with low soil fertility is screened out, then preliminary analysis and evaluation of soil nutrients are carried out, soil with insufficient nutrients is adjusted, next analysis is carried out on soil with sufficient nutrients, secondary analysis is carried out, the compaction degree of a soil structure is evaluated, and adjustment is carried out on the situation that the compaction degree of the soil is high. Next-step pH value detection is carried out under the condition that the soil compaction degree is not high, whether the pH value of the soil needs to be adjusted or not is judged, the soil without pH value adjustment is continuously monitored, the pH value of the soil with the pH value not reaching the standard is adjusted, and the improvement effect of the farmland fertility is monitored in real time.

The invention discloses a manhole cover hidden danger integrated identification method and system fusing target detection and segmentation, and the method comprises the steps: carrying out the preprocessing of manhole cover image data, and dividing the manhole cover image data into cells and anchor frames; and secondly, extracting multi-scale features of the preprocessed well lid picture through YOLO v8x, and outputting a well lid prediction result. And a Bagging ensemble learning strategy is adopted, and based on the well lid prediction result, a weighted voting mechanism is utilized to obtain a discrimination result of the well lid hidden danger category. And inputting the preprocessed well lid picture into an encoder module composed of PVTv2-B4 for well ring feature extraction, and outputting a well ring prediction result. And carrying out secondary analysis on the walling crib based on the walling crib prediction result to obtain a judgment result of the type of the hidden danger of the walling crib. And finally, obtaining a hidden danger category by combining the distinguishing results of the well lid and the well ring. According to the invention, the refined identification capability of the well lid state in a complex scene is improved, and accurate outlining and positioning of the well ring boundary are realized.

The invention discloses an AI-based network log analysis processing method, which comprises the following steps of: firstly, collecting network equipment log information through a collection script supporting multiple protocol modes, and writing the collection script and deploying and starting after determining equipment and protocol types; thirdly, carrying out preliminary classification, data supplementation and formatting processing on the original logs; and then, sending the processed log data to an AI large model for semantic analysis, including deep understanding, basic classification, secondary analysis and providing of processing suggestions. An analysis result is stored in a data think tank for historical comparison, and an inconsistent result is manually confirmed and corrected; when the network equipment triggers a fault alarm, calling the data think tank to supplement alarm content; and meanwhile, equipment related data handbooks provided by manufacturers are uploaded regularly, the analysis accuracy of the AI model is corrected, verification and testing are carried out, it is ensured that the scheme can comprehensively and efficiently collect and process logs, accurately analyze faults and continuously optimize the model, and effective support is provided for network fault processing.

The invention relates to a dynamic identification and measurement method and system for a flexible pantograph-catenary contact point of a large iron, and the method is realized by adopting an overall architecture of two processes: the first process is located at a vehicle-mounted end, and the pantograph-catenary contact point is identified by adopting an infrared camera to obtain an image, gray matching NCC and Hough straight line detection; and then real-time rapid analysis is carried out to store possible overrun problem data. And in the second process, the main control device located at the ground end carries out secondary analysis on the abnormal video to obtain final abnormal alarm information. Wherein the second process is realized by adopting target detection of deep learning, and an alexnet model framework is used for training to obtain model parameters of the pantograph and the contact point for forward reasoning. The method comprises the following two steps of: identifying a pantograph area, longitudinally expanding part of the area according to a result of the first step, and performing target detection on a cross-like intersection point in pantograph-catenary contact in the area. The method is good in real-time performance and high in anomaly detection accuracy. The practicability is high, and the popularization significance is high.

The invention provides a welding spot cavity and bridging defect intelligent automatic test system, and relates to the technical field of defect detection, and the method comprises the steps: a data collection module is used for collecting image data of a to-be-detected welding spot, carrying out the standardization processing, and obtaining standard image data; the void defect detection module is used for identifying a low-density region in a standard image data welding spot, calculating an area proportion, and if the area proportion exceeds a preset area threshold value, judging that the welding spot is a void defect; the bridging defect detection module is used for calculating the minimum distance between standard image data welding spots, and if the minimum distance is smaller than a preset distance threshold value, a bridging defect is judged; the confidence coefficient analysis module is used for obtaining the detection confidence coefficient and re-collecting image data for secondary analysis of a detection result lower than a confidence coefficient threshold value; and the report generation module is used for generating a structured detection report comprising the defect type and the defect severity level. The technical problem that in the prior art, welding spot cavities and bridging defect detection accuracy is not high is solved.

The invention relates to the technical field of data reasoning, in particular to an intelligent dispute analysis system based on big data, and the system comprises an event collection module, a dispute analysis module, a scheme generation module, an emergency scheme module and an auxiliary decision generation module. Generating an original data set; extracting event key elements of the original data set to obtain a preliminary analysis report; in combination with the attribution feature data and the party background data, carrying out attribution secondary analysis on the preliminary analysis report to generate a personalized mediation scheme; when an emergency triggering condition is identified, rescue resource data are integrated based on an event geographic position, and an emergency disposal plan is automatically generated; and generating an auxiliary decision report based on the personalized mediation scheme and the emergency disposal plan. The dispute solving efficiency can be improved.

The invention discloses a bridge detection method and system based on Internet of Things and big data, and the method comprises the steps: regularly obtaining bridge image data and bridge vibration data, and carrying out the preprocessing of the bridge image data and the bridge vibration data, and obtaining first monitoring data; performing state evaluation on the bridge according to the first monitoring data to obtain a bridge evaluation report, acquiring bridge real-time vibration data as second monitoring data according to the bridge evaluation report, and analyzing the second monitoring data to obtain a bridge health report; and generating a bridge use decision and a maintenance suggestion according to the bridge health report. Through secondary analysis and processing of the data, a comprehensive and complete bridge health report is obtained; the generated use decision can play a role in warning and early warning, for example, for a use decision in a dangerous state, a manager can be prompted to adjust the use of the bridge in time, cope with the crisis of the bridge in time, make up defects in time, and avoid danger caused by update lag.

The invention discloses a slope anchor rod prestress monitoring system and method, and relates to the technical field of slope abnormity supervision. Comprising a data acquisition module, an acquisition and analysis module, a secondary analysis module and an anomaly analysis module, wherein the data acquisition module is used for acquiring multipoint stress data of a slope anchor rod, slope soil data and slope environment data and preprocessing the acquired data; according to the technical key points, prestressing forces at different positions of the side slope anchor rod can be collected, then the collected prestressing force data at different positions of the side slope anchor rod are evaluated, the attenuation of the prestressing force data is researched, whether the collected data are abnormal or not is further analyzed, and whether the data are abnormal or not can be found in time; therefore, the conditions of low efficiency and high cost caused by inaccurate analysis result, repeated acquisition and analysis due to abnormal acquired data can be effectively avoided, the use effect is good, and the method has a good use prospect.

The invention discloses a method and a system for auxiliary determination and grading of brain glioma, and belongs to the technical field of biological genes and bioinformatics. The method comprises the following steps: acquiring sample information of a patient and preprocessing a sample; carrying out FISH detection, NGS detection auxiliary biological information analysis and pathological detection on the sample to respectively obtain N1 FISH detection indexes, N2 NGS detection indexes, N3 pathological detection indexes and gene variation phenomenon characteristics corresponding to the detection indexes; and judging the type and classification of the brain glioma based on the detection indexes and the gene variation phenomenon characteristics corresponding to one or more of the detection indexes. The invention further discloses a corresponding system, electronic equipment and a computer readable storage medium, the NGS sequencing technology is used for replacing a part of conventional FISH detection modes, meanwhile, NGS data are reserved, secondary analysis is carried out through a signal generation method to obtain guide newly-added molecular indexes, the detection types and times are reduced, and the detection modes are optimized.

The invention relates to the technical field of uranium mineralization prediction, in particular to a uranium ore positioning method for oil and gas dissipation channel control, which comprises the following steps: for a drilling point preliminarily meeting a uranium mineralization standard, determining whether a temperature range of a gas generation threshold meets a mineralization achievement condition or not, and transferring a preliminary drilling area according to a fluid activity path; the direction of moving the preliminary drilling area is determined, and the initial drilling parameters are adjusted; fluid property classification is carried out, and it is determined that the uranium metallogenic potential of the paleoenvironment where the sample is located meets expectation or secondary analysis of the uranium metallogenic potential is needed when the sample is formed; determining composition characteristic types of the samples, judging whether the causes of the samples are related to biological action or not according to the proportions of the samples in different composition characteristic types, and adjusting a judgment standard for determining uranium mineralization potential; and when the uranium mineralization potential meets the expectation or the cause of the sample is irrelevant to the biological action, the coexistence of the uranium ore and the hydrocarbon inclusion is verified through drilling. According to the invention, efficient uranium ore positioning is realized by identifying the oil-gas dissipation channel and the associated ore control elements.

The invention relates to the technical field of dough processing quality monitoring, in particular to a biscuit dough processing information monitoring management system.According to the biscuit dough processing information monitoring management system, the quality of dough is preliminarily estimated according to corresponding parameters in the dough preparation process in big data, then the shelf life of the current batch of dough is predicted, and the more the water content of the dough is, the more likely the dough goes bad; the better the elasticity, the better the quality and the longer the shelf life of the dough are, the actual quality of the dough is analyzed by periodically detecting the moisture content of the prepared dough and the elasticity of the dough, the control precision aiming at the actual situation of the dough is improved, the longer the storage time of the prepared dough is, the more variables in the storage process are, and the better the quality is. For example, the environment temperature changes in the dough taking process, so that the dough is more prone to deterioration, secondary analysis is conducted on the pre-estimation condition of the quality guarantee duration of the dough according to the storage duration of the dough, and misjudgment caused by the environment changes in the dough storage process is avoided.

The invention provides a traffic load monitoring management method and system for an electric power communication network, and the method comprises the steps: carrying out the initial multilayer analysis of a collected traffic data package based on a DPI probe, and obtaining the initial analysis data of the traffic; performing windowing processing on the flow initial analysis data based on the time sliding windows to obtain a flow load index and a service load index corresponding to each time sliding window, and associating the flow load indexes with the service load indexes to form structured flow feature data; inputting the structured traffic feature data into an anomaly detection model to obtain an anomaly recognition result output by the anomaly detection model; carrying out secondary analysis on an original abnormal data packet extracted from the traffic data packet based on an abnormal identification result, associating with the traffic primary analysis data, and constructing an abnormal traffic traceability chain; and determining a load regulation and control strategy based on the abnormal traffic traceability chain in combination with a pre-constructed traffic load regulation and control decision library. The fault recovery efficiency of the power communication network is improved.

The invention relates to a test central control method and system for a test station of a transformer manufacturer, and belongs to the technical field of test control. Control demand information of current test equipment is acquired, control equipment is initially selected for control according to the control demand information of the current test equipment, and an initialization control result is generated; the method comprises the following steps: initializing a control result of each piece of control equipment, acquiring real-time state data information of each piece of control equipment, performing secondary analysis on the initialized control result according to the real-time state data information of each piece of control equipment to generate a secondary control result, and finally performing control interference analysis on the secondary analysis control result to generate a final control result. And the test equipment is controlled according to the final control result. According to the condition of the control equipment, the phenomenon of misoperation when the test equipment needing to be controlled in the central control network system of the test station of the transformer manufacturing plant is controlled is avoided, so that the control equipment can be in order.

The present invention discloses a method and system for intrusion analysis based on web logs. The analysis method includes: cutting the original log into fields according to the default delimiter and the default quote character; automatically identifying the field format, performing rapid matching of the field format, parsing and storing in a database; reading the stored logs in batches and performing log matching rule detection; updating the logs and writing them back to the database; aggregating the logs to generate web attack events and determine whether the intruder's attack was successful; generating and submitting a protection and processing report based on the web attack time and the analysis results and the impact range of the intrusion confirmed manually. The present invention realizes intrusion analysis of the attacker's IP address by cutting the web logs, uniformly parsing, rule matching detection and log aggregation. The present invention adopts a method combining automatic detection and manual confirmation, which has the advantages of high efficiency, high result accuracy, high log parsing strength and convenience for secondary analysis, thereby greatly improving the efficiency of web analysis.

The embodiment of the invention belongs to the technical field of research, development and design, is applied to a data management statistical scene under a big data scale, and relates to a data management statistical method based on a dynamic open point line segment tree and related equipment thereof. All the task index data in the source database are sorted, summarized and analyzed through the dynamic open-point line segment tree data structure, so that secondary analysis is carried out on all the task index data in the source database subsequently; according to the method, secondary analysis can be carried out by directly adopting the arrangement and summarization result of the dynamic open point line segment tree data structure, and compared with analysis directly carried out from a source database, the time complexity of query and update is reduced from O (N) to O (logN), and the analysis efficiency of subsequent secondary analysis is improved. The method can be applied to management statistics between the insurance sales price and the sales volume, and the statistical analysis efficiency between the insurance sales price and the sales volume is improved by taking the insurance sales price as task index data and taking the sales volume as a statistical result at different nodes.

The application discloses a quantitative analysis method of tobacco medium-polarity VOCs, which comprises the following steps: S1, inert gas is used to purge tobacco, and meanwhile, the tobacco is heated and sampled, so that the medium-polarity VOCs in the tobacco are collected; and S2, the medium-polarity VOCs in the tobacco are enriched by using MWCNTs-COOH / rGO, then, the medium-polarity VOCs are subjected to thermal desorption, and then, the medium-polarity VOCs are subjected to secondary enrichment and secondary analysis by using a cold trap, and finally, the medium-polarity VOCs are subjected to quantitative analysis by using GC-MS. When the MWCNTs-COOH / rGO is used as the adsorption medium of the thermal desorption of the purge and trap method, the flow rate, time and secondary enrichment temperature of the inert gas purging during the enrichment of the medium-polarity VOCs in the tobacco are simultaneously controlled, so that the quantitative analysis method of the medium-polarity VOCs in the tobacco is established. The enrichment effect of the medium-polarity VOCs in the tobacco is improved, and the accuracy of the quantitative analysis of the medium-polarity VOCs in the tobacco is also improved.

The present application relates to the technical field of flow monitoring, and discloses a method and system for real-time monitoring of flow in an At-211 column separation process, the method comprising: collecting the conductivity of eluent in the outlet pipeline of the At-211 chromatographic column in real time, continuously analyzing the conductivity, and generating a state flag; wherein when it is detected that the eluent is switched to anhydrous ethanol phase, the state flag is valid; otherwise, the state flag is invalid; by collecting the conductivity and generating the state flag, the timing of anhydrous ethanol phase switching is accurately determined, the collected pressure time series data is analyzed to generate a steady-state pressure value, then secondary analysis is performed to obtain a pressure memory value, the stable state of pressure and the continuous influence of flow are comprehensively captured, and finally a flow stability index is generated, which can evaluate the flow state of the whole alcohol phase elution process. The present application does not need to rely on the continuity of the conductivity signal, effectively filters transient interference, and improves the accuracy of flow monitoring.

A determination system includes: a log obtainer that obtains log information; a preprocessor that generates preprocessed log information from the log information; a primary analyzer that generates primary determination result information by performing primary analysis on the preprocessed log information; a prompt generator that generates a prompt for generative artificial intelligence (AI) based on the preprocessed log information; a secondary analyzer that performs, as secondary analysis, a process of obtaining secondary determination result information by inputting the prompt to the generative AI; an overall determiner that generates overall determination result information based on the primary determination result information and the secondary determination result information; and an outputter that outputs a determination result report that includes a determination result indicated by the overall determination result information.

The invention relates to the technical field of model design, in particular to a model design method and system for a long-distance steam supply pipeline. The method comprises the following steps: acquiring the positions of a pipeline starting point and a pipeline target point; performing mid-distance landform analysis based on the pipeline starting point and the pipeline target point to generate mid-distance landform division data, the mid-distance landform division data including cross-road landform data and cross-river landform data; performing basic pipeline modeling according to the positions of the pipeline starting point and the pipeline target point, and generating basic pipeline model design data; and secondary internal stress analysis of the pipeline sleeve is carried out on the basic pipeline model design data through the cross-road landform data, and pipeline cross-road internal stress data are generated. According to the method, the problems that in the prior art, under the complex terrain, the pipeline design precision is insufficient, stress analysis is limited, and comprehensive optimization is insufficient are solved by introducing an optimization design method combining middle-distance landform analysis, pipeline stress secondary analysis and natural vibration analysis.

The application provides a data processing method, device, equipment and medium. Data of multiple heterogeneous systems is collected multiple times, and the data of the multiple heterogeneous systems collected each time is transmitted to an analysis system respectively. Each analysis system runs different unsupervised learning algorithms and outputs corresponding analysis results. Each analysis result includes a first interworking heterogeneous system matched by each heterogeneous system. Then, each analysis result is integrated into a classification matrix. Rows of the classification matrix include matching results of each heterogeneous system under different analysis systems, and columns of the classification matrix include matching results of each heterogeneous system under each analysis system. Secondary analysis is performed on data in each row of the classification matrix to analyze analysis results of each heterogeneous system under different analysis systems, and a second interworking heterogeneous system matched by each heterogeneous system is obtained, so that an interworking heterogeneous system can be effectively obtained.

This invention relates to a method and system for disaster overlay analysis in nuclear power plants, comprising the following steps: acquiring standard specifications and experience feedback data of the nuclear power plant; conducting preliminary screening analysis based on the standard specifications, experience feedback data, and screening criteria to obtain a preliminary screening list of disaster overlays; and conducting secondary analysis based on the preliminary screening list of disaster overlays and in conjunction with a list of overlay disaster analysis scopes to obtain a disaster overlay list. This invention, through preliminary screening analysis, obtains a preliminary screening list of disaster overlays, achieving a comprehensive and effective review of possible disaster overlay types and avoiding omissions; simultaneously, through secondary analysis, it achieves a comprehensive and effective review and determination of the disaster overlay types that need to be considered, further ensuring the comprehensiveness and effectiveness of the disaster overlay analysis and avoiding omissions.

The invention relates to the technical field of data processing in an automatic recruitment system (ATS), in particular to a double-stage asynchronous processing-based resume analysis optimization method in the ATS, which comprises the following steps: S1, a primary analysis stage: quickly extracting key fields in a resume by adopting a lightweight analysis module formed on the basis of regular rules, entity recognition and other technologies, and carrying out secondary analysis on the key fields in the resume; comprising name, gender, contact information, graduation colleges, educational background and professional information; the module has high real-time performance and can return an analysis result within a second level; s2, user response and business promotion: returning a primary analysis result to a user interface in real time and storing the primary analysis result into a system, and supporting the user to carry out subsequent operation without delay, the user experience is remarkably improved, the user does not need to wait for a long-time analysis process, and non-inductive import of'completion once submission 'is realized; the analysis quality is greatly improved; the accurate extraction of deep and complex fields is realized through a multi-modal large model, and the data basis of the subsequent man-post matching and screening process is improved.

The present invention discloses a somatosensory interactive display system based on a depth camera, which relates to the field of somatosensory interaction technology; the system includes a data retrieval module, a parameter analysis module, a secondary analysis module, an image processing module and a verification and display module: the data retrieval module: extracts the user's most recent N historical somatosensory interaction data from a database based on a data retrieval algorithm, and preprocesses the data; the parameter analysis module: analyzes the preprocessed data, obtains a dimming strategy, sequentially collects and analyzes the user's distance data and infrared image data, and marks the interaction area; its technical key points are: a cross-dimensional analysis method is constructed for multimodal data such as the fusion of infrared images, depth data, user habits and the user's own conditions, which can accurately grasp the user's intentions and construct somatosensory interaction display parameters exclusive to the user, thereby improving the interaction effect and greatly improving the display effect, and has good usage prospects.

The invention discloses an automobile part information construction method and device based on export requirements and a storage medium, and relates to the field of automobile part information analysis. The method comprises the steps that after market characteristics of each market needing to be exported are determined, automobile parts corresponding to the market characteristics are obtained; associating the automobile part corresponding to each market feature with the market feature to form a market feature module; defining corresponding label information for each market feature module; and forming the market feature module corresponding to the label information required by the market needing to be exported into the automobile part information of the market needing to be exported. According to the method, the operation process is remarkably simplified, the working efficiency is improved, only one-time analysis is carried out when the automobile part corresponding to the market feature is initially determined, secondary analysis is not needed subsequently, the error probability is further reduced, and the working precision is improved.