Emergency disposal tracing system

Abstract

The invention provides an emergency disposal tracing system, and belongs to the technical field of electric power emergency disposal. The system adopts a layered architecture design, comprises a data acquisition and transmission module, a data processing and storage module, a data analysis module, a scheme distribution module and an emergency disposal tracing module, integrates various emergency related data, realizes whole-process closed-loop management of real-time monitoring, analysis decision-making, emergency disposal and afterward tracing, and is high in practicability. After the emergency event is processed, the reason and the development process of the accident can be comprehensively understood by reviewing and analyzing the data of the whole process, valuable experience is extracted from the reason and the development process, the ability of the power enterprise to deal with the emergency event is improved, and the safe and stable operation of the power system is ensured.

Description

Technical Field

[0001] The present invention belongs to the technical field of power emergency disposal, and in particular relates to an emergency disposal tracing system. Background Art

[0002] The importance of the power industry is self-evident. From lighting and home appliances in daily life to the operation of large-scale industrial equipment and support for key sectors such as communications and transportation, power supply cannot be interrupted for a single moment. Any power outage caused by a power failure can cause factory shutdowns, traffic paralysis, communication interruptions, and even endanger people's lives, resulting in massive economic losses and social impact.

[0003] Although some systems already exist within the existing power emergency scenario handling technology system, which can, to a certain extent, achieve the deployment of emergency resources, preliminary monitoring of on-site conditions, and the execution of basic handling processes, they generally suffer from a significant flaw: a lack of traceability. After the emergency incident is handled, it is difficult for staff to systematically review and analyze the massive amount of data from the entire process. This makes it impossible to accurately and comprehensively analyze the cause of the accident, as well as the key nodes and changes that the accident experienced during its development. Power companies find it difficult to fully learn from past emergency incidents. When faced with similar situations in the future, they are unable to specifically optimize emergency plans and improve handling efficiency. As a result, it is difficult to fundamentally reduce the risk of recurring power accidents and effectively ensure the long-term stable and reliable operation of the power system.

[0004] Therefore, in emergency scenarios in the power industry, it is crucial to build an efficient, accurate and traceable emergency system, which can enhance the ability of power companies to respond to emergencies and ensure the safe and stable operation of the power system. Summary of the Invention

[0005] In view of this, the present invention aims to provide an emergency response tracing system, which can, after the emergency event is handled, comprehensively understand the cause and development process of the accident through data review and analysis of the entire process, extract valuable lessons from it, enhance the ability of power companies to respond to emergencies, and ensure the safe and stable operation of the power system.

[0006] In order to achieve the above object, the technical solution provided by the present invention is as follows:

[0007] The present invention provides an emergency response tracing system, comprising:

[0008] The data acquisition and transmission module, data processing and storage module, data analysis module, solution distribution module and emergency response tracing module are sequentially connected in communication;

[0009] The data acquisition and transmission module is used to regularly collect multi-source data of the target area and transmit it to the data processing and storage module;

[0010] The data processing and storage module is used to pre-process and store the collected multi-source data;

[0011] The data analysis module is used to extract pre-processed multi-source data for analysis and determine the event status based on preset evaluation criteria;

[0012] The solution distribution module is used to match and distribute the corresponding emergency response solutions from the preset solution library based on the judgment results of the event status and the distribution matching algorithm;

[0013] The emergency response traceability module is used to record the execution status of the data collection and transmission module, data processing and storage module, data analysis module and solution distribution module during the emergency response process; the execution status is used to trace the emergency response process.

[0014] Furthermore, multi-source data includes:

[0015] Locate emergency personnel, vehicles, and supplies.

[0016] Furthermore, the data collection and transmission module is also used to collect emergency response information related to the target area based on a pre-built mathematical model, including:

[0017] Collect multi-channel data related to emergency response in the target area;

[0018] Clean the collected multi-channel data and label different types of data;

[0019] Divide the cleaned and labeled data into training, validation, and test sets;

[0020] Determine training parameters for natural language processing models and convolutional neural network-based image recognition models;

[0021] According to the data type of the training set, the data is input into the corresponding model for training, and the model parameters are updated using the loss function and back propagation algorithm. Among them, the natural language processing model is used to process text data, and the image recognition model is used to process image data.

[0022] Determine the evaluation indicators and use the test set data and validation set data to evaluate and verify the trained model;

[0023] Collect emergency-related data from the target area in real time, input them into the corresponding model for analysis according to the data type, and obtain emergency response information related to the target area;

[0024] The extracted emergency response information is stored in the database according to the date, and the priority is determined according to the pre-set priority evaluation model. Important and urgent information is stored in a focused manner and relevant personnel are reminded in a timely manner.

[0025] Furthermore, in the data collection and transmission module, the specific process of constructing the priority evaluation model includes:

[0026] Identify factors that influence the priority of emergency events;

[0027] The hierarchical analysis method was used to compare and score each influencing factor in pairs to form a personal judgment matrix;

[0028] Conduct consistency checks on each individual judgment matrix;

[0029] For the individual judgment matrix that passed the consistency test, the eigenvector method was used to calculate the weight vector of each factor;

[0030] Divide the priority of emergency events into several evaluation levels and set a corresponding score range for each evaluation level;

[0031] For each emergency event to be evaluated, a fuzzy evaluation is performed on each influencing factor, and the evaluation results are converted into a fuzzy evaluation vector;

[0032] Using the fuzzy transformation principle, the fuzzy evaluation vector of each factor and the corresponding weight vector are synthesized and calculated. According to the evaluation level corresponding to the maximum value of each element in the comprehensive evaluation vector, the priority level of the emergency event is determined, and the priority score is calculated according to the score range.

[0033] Furthermore, the data analysis module analyzes multi-source data based on the long short-term memory network model;

[0034] Extract pre-processed multi-source data for analysis and determine the event status based on pre-set evaluation criteria, including:

[0035] Collect data of the target area and emergency response information in real time;

[0036] Pre-process and fuse the collected data and emergency response information of the target area, and use the timestamp as the benchmark to associate the data from different data sources at the same time to form a complete event-related data set;

[0037] Extract key features from event-related datasets and select the most influential features for event status judgment based on feature selection algorithms;

[0038] Build a long short-term memory network model and train the model using historical event data;

[0039] The data collected in real time and subjected to preprocessing and feature extraction are input into the trained model so that the model can predict the current event status based on the evaluation criteria and output the prediction results.

[0040] Furthermore, in the data analysis module, the evaluation criteria include power equipment status standards, personnel and vehicle action standards, emergency response process standards, and incident impact scope and consequence standards;

[0041] Power equipment status standards include equipment operating parameters and equipment appearance characteristics;

[0042] Human and vehicle movement standards include abnormal human behavior and vehicle trajectory and speed;

[0043] Emergency response process standards include corresponding time and implementation of response measures;

[0044] The scope and consequence standards of the incident include the scope of power outage and estimated economic losses.

[0045] Furthermore, in the solution distribution module, based on the distribution matching algorithm, the corresponding emergency response solutions are matched and distributed from the preset solution library. The specific process includes:

[0046] Collect data related to the event, extract key features from the collected data, and judge and classify the status of the event based on preset standards and models;

[0047] Collect and organize various emergency response plans, classify them according to different event types, scenarios and response objectives, annotate each emergency response plan with features, and store the organized and annotated emergency response plans in the plan library;

[0048] Input the key features of the event status into the distribution matching algorithm, and perform similarity calculation and matching with each solution in the solution library;

[0049] Determine the relevant units and staff who need to receive the emergency response plan based on the nature of the incident, the location of the incident, and the responsibilities of the relevant units;

[0050] Distribute the matched emergency response plan to relevant units and staff through multiple channels;

[0051] After receiving the plan, relevant units and staff will organize and implement emergency response work in accordance with the plan requirements, and collect feedback information in a timely manner during the implementation process.

[0052] Furthermore, it also includes: a visual display module;

[0053] The visualization display module is used to display the information of each process of emergency response in real time on the visualization platform.

[0054] Furthermore, in the visualization display module, the specific process of building a visualization display platform includes:

[0055] Set up a layered architecture of data collection layer, data transmission layer, data processing layer, business logic layer and visual display layer;

[0056] At the emergency response site, the sensors of the mobile devices installed in the data collection layer collect data from the emergency response process in real time, and the collected data is preliminarily sorted and compressed, and uploaded to the data transmission layer in real time;

[0057] The matched emergency response plan is broken down into multiple specific tasks through the data processing layer and business logic layer. Each task is assigned a unique task ID, and the task start time, expected completion time, and task status are set.

[0058] Associate and update the real-time uploaded data with the corresponding task through the task ID;

[0059] Calculate the overall progress of emergency response in real time based on task status and time information;

[0060] Based on map visualization, flow chart visualization and data panel visualization, the emergency response progress information is displayed in real time on the visualization platform through the visualization display layer.

[0061] Furthermore, the system also includes a feedback suggestion module, which is used to collect emergency response measures suggestions and rectification suggestions for emergency response measures in various emergency scenarios from relevant units and staff during the actual work process.

[0062] In summary, the present invention provides an emergency response tracing system, which adopts a layered architecture design and includes a data acquisition and transmission module, a data processing and storage module, a data analysis module, a solution distribution module and an emergency response tracing module. It integrates various emergency-related data to achieve a closed-loop management of the entire process of "real-time monitoring - analysis and decision-making - emergency response - post-event tracing". After the emergency event is handled, the system can review and analyze the data of the entire process to fully understand the cause and development process of the accident, extract valuable lessons from it, enhance the ability of power companies to respond to emergencies, and ensure the safe and stable operation of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0064] Figure 1 This is a system architecture diagram of an emergency response tracing system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0065] In order to make the purpose, features, and advantages of the present invention more obvious and easy to understand, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described below are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0066] See also Figure 1 As shown, an embodiment of the present invention provides an emergency disposal tracing system, comprising a data acquisition and transmission module, a data processing and storage module, a data analysis module, a solution distribution module, and an emergency disposal tracing module, which are communicatively connected in sequence;

[0067] Data collection and transmission module: used to regularly collect multi-source data in the target area, build a mathematical model to collect emergency response information related to the target area, store it in a database according to preset priorities, and transmit all collected data to the data center;

[0068] Data processing and storage module: used to pre-process and store the collected data;

[0069] Data analysis module: used to analyze the data collected in real time and determine the event status based on preset evaluation criteria;

[0070] Solution distribution module: used to match appropriate emergency response solutions from the preset solution library based on the event judgment results and distribution matching algorithm, and distribute them to relevant units or staff;

[0071] Emergency response traceability module: After the emergency incident is handled, the system automatically generates an incident process report.

[0072] The emergency response tracing system provided by the present invention features a data acquisition and transmission module that regularly collects multi-source data from a target area, utilizes mathematical models to gather emergency response information, and stores and transmits it according to priority, ensuring comprehensive and important data. A data processing and storage module preprocesses and stores the collected data, providing a reliable data foundation for subsequent analysis. A data analysis module analyzes real-time data based on preset evaluation criteria to determine event status, enabling timely understanding of the emergency situation. Based on the event judgment results, a solution distribution module uses a distribution matching algorithm to match appropriate emergency response solutions from a preset solution library and distributes them to relevant personnel, ensuring the timeliness and effectiveness of emergency response. The emergency response tracing module automatically generates an event process report after the emergency event is handled. This key feature overcomes the lack of tracing functionality in existing technologies. This report allows staff to systematically review and analyze the massive amount of data from the entire emergency process, accurately and comprehensively analyzing the causes, key nodes, and developments of the accident. This allows power companies to fully learn from past emergency incidents and, when faced with similar situations in the future, optimize emergency plans and improve response efficiency, thereby fundamentally reducing the risk of recurrence of power accidents and ensuring the long-term stable and reliable operation of the power system.

[0073] In one embodiment, the data collection and transmission module regularly collects multi-source data of each sub-area of the target, including emergency personnel location, vehicle location, and material location, to facilitate the subsequent tracing of data in different time periods.

[0074] Emergency personnel are equipped with smart terminal devices that integrate Beidou satellite navigation (BDS) and Bluetooth indoor positioning technology.

[0075] On-board positioning equipment is installed on emergency vehicles. Under normal circumstances, the global positioning system provides the main positioning information, with positioning accuracy up to meter level, and the positioning information is uploaded once per second. When the vehicle enters areas with weak global positioning system signals such as tunnels and underground parking lots, the inertial navigation system automatically switches to the main positioning mode, and calculates the position by measuring the acceleration and angular velocity of the vehicle to ensure the continuity of positioning. Mobile communication network positioning technology is used as a backup means to provide emergency positioning services when the global positioning system and inertial navigation system fail.

[0076] Stick barcode or QR code labels on emergency supplies and storage locations respectively, build a database containing emergency supply information, such as name, specifications, quantity, production date, shelf life, etc., enter the emergency supply storage location information into the database, and associate the labels on the emergency supplies with the database. By scanning the labels on the emergency supplies, you can understand the in and out information.

[0077] In one embodiment, for the data collection and transmission module, a mathematical model is constructed to collect emergency response information related to the target area. The specific process includes:

[0078] Collect data related to emergencies in the target area through multiple channels. For example, relevant data can be collected from multiple channels such as the power company's internal database, historical accident reports, real-time monitoring systems, and external power industry information platforms. The data includes text-based accident descriptions, operation manuals, emergency plans, as well as structured equipment operating parameters, personnel and vehicle location information, etc.

[0079] The collected data is cleaned to remove duplicate, erroneous and incomplete data records, and different types of data are labeled. For text data, power industry experts and data labelers work together to mark key information, such as the cause of the accident, disposal measures, and equipment involved.

[0080] Divide the cleaned and labeled data into training, validation, and test sets.

[0081] Use natural language processing models to process text data, such as pre-trained language models based on the Transformer architecture, and use image recognition models based on convolutional neural networks to process image data, such as VGG and ResNet.

[0082] Determine the training parameters of the model and determine the parameters according to the actual situation. For example, the learning rate is set to 0.001 and the batch size is set to 21.

[0083] According to the data type of the training set, it is input into the corresponding model for training, and the model parameters are updated using the loss function and back propagation algorithm; that is, the text data of the training set is input into the natural language processing model, and the loss function (such as the cross entropy loss function) between the prediction results and the annotation results is calculated. The model parameters are continuously adjusted using the back propagation algorithm so that the loss function value gradually decreases. During the training process, the model performance is regularly evaluated on the validation set, and the parameters are adjusted according to the validation results.

[0084] Determine the evaluation indicators. For text classification tasks, use indicators such as accuracy, recall rate, and F1 value; for image recognition tasks, use indicators such as accuracy, precision, recall rate, and mean average precision (mAP). Use the test set data to evaluate the trained model.

[0085] Emergency-related data from the target area, such as new accident reports, is collected in real time and fed into corresponding models for analysis based on data type, yielding relevant emergency response information for the target area. Specifically, the natural language processing model analyzes new text data, extracting key emergency response information such as the time, location, cause, and measures taken, and classifying the accident type, such as equipment failure or power outages caused by natural disasters.

[0086] The extracted emergency response information is stored in the database according to the date, and the priority is determined according to the pre-set priority evaluation model. Important and urgent information is stored in a focused manner and relevant personnel are reminded in a timely manner.

[0087] In one embodiment, for the data collection and transmission module, the specific process of constructing the priority evaluation model includes:

[0088] Determine the factors that affect the priority of emergency events, including the scope of power supply impact (consider whether the emergency event will cause a large-scale power outage, count the number of users in the power outage area, and the impact on important users), the urgency of the event (classify different types of emergencies based on historical data and industry standards), the degree of equipment damage (assess the degree of damage caused by the emergency event to power equipment, including the criticality of the equipment, the difficulty and time of repair, etc.), and the probability of the event (analyze the historical frequency of similar emergency events).

[0089] Experts were invited to conduct assessments, using the Analytic Hierarchy Process (AHP) to compare and score each influencing factor pairwise, creating a personal judgment matrix. For example, they compared "power supply impact range" and "incident urgency" to determine which factor had a greater impact on the incident's priority. Scoring was performed on a scale of 1 to 9. 1 indicates both factors are equally important; 3 indicates the power supply impact is slightly more important than the incident; 5 indicates the power supply impact is significantly more important than the incident; 7 indicates the power supply impact is significantly more important than the incident; 9 indicates the power supply impact is extremely important; 2, 4, 6, and 8 are intermediate values.

[0090] Perform consistency check on each judgment matrix and calculate the maximum eigenvalue of the judgment matrix , and according to the formula Calculate the consistency index CI, where n is the order of the judgment matrix, find the random consistency index RI (there is a corresponding standard value according to the matrix order), and calculate the consistency ratio ,when

[0091] When CR < 0.1, the judgment matrix is considered to have acceptable consistency.

[0092] For the judgment matrix that has passed the consistency test, the eigenvector method is used to calculate the weight of each factor. The eigenvector corresponding to the maximum eigenvalue of the judgment matrix is calculated and normalized to obtain the relative weight of each factor. For example, for a judgment matrix containing four influencing factors, the calculation results are

[0093] The normalized eigenvector of , where ωi is the weight of the i-th factor, and The weight calculation results of multiple experts are weighted averaged to obtain the final weight of each influencing factor.

[0094] The priority of emergency events is divided into several evaluation levels, and a corresponding score range is set for each level, for example, "very high" corresponds to 81-100 points, "high" corresponds to 61-80 points, "medium" corresponds to 41-60 points, "low" corresponds to 21-40 points, and "very low" corresponds to 0-20 points.

[0095] For each emergency event to be assessed, professionals perform a fuzzy evaluation of each influencing factor based on its actual situation and convert the evaluation results into a fuzzy evaluation vector. For example, if the "power supply impact range" factor is evaluated as "high," its fuzzy vector can be represented as [0, 1, 0, 0, 0] (assuming the five evaluation levels correspond to the vector elements in order).

[0096] Using the fuzzy transformation principle, the fuzzy evaluation vector of each factor and the corresponding weight vector are synthesized and calculated. Let the factor weight vector be , the evaluation matrix composed of the fuzzy evaluation vectors of each factor is R, then the comprehensive evaluation vector B=W·R. For example, , ,but The priority level of the emergency event is determined based on the evaluation level corresponding to the maximum value of each element in the comprehensive evaluation vector B. A specific priority score is then calculated based on the score range. For example, if the maximum value in B is 0.5, corresponding to a "medium" evaluation level, the priority score for this event can be determined within the range of 41-60, such as 50, based on specific rules. Based on the priority score, high-priority events are stored and promptly notified to relevant personnel through various means.

[0097] Emergency response information includes the accident type, time, location, cause, and emergency measures taken. Data transmission is performed using both wired and wireless networks, depending on the device type. For highly mobile devices like personnel positioning terminals and vehicle-mounted positioning equipment, data transmission is performed via 4G / 5G mobile communication networks. Data compression technologies, such as the Zlib algorithm, are used to compress positioning data, reducing data volume and improving transmission efficiency.

[0098] In one embodiment, for the data processing and storage module, the Apache Flink distributed real-time computing framework is used to perform real-time cleaning and preprocessing on the flowing data.

[0099] Structured data such as personnel location, vehicle location, and material location are stored in the distributed relational database TiDB. The data is partitioned and stored according to time series, with each day's data stored in a separate partition. At the same time, indexes are created for key fields (such as time, device ID, etc.) to speed up data queries.

[0100] In one embodiment, the data analysis module analyzes the real-time collected data based on the long short-term memory network model and determines the event status according to the preset evaluation criteria. The specific process includes:

[0101] Real-time collection of target sub-area data (including personnel location, vehicle location, material location, etc.) and emergency response information.

[0102] The collected real-time data is preprocessed and fused, and the data from different data sources at the same time are associated based on the timestamp to form a complete event-related data set.

[0103] For the fused data, key features are extracted for event status judgment, and the features that have the greatest impact on event status judgment are screened out based on the feature selection algorithm.

[0104] Build a long short-term memory network model and use historical event data (including labeled event states) to train the model.

[0105] The data collected in real time and subjected to preprocessing and feature extraction is input into the trained model. The model predicts the current event status based on the evaluation criteria and outputs the prediction results.

[0106] In one embodiment, for the data analysis module, the evaluation criteria include power equipment status criteria, personnel and vehicle action criteria, emergency response process criteria, and event impact scope and consequence criteria.

[0107] Power equipment status standards include equipment operating parameters and equipment appearance characteristics;

[0108] Human and vehicle movement standards include abnormal human behavior and vehicle trajectory and speed;

[0109] Emergency response process standards include corresponding time and implementation of response measures;

[0110] The scope and consequence standards of the incident include the scope of power outage and estimated economic losses.

[0111] In one embodiment, the solution distribution module distributes emergency response solutions based on a distribution matching algorithm. The specific process includes:

[0112] Collect various information related to the event, such as event type, time of occurrence, location, scope of impact, and severity. Extract key features from the collected data to accurately describe the event status. For example, for a fire incident, features such as fire size, burning materials, and whether there are trapped people may be extracted. The event status is judged and classified based on pre-set standards and models.

[0113] Collect and organize various emergency response plans, categorizing them according to event type, scenario, and response objective. Label each emergency response plan with features, clarifying key information such as applicable event type, scenario conditions, response measures, and resource requirements. Store the organized and labeled emergency response plans in a plan library, and establish an efficient indexing and query mechanism for rapid retrieval and matching.

[0114] The feature vector of the event state is input into the distribution matching algorithm, and the similarity is calculated and matched with each solution in the solution library, and the matching threshold is determined. When the similarity score is higher than a certain threshold, the solution is considered to be matched; or the solutions are sorted according to the similarity score, and the solutions with the highest scores are selected as matching solutions.

[0115] Based on the nature of the incident, the location of the incident, and the responsibilities of the relevant units, determine the relevant units and personnel who need to receive the emergency response plan. For example, a fire incident needs to be distributed to the fire department, emergency management department, and community staff at the incident site.

[0116] Distribute the matched emergency response plan to relevant units and personnel through multiple channels, such as email, text messages, and push notifications on the emergency command platform, to ensure that the plan is communicated in a timely and accurate manner.

[0117] After receiving the plan, relevant units and staff will organize and implement emergency response work in accordance with the plan requirements. During the implementation process, timely feedback information, such as response progress and problems encountered, will be collected to adjust and optimize the plan.

[0118] In one embodiment, the system further includes a visualization display module, which is used to build a visualization display platform. Relevant units or staff handle the emergency response according to the matched plan and upload the emergency response process data in real time. The system tracks and records the emergency response progress information throughout the entire process and displays the information of each process in real time on the visualization platform.

[0119] In a further embodiment, for the visualization display module, the specific process of constructing the visualization display platform includes:

[0120] Design a layered architecture consisting of data acquisition layer, data transmission layer, data processing layer, business logic layer and visual display layer. The data acquisition layer is used to collect data from emergency response on-site equipment and staff; the data transmission layer transmits data to the data processing layer through wired and wireless networks; the data processing layer cleans, organizes and converts the data into a format; the business logic layer analyzes and processes the data according to the emergency response process to determine the display content and method; the visual display layer is used to present the processed data to users in an intuitive visual form.

[0121] At the emergency response site, staff use mobile devices (such as smartphones and tablets) equipped with specific applications. The mobile devices collect data from the emergency response process in real time through sensors. The application will preliminarily organize and compress the collected data and upload it to the data transmission layer in real time through the 4G / 5G network.

[0122] The data processing layer and business logic layer break down the matched emergency response plan into multiple specific tasks, assign a unique task ID to each task, and set the task's start time, estimated completion time, and task status (not started, in progress, completed).

[0123] The system associates and updates real-time uploaded data with the corresponding task using the task ID. When a worker uploads data, the system automatically identifies the task to which the data belongs and updates relevant information about the task, such as task progress and completion status.

[0124] Based on the status and time information of the task, the system calculates the overall progress of emergency response in real time.

[0125] Based on map visualization, flow chart visualization and data panel visualization, emergency response progress information is displayed in real time on the visualization platform.

[0126] GIS maps display the geographic location of the emergency response site and the real-time positions of personnel, vehicles, and supplies. Different icons and colors are used to distinguish different objects, such as red icons representing personnel performing emergency tasks and green icons representing vehicles on standby. The map also marks the execution location of each task, and the progress of tasks is visually displayed through the color changes of task status (e.g., gray for unstarted tasks, yellow for ongoing tasks, and green for completed tasks).

[0127] The emergency response plan process is presented in the form of a flowchart, with each task presented as a node, and the lines connecting the nodes indicate the order of tasks. Nodes display different colors and icons based on the real-time status of the task, such as flashing yellow for ongoing tasks and green checkmarks for completed tasks. Hovering the mouse over a node displays detailed task information, including the task name, start time, estimated completion time, actual completion time, and a description.

[0128] A data dashboard is set up on the platform to display key emergency response data in real time, such as the overall progress percentage, the number of completed tasks, the number of ongoing tasks, the number of remaining tasks, and the estimated completion time. Task execution time statistics are also displayed in the form of charts, such as a bar chart comparing the actual execution time of each task with the estimated execution time, allowing users to intuitively understand the efficiency and deviation of task execution.

[0129] After an emergency incident is handled, the system automatically extracts data related to the entire incident from the database, including data collected during the incident, changes in emergency score, plan execution status, and handling progress records. A detailed incident report is also generated, covering an overview of the incident, cause analysis, handling process, and lessons learned. The report is stored in PDF or Word format and can be accessed at any time.

[0130] In one embodiment, the system also includes a feedback module for collecting feedback from relevant units and staff on emergency response measures and rectification suggestions for emergency response measures in various emergency scenarios during the actual work process. By continuously collecting suggestions from all parties, the system can be continuously optimized.

[0131] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. An emergency disposal tracing system, characterized in that: include: The data acquisition and transmission module, data processing and storage module, data analysis module, solution distribution module and emergency response tracing module are sequentially connected in communication; The data acquisition and transmission module is used to regularly collect multi-source data of the target area and transmit it to the data processing and storage module; The data processing and storage module is used to pre-process and store the collected multi-source data; The data analysis module is used to extract the pre-processed multi-source data for analysis and determine the event status according to preset evaluation criteria; The solution distribution module is used to match and distribute corresponding emergency response solutions from a preset solution library based on the judgment result of the event status and the distribution matching algorithm; The emergency response tracing module is used to record the execution status of the data acquisition and transmission module, the data processing and storage module, the data analysis module and the solution distribution module during the emergency response process; the execution status is used to trace the emergency response process.

2. The emergency response tracing system according to claim 1, characterized in that: The multi-source data includes: Locate emergency personnel, vehicles, and supplies.

3. The emergency response tracing system according to claim 2, characterized in that: The data acquisition and transmission module is further used to collect emergency response information related to the target area based on a pre-built mathematical model, including: Collect multi-channel data related to emergency response in the target area; Cleaning the collected multi-channel data and labeling different types of data; Divide the cleaned and labeled data into training, validation, and test sets; Determine training parameters for natural language processing models and convolutional neural network-based image recognition models; According to the data type of the training set, the data is input into the corresponding model for training, and the model parameters are updated using the loss function and the back propagation algorithm; wherein the natural language processing model is used to process text data, and the image recognition model is used to process image data; Determine the evaluation indicators and use the test set data and validation set data to evaluate and verify the trained model; Collect emergency-related data from the target area in real time, input them into the corresponding model for analysis according to the data type, and obtain emergency response information related to the target area; The extracted emergency response information is stored in the database according to the date, and the priority is determined according to the pre-set priority evaluation model. Important and urgent information is stored in a focused manner and relevant personnel are reminded in a timely manner.

4. The emergency response tracing system according to claim 3, characterized in that: In the data collection and transmission module, the specific process of constructing the priority evaluation model includes: Identify factors that influence the priority of emergency events; The hierarchical analysis method is used to compare and score each of the influencing factors to form a personal judgment matrix; Performing a consistency test on each of the individual judgment matrices; For the personal judgment matrix that passes the consistency test, the weight vector of each factor is calculated using the eigenvector method; Divide the priority of emergency events into several evaluation levels and set a corresponding score range for each evaluation level; For each emergency event to be evaluated, a fuzzy evaluation is performed on each influencing factor, and the evaluation results are converted into a fuzzy evaluation vector; By using the fuzzy transformation principle, the fuzzy evaluation vector of each factor is synthesized with the corresponding weight vector. The priority level of the emergency event is determined according to the evaluation level corresponding to the maximum value of each element in the comprehensive evaluation vector, and the priority score is calculated according to the score range.

5. The emergency response tracing system according to claim 1, characterized in that: The data analysis module analyzes the multi-source data based on a long short-term memory network model; Extract the pre-processed multi-source data for analysis and determine the event status based on preset evaluation criteria, including: Collect data of the target area and emergency response information in real time; Preprocessing and fusing the collected data of the target area and the emergency response information, and correlating the data from different data sources at the same time based on the timestamp to form a complete event-related data set; Extract key features from the data in the event-related dataset and select the features that have the greatest impact on event status judgment based on a feature selection algorithm; Build a long short-term memory network model and train the model using historical event data; The data collected in real time and subjected to preprocessing and feature extraction are input into the trained model so that the model can predict the current event status based on the evaluation criteria and output the prediction results.

6. The emergency response tracing system according to claim 5, characterized in that: In the data analysis module, the evaluation criteria include power equipment status criteria, personnel and vehicle action criteria, emergency response process criteria, and event impact scope and consequence criteria; Power equipment status standards include equipment operating parameters and equipment appearance characteristics; Human and vehicle movement standards include abnormal human behavior and vehicle trajectory and speed; Emergency response process standards include corresponding time and implementation of response measures; The scope and consequence standards of the incident include the scope of power outage and estimated economic losses.

7. The emergency response tracing system according to claim 1, characterized in that: In the solution distribution module, based on the distribution matching algorithm, the corresponding emergency response solutions are matched and distributed from the preset solution library. The specific process includes: Collect data related to the event, extract key features from the collected data, and judge and classify the status of the event based on preset standards and models; Collect and organize various emergency response plans, classify them according to different event types, scenarios and response objectives, annotate each emergency response plan with features, and store the organized and annotated emergency response plans in the plan library; Input the key features of the event state into the distribution matching algorithm, and perform similarity calculation and matching with each solution in the solution library; Determine the relevant units and staff who need to receive the emergency response plan based on the nature of the incident, the location of the incident, and the responsibilities of the relevant units; Distribute the matched emergency response plan to relevant units and staff through multiple channels; After receiving the plan, relevant units and staff will organize and implement emergency response work in accordance with the plan requirements, and collect feedback information in a timely manner during the implementation process.

8. The emergency response tracing system according to claim 1, characterized in that: Also includes: Visual display module; The visualization display module is used to display the information of each process of emergency response on the visualization platform in real time.

9. The emergency response tracing system according to claim 8, characterized in that: In the visualization display module, the specific process of building a visualization display platform includes: Set up a layered architecture of data collection layer, data transmission layer, data processing layer, business logic layer and visual display layer; At the emergency response site, the data collected during the emergency response process is collected in real time by the sensors of the mobile devices provided in the data collection layer, and the collected data is preliminarily sorted and compressed, and uploaded to the data transmission layer in real time; Decomposing the matched emergency response plan into multiple specific tasks through the data processing layer and the business logic layer, assigning a unique task ID to each task, and setting the task's start time, expected completion time, and task status; Associating and updating the real-time uploaded data with the corresponding task through the task ID; Calculate the overall progress of emergency response in real time based on task status and time information; Based on map visualization, flow chart visualization and data panel visualization, the emergency response progress information is displayed in real time on the visualization platform through the visualization display layer.

10. The emergency response tracing system according to claim 1, characterized in that: The system also includes a feedback suggestion module for collecting emergency response suggestions from relevant units and staff during the actual work process, as well as rectification suggestions for emergency response measures in various emergency scenarios.

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