451results about How to "Guaranteed timeliness" patented technology

The invention relates to the technical field of ecological environment protection, in particular to an ecological environment damage intelligent identification management method based on multi-source data fusion, which comprises the steps of data acquisition and preprocessing, data fusion and feature extraction, ecological environment damage evaluation model construction, damage dynamic analysis and trend prediction, restoration suggestion generation and visual display. The method comprises the following steps: collecting water quality, atmosphere, biological and social economic data in real time through a sensor network deployed in an ecological sensitive area, preprocessing the data, extracting key features by adopting a multi-modal data fusion technology and a deep learning model, and constructing an ecological damage assessment model; the change rule of ecological damage and the potential high-risk area are predicted based on a time sequence analysis method, and an optimal ecological restoration scheme is generated in combination with a particle swarm optimization algorithm. According to the method, the evaluation result and the restoration suggestion are displayed through the visual interface, a scientific basis is provided for decision makers, and the efficiency and the effect of ecological environment restoration work can be improved.

The invention discloses a mirror image type industry linkage engine system and method based on a knowledge graph, and relates to the technical field of industry linkage engines, and the system comprises a data collection module which collects multi-industry data in multiple modes, and the cleaning accuracy is 90% or above; the knowledge graph construction module is combined from bottom to top and from top to bottom, and is used for distributed storage and block chain evidence storage; the industry mirror image model generation module is used for generating a model through machine learning, and the association accuracy is 95% or above; the linkage analysis and decision module is used for finding a linkage strategy through an intelligent algorithm; and the user interaction module has a graphical interface, is fast in response, can feed back optimization, and supports multi-language and VR / AR display. The system is accurate and efficient in data processing, integrates multi-source data to construct the knowledge graph, is deep and comprehensive in industry analysis, simulates industry relations to assist decision making, is intelligent and reliable in decision making support, provides strategies and evaluation, is convenient and friendly in user experience, improves interactivity through multiple technologies, promotes accurate decision making of enterprises, and promotes collaborative development and innovation of the industry.

The invention discloses a multi-terminal vehicle management system based on an intelligent algorithm, relates to the technical field of vehicle management, and is used for solving the problem of insufficient real-time performance and cooperative efficiency of vehicle management. According to the method, a multi-modal sensor fusion technology is combined with Gaussian process regression, complex environment data is modeled in real time, dynamic target and static background features are accurately extracted, environment changes are analyzed and predicted in combination with a time sequence, accurate input is provided for path planning and task allocation, a path is dynamically adjusted through an incremental optimization algorithm, emergencies are coped, and the path planning and task allocation efficiency is improved. According to a distributed path optimization mechanism, multi-vehicle task path conflicts are eliminated, the cooperation efficiency is improved, a priority model is constructed based on the task emergency degree, the time constraint and the resource matching degree, the weight is dynamically adjusted, vehicle and task matching is optimized in combination with reinforcement learning, the task execution efficiency is ensured, resource competition and path conflicts are solved, and the task execution efficiency is improved. Efficient cooperation in a complex dynamic environment is realized, and the real-time performance and management efficiency of vehicle management are improved.

The invention discloses an industrial firewall isolation method of a DCS (Distributed Control System). The objective of the invention is to solve the problem that existing industrial network security protection has defects for a DCS system. According to the method, firstly, DCS architecture analysis and risk assessment are carried out, and protection key points and data flow directions are accurately determined; then, a firewall strategy fitting the characteristics of each area (such as a core control unit, an operation station and an external network interface) is customized, and mechanisms such as multiple data encryption are covered; and then firewall deployment and configuration optimization are carried out to ensure protection continuity and rule timeliness. And abnormal conditions are timely handled through a real-time monitoring and dynamic adjustment mechanism, and system recovery under a security event is guaranteed through an emergency response and backup recovery mechanism. And a regular safety drill and evaluation mechanism is arranged to continuously strengthen the protection capability. According to the method, the network security level of the DCS is comprehensively improved, stable and safe operation of industrial production is guaranteed, and the method has remarkable practical value.

The invention relates to the technical field of geological disaster monitoring, and discloses a debris flow monitoring and early warning system based on Internet of Things, comprising a multi-source data acquisition module used for acquiring environmental data related to debris flow; the data transmission module is used for transmitting the environment data to a data center or a cloud end from a collection point; the data preprocessing module is used for carrying out abnormal value cleaning, time synchronization and normalization processing on the environment data; the deep learning analysis module is used for analyzing the preprocessed data so as to predict the occurrence risk of debris flow; and the dynamic early warning and publishing module is used for generating a multi-stage early warning signal according to an output result of the deep learning analysis module and publishing early warning information to a user in multiple modes. The accuracy, the real-time performance and the adaptability of the debris flow early warning system are improved through multi-source data fusion, edge calculation and a dynamic learning mechanism so as to cope with debris flow disasters in a complex geological environment.

The invention relates to the technical field of artificial intelligence, and discloses an anti-fraud model training method based on deep learning, which comprises the following steps: step 1, preprocessing and constructing multi-modal data, carrying out abnormal value processing, missing value filling and feature standardization on original data, representing processed transaction data and geographic information through a feature extraction module, and carrying out feature extraction; the equipment information and the social relation graph are embedded through an embedding layer and a graph neural network; step 2, based on data enhancement of self-supervised learning, pre-training unlabeled data by designing a behavior prediction task and a mask filling task so as to initialize a main model by using generated model parameters, and generating a synthetic sample extension training data set; by designing a behavior prediction task and a mask filling task, automatic learning and enhancement of features are realized by using label-free data, the dependence on labeled data is reduced, the detection capability of the model on fraudulent behaviors is remarkably improved, and the diversity of training data is enriched.

The invention relates to the technical field of education informatization, and discloses a digital teaching resource library intelligent management and application method and system. The method comprises the steps of constructing a digital resource library, constructing a resource application big data analysis model to calculate a resource quality index, a popularity index and an update index, performing intelligent resource sorting and elimination based on the indexes, analyzing the correlation between a user retrieval request and resources, and performing intelligent recommendation. According to the method, efficient management and accurate application of teaching resources are realized, the resource utilization rate and the teaching effect are improved, the storage space and the management cost are saved, and data support is provided for education decision making.

The invention discloses a one-way optical shutter video stream transmission method based on weak supervision semantic segmentation, and belongs to the technical field of one-way optical shutter information transmission. Comprising the following steps: carrying out key region and background region classification and marking on all video data received by an optical shutter outer end through a weak supervision semantic segmentation model; performing priority division on a key region and a background region in the video frame; dividing the data packets according to time windows, and packaging the data packets; performing dynamic data compression and segmented coding on the packaged data packet; reed-Solomon redundant coding is carried out on all the data packets of the key area after compression coding is carried out on the data packets of the key area; dynamically adjusting the transmission sequence of the data packets to perform link scheduling; and a receiving end carries out de-encapsulation and data restoration, and reconstructs the restored video frame. According to the method, redundancy and compression of data transmission contents are accurately controlled, so that the transmission efficiency and reliability are remarkably improved, the bandwidth utilization is optimized, and the transmission reliability and stability are enhanced.

The invention discloses an official document generation system and method based on a large model, belongs to the technical field of artificial intelligence, and aims to solve the technical problems of low official document format and content accuracy, poor material failure and poor content innovation in official document generation. Comprising a model training module used for performing model training on an official document generation model based on official document samples in an official document sample library; the model fine tuning module is used for performing layered fine tuning on the trained official document generation model; the cue word optimization module is used for carrying out cue word matching on user input based on a built-in cue word set; the outline generation module is used for calling a final official document generation model to generate an official document outline based on the cue word combination and user input; and the official document generation module is used for calling the final official document generation model to generate and output an official document body based on the user input and the final official document outline, and calling the privacy data shielding module to shield the privacy data in the official document body.

The invention discloses a big data prediction system based on machine learning, and relates to the technical field of machine learning and big data, and the system comprises a data collection module, a data preprocessing module, a feature engineering module, a machine learning prediction model module, a model training module and a prediction output module. According to the method, a specific algorithm is applied through the feature engineering module, correlation is considered, a penalty factor is introduced, related feature subsets are focused to improve precision, a hidden layer and parameters of the model module can be adaptively adjusted, for example, high precision is obtained in power load prediction, the data acquisition module updates and processes a format in real time, and data timeliness and availability are ensured. The preprocessing module processes abnormal values by adopting a clustering algorithm, corrects and normalizes parameters after determining the parameters, meets the requirements of the algorithm and improves the stability and accuracy, and the characteristics act together, so that the system is excellent in performance in a prediction task, and a solid foundation is laid for accurate prediction.

The invention belongs to the technical field of low-voltage distribution cable fault identification, and particularly relates to a method for judging a series arc fault of a low-voltage distribution cable based on disturbance current characteristics, which comprises the following steps of: S1, acquiring current data of three-phase current of the cable; s2, analyzing the similarity of the current waveforms of the adjacent current periods by adopting a Pearson correlation coefficient PCC, and judging whether the cable has current disturbance or not; s3, when it is judged that current disturbance occurs, intercepting current data of a disturbance starting point and a plurality of current periods after the disturbance starting point, and recording the current data as disturbance current; calculating the time-frequency domain characteristic quantity of the intercepted disturbance current; and S4, inputting the time-frequency domain characteristic quantity of the disturbance current into a preset fault identification model, carrying out detection and identification on a cable series arc fault, and judging whether the fault causing current disturbance is the series arc fault or not. According to the method, the low-voltage distribution cable series arc fault can be efficiently, accurately and timely monitored and identified, and the safety and stability of a power system are improved.

The invention provides a knowledge graph dynamic updating method and device based on a crop growth process, and the method comprises the steps: inputting environment factor data and a crop growth stage into a trained Transform model, and obtaining a time sequence feature; performing relation graph construction based on the environmental factor data and the crop growth indexes through a graph convolutional network to obtain an environmental crop growth relation graph; performing feature extraction based on the environment crop growth relation graph through a graph attention network to obtain a spatial feature matrix; performing feature extraction based on the spatial feature matrix through a spatial graph convolutional network to obtain high-dimensional spatial features; performing alignment and conversion based on the high-dimensional spatial features through a spatial transformation network to obtain geographic spatial features; performing feature fusion based on the time sequence features and the geographic space features through a deep fusion model to obtain comprehensive features; and performing incremental updating on a preset knowledge graph based on the environmental factor data, the crop growth indexes and the comprehensive characteristics.

The invention belongs to the technical field of ground unmanned platforms, and particularly relates to a sky-air-ground integrated cross-country environment high-precision map construction method. Comprising the following steps: 1, acquiring a cross-country environment high-precision map; and 2, making a cross-country environment high-precision map. According to the invention, automatic extraction of elements is realized by combining an automatic extraction technology; in the aspect of map making, making specifications are provided, and the running efficiency of a high-precision geographic information system is improved while it is guaranteed that the cross-country maneuvering requirement of a ground unmanned platform in the cross-country environment is met; incremental data updating is carried out on the high-precision map based on a real-time mobile environment perception result of the ground unmanned platform so as to ensure the timeliness of the high-precision map. Through the method, the blank of a high-precision map technology in the field of automatic driving in the cross-country environment is filled up, and support is provided for improving the cross-country maneuverability of a ground unmanned platform.

The invention discloses a WebSocket-based instant message task assigning system. The system comprises two parts, i.e., a server and clients, wherein the server is deployed on an application server and configured with ports for providing external services, and the server is then started; the clients comprises an APP client and a PC client; and information data transmission among the server, the APP client and the PC client is achieved based on the WebSocket protocol, that is, the server provides the instant messaging service, and the clients achieve acquisition of data from the server. Compared with the prior art, the system provided by the invention has the advantages that WebSocket-based instant message task assigning is achieved, and information can be synchronized between the information acquisition clients, so that high timeliness, effectiveness and safety of the information are ensured.

The invention discloses an Internet of Things data analysis system based on artificial intelligence, and relates to the technical field of telecommunication, the system analyzes the coupling effect in signal propagation through a path selection and prediction module and an AI algorithm, and outputs an environmental influence function TW; and the dimension reduction feature data set F is combined with the environmental influence function TW to calculate and output a prediction path optimization parameter Pout. The intelligent path selection model not only considers factors such as signal intensity attenuation, noise interference and building reflection, but also combines the influence of terrain and weather, so that the prediction accuracy is greatly improved. And the dynamic path adjustment switching module realizes intelligent adjustment of the communication path based on the quality score comparison of the current path Pcurr and the predicted optimal path Pout and the setting of a threshold value M. And the response speed and the decision quality of the system in the case of network condition change are enhanced.

The invention relates to the field of power systems, in particular to a power system load type intelligent identification method, system and equipment. The method comprises the steps that clustering centers of a plurality of load type subgroups are determined respectively, and each clustering center represents a typical load type calibrated in advance; utilizing a dynamic time warping (DTW) algorithm to calculate a DTW distance between the to-be-identified load node and each clustering center; according to the DTW distance obtained through calculation, distributing the to-be-identified load node to a subgroup of the nearest clustering center, and determining the load type of the to-be-identified load node; and periodically scanning and identifying load type changes of the load units in the domain, and updating a load type identification result. The invention further comprises a power system load type intelligent identification system and electronic equipment, the identification accuracy of the multi-type industrial load composite bus is improved, and misjudgment and deviation are reduced. The bus load type can be dynamically identified and changed and can be updated in time.

The invention discloses an intelligent data analysis method based on spatio-temporal big data, and belongs to the technical field of spatio-temporal big data, the method comprises the step of obtaining original spatio-temporal data of different sources in real time, and the method comprises the steps of performing feature extraction on preprocessed spatio-temporal data, including unification of a time format, spatial coordinates and a data unit, deduplication fusion and the like, so as to obtain the spatio-temporal data of different sources. According to the method, multi-source spatio-temporal data is formed, a spatio-temporal data analysis model is constructed based on deep learning, spatio-temporal dynamic changes in data can be captured, modes in the spatial data can be effectively extracted through models such as a convolutional neural network, classification is performed through cross entropy loss, high-precision spatio-temporal data analysis is achieved, and the spatial data analysis efficiency is improved through the trained spatio-temporal data analysis model. According to the method, new spatio-temporal data are analyzed in real time, abnormal modes and trend changes in the data are rapidly identified, an anomaly detection threshold value is set, model output is compared with the set threshold value, comprehensive judgment is carried out in combination with context information, and the accuracy and efficiency of anomaly detection are improved.

The invention discloses a case information processing method and system based on a document generation model, and the method comprises the steps: carrying out the data analysis and processing of case information through employing a semantic understanding and data analysis model based on a deep learning technology, extracting a named entity from the case information, determining the type of the entity, and carrying out the processing of the named entity. Meanwhile, key events are extracted from the case information, corresponding legal terms are intelligently matched based on semantic features of the key events, and then the obtained legal terms, key events, entities and entity category information are integrated into a designed case related document template through a condition generation model to generate case related documents. According to the invention, the efficiency and accuracy of case information processing can be significantly improved through an automation technology, the burden of law enforcement officers is effectively reduced, and the timeliness of case processing is ensured.

The invention provides a slope stability dynamic prediction and geological parameter inversion method, and the method comprises the steps: building a slope stability simulation model through a COMSOL multi-physics field simulation platform, and generating simulation data in combination with an orthogonal experiment design and automatic parameter optimization technology; then, training and optimizing the simulation data based on a variational auto-encoder and a generative adversarial network to obtain enhanced simulation data; and performing geological parameter inversion based on SBAS-InSAR ground deformation data and the enhanced simulation data, generating a safety coefficient distribution map in the region by using geological parameters obtained by inversion, and fusing the safety coefficient distribution map with a geological disaster potential map to evaluate disaster risks of different regions. According to the method, the timeliness and the real-time performance of a prediction result are ensured, and a scientific basis is provided for engineering design, disaster monitoring and decision making.

The invention relates to an operation method of an unattended intelligent mobile emergency cabin. Comprising the steps of system initialization, material information configuration, authority verification, order generation, material warehouse-in and warehouse-out, inventory monitoring, intelligent prediction, decision support, data processing and synchronization and the like. Various parameters are obtained and configured through the system configuration module, material information is managed through the material detail directory tree module, accuracy of access authority is ensured through the face recognition and authority verification module, and automatic order generation and inventory updating are achieved through the automatic order generation and inventory updating module. A gravity sensing unit is used for monitoring warehouse-in and warehouse-out of materials, an inventory alarm and decision-making auxiliary module is used for monitoring an inventory state and sending an alarm signal, an intelligent prediction unit is used for predicting a material use trend and generating a replenishment plan, and finally data processing and synchronous management are realized through a data processing module. Manual operation is reduced, management efficiency is improved, timeliness and reliability of material supply are ensured, and the method is suitable for the fields of electric power engineering and the like.

The invention relates to the field of scientific research project management, in particular to an intelligent management and control system and method for scientific research project budget execution, and the system comprises a data collection module, a budget analysis module, an execution monitoring module, a feedback adjustment module and a decision support module. Through modular design, the system realizes whole-process monitoring and intelligent adjustment of scientific research project budget. The data acquisition module can acquire budget-related data in real time, so that the timeliness and accuracy of data input are ensured; the budget analysis module generates stage budget demand prediction based on the collected multi-dimensional data to ensure that budget distribution is more reasonable; the execution monitoring module provides real-time budget execution monitoring through a deviation monitoring algorithm, so that the risk of hyper-branch or insufficient budget is reduced; the feedback adjustment module dynamically adjusts budget allocation according to monitoring feedback, and the adaptability of fund allocation is ensured; the decision support module provides management suggestions, and improves the accuracy and decision efficiency of scientific research project budget management.

The invention discloses an intelligent power distribution network coordination control method and system based on data analysis and a medium, and relates to the technical field of power distribution network coordination control, and the main scheme is that real-time load data, historical load data and historical meteorological data of each node in a power distribution network are collected, and a future load prediction value is calculated according to the data; estimating the load demand of the power distribution network; the state of the power distribution network is evaluated by calculating the capacity-load ratio of the power grid and comparing the capacity-load ratio with a preset optimal value; detecting an abnormal value according to the historical load data and the real-time load data, and judging whether the power distribution network has an abnormal operation condition or not; setting a coordination control strategy; according to load prediction, power grid state evaluation and anomaly detection results, a corresponding control strategy is intelligently selected and triggered so as to optimize resource configuration of the power distribution network, balance load requirements and maintain voltage stability; the control strategy is dynamically adjusted by collecting real-time data in the execution process of the coordination control strategy, and efficient and stable operation of the power distribution network is ensured.

The invention discloses an LLM-based intelligent question and answer method and system for operation and maintenance knowledge, relates to the technical field of artificial intelligence, and provides an efficient knowledge management and update mechanism which can continuously absorb latest operation and maintenance knowledge and technical practice, and operation and maintenance personnel can upload new knowledge documents to the system through a data entry unit, so that the operation and maintenance knowledge can be quickly and conveniently answered. After verification of the data checking unit, the knowledge can be automatically partitioned and vectorized and supplemented into the knowledge base, the mechanism not only ensures timeliness and accuracy of the knowledge base, but also promotes accumulation and inheritance of the knowledge, new operation and maintenance personnel can quickly obtain necessary knowledge and skills through the system, and the operation and maintenance efficiency is improved. In addition, the system also supports the processing of multi-round dialogues and context relevance problems, so that the solution of complex problems is smoother and more efficient.

The invention relates to the technical field of construction monitoring, and particularly discloses a large-span open-web arch bridge construction stability monitoring and control system and method, and the system comprises a structure data obtaining module which obtains the structure data of all key parts currently existing in a large-span open-web arch bridge construction structure in real time; the structural data analysis module analyzes structural characteristics, structural data change characteristics and a current construction stage of the construction structure of the large-span open-web arch bridge; the stability monitoring module inputs the current construction stage, the structural characteristics and the structural data change characteristics of the large-span open-web arch bridge construction structure into the construction stability evaluation model to obtain a structural stability index in the current construction state; the stability control module starts a corresponding control measure when monitoring that the structural stability index is close to a critical value, and obtains a construction stability control result; real-time monitoring and effective control of the construction stability of the large-span open-web arch bridge are achieved, and the safety and reliability of construction are improved.

The present invention relates to the technical field of sleep diagnosis, and in particular to a sleep staging method based on deep learning, the method comprising: collecting a number of target physiological signals, preprocessing each target physiological signal to obtain a number of standard physiological signals; synchronously aligning and integrating each standard physiological signal in turn to obtain a standard sleep data set; determining the actual correlation between each physiological signal to be integrated in the standard sleep data set, and determining an actual signal combination strategy according to the actual correlation; generating an actual sleep data set according to the actual signal combination strategy, and extracting features from the actual sleep data set to generate actual feature parameters; inputting the actual feature parameters into a deep learning model to determine the actual sleep state; analyzing the actual sleep state, and performing corresponding sleep intervention according to the analysis results. The present invention realizes low-cost non-intrusive sleep monitoring, thereby improving people's physical health.

The invention discloses a point cloud intelligent cutting and dynamic optimization method based on edge computing, and belongs to the technical field of intelligent traffic systems and edge computing, and the method comprises the steps: collecting three-dimensional point cloud data through a multi-line laser radar disposed on an RSU; generating a three-dimensional bounding box through a three-dimensional target detection network in an RSU edge calculation unit, generating an ROI mask, expanding a buffer region, and performing adaptive region cutting on the point cloud to form an ROI point set and a background point set; a perception-driven cutting decision is realized, and ROI extraction is converted into a self-learning process based on semantic features from fixed threshold judgment; voxelizing the non-uniform resolution point cloud, generating a unique hash index for each voxel unit, comparing a hash set of a current frame with a hash set of a previous frame, extracting newly added and disappeared point sets, and constructing a differential data packet according to a change ratio; while high timeliness is kept, data redundancy is reduced, and the point cloud updating rate and the incremental transmission efficiency are improved; and dynamic optimization of communication-calculation cooperation is realized.

The invention relates to the technical field of ship safety, and discloses a ship safety management monitoring system based on the Internet and a Beidou satellite, the system comprises a coordinate analysis module, a ship state module, a data fusion module, an abnormity identification module and an early warning message sending module, a satellite positioning signal is analyzed, and real-time latitude and longitude coordinates of a ship are obtained; generating comprehensive operation parameters of the ship according to the attitude, the navigational speed, the load and the main engine operation parameters of the ship; performing data fusion on the longitude and latitude coordinates after timestamp alignment and the comprehensive operation parameters to obtain an operation feature vector of the ship; performing abnormal mode identification on the operation feature vector based on historical navigation data and a preset safety threshold to obtain a risk category of the ship; when a risk category occurs, generating a treatment measure message of the risk category, and sending the treatment measure message to a ship-end display terminal and a shore-based management terminal through communication between the Internet and a Beidou short message; the ship safety management efficiency can be improved.

The invention relates to the technical field of substation inspection robots, and discloses an autonomous inspection method and system for a substation inspection robot. The method comprises the following steps: acquiring current environment parameters of a transformer substation and real-time state information of an inspection robot, and constructing an inspection environment model; analyzing historical inspection records, classifying historical inspection strategies into a plurality of experience levels, and establishing a multi-layer experience pool; according to a preset target inspection state in the inspection environment model, calculating an intelligent inspection strategy required for achieving the target inspection state; the intelligent inspection strategy and the experience inspection strategy are corrected in real time by adopting a dual-channel coordination mechanism to respond to the dynamic change of the transformer substation environment, and a final inspection strategy is generated; executing a control instruction based on the final inspection strategy, completing an inspection task and recording an actual inspection state; and comparing the deviation reason between the actual inspection state and the target inspection state, adjusting the experience content in the multilayer experience pool according to the deviation reason, and optimizing the experience data.

The invention discloses an underground space personalized intelligent guidance system based on spatial layout, which comprises a positioning hardware network, a navigation calculation server, user terminal equipment and a data transmission network, and is characterized in that the navigation calculation server is internally provided with a complex spatial database and stores a three-dimensional map of an underground space and function distribution data of each region; the system further comprises a special positioning algorithm processing unit and a path planning processing unit, the positioning algorithm processing unit is responsible for fusing multi-source data from Wi-Fi, Bluetooth and a high-precision inertial measurement unit IMU to achieve accurate positioning, and the path planning processing unit is based on a NavMesh navigation grid and an improved A * algorithm. And a multi-target path is generated in combination with real-time personnel density distribution data, an optimal path and a plurality of recommended paths are provided, and personalized route suggestions are generated according to specific requirements of users. According to the method, the limitation of a traditional navigation mode in dealing with the complexity of the underground space is overcome, and accurate navigation and personalized guidance of the user in the underground space are realized.

The invention discloses a multi-source heterogeneous data dynamic causal blood relationship tracing method and a related device, and belongs to the technical field of data blood relationship detection.The method comprises the steps that static blood relationship metadata and dynamic index time sequence data of aviation test data are collected, a dynamic causal blood relationship map is constructed, each edge of the map is associated with a causal strength weight, and the dynamic causal blood relationship is obtained; and reverse traceability is carried out through the dynamic causal blood relationship map, and root cause nodes causing abnormity are determined. Static consanguinity and dynamic indexes are collected, a dynamic causal consanguinity map with nodes as triads and edges carrying causal strength weights is constructed, and dynamic attributes of the map break through static relevance limitation, so that traceability can reveal state change and causal dynamics; the influence degree between nodes is quantized by introducing a causal strength weight, and the root cause is determined by combining a probabilistic reasoning model, so that the spanning from fuzzy association to quantitative positioning is realized, and the problems that the root cause positioning is fuzzy and is lack of quantitative analysis are solved.