101 results about "Big data management" patented technology

The invention discloses a multi-agent collaborative application big data management system and method. The system comprises a data acquisition unit, a data preprocessing unit, a multi-agent collaborative processing unit, a data storage unit and a data scheduling unit. The data acquisition unit is used for acquiring multi-source heterogeneous original big data and transmitting the multi-source heterogeneous original big data to the data preprocessing unit; and the data preprocessing unit is used for executing preprocessing operation including de-duplication and format standardization on the original big data. The invention relates to the technical field of big data management. According to the multi-agent collaborative application big data management system and method, through a multi-agent collaborative algorithm, the system can realize a self-adaptive data classification and optimization strategy, a data processing mode is dynamically adjusted, the processing efficiency problem of a traditional static rule during data mode fluctuation is improved, and the data processing efficiency is improved. The system adopts a dynamic resource scheduling strategy based on a data access demand and a system load, efficiently distributes calculation and storage resources, and facilitates priority processing of high-priority tasks.

The invention relates to the technical field of big data management, in particular to a data tracing method based on big data mining, which comprises the following steps of: acquiring an unstructured operation log, analyzing the unstructured operation log into a structured event record, and associating to generate a heterogeneous operation session set; performing access mode association analysis on the session set to extract a read-write path mode, and combining association strength and a time decay factor to determine a dependency weight and construct a blood relationship map; eliminating a loop of the graph to generate a directed acyclic graph, constructing a spanning tree serving as a retrieval trunk and a node index coding interval, and mapping a cross-branch associated edge to a bitmap index to generate a probability skeleton tree index; and positioning a trunk path by using the interval, calling a bitmap to perform multi-path verification, and outputting a traceability path and confidence. A probability consanguinity map and a probability skeleton tree index are constructed through mining logs, and data consanguinity reconstruction and large-scale node low-delay retrieval in the heterogeneous black box environment are achieved.

The invention discloses a total-factor integrated large Token data management system, and the system comprises an identity credential collection unit which is used for obtaining an identity label and associated business data from a plurality of social subjects; the identifier abstraction unit is used for mapping the identity identifier into a standardized Token primary key according to a main body type and structuring the associated business data into a corresponding digital asset package; the total element aggregation unit is used for aggregating and packaging the digital asset package into a single verifiable large Token data structure, and the large Token comprises a Token primary key and a dynamic trust level; the dynamic trust engine is used for calculating and updating the trust level of each main body in real time and binding the trust level to the corresponding large Token; and the intelligent service execution unit is used for responding to an external business instruction and calling the large Token to realize intelligent service.

The invention relates to the technical field of financial industry big data management, in particular to a financial knowledge graph updating and compliance verification method based on multi-source heterogeneous data, and the method comprises the steps: building a global sensing network for the financial field, collecting multi-source heterogeneous financial data in real time, and carrying out the normalization processing; performing named entity recognition and relation extraction on the normalized financial data by using a pre-trained large language model, and constructing a financial knowledge graph; when a new supervision rule is released, detecting whether there is a conflict between the new supervision rule and the old supervision rule, and if there is a conflict, carrying out lossless dynamic updating on the financial knowledge graph; and when a financial service request is received, taking a service main body and a service type corresponding to the current financial service request as anchor points, extracting a local sub-graph from the financial knowledge graph, reasoning the local sub-graph by using the graph neural network, and finally outputting an interpretable compliance audit report.

The invention relates to the technical field of computers, discloses a full-life-cycle big data management method and system for a meteorological engineering project, and aims to solve the problems of data islands, different standards, processing splitting, decision lag and the like in the prior art. The method comprises the following steps: collecting multi-source heterogeneous original data; performing standardization processing to form a data set with unified semantics and space-time reference; classifying and storing according to the full life cycle stage of the project, and constructing a project entity association index; calling a stage adaptation model based on the data set to execute prediction, diagnosis or optimization; generating visual early warning and operation and maintenance suggestions; and pushing a decision instruction to realize closed-loop management. Through adoption of the technical scheme, unified convergence, efficient retrieval, intelligent analysis and closed-loop decision of meteorological engineering project data can be realized, and data availability, early warning timeliness and management automation level are remarkably improved.

This invention discloses a big data-based method for maximizing efficiency management in a hardware processing workshop, relating to the field of big data management technology. The method includes: acquiring historical video data of hardware workpiece processing tasks; quantifying the skill levels of processing personnel in the hardware processing workshop; clustering and classifying the processing personnel according to their skill levels; initializing the time distribution of hardware workpiece processing actions under each skill level; constructing a hardware workpiece processing action-effective / ineffective time identification model; marking the types of ineffective time-consuming tasks for each worker's skill level; performing causal tracing for these ineffective time-consuming task types; constructing a causal list of ineffective time-consuming tasks; and generating a sequence of causal improvement tasks for ineffective time-consuming tasks. The beneficial effects of this invention are: improving the accuracy of ineffective time analysis and improvement, and maximizing the efficiency of the hardware processing workshop.

The application discloses a kind of intelligent scheduling and distribution method of protein complex detection task in cloud platform, it is related to big data management technical field;By constructing task feature model;Real-time sequencing is carried out to task using dynamic priority evaluation function containing vulnerability index, and dynamic scheduling sequence is generated;Using improved space-time graph convolutional neural network to construct resource matching prediction model, and generating resource-task matching matrix;Using improved adaptive genetic algorithm, with minimizing completion time and maximizing equipment utilization as target, under the constraint of resource-task matching matrix, the optimal allocation strategy and execution instruction set are generated;Through abnormal hierarchical judgment and dynamic adjustment resource allocation, and generate exception traceability feedback report;Realize biological characteristic perception and physical resource state prediction deep coupling, improve the survival rate of easy-degradable sample and the utilization efficiency of experimental equipment, guarantee the intelligentization and high robustness of cloud platform detection whole process.

This invention discloses a big data-based safety design management method for chemical projects, relating to the field of big data technology in chemical safety. The method includes: collecting multi-source heterogeneous data and preprocessing it to form a project baseline dataset; collecting design change data and performing differential comparison with a safety knowledge graph to identify differential objects; starting from the differential objects, performing dependency propagation analysis along the safety dependencies in the safety knowledge graph to output affected objects and risk propagation chains; determining the review order of affected objects based on the risk propagation chains, sequentially extracting the specifications to be reviewed that match the affected objects from the specification knowledge base, determining whether the affected objects meet the specifications to be reviewed, and outputting the specification review conclusion. This invention realizes dynamic and precise management and automated compliance review of chemical project safety design, shifting from experience-driven to big data-driven management.

The invention discloses a sewage treatment equipment operation intelligent monitoring system based on big data management, and relates to the technical field of big data. The method comprises the following steps: acquiring an Internet of Things sensor acquisition network of target sewage through big data, acquiring sewage characteristics, acquiring a sewage characteristic putting data pool through the sewage characteristics, and acquiring a sewage putting data pool of the Internet of Things sensor acquisition network through the sewage characteristic putting data pool; a preprocessing mechanism is set, and the sewage data of the sewage data pool is preprocessed through the preprocessing mechanism to obtain a to-be-put sewage data pool; the unit sewage abnormal coefficient of the to-be-put sewage data pool is obtained, and then abnormal sewage water quality data is obtained through the unit sewage abnormal coefficient; the monitoring module is used for carrying out real-time monitoring treatment on the abnormal sewage treatment data; and the operation monitoring accuracy of the sewage treatment equipment is improved.

The present application relates to the technical field of data management, and particularly relates to a land space planning geographic information big data management method and system; according to the suspected abnormal degree of geographic data of a coordinate point and neighborhood coordinate points in the same dimension; according to the single-dimensional abnormal confidence obtained by the ranking characteristics of the geographic data of the coordinate point in the neighborhood, the geographic data of the coordinate point and the neighborhood coordinate points; clustering is performed according to the geographic data of all dimensions of the coordinate point and the neighborhood coordinate points, and the cluster characteristic value is obtained according to the point cluster where the coordinate point is located and the distance characteristics between the point cluster where the coordinate point is located and other point clusters; the dimension correlation characteristic value is obtained according to the change difference characteristics of the geographic data between the dimensions; the multi-dimensional abnormal confidence is obtained according to the cluster characteristic value and the dimension correlation characteristic value; the abnormal degree of the coordinate point in any dimension is obtained according to the comprehensive abnormal confidence and the dimension suspected abnormal degree, and the geographic data is detected, and the accuracy of land space planning is improved.

This invention discloses an optimization prediction method for software testing based on big data management, specifically including: Step 1, collecting test data and performing time index alignment and numerical processing to form a test feature set; Step 2, constructing a static Markov blanket and a dynamic Markov blanket, and determining the target feature subset by switching input boundaries; Step 3, constructing a neural prediction model based on the target feature subset and outputting predicted values; Step 4, dividing the target feature subset into relevant feature clusters and updating the model parameters using the decorrelation-based isoangular minimum angle regression rule; Step 5, outputting test results, defect risk, and resource consumption prediction values; Step 6, generating test optimization configuration results based on the predicted values. This invention completes the prediction through isoangular regression using a two-layer Markov blanket.

The invention relates to the technical field of big data management, in particular to a control method and system and a vector network analyzer thereof, and the method comprises the following steps: obtaining frequency control parameters, generating an excitation frequency scanning control sequence, driving reflection and transmission paths to collect data, and analyzing amplitude fluctuation and direction change. And identifying the abnormal paragraph and adjusting the amplitude to a target state, generating a measurement channel compensation completion flag bit table, and analyzing a control instruction and a state to obtain a compensation data synchronization control path set. According to the invention, by generating the excitation frequency sequence and combining the channel number and the port number, the frequency excitation and the measurement path are coordinated. An amplitude fluctuation direction identification mechanism is introduced into a frequency band, symmetry deviation paragraphs are dynamically marked, amplitude values are adjusted point by point, and abnormal frequency response expansion is avoided. By updating the compensation flag bit and the synchronous control data in real time, the data processing stability and the measurement result reliability are improved in a multi-channel parallel test and high-frequency change scene.

The invention relates to the technical field of big data management, in particular to a whole-process performance data management method based on big data. The specific implementation process comprises the steps of obtaining heterogeneous behavior traces, deconstructing and packaging the heterogeneous behavior traces into space-time cross-domain data streams, and inputting the space-time cross-domain data streams into a distributed cooperative monitoring model; an asynchronous pause frequency and a cross-domain retrieval track are extracted, a cooperative friction coefficient is calculated, and a cooperative retardation degree is generated; inputting the collaborative friction coefficient and the collaborative retardation degree into a performance evolution engine, generating a targeted dynamic weight, and outputting a targeted collaborative performance report; and outputting a resource intervention instruction when the cooperative retardation degree change slope exceeds a preset warning threshold. According to the method, real-time unified access of multi-source heterogeneous process data, continuous association modeling of cross-system behavior tracks, detection of flow blocking nodes and online dynamic updating of evaluation weights are realized by utilizing a distributed collaborative monitoring model and a performance evolution engine, so that the association processing delay of the cross-system data is reduced; and the timeliness and accuracy of flow anomaly detection are improved.

The invention discloses a big data management method and system based on semantic analysis, and relates to the technical field of big data management. A multilayer semantic abstract data organization structure comprising a base layer, a mode layer and a statistical layer is constructed, real-time semantic analysis is performed on input data, and semantic descriptors are generated; the high-frequency data instance in the base layer is stored to a corresponding level according to a central semantic index, a semantic folding operation is triggered based on a preset rule, the high-frequency data instance in the base layer is automatically abstracted into a modeled representation of a mode layer, and similar representations in the mode layer are further aggregated into an aggregated representation of a statistical layer; when the query is responded, the system analyzes a query intention and determines a target data abstraction level, high-level aggregation or modeling representation is restored into bottom-layer detailed data as required through reverse expansion operation, and a query result is generated; through the multi-layer semantic abstract data organization structure and the data folding operation, the storage efficiency of mass complex data and the performance of multi-granularity intelligent query are effectively improved.

The invention relates to an intelligent auxiliary management method and system for spare parts such as a nuclear power plant, a medium and equipment. Identifying the state of the spare part work order, and screening based on the state of the spare part work order to obtain a spare part work order list; classifying the equal spare part work orders to obtain equal spare part work orders of different management classification types; performing statistical counting and purchase progress classified statistics on the spare part work orders of different management classification types to obtain statistical data and corresponding summarized lists and classified statistical data tables; generating a visual display chart based on the statistical data and the progress classification statistical data table, and generating a form which can be exported according to each management classification; and setting data items which can be configured individually, generating a management mail and an attachment according to the data items, and sending the mail and the attachment. According to the invention, a classification list for auxiliary management can be automatically provided for spare part demand management manual intervention, automatic updating is carried out to focus on development of work order and spare part businesses concerned by management, and the spare part management efficiency is remarkably improved.

The application relates to the technical field of big data management in the financial industry, in particular to a financial knowledge graph updating and compliance verification method based on multi-source heterogeneous data. The method comprises the following steps: establishing a global perception network for the financial field, collecting multi-source heterogeneous financial data in real time, and performing normalization processing; using a pre-trained large language model to perform named entity recognition and relationship extraction on the normalized financial data, and constructing a financial knowledge graph; when a new regulatory rule is released, detecting whether there is a conflict between the new regulatory rule and the old regulatory rule, and if there is a conflict, performing lossless dynamic updating on the financial knowledge graph; when a financial business request is received, taking the business subject and the business type corresponding to the current financial business request as an anchor point, extracting a local subgraph in the financial knowledge graph, using a graph neural network to reason the local subgraph, and finally outputting an interpretable compliance audit report.

The public opinion big data management method based on the multi-modal intelligent technology comprises the steps that relational enterprises of to-be-managed enterprises are acquired, and the relational enterprises comprise competitive enterprises and upstream and downstream enterprises; obtaining social media and information streams about the related enterprises in a network platform, and converting the information streams into public opinion information; evaluating the public opinion information to obtain a value degree, and outputting the public opinion information of which the value degree is greater than a preset threshold value as important public opinions; performing positive and negative judgment on the important public opinions; the to-be-managed enterprise takes corresponding processing measures according to different types of relational enterprises and different positive and negative public opinions; public opinion states of competitive enterprises and upstream and downstream enterprises are monitored, whether public opinion information is important public opinions needing to be processed or not is recognized, meanwhile, the positive and negative directions of the important public opinions are recognized, and different processing measures are taken based on the types of the relational enterprises and the positive and negative directions of the important public opinions; and the public opinions of the related enterprises are prevented from influencing the normal operation of the to-be-managed enterprises.

The application discloses a gas extraction big data management system, which comprises a gas database module and an extraction equipment planning module; the gas database module stores the following: direct mineral gas content x1, real-time spatial gas content x2, mine cross-sectional area S, mine depth advancing speed V and total mine depth H; the extraction equipment planning module is used for calculating the required number N of gas extraction equipment of the current mine; the extraction equipment planning module calculates the required number N of gas extraction equipment of the current mine according to the following steps: S11, setting the extraction speed of each gas extraction equipment as Q1, which is the volume of air extracted per hour. The gas extraction big data management system solves the problem in the prior art that the gas extraction equipment cannot be controlled according to the direct mineral gas content detected on site, the real-time spatial gas content and the size of the current mine space.

The invention provides a data scheduling method and device of a distributed storage system, electronic equipment, a storage medium and a program product, and relates to the technical field of big data management. The method comprises the following steps: predicting a state change trend of each node based on multi-source state data of the nodes of the distributed storage system; determining an initial allocation scheme and a reward score of the target task to determine a task allocation scheme; and scheduling the data blocks of the distributed storage system based on the task allocation scheme. According to the method, the state change trend of the node is predicted according to the multi-source state data, the state data of the node in multiple different dimensions are fully considered, and accurate prediction of the current state and the future state of the node is realized. The task allocation scheme is determined through the state change trend and the features of the target task, the task allocation scheme is made in advance in combination with the future resource demand change of the node and the features of the to-be-allocated task, and the utilization rate of the node at the current moment and in the future time period can be improved.

The invention provides a tobacco leaf raw material spectrogram data management method and system and a storage medium. The tobacco leaf raw material spectrogram data management method comprises the steps of S1, obtaining initial spectrogram data of tobacco leaf raw materials; s2, classifying and managing the initial spectrogram data according to preset spectrogram data types to obtain target spectrogram data; and S3, performing data analysis processing on the target spectrogram data to generate an analysis result corresponding to the tobacco leaf raw material. The invention aims to provide core technical support for design and implementation of a tobacco scientific research big data management system. According to the method, spectrogram sources and formats are investigated, spectrogram data are classified, deep analysis mining is performed according to the classified spectrogram data in combination with multiple chemometrics algorithms, a foundation is laid for centralized storage, efficient retrieval, unified management and safe sharing of the spectrogram data and other scientific research data, the tobacco scientific research efficiency can be improved, and the tobacco scientific research efficiency can be improved. And powerful support is provided for industry scientific and technological innovation and decision making.

The invention relates to the technical field of home equipment detection, and discloses an Internet of Things home data management method and system based on big data analysis. According to the method, the total power consumption of all smart homes in two months adjacent to the same quarter is obtained, the total power consumption difference is calculated, and the total power consumption difference is compared with a preset total power consumption difference threshold. If the total power consumption difference is smaller than or equal to the threshold, the power consumption difference is reasonable, and the system does not need intervention; and if the total power consumption difference is larger than the threshold value, it is judged that power consumption is abnormal, and the air conditioner with the maximum power consumption proportion is detected preferentially. Through the dynamic monitoring and priority response mechanism, the energy consumption management of the smart home equipment is optimized, the resource waste is avoided, the system cooperation efficiency is improved, and the core demand of smart home big data management is met.

The invention discloses a communication maintenance field operation big data management system based on visual analysis, and belongs to the technical field of detection and analysis. By separating illumination and reflection components, the detail definition of the target in strong light, backlight and dark scenes can be effectively improved, and target missing detection caused by uneven illumination is avoided; the high-frequency texture is reconstructed through the generative adversarial network, so that the detail recovery rate of the compressed video can be effectively improved, the problem of information loss in a compression-analysis link is solved, and data support can be provided for equipment defect identification; the extracted illumination feature sequence provides an environment context for subsequent risk early warning, and the false alarm rate is reduced; after YOLOv5s is combined with illumination features, the target detection precision in strong light and backlight scenes can be effectively improved, and the omission factor is reduced; the multi-modal feature matrix can be migrated to different maintenance scenes, the model weight is dynamically adjusted through the illumination features, and the robustness of the system is enhanced.

The invention relates to the technical field of big data management and retrieval enhancement generation, and particularly discloses a college data management large model platform based on a vector database and retrieval enhancement, comprising a multi-modal vectorization engine integrating a multi-modal large language model to realize deep semantic coding and representation learning of education data; the intelligent vector retrieval system realizes high-precision rapid retrieval based on neural information retrieval and learnable indexes; the advanced RAG inference engine improves the generation quality through an adaptive retrieval strategy, a multi-hop inference mechanism and knowledge fusion; the intelligent educational knowledge engine provides domain knowledge support and intelligent analysis based on dynamic knowledge graph and educational ontology modeling. By integrating the latest artificial intelligence technology, an advanced college data management platform is constructed, powerful technical support is provided for education informatization, and important theoretical significance and practical value are achieved.

The invention belongs to the technical field of campus food safety management, and particularly discloses and provides a campus food safety big data management method and system, and the method comprises the steps: monitoring food material quality parameters, binding an identity traceability identifier, obtaining a current risk state through real-time deviation degree analysis, building a deviation degree change curve based on historical data, and carrying out the real-time deviation degree analysis. Analyzing the quality deviation growth degree to predict the risk trend, comparing and judging the food material safety risk according to double thresholds, tracing the food material safety risk, determining a potential risk student group, obtaining the doctor seeing data of the student group, and outputting the safety risk level through a risk assessment matrix by integrating the quality risk index and the health risk diffusion degree. And finally starting graded early warning according to the risk grade. According to the method, the comprehensive quality risk index is calculated, the health risk diffusion degree is assessed in combination with student doctor-seeing data, rule judgment is carried out on the comprehensive quality risk index and the health risk diffusion degree in the risk assessment matrix according to the grade comparison principle, and closed-loop assessment from risk prediction to consequence verification is formed.

The invention discloses an intelligent talent recruitment screening method based on multi-source data fusion, and relates to the technical field of big data management, and the method comprises the steps: collecting candidate multi-source heterogeneous data, carrying out the feature extraction, and generating a multi-modal initial feature vector; fairness related attribute data and historical recruitment result data of candidates are collected and combined with the skill confidence feature set, and a recruitment decision causal graph model is constructed; and based on a recruitment decision causal graph model, performing causal effect estimation and anti-fact reasoning by taking the multi-source verification confidence as a weight, performing fair correction calculation on candidate ability evaluation represented by the skill confidence feature set, and outputting a fair correction talent sorting list. According to the method, the multi-source evidence cluster is constructed in the shared semantic space, and the multi-source verification confidence is calculated, so that the unified management and credibility quantification of the candidate multi-source heterogeneous data in the skill dimension are realized, and the controllability and traceability of a skill assessment result in a big data management scene are improved.

The invention relates to an electric power big data management system and method, and the system comprises a data source access module which is connected with an external platform and is used for obtaining first electric power data, and the first electric power data is multi-modal data; the data processing module is used for carrying out quality evaluation on the first electric power data and carrying out data cleaning on the original electric power data based on an evaluation result to generate second electric power data, and the quality evaluation is realized through a multi-modal deep learning evaluation model. The multi-modal deep learning evaluation model comprises a feature extraction unit, a cross-modal fusion unit, a distributed quality evaluation unit and a comprehensive evaluation unit which are connected in sequence, and the multi-modal deep learning evaluation model is trained based on a weighted quality loss function; the data storage module is used for storing the second power data; and the data application module is used for calling the second power data in the data storage module according to an application request. Compared with the prior art, the method has the advantages of high data processing reliability, effective improvement of data quality and the like.

This application provides a community ecological data management method, system, electronic device, and storage medium, relating to the field of big data management technology. The method includes: acquiring multiple sets of resident data; de-identifying each set of resident data to obtain multiple sets of de-identified resident data; obtaining data requirements from enterprises; performing multi-dimensional analysis on each set of de-identified resident data based on the enterprise's data requirements to obtain multiple multi-dimensional data sample sets; encrypting each multi-dimensional data sample set to obtain multiple encrypted datasets; uploading each encrypted dataset to a blockchain; obtaining a data acquisition request from an enterprise terminal; retrieving a target encrypted dataset from the multiple encrypted datasets on the blockchain according to the data acquisition request; and sending the target encrypted dataset to the enterprise terminal. This application provides enterprises with accurate and usable cross-domain data while ensuring resident privacy and security, realizing the commercial value transformation of data.

The present application relates to the technical field of big data management-based wisdom pension, in particular to a wisdom pension comprehensive service platform, comprising a cloud service platform and a pension institution service end, the cloud service platform is used for maintaining the basic information of each pension institution, including creating a new pension institution account, managing the pension institution account list, counting the indicators of the pension institution, receiving pension consultation information and publishing pension demand information based on the pension consultation, the pension institution service end comprises a management end and a nursing end, the management end is used for configuring service items and managing each service item, and is also used for managing the catering service, nursing level, assets and nurse scheduling of the pension institution; the nursing end is used for inquiring the daily shift information and the list of responsible old people, displaying the nursing tasks and recording the execution of the nursing tasks, recording the house inspection and fire patrol conditions; the present application can realize the all-round, efficient and intelligent management of the pension institution.

The invention discloses an energy management intelligent control method and system, and particularly relates to the technical field of intelligent energy of end-cloud cooperation and big data management, and the method comprises the steps: obtaining energy data of each device in an energy management system through an edge device, and carrying out the time sequence abnormality detection of the energy data, analyzing the change trend of the energy data by setting a sliding time window, identifying and removing abnormal values deviating from a normal fluctuation range, filling missing data at the same time, arranging the energy data into a uniform format according to a time sequence, and generating a real-time data set; according to the method, sliding time window analysis and abnormal value correction are executed at the edge end, and energy efficiency calculation and dynamic optimization are carried out at the cloud end based on time sequence index and big data management, so that the problems that in the prior art, response of an energy system is lagged, feedback is not timely, and multi-source data fusion efficiency is low are solved.

The invention relates to the technical field of data processing, and particularly discloses a big data management-based substation maintenance operation positioning and safety early warning method, which comprises the following steps of: fusing an unstructured operation task book, real-time SCADA (Supervisory Control And Data Acquisition) equipment state data and GIS (Geographic Information System) spatial data; and a safe operation area and a danger warning area which are completely matched with the field electrical state are automatically and dynamically constructed for each operation, so that the accuracy and scenario of warning are realized from the source. On the basis, in order to overcome the hysteresis of a traditional alarm, the dynamic geofence is abstracted into an environmental potential field providing gravitational force and repulsive force in the scheme, and high-precision nonlinear prediction is carried out on a future trajectory through fusion of personnel motion inertia and environmental potential field force. According to the mechanism, the system can pre-judge an imminent danger approaching behavior in advance, and the fundamental conversion from post-event response to pre-warning is realized, so that the safety risk is actively solved.