High-speed emergency deployment and control management method and system

Abstract

The invention provides a highway emergency deployment and control management method and system, and relates to the technical field of highway management. According to the invention, the emergency task information is directly obtained from the task issuing center system, and diversified data sources are processed in real time, so that efficient task response is realized; meanwhile, intelligent prediction and evaluation are carried out by utilizing a pre-constructed event association prediction model and a pre-constructed department association model, so that the occurrence probability of an accident and the to-be-executed probability of a backup department can be accurately predicted; on the basis, by calculating the weight of the backup execution department and dividing the order waiting level, reasonable allocation of emergency resources is achieved, it is ensured that the key department can respond rapidly at the key moment, and the measures jointly ensure rapidness, accuracy and high efficiency of emergency processing.

Description

Technical Field

[0001] The present invention relates to the technical field of highway management, and in particular to a highway emergency control management method and system. Background Art

[0002] In highway emergency management, response speed is one of the key factors that determine the rescue effect. However, traditional emergency management methods often rely on communication tools such as telephones and walkie-talkies to transmit information, resulting in a long information transmission chain and many links, which is prone to information delays or distortion. In addition, the deployment of emergency resources often relies on manual coordination, making it difficult to quickly assemble and deploy resources in a short period of time.

[0003] Traditional emergency management methods often lack scientific forecasting models and evaluation methods, resulting in low forecast accuracy and difficulty in accurately judging the development trend and possible impact range of emergency events. This brings great uncertainty to emergency decision-making and increases the difficulty and risk of emergency management.

[0004] In highway emergency management, the rational allocation of resources is an important guarantee for ensuring the rescue effect. However, traditional emergency management methods often rely on experience and judgment to allocate resources, lacking scientific allocation methods and standards. This often leads to resource shortages or waste in emergency events, affecting the rescue effect.

[0005] Therefore, it is necessary to provide a high-speed emergency control management method and system to solve the above technical problems. Summary of the invention

[0006] In order to solve the above technical problems, the present invention provides a high-speed emergency control management method and system for solving the problem that the traditional emergency management model often relies on manual coordination for the allocation of emergency resources, making it difficult to quickly assemble and deploy resources in a short period of time, and it is difficult to accurately judge the development trend and possible impact range of emergency events, resulting in difficulty and high risk of emergency management.

[0007] The present invention provides a high-speed emergency control management method, the management method comprising the following steps:

[0008] S100, obtaining current emergency task information and corresponding execution plan from the task release center system, and extracting key semantic information of the current emergency task information;

[0009] S200, acquiring diversified data in real time and performing preprocessing, wherein the diversified data sources include but are not limited to traffic monitoring data, environmental sensor data, vehicle GPS data, and social media data;

[0010] S300, according to the acquired current emergency task information, extracting data related to the current emergency task information from the pre-processed diversified data by parallel processing, and judging whether the current emergency task information is accurate;

[0011] S400, in response to the result that the current emergency task information is determined to be accurate, the probability of the occurrence of the unexpected event and the probability of the backup department to be executed are analyzed and estimated through the pre-built event association prediction model and department association model according to the key semantic information of the current emergency task information;

[0012] S500, based on the estimated probability of occurrence of unexpected events and the probability of relevant departments to be executed, the weights of the backup execution departments are obtained by multiplication, and the waiting orders of the backup execution departments are divided into waiting orders levels;

[0013] S600, transmitting the emergency task information, execution target and the information on the waiting level of the backup execution department to the corresponding execution department and the backup execution department;

[0014] S700. Each execution department receives the current emergency task information and the corresponding execution plan to execute, and the backup execution department waits for orders according to the waiting order level.

[0015] Preferably, the step S100 specifically includes the following steps:

[0016] S101, establish a connection with the task release center system to obtain the current emergency task information and its corresponding execution plan in real time;

[0017] S102. Using semantic analysis technology, extract key semantic information from the acquired current emergency task information and execution plan, including task type, location, time requirement and urgency.

[0018] Preferably, the step S200 specifically includes the following steps:

[0019] S201, real-time collection of diverse data including traffic surveillance videos, environmental sensors, data, vehicle GPS location information, and social media;

[0020] S202: Clean, denoise and unify the format of the collected diversified data to obtain pre-processed diversified data, and store them in a distributed database.

[0021] Preferably, the step S300 specifically includes the following steps:

[0022] S301, presetting multiple processing sub-cards, and using the multiple processing sub-cards to simultaneously scan pre-processed diversified data from different sources, extracting data information directly related to the current emergency task information, and obtaining relevant data;

[0023] S302, comparing the current emergency task information with the extracted relevant data to determine whether the description of the current emergency task information truly reflects the current situation;

[0024] S303: If it is determined that the description of the current emergency task information truly reflects the current situation, the task information is confirmed to be accurate; otherwise, the task information is marked as inaccurate.

[0025] Preferably, in step S400, the specific steps of constructing the event association prediction model are:

[0026] Collect historical emergency task data, including emergency task information, relevant event records, execution department records, and execution results, and clean them;

[0027] Extract features associated with emergency tasks from the cleaned historical emergency task data, including event type, occurrence time, location, weather conditions, and traffic conditions;

[0028] Identify the mapping relationship between each feature and the corresponding accident through data statistical analysis, and use it as a training set;

[0029] The classification algorithm model is adopted, and the mapping relationship between each feature and the corresponding accident is used as a training set to train the classification algorithm model to obtain an event association prediction model.

[0030] Preferably, in step S400, the specific steps of constructing the department association model are:

[0031] Collect historical emergency task data, including emergency task information, relevant event records, execution department records, and execution results, and clean them;

[0032] Extract the characteristics of department type, historical response time, resource allocation, and personnel experience from the cleaned historical emergency task data;

[0033] Use the correlation analysis algorithm to analyze the correlation between the extracted department type, historical response time, resource allocation, and personnel experience characteristics, and use the corresponding relationship between the analysis results and the characteristics as the training set

[0034] The association rule mining algorithm model is adopted, and the corresponding relationship between the analysis results and the features is used as a training set to train the association rule mining algorithm model to obtain the department association model.

[0035] Preferably, the specific steps of step S500 are:

[0036] S501, based on the probability value of the occurrence of the accident and the probability value of the backup department to be executed output by the event association prediction model and the department association model;

[0037] S502, multiplying the estimated probability value of the accident by the probability value to be executed by the relevant department, and calculating the weight of the backup execution department;

[0038] S503, according to the calculated weights of the backup execution departments, sorting the weights of the backup execution departments, and dividing the weight values ​​into different intervals, specifically, a first-level weight interval, a second-level weight interval, and a third-level weight interval;

[0039] S504. According to the result of dividing the weight values ​​into different intervals, corresponding waiting levels are set, specifically, level one waiting, level two waiting, and level three waiting.

[0040] Preferably, in step S700, the specific steps of the backup execution department performing the waiting order according to the waiting order level are:

[0041] Each backup execution department receives a corresponding level of standby orders. Among them, if a level one standby order is received, it is a level one standby department, and it shall keep more than 70% of the personnel and equipment and tools in the department in a ready state; if a level two standby order is received, it is a level two standby department, and it shall keep more than 40% of the personnel and equipment and tools in the department in a ready state; if a level three standby order is received, it is a level three standby department, and it shall keep more than 20% of the personnel and equipment and tools in the department in a ready state.

[0042] A high-speed emergency control management system, the management system comprising:

[0043] The management system comprises:

[0044] The task acquisition module is used to obtain the current emergency task information and the corresponding execution plan from the task release center system, and extract the key semantic information in the current emergency task information and the execution plan;

[0045] A data collection module, used to obtain diversified data in real time and perform pre-processing, wherein the diversified data sources include but are not limited to traffic monitoring data, environmental sensor data, vehicle GPS data and social media data;

[0046] The task judgment module is used to extract data related to the current emergency task information from the pre-processed diversified data through parallel processing based on the acquired current emergency task information, and judge whether the current emergency task information is accurate;

[0047] The estimation module is used to respond to the result that the current emergency task information is judged to be accurate, and then analyze and estimate the probability of the occurrence of unexpected events and the probability of the backup department to be executed through the pre-built event association estimation model and department association model according to the key semantic information of the current emergency task information;

[0048] The level classification module is used to obtain the weight of the backup execution department by multiplying the estimated probability of the occurrence of unexpected events and the probability of the relevant departments to be executed, and to classify the waiting level of the backup execution departments;

[0049] An information transmission module is used to transmit emergency task information, execution targets and information on the waiting level of the backup execution department to the corresponding execution department and backup execution department;

[0050] The instruction receiving module is used for each execution department to receive the current emergency task information and the corresponding execution plan for execution, and the backup execution department is on standby according to the waiting order level.

[0051] Compared with the related art, the high-speed emergency control management method and system provided by the present invention has the following beneficial effects:

[0052] The present invention achieves efficient task response by directly acquiring emergency task information from the task release center system and processing diversified data sources in real time. At the same time, it uses pre-built event association prediction models and department association models for intelligent prediction and evaluation, and can accurately estimate the probability of unexpected events and the probability of backup departments to be executed. On this basis, by calculating the weights of backup execution departments and dividing the waiting order levels, it achieves the rational allocation of emergency resources and ensures that key departments can respond quickly at critical moments. These measures jointly ensure the rapidity, accuracy and efficiency of emergency handling. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 A flowchart of a high-speed emergency control management method of the present invention;

[0054] Figure 2 The system block diagram of a high-speed emergency control management system of the present invention. DETAILED DESCRIPTION

[0055] The present invention will be further described below in conjunction with the accompanying drawings and implementation modes.

[0056] Embodiment 1

[0057] like Figure 1 As shown, a high-speed emergency control management method includes the following steps:

[0058] S100, obtaining current emergency task information and corresponding execution plan from the task release center system, and extracting key semantic information in the current emergency task information and execution plan;

[0059] In the specific implementation process, step S100 specifically includes the following steps:

[0060] S101, establish a connection with the task release center system to obtain the current emergency task information and its corresponding execution plan in real time;

[0061] Specifically, through the docking of API, or application programming interface, it is ensured that the two systems can exchange data in real time. During the interface docking process, the format, encryption method, and verification mechanism of data transmission are agreed upon to ensure the security and accuracy of data transmission. When the task release center system responds to the request or actively pushes it, it receives a data packet containing the current emergency task information and its corresponding execution plan. The data packet contains detailed information such as the task number, task description, location coordinates, time requirements, and degree of urgency.

[0062] S102, using semantic analysis technology, extracting key semantic information from the acquired current emergency task information and execution plan, including task type, location, time requirement, and urgency;

[0063] Specifically, the received task information and execution plan text are first preprocessed, including removing irrelevant characters and word segmentation steps for subsequent processing. Then, the preprocessed text is analyzed using semantic analysis technology in natural language processing (NLP) to obtain analysis results. According to existing emergency management demand standards, key semantic information is extracted from the analysis results. For high-speed tunnel emergency tasks, key semantic information includes but is not limited to task type (such as traffic accident handling), location (specific tunnel location and accident point), time requirements (such as immediate response), degree of urgency, etc.

[0064] For example, through semantic analysis technology, the following key semantic information is successfully extracted from the task information and execution plan: the task type is "traffic accident handling", the location is "XX expressway tunnel K50+300", the time requirement is "immediate response", and the urgency is "high".

[0065] S200, acquiring diversified data in real time and performing preprocessing, wherein the diversified data sources include but are not limited to traffic monitoring data, environmental sensor data, vehicle GPS data, and social media data;

[0066] In the specific implementation process, step S200 specifically includes the following steps:

[0067] S201, real-time collection of diverse data including traffic surveillance videos, environmental sensors, data, vehicle GPS location information, and social media;

[0068] S202, cleaning, denoising and formatting the collected diversified data to obtain pre-processed diversified data, and storing the pre-processed diversified data in a distributed database;

[0069] Specifically, the collected diversified data is identified and invalid data is deleted, such as duplicate data, null value data, and obviously erroneous data (such as timestamp anomalies, geographic location outside the tunnel range, etc.). The video data is then compressed and encoded to save storage space and speed up subsequent processing. The social media data is then preprocessed, such as removing irrelevant tags and emoticons, and stored in a distributed database after preprocessing.

[0070] S300, according to the acquired current emergency task information, extracting data related to the current emergency task information from the pre-processed diversified data by parallel processing, and judging whether the current emergency task information is accurate;

[0071] In the specific implementation process, step S300 specifically includes the following steps:

[0072] S301, presetting multiple processing sub-cards, and using the multiple processing sub-cards to simultaneously scan pre-processed diversified data from different sources, extracting data information directly related to the current emergency task information, and obtaining relevant data;

[0073] Specifically, according to different data types and sources, multiple special processing sub-cards are designed. Each processing sub-card is responsible for scanning data of a specific type or source and extracting key information related to the current emergency task information. Then, using a multi-core processor or distributed computing resources, multiple processing sub-cards are started at the same time for parallel processing to scan pre-processed diversified data from different sources, and extract data information directly related to the current emergency task information from the pre-processed data.

[0074] For example, the video processing sub-card can identify the vehicle type, number, and personnel activities at the accident scene; the environmental data processing sub-card can monitor changes in environmental parameters such as smoke concentration and temperature; and the GPS processing sub-card can track the driving trajectory and speed changes of vehicles around the accident.

[0075] S302, comparing the current emergency task information with the extracted relevant data to determine whether the description of the current emergency task information truly reflects the current situation;

[0076] Specifically, the description in the current emergency task information is compared with the extracted relevant data one by one to determine whether the accident type, location, time, impact range, etc. are consistent with the information in the data report.

[0077] S303: If it is determined that the description of the current emergency task information truly reflects the current situation, the task information is confirmed to be accurate; otherwise, the task information is marked as inaccurate.

[0078] S400, in response to the result that the current emergency task information is determined to be accurate, the probability of the occurrence of the unexpected event and the probability of the backup department to be executed are analyzed and estimated through the pre-built event association prediction model and department association model according to the key semantic information of the current emergency task information;

[0079] In the specific implementation process, step S400 specifically includes the following steps:

[0080] S401, extracting event type, occurrence time, location, weather conditions and traffic status characteristics from key semantic information of current emergency task information;

[0081] S402: Input the extracted event type, occurrence time, location, weather conditions and traffic status characteristics into an event association prediction model, and output a probability value of an accident occurring.

[0082] S403, extracting features of department type, historical response time, resource allocation, and personnel experience from key semantic information of the current emergency task information;

[0083] S403: Input the extracted department type, historical response time, resource allocation, and personnel experience characteristics into the department association model, and output the probability value of the backup department to be executed.

[0084] S500, based on the estimated probability of occurrence of unexpected events and the probability of relevant departments to be executed, the weights of the backup execution departments are obtained by multiplication, and the waiting orders of the backup execution departments are divided into waiting orders levels;

[0085] In the specific implementation process, step S500 specifically includes the following steps:

[0086] S501, based on the probability value of the occurrence of the accident and the probability value of the backup department to be executed output by the event association prediction model and the department association model;

[0087] S502, multiplying the estimated probability value of the accident by the probability value to be executed by the relevant department, and calculating the weight of the backup execution department;

[0088] Specifically, for each backup execution department, its weight = probability of an accident occurring × probability of the department to be executed. For example, if the current task is to handle a serious traffic accident occurring on a highway, the event association prediction model outputs a probability of an accident occurring of 80%, the department association model outputs a probability of the traffic police department to be executed of 90%, and a probability of the medical rescue department to be executed of 85%, then the weight of the traffic police department = 80% × 90% = 72%, and the weight of the medical rescue department = 80% × 85% = 68%.

[0089] S503, according to the calculated weights of the backup execution departments, sorting the weights of the backup execution departments, and dividing the weight values ​​into different intervals, specifically, a first-level weight interval, a second-level weight interval, and a third-level weight interval;

[0090] Specifically, according to the sorting results and the preset division criteria, the present implementation uses percentage range as the division criteria, dividing weight values ​​above 70% into the first-level weight interval, weight values ​​between 50% and 70% into the second-level weight interval, and weight values ​​below 50% into the third-level weight interval.

[0091] S504. According to the result of dividing the weight values ​​into different intervals, corresponding waiting levels are set, specifically, level one waiting, level two waiting, and level three waiting.

[0092] Specifically, for departments in the first-level weight range, they are set to level one on standby, indicating that these departments need to immediately prepare for emergency response; for departments in the second-level weight range, they are set to level two on standby, indicating that these departments need to pay close attention and be ready to respond at any time; for departments in the third-level weight range, they are set to level three on standby, indicating that these departments can maintain normal working conditions but also need to pay attention to emergency situations.

[0093] S700, each execution department receives the current emergency task information and the corresponding execution plan to execute, and the backup execution department waits for orders according to the waiting order level;

[0094] In the specific implementation process, in step S700, the specific steps for the backup execution department to wait for orders according to the waiting order level are:

[0095] Each backup execution department receives a corresponding level of standby orders. Among them, if a level one standby order is received, it is a level one standby department, and it shall keep more than 70% of the personnel and equipment and tools in the department in a ready state; if a level two standby order is received, it is a level two standby department, and it shall keep more than 40% of the personnel and equipment and tools in the department in a ready state; if a level three standby order is received, it is a level three standby department, and it shall keep more than 20% of the personnel and equipment and tools in the department in a ready state.

[0096] Embodiment 2

[0097] like Figure 2 As shown, a high-speed emergency control management system applied to a high-speed emergency control management method specifically includes:

[0098] The task acquisition module is used to obtain the current emergency task information and the corresponding execution plan from the task release center system, and extract the key semantic information in the current emergency task information and the execution plan;

[0099] A data collection module, used to obtain diversified data in real time and perform pre-processing, wherein the diversified data sources include but are not limited to traffic monitoring data, environmental sensor data, vehicle GPS data and social media data;

[0100] The task judgment module is used to extract data related to the current emergency task information from the pre-processed diversified data through parallel processing based on the acquired current emergency task information, and judge whether the current emergency task information is accurate;

[0101] The estimation module is used to respond to the result that the current emergency task information is judged to be accurate, and then analyze and estimate the probability of the occurrence of unexpected events and the probability of the backup department to be executed through the pre-built event association estimation model and department association model according to the key semantic information of the current emergency task information;

[0102] The level classification module is used to obtain the weight of the backup execution department by multiplying the estimated probability of the occurrence of unexpected events and the probability of the relevant departments to be executed, and to classify the waiting level of the backup execution departments;

[0103] An information transmission module is used to transmit emergency task information, execution targets and information on the waiting level of the backup execution department to the corresponding execution department and backup execution department;

[0104] The instruction receiving module is used for each execution department to receive the current emergency task information and the corresponding execution plan for execution, and the backup execution department is on standby according to the waiting order level.

[0105] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0106] Those skilled in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium, and the storage medium includes a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable rewritable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0107] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

Claims

1. A high-speed emergency control management method, characterized in that: The management method comprises the following steps: S100, obtaining current emergency task information and corresponding execution plan from the task release center system, and extracting key semantic information of the current emergency task information; S200, acquiring diversified data in real time and performing preprocessing, wherein the diversified data sources include but are not limited to traffic monitoring data, environmental sensor data, vehicle GPS data, and social media data; S300, according to the acquired current emergency task information, extracting data related to the current emergency task information from the pre-processed diversified data by parallel processing, and judging whether the current emergency task information is accurate; S400, in response to the result that the current emergency task information is determined to be accurate, the probability of the occurrence of the unexpected event and the probability of the backup department to be executed are analyzed and estimated through the pre-built event association prediction model and department association model according to the key semantic information of the current emergency task information; S500, based on the estimated probability of occurrence of unexpected events and the probability of relevant departments to be executed, the weights of the backup execution departments are obtained by multiplication, and the waiting orders of the backup execution departments are divided into waiting orders levels; S600, transmitting the emergency task information, execution target and the information on the waiting level of the backup execution department to the corresponding execution department and the backup execution department; S700. Each execution department receives the current emergency task information and the corresponding execution plan to execute, and the backup execution department waits for orders according to the waiting order level.

2. A high-speed emergency control management method according to claim 1, characterized in that: The step S100 specifically includes the following steps: S101, establish a connection with the task release center system to obtain the current emergency task information and its corresponding execution plan in real time; S102. Using semantic analysis technology, extract key semantic information from the acquired current emergency task information and execution plan, including task type, location, time requirement and urgency.

3. A high-speed emergency control management method according to claim 1, characterized in that: The step S200 specifically includes the following steps: S201, real-time collection of diverse data including traffic surveillance videos, environmental sensors, data, vehicle GPS location information, and social media; S202: Clean, denoise and unify the format of the collected diversified data to obtain pre-processed diversified data, and store them in a distributed database.

4. A high-speed emergency control management method according to claim 1, characterized in that: The step S300 specifically includes the following steps: S301, presetting multiple processing sub-cards, and using the multiple processing sub-cards to simultaneously scan pre-processed diversified data from different sources, extracting data information directly related to the current emergency task information, and obtaining relevant data; S302, comparing the current emergency task information with the extracted relevant data to determine whether the description of the current emergency task information truly reflects the current situation; S303: If it is determined that the description of the current emergency task information truly reflects the current situation, the task information is confirmed to be accurate; otherwise, the task information is marked as inaccurate.

5. A high-speed emergency control management method according to claim 1, characterized in that: In step S400, the specific steps of constructing the event association prediction model are: Collect historical emergency task data, including emergency task information, relevant event records, execution department records, and execution results, and clean them; Extract features associated with emergency tasks from the cleaned historical emergency task data, including event type, occurrence time, location, weather conditions, and traffic conditions; Identify the mapping relationship between each feature and the corresponding accident through data statistical analysis, and use it as a training set; The classification algorithm model is adopted, and the mapping relationship between each feature and the corresponding accident is used as a training set to train the classification algorithm model to obtain an event association prediction model.

6. A high-speed emergency control management method according to claim 1, characterized in that: In step S400, the specific steps of constructing the department association model are: Collect historical emergency task data, including emergency task information, relevant event records, execution department records, and execution results, and clean them; Extract the characteristics of department type, historical response time, resource allocation, and personnel experience from the cleaned historical emergency task data; The correlation analysis algorithm is used to perform correlation analysis on the extracted department type, historical response time, resource allocation, and personnel experience characteristics, and the association rule mining algorithm model is trained by using the corresponding relationship between the analysis results and the characteristics as the training set to obtain the department association model.

7. A high-speed emergency control management method according to claim 1, characterized in that: The specific steps of step S500 are: S501, based on the probability value of the occurrence of the accident and the probability value of the backup department to be executed output by the event association prediction model and the department association model; S502, multiplying the estimated probability value of the accident by the probability value to be executed by the relevant department, and calculating the weight of the backup execution department; S503, according to the calculated weights of the backup execution departments, sorting the weights of the backup execution departments, and dividing the weight values ​​into different intervals, specifically, a first-level weight interval, a second-level weight interval, and a third-level weight interval; S504. According to the result of dividing the weight values ​​into different intervals, corresponding waiting levels are set, specifically, level one waiting, level two waiting, and level three waiting.

8. A high-speed emergency control management method according to claim 1, characterized in that: In step S700, the specific steps for the backup execution department to wait for orders according to the waiting order level are: Each backup execution department receives a corresponding level of standby orders. Among them, if a level one standby order is received, it is a level one standby department, and it shall keep more than 70% of the personnel and equipment and tools in the department in a ready state; if a level two standby order is received, it is a level two standby department, and it shall keep more than 40% of the personnel and equipment and tools in the department in a ready state; if a level three standby order is received, it is a level three standby department, and it shall keep more than 20% of the personnel and equipment and tools in the department in a ready state.

9. A high-speed emergency control management system, applied to a high-speed emergency control management method as claimed in claims 1-8, characterized in that: The management system comprises: The task acquisition module is used to obtain the current emergency task information and the corresponding execution plan from the task release center system, and extract the key semantic information in the current emergency task information and the execution plan; A data collection module, used to obtain diversified data in real time and perform pre-processing, wherein the diversified data sources include but are not limited to traffic monitoring data, environmental sensor data, vehicle GPS data and social media data; The task judgment module is used to extract data related to the current emergency task information from the pre-processed diversified data through parallel processing based on the acquired current emergency task information, and judge whether the current emergency task information is accurate; The estimation module is used to respond to the result that the current emergency task information is judged to be accurate, and then analyze and estimate the probability of the occurrence of unexpected events and the probability of the backup department to be executed through the pre-built event association estimation model and department association model according to the key semantic information of the current emergency task information; The level classification module is used to obtain the weight of the backup execution department by multiplying the estimated probability of the occurrence of unexpected events and the probability of the relevant departments to be executed, and to classify the waiting level of the backup execution department; An information transmission module is used to transmit emergency task information, execution targets and information on the waiting level of the backup execution department to the corresponding execution department and backup execution department; The instruction receiving module is used for each execution department to receive the current emergency task information and the corresponding execution plan for execution, and the backup execution department is on standby according to the waiting order level.

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