824 results about "Rapid processing" patented technology

Disclosed in the present invention are a fault identification method and system based on the operating condition of a continuous system in an open-pit mine. The method comprises: establishing a simulation model for a continuous system in an open-pit mine, and monitoring device data in real time; pre-processing the data, and performing potential fault identification; on the basis of a system pressure change rate and an adaptive adjustment mechanism of the continuous system in the open-pit mine, optimizing fault identification output; and designing a fault response and real-time adjustment mechanism to prevent fault occurrence. The fault identification method and system based on the operating condition of a continuous system in an open-pit mine provided in the present invention improve the speed and accuracy of fault diagnosis, particularly the rapid processing capability for complex data relationships. A breakthrough is achieved in fault prevention, thus enabling early warning and adaptive adjustment to be implemented before faults occur. Thus, the stability and safety of continuous systems in open-pit mines are significantly improved, and a more efficient technical solution is provided for operation management of modern open-pit mines.

The invention discloses a point cloud data rapid processing system based on 3D vision, and relates to the technical field of point cloud data processing. The binocular acquisition module generates an anti-interference high-precision point cloud; the preprocessing module is used for reducing noise and dimensionality and retaining key geometric features; the hierarchical feature extraction module fuses curvature weight and a non-maximum suppression strategy, and improves the robustness of an ORB algorithm in rotation, scale and noise scenes; the industrial scene segmentation module accurately separates stacked objects through normal vector clustering and a dynamic region growing algorithm, and secondarily verifies boundaries in combination with a lightweight semantic model; the two-stage registration module dynamically adjusts a threshold value based on error feedback to realize pose optimization from coarse registration to fine registration; the coordinate mapping module establishes a space mapping model through multi-attitude calibration and robot multi-axis linkage trajectory optimization. The problem that in the prior art, a 3D machine vision algorithm is high in time delay is solved, the real-time performance and rapidity of 3D model recognition and feature extraction are improved, and the efficiency requirement of industrial production is met.

The invention relates to the technical field of electric power government affair management, and provides a cross-department government affair big data business co-processing method based on a computing power platform and data fusion, comprising the following steps: S1, deploying a heterogeneous computing power cluster, and establishing a computing power resource dynamic scheduling data processing platform comprising a CPU, a GPU, an FPGA and an edge computing node; s2, acquiring multi-dimensional data of power grid equipment data, power grid load data, industry power consumption data and government policy data; and S3, designing a cross-department data access system based on the zero-trust architecture, and developing a multi-dimensional model of a power grid risk prediction model, an industry energy consumption analysis model and a carbon emission accounting model in power management. The space-time joint probability prediction and conflict priority ranking algorithm is applied to power-government affair emergency disposal, intellectualization and precision of cross-department business collaboration are achieved, power faults with serious consequences are rapidly handled according to the priority level, and the harmfulness and unpredictability of the power faults can be reduced.

The invention provides a knowledge question and answer rapid processing method and system based on artificial intelligence, and relates to the field of artificial intelligence. A multi-modal knowledge graph is constructed, collected multi-source teaching data is fused through a mixed retrieval strategy, and the mixed retrieval strategy comprises semantic retrieval, vector retrieval and metadata retrieval; multi-level question and answer processing is executed based on an RAG enhancement framework, a multi-modal input intention is analyzed, cross-library joint retrieval is performed, and an optimization answer is generated in combination with a teaching scene; distilling the global model to a lightweight TinyBERT architecture, dynamically optimizing question and answer quality through a cognitive reinforcement learning framework, positioning a key document from a comprehensive retrieval list, evaluating an optimized answer, and reconstructing an answer with a key document verification score; according to the invention, the professional skill level of teachers and students in the fields of artificial intelligence and large model application can be improved, and the personalized requirements of teachers and students in teaching, scientific research and innovation courses can be met.

The invention relates to a data cleaning method and device based on a Drools rule engine. The method comprises the following steps: acquiring original data from various data sources through a data acquisition module; converting the collected original data into a data format which can be processed by a Drools rule engine, and inserting the data format into a working memory; the data cleaning module carries out cleaning processing on the data according to a predefined cleaning rule, wherein the cleaning processing comprises operations such as duplicate removal, missing value filling and format correction; outputting the cleaned data to a target system through a data output module; and recording a cleaning log through the monitoring module, counting a cleaning result, and providing visual display. According to the scheme, a Drools rule engine is adopted, dynamic loading and updating rules are supported, and the dynamic loading and updating rules can take effect without restarting a system; the flexibility is high, and updating of various data sources and dynamic rules is supported; the cleaning efficiency is high, and large-scale data can be rapidly processed through the rule engine; the monitoring function is complete, real-time log recording and visual display are provided, and the cleaning process is convenient to manage and optimize.

The invention provides an HY-2A ocean water vapor inversion method based on machine learning, and the method comprises the steps: constructing a high-quality HY-2A scanning microwave radiometer data set through the data preprocessing, matching, feature extraction and normalization processing of HY-2A satellite scanning microwave radiometer data and ERA5 reanalysis data. A plurality of machine learning models are used for training and testing, Bayesian optimization is used for realizing hyper-parameter automatic adjustment, an optimal solution in finite time is obtained, an SHAP method is used for explaining the models, contribution of each characteristic variable is clear, interpretability of the used machine learning models is improved, and finally high-precision inversion of the water vapor over the sea is realized. The method has efficient calculation performance, can realize rapid processing of large-scale marine meteorological data, and improves the interpretability of a machine learning model. The method has important application value in numerical weather forecast, ocean extreme weather forecast, climate monitoring and other geophysical and meteorological fields.

The invention discloses a network flow measurement method and device based on a data packet interval sketch data structure and a medium. Each arriving data packet is mapped to each corresponding bucket in a sketch data structure through a plurality of independent hash functions, the sketch data structure is a two-dimensional array form structure comprising multi-row and multi-column buckets, and empty bucket processing, identical flow updating or conflict flow replacement judgment is executed according to the conditions of the data packets and the buckets. According to the method, a brand-new interval value sketch is designed, and efficient and accurate large flow detection is realized on the premise of high processing speed and low memory occupation by comprehensively utilizing two pieces of multi-dimensional statistical information of the flow estimation value and the data packet interval. Meanwhile, the large flow and the small flow in the network environment can be well distinguished, so that the method can be better suitable for network measurement tasks in different scenes such as large flow detection and abrupt flow detection.

The invention discloses a power distribution cabinet cable monitoring method, system and device based on edge calculation, and relates to the technical field of power system monitoring and early warning, and the method comprises the steps: arranging a sensor in a power distribution cabinet, and collecting the displacement and deformation data of a cable; carrying out preprocessing and preliminary analysis on the collected data by utilizing edge computing equipment; the method comprises the following steps: determining whether cable displacement deformation is in an abnormal state or not according to a preset judgment condition through edge computing equipment, and uploading abnormal data to a cloud server if the cable displacement deformation is judged to be abnormal; and the cloud server receives the abnormal data and carries out deep analysis in combination with historical data. Through rapid processing of the edge computing device, once the cable is abnormal, the system can timely perceive and upload abnormal data, the real-time performance of monitoring is remarkably improved, it is ensured that the state change of the cable can be rapidly captured, meanwhile, a deep learning algorithm is introduced, more accurate anomaly detection is achieved, and the accuracy and reliability of early warning are improved.

The invention discloses a computer vision target detection method and system, and relates to the technical field of vision detection, the computer vision target detection system comprises a camera and a data processing terminal, and the computer vision target detection system further comprises the following modules: a data acquisition module acquires image and video data through the camera, preprocesses the acquired data, and sends the preprocessed data to a display module; according to the technical scheme of the present invention, the target detection module dynamically selects a lightweight, medium-complexity or high-complexity sub-network for detection based on the complexity score by means of the dynamic weight and the adaptive threshold value, so that the complexity of the target detection module is improved, and the resolution of the image is improved by means of the image enhancement algorithm, and the noise reduction processing is performed on the video data by means of the filtering algorithm. The calculation amount of the model is adjusted in a self-adaptive manner, when a simple image is faced, the lightweight sub-network can be selected for rapid processing, the detection efficiency is improved, the high-complexity sub-network is adopted for a complex image, the detection precision is ensured, the calculation resources and the detection effect are balanced, and the adaptability of the model in different scenes is enhanced.

Embodiments of the present disclosure provide a data processing method and apparatus, an electronic device and a storage medium. The data processing method comprises: displaying table data, the table data comprising at least one data object and at least one piece of basic statistical information corresponding to the data object; in response to a trigger operation for a preset control, presenting a dialogue component window on a page of the table data; and in response to receiving, by means of the dialogue component window, a first message comprising a target demand, displaying a reply message in the dialogue component window, the reply message comprising conclusion content and / or a recommended demand template for the target demand, wherein the conclusion content comprises at least one of a text, a table and a chart. Once the table data is displayed, the table data is analyzed on the basis of the target demand and a language model to obtain the reply message and display same, thereby realizing rapid processing and analysis of the table data.

The invention provides a heavy-load unmanned aerial vehicle cooperative hoisting method, device and equipment and a medium, and relates to the technical field of edge calculation, and the method comprises the steps that a virtual pilot unmanned aerial vehicle in an unmanned aerial vehicle cluster calculates total load data of loads hoisted by a plurality of edge nodes based on load distribution data of each edge node; based on the total load data and the node position, determining an overall motion track of the unmanned aerial vehicle cluster, and sending the overall motion track to the edge node, so that the edge node finds a reference tension value needing to be provided and a node motion track based on an ant colony algorithm; if it is determined that the lifting rope of the first unmanned aerial vehicle in the multiple unmanned aerial vehicles is broken in the flight process, according to a preset load redistribution scheme, synchronous tension is distributed to each second unmanned aerial vehicle; and setting the first unmanned aerial vehicle as a proxy center node, and receiving an updated movement track of the proxy center node for the unmanned aerial vehicle cluster. According to the invention, the sudden fault in the cooperative lifting process of the multiple unmanned aerial vehicles can be quickly processed.

The invention provides an image real-time filter processing method based on edge calculation, and the method comprises the steps: obtaining the local texture feature distribution of an input image, calculating the gradient variation of each pixel point in the horizontal and vertical directions, and calculating a quantitative index value reflecting the image texture complexity based on the statistical distribution feature of the gradient variation; executing a parallelized filter effect rendering operation based on the optimized processing task allocation scheme, and applying corresponding filter parameters to each image block through a multi-thread cooperative processing mechanism to form filter rendering configuration information suitable for the performance of the current mobile device; and carrying out fusion reconstruction on brightness, saturation and hue component data after filter processing, generating a final filter processing image through a reverse conversion process from HSV to RGB, and recording parameter configuration information in the processing process at the same time for rapid processing of subsequent similar images.

The invention relates to the technical field of data processing, in particular to an LLM-based multi-source heterogeneous data intelligent fusion and self-adaptive processing method, which comprises the following steps of: receiving multi-source heterogeneous data, matching character sequences of mailbox fields and identity card number fields in records one by one by adopting a preset regular expression model, according to a preset key value pair mapping dictionary, items are uniformly converted, data records which cannot pass format verification, type conversion and value domain mapping are marked as doubt, and data records which cannot be processed or are marked as suspicious are separated. According to the method, a hierarchical processing flow is constructed, and deterministic format verification, type conversion and value domain mapping are arranged at the front end of a processing link, so that most simple data quality problems can be quickly processed with low calculation overhead. Then, numeric statistics and character string comparison are used to carry out fuzzy correction on the data, and spelling errors and numeric abnormities which cannot be covered by rules are further solved.

The invention relates to an image registration method and a real-time tracking method, and the method comprises the steps: dividing a reference frame and a subsequent frame into a plurality of regions according to the same rule, carrying out the registration of the reference frame and the subsequent frame, and obtaining a region combination (a reference frame region and a subsequent frame region) where each pair of registration points are located; screening out matching point pairs according to the distribution condition of the registration point pairs in all region combinations; and calculating a transformation matrix according to the matching point pairs. As the change of the images between the continuous frames is not large, the method calculates the transformation matrix for registration by using the most representative local area, can avoid interference information brought by the influence of the environment and noise on the partial area in the image, can ensure that the screened matching point pair for registration has high reliability, and can improve the registration accuracy. In addition, calculation is simple and convenient, the rapid processing requirement of real-time registration can be met, and the method has wide application scenes and value.

The invention discloses a memory fault processing method and device, electronic equipment and a readable storage medium, and relates to the technical field of computers.The method comprises the steps that when a memory line fault exists in a memory of a target system, an isolation operation is triggered for a target physical address, the target physical address at least comprises the physical address of the fault line with the memory line fault in the memory, the technical problem of system downtime caused by the fact that the memory line fault cannot be rapidly processed in the related technology is solved, the isolation process is started for the target physical address in time when the memory line fault is detected, and the system downtime is reduced. Performance reduction and data error accumulation caused by the fact that the fault line continues to participate in data reading and writing are avoided, and then the technical effect of reducing the risk of system downtime is achieved.

The invention provides a shale total organic carbon content logging prediction method and device based on a random forest algorithm. The method comprises the steps that logging curve data are collected and processed; constructing a TOC prediction model according to the collected logging curve data by using a random forest algorithm; training the prediction model by using a training data set of TOC values; and predicting the shale TOC by using the trained prediction model. According to the method, the mud shale TOC prediction precision is improved, the influence of human factors is eliminated, rapid processing and comprehensive analysis of a large amount of logging data can be achieved, key information related to TOC is extracted, the defects of a linear or nonlinear model and a single machine learning algorithm model are overcome, the mud shale TOC prediction precision is effectively improved, and the prediction accuracy of the mud shale TOC is improved. Powerful support is provided for exploration and development of shale oil and gas.

The invention discloses an artificial intelligence-based prescription multi-modal intelligent auditing system, which comprises a multi-modal data acquisition module, a data preprocessing module, a feature fusion module, an intelligent auditing model and a result feedback and knowledge base updating module, the multi-modal data comprises basic information of a patient, diagnosis information, inspection report data, medical image data and prescription text information, the data is automatically collected through the multi-modal data collection module, a deep learning algorithm is used for rapid processing and analysis, rapid examination and verification of a prescription are completed, comprehensive analysis is conducted in combination with the multi-modal data, and the accuracy of the prescription is improved. Comprising basic information, diagnosis information, inspection report data, medical image data and the like of the patient, whether the prescription has the problems of drug interaction, dosage abnormity, mismatching of indications and the like or not can be judged more comprehensively and accurately, in this way, auditing errors are effectively reduced, the drug use risk of the patient is reduced, and the drug use safety of the patient is guaranteed.

The invention discloses an AI big data real-time processing and analysis method, and solves the problems of insufficient real-time performance and resource waste in traditional processing. The method comprises the steps of collecting heterogeneous data from multiple sources, dividing priorities through feature vector construction and a dynamic evaluation model, and shunting to an edge rapid processing channel, an edge-cloud collaboration channel and a cloud batch processing channel. The edge node preprocesses the high / medium priority data and compresses an analysis result in a layered manner; and the cloud receives the compression result, the middle-priority residual data and the low-priority data, a unified view is established through fusion, and the edge analysis model parameters are iteratively optimized in real time. And finally feeding back the edge preliminary analysis and the cloud depth result to the terminal. According to the method, through dynamic distribution, cooperative processing, differential compression and model iteration, real-time response of high-priority data, efficient resource allocation and continuous improvement of analysis precision are achieved, and the method is suitable for multi-scene heterogeneous data processing.

The invention provides a video processing method and device, equipment and a storage medium, and the method comprises the steps: segmenting a video material into a plurality of semantically coherent video clips, automatically extracting related video clips from the video material according to the text content provided by a user, and generating corresponding subtitles and voice broadcast ports matched with the related video clips, and finally synthesizing a section of finished video with clear logic and coherent content. According to the scheme, full-process automation from video material generation to final video file generation is realized, a large number of video materials can be quickly processed, the tedious manual editing process in traditional video production is reduced, the dependence on manual intervention is greatly reduced, the operation cost is reduced, the video production process is simplified, the video production efficiency is improved, and the user experience is improved. And the quality and the efficiency of finished products are also improved.

The invention relates to an image processing technology, and discloses a rapid image processing method during intelligent driving, which comprises the following steps: respectively carrying out targeted analysis on front view image data, all-round view image data and side view image data acquired in the driving process of a target vehicle to obtain road marking data, surrounding vehicle data and road pedestrian data; the method comprises the following steps: converting road marking data, surrounding vehicle data and road pedestrian data into coordinate data, carrying out three-dimensional space rapid modeling based on the coordinate data to obtain an intelligent driving space model, generating a driving suggestion according to the intelligent driving space model, carrying out three-dimensional rough modeling according to collected wide-angle image data to obtain an intelligent driving rough space model, and carrying out three-dimensional modeling according to the collected wide-angle image data to obtain a driving suggestion. And comparing the intelligent driving space model with the intelligent driving rough space model in real time to obtain a model error, and performing driving suggestion adaptive broadcasting based on the model error. The invention also provides an image rapid processing device during intelligent driving and a medium. The image processing efficiency can be improved.

The invention relates to the technical field of cable surface defect detection, and provides a cable surface defect detection method, in terms of detection efficiency, a hybrid quantification technology and a hardware acceleration means are adopted, the model reasoning speed is greatly improved, a system can rapidly process a large amount of detection data, a unique dynamic tracking technology reduces unnecessary calculation amount, and the detection efficiency is improved. The detection process is more efficient and can adapt to the detection rhythm of high-speed production of cables; in terms of detection precision and robustness, a multi-modal feature fusion strategy fully integrates advantage information of different sensors, and fusion from pixels and feature patterns to decision levels effectively enhances the capture and analysis ability of defect features and the structural optimization and innovative training mode of a deep learning model, significantly improves the detection ability of micro defects, and improves the detection accuracy and robustness of the micro defects. And a relatively low false detection rate can still be kept in a complex environment.

The invention discloses a precise dosing control system based on an industrial wastewater advanced treatment Fenton process, and the system comprises a water quality and water quantity monitoring module which is used for monitoring the key water quantity and water quality parameters of wastewater online in real time; the data processing and analyzing module is used for receiving the real-time water quality data transmitted by the water quality and water quantity monitoring module and quickly processing and analyzing the data; the dosing control module is used for accurately controlling the adding process of the Fenton reagent according to the dosing instruction output by the data processing and analyzing module; the reaction monitoring and feedback module is mounted in the Fenton reaction system and at a water outlet and is used for monitoring key parameters in the reaction process and water quality indexes of treated wastewater in real time; the remote monitoring and management platform is used for being connected with the whole precise dosing control system through a wired or wireless network to realize remote data transmission and monitoring management functions; the problems of agent waste, poor treatment effect, high cost and the like caused by inaccurate and unstable agent feeding of the existing Fenton process are solved.

The invention discloses a rail transit dispatching simulation practical training and automatic examination system integrated with emergency assistance, and relates to the technical field of simulation practical training, and the system comprises a data collection module which is used for collecting basic data, operation data and historical emergency event data of a rail transit system; and the simulation environment construction module is used for constructing a high-fidelity rail transit simulation environment by applying a digital twin technology based on the data acquired by the data acquisition module, mapping a geometric field model, a physical field model, a behavior field model and a rule field model which are required by virtual equipment, constructing a four-model mapping rule base and fusing a multi-field model. According to the invention, the simulation environment construction module relies on a digital twinning technology and real acquired data, combines geometric, physical, behavioral and regular four-field models and a mapping rule base, constructs a high-fidelity simulation environment and meets diversified practical training requirements, and the emergency aid decision-making module introduces a quantum computing technology and can quickly process mass data in a complex scene.

The invention relates to the technical field of mine restoration, and discloses an intelligent surface mine environment monitoring and treatment system, which comprises an environment monitoring and multi-modal data acquisition module, a data processing module and a data processing module, the data acquisition module is used for acquiring various types of environmental data such as air quality, noise, water pollution, soil erosion and geological disasters through a sensor network, an unmanned aerial vehicle, satellite remote sensing and other detection devices, and performing local data processing; and the environment optimization and decision-making module based on edge computing and deep learning is configured to deploy computing resources at a data acquisition end close to a mining area by utilizing an edge computing technology and perform real-time processing and complex multivariate analysis on the multi-source environment data through a deep learning algorithm. Through multi-modal data acquisition and an environment optimization and decision module based on edge calculation and deep learning, multi-dimensional environment data from a mining area can be rapidly processed.

The invention discloses an SVM algorithm automatic driving road detection system based on quantum computing enhancement, and relates to the technical field of automatic driving road detection in which quantum computing, multi-sensor fusion and artificial intelligence technologies are combined and applied. By combining quantum calculation and an SVM algorithm, the adaptability of the system to complex road conditions and illumination changes is enhanced, and the accuracy and stability of road detection are improved; a large amount of data in a complex scene can be quickly processed by means of the powerful computing power of quantum computing, and the model robustness is good. And by utilizing the characteristic that the SVM algorithm has low requirements on annotation data, the cost and time of data annotation are reduced. Quantum calculation is introduced to efficiently optimize parameters of the SVM model, an optimal parameter combination can be found in a shorter time through quantum particle swarm optimization, and the generalization ability and performance of the model are improved. And the parallel processing capability of quantum calculation is utilized to accelerate the data processing and model reasoning process, so that the real-time performance of the system is improved, and the requirements of automatic driving on the real-time performance and high efficiency are met.

The invention provides a lane toll collection system, which relates to the technical field of traffic toll collection and comprises a global sensing layer, an edge intelligent layer, a cloud intelligent layer and a lane execution layer. The global sensing layer collects data based on the lane execution layer through a multi-modal sensing matrix, and uploads the data to the edge intelligent layer after the data is preprocessed by an edge computing node; the edge intelligent layer completes vehicle identification and rate calculation based on a distributed AI engine, and issues a decision instruction to the lane execution layer; wherein the cloud intelligent layer provides global data storage, model training and cross-domain cooperation functions, and communicates with the edge intelligent layer in real time through a 5G / optical fiber network; the multi-modal sensing matrix collects data based on the lane execution layer, the data are preprocessed by the edge computing node and then uploaded to the edge intelligent layer, the edge intelligent layer completes vehicle identification and rate calculation and issues a decision instruction to the lane execution layer, the architecture realizes rapid processing and efficient decision of the data, and the overall efficiency of the toll collection system is improved.

The invention discloses an automatic integrated system of an outdoor circuit breaker. The automatic integrated system comprises a circuit breaker body, a data acquisition module, a control module, a communication module, a remote monitoring platform, an execution mechanism and the like. The system acquires data through multiple types of sensors, analyzes and processes the data through the intelligent control module, and realizes operation state monitoring, fault diagnosis and processing and parameter adaptive adjustment of the circuit breaker in combination with the adaptive protection module, the energy storage management module, the environment monitoring module and the like. And the remote monitoring platform supports visual operation and maintenance. Through an innovative system architecture and an intelligent control technology, multi-dimensional monitoring of the operating state of the circuit breaker, intelligent diagnosis and rapid processing of faults and self-adaptive adjustment of system parameters are achieved, the technical problems existing in a traditional outdoor circuit breaker are solved, the intelligent level and operation and maintenance efficiency of a power system are improved, and the service life of the outdoor circuit breaker is prolonged. The technical problem of a traditional circuit breaker is effectively solved, and the intelligent level and operation and maintenance efficiency of a power system are improved.

The invention discloses a preparation process of a high-strength copper wire, and relates to the technical field of copper wire preparation, and the preparation process comprises the following steps: S1, smelting raw materials, removing impurities, and casting into a copper blank; s2, the copper billet is subjected to heat treatment; s3, the copper blank subjected to heat treatment is cut into blocks with proper lengths; s4, under monitoring of the real-time monitoring system, the copper block is gradually drawn into the copper wire with the required diameter through a plurality of drawing dies by using a wire drawing machine; s5, carrying out annealing treatment on the drawn copper wire; and S6, the annealed copper wire is subjected to surface treatment, and a finished product is obtained. Through the real-time monitoring system, the temperature, tension and displacement key parameters can be monitored in real time in the drawing process, the abnormal risk occurring in the production process is effectively reduced, the need of manual intervention is reduced, the automation and efficiency of the production process are improved, the abnormal situation can be quickly treated, long-time production stagnation is avoided, and the production efficiency is improved. Therefore, the overall production efficiency is improved.

The invention relates to an intelligent identification and evaluation technology for tornado ground disasters, and the technology comprises a data collection and preprocessing module which collects image data and text data of tornado ground disasters from a plurality of data sources, carries out the preprocessing and cleaning of the data, and generates a structured data set; the multi-modal disaster assessment model is based on a VLLM and an advanced visual identification technology, adopts a double-flow network structure and comprises an image feature extraction branch and a text processing branch, the image feature extraction branch extracts key disaster features from a high-resolution image, and the text processing branch deeply analyzes text information related to disaster grade assessment; and the interactive learning mechanism allows the multi-modal disaster assessment model to request feedback to a user after preliminary analysis, and performs self-adjustment and optimization according to the feedback. The invention further provides a map display method. According to the method, the problems of data scarcity, image diversity, low damage evaluation efficiency, rapid processing requirements and model generalization ability can be effectively solved.

The invention relates to the technical field of traffic management systems, in particular to a traffic incident alarm, early warning and rescue rapid processing method and system, and the method comprises the following steps: S1, configuring hardware facilities; s2, in the hardware facility, the server carries a traffic operation and maintenance platform; according to the intelligent delineator, through hardware setting of the intelligent delineator, the distance between the intelligent delineator and a vehicle is detected through an infrared distance measuring probe, a flashing light is started for early warning when the intelligent delineator reaches a designated range, the designated range can be adjusted based on fog amount, sunlight and other data, and vibration data generated by collision and rubbing are collected through a displacement sensor and a vibration sensor; the automatic alarm function is achieved, based on the two-dimensional code label manual code scanning alarm or the pressing mode of the pressing sensor, quick alarm is achieved, the accident prevention function is achieved, convenient alarm is achieved, the precise coordinate transmission function is achieved, guarantee is provided for timeliness of rescue work, burst flashing is called for road section burst flashing warning, and serial accidents are avoided.