A drainage and flood control emergency response method, device, equipment and medium

Abstract

This disclosure provides a drainage and flood control emergency response method, apparatus, equipment, and medium. The method includes: collecting drainage and flood control monitoring data obtained in real time from various terminal sensing devices; using a preset AI model and drainage knowledge base, combined with the monitoring data, predicting abnormal drainage and flood control events, and automatically generating a corresponding type of structured response work order. The structured response work order carries a response responsibility person and supporting response plan intelligently recommended by the AI ​​model based on historical data in the drainage knowledge base; and performing hierarchical review and control of the generation, execution, and archiving stages of the structured response work order based on a multi-level work order control strategy to complete the corresponding drainage emergency response task. Embodiments of this disclosure can improve the response efficiency and accuracy of task dispatch, and ensure the controllability and standardization of the entire emergency response process.

Description

Technical Field

[0001] This disclosure relates to the field of drainage and flood control technology, and in particular to a drainage and flood control emergency response method, device, equipment and medium. Background Technology

[0002] With the increasing frequency of extreme weather events in recent years, strengthening urban drainage and flood control management during the flood season and ensuring urban safety have received increasing attention, leading to continuous increases in related construction and investment. Currently, most cities have established urban drainage and flood control emergency response teams and basic handling procedures to address urban flooding issues. However, existing urban drainage and flood control emergency response methods still have the following shortcomings: (1) Relying on manual experience for order dispatching results in slow response and easy omissions. Historical experience cannot be accumulated and reused, and the lack of standardized processes leads to inconsistent processing quality. (2) The acquisition and analysis of drainage and flood control monitoring data are not timely, and there is a lack of targeted, structured, and rapid response plans for emergency response, making it difficult to respond quickly and in accordance with the actual situation. Summary of the Invention

[0003] This disclosure provides a drainage and flood control emergency response method, apparatus, equipment, and medium to at least solve the problems of existing urban drainage and flood control emergency response methods that rely on manual experience for dispatching, have slow response times and are prone to omissions, cannot accumulate and reuse historical experience, and lack targeted, structured, and rapid response solutions for emergency situations.

[0004] In a first aspect, this disclosure provides a drainage and flood control emergency response method, the method comprising: Collect drainage and flood control monitoring data obtained in real time from various terminal sensing devices; Using a pre-set artificial intelligence (AI) model and drainage knowledge base, combined with the monitoring data, abnormal drainage and flood control events are predicted, and a corresponding structured disposal work order is automatically generated. The structured disposal work order carries the disposal responsible person and supporting disposal plan intelligently recommended by the AI ​​model based on historical data in the drainage knowledge base. The structured disposal work order is subject to hierarchical review and control during its generation, execution, and archiving stages based on a multi-level work order control strategy, in order to complete the corresponding drainage emergency disposal tasks.

[0005] Furthermore, the drainage and flood control monitoring data obtained in real time from the collection of various terminal sensing devices includes at least two of the following: Water level measurement data is collected using a pre-installed liquid level detector; Instantaneous flow rate, cumulative flow rate, and flow velocity monitoring data are collected using pre-installed flow meters. Data on manhole cover displacement and tilt status are collected using a pre-installed manhole cover detector. Rainfall monitoring data is collected using pre-installed rain gauges; Video image data of key locations in drainage facilities are collected through pre-installed video monitoring cameras.

[0006] Furthermore, before automatically generating corresponding structured response work orders by utilizing a preset artificial intelligence (AI) model and drainage knowledge base, combined with the monitoring data to predict drainage and flood control anomalies, the method further includes: Collect at least one of the following information related to drainage and flood control: technical standards and specifications, engineering design information, construction technology information, pipeline and equipment information, pipeline and equipment operation and maintenance information, historical event process data, historical event analysis information, and business management workflows, and construct the drainage knowledge base based on the collected information.

[0007] Furthermore, the AI ​​model includes a general basic model and a drainage and flood control-specific model; The general basic model includes at least one of a general large model and a visual large model; The drainage and flood control model includes at least one of the following: rainfall prediction model, rainstorm and flood management model (SWMM), Naive Bayes classification model, and flood-prone area clustering model.

[0008] Furthermore, the hierarchical review and control of the generation, execution, and archiving stages of the structured processing work order based on the multi-level work order control strategy specifically includes: After the structured processing work order is generated, the system receives a first confirmation or correction instruction from a Level 1 control specialist regarding the work order type, work order content, and the person responsible for processing the work order. In response to the first confirmation or correction instruction, the system updates the structured processing work order. During the execution phase of the structured handling work order, the work order path is calculated, personnel capability matching is verified, handling suggestions are verified, and processing time is estimated based on the work order content. The verification results are then pushed to the secondary control specialist. The system receives a second confirmation or correction instruction from the secondary control specialist regarding the person responsible for handling the structured handling work order, the handling method, and the processing time. In response to the second confirmation or correction instruction, the structured handling work order is updated and issued to the confirmed person responsible for handling the work order. After receiving feedback from the confirmed responsible party confirming the completion of the work order, the system obtains the evaluation input from the Level 3 control specialist regarding the handling result and quality of the structured work order, as well as the algorithm recommendation feedback input submitted by the Level 3 control specialist after comparing the work order path calculation result, personnel capability matching review result, handling suggestion review result, processing time estimation result with the actual execution of the work order. Based on the evaluation input and the algorithm recommendation feedback input, the system performs a re-dispatch operation for structured work orders that are not properly handled, closes and archives completed structured work orders, and evaluates and records the work capability of the confirmed responsible party.

[0009] Furthermore, the method also includes: Receive at least one type of data from the confirmed responsible party, collected and uploaded by the party in charge of drainage emergency response through IoT wearable devices and / or mobile terminals during the execution of the task; on-site images, on-site measured data, on-site response methods, and on-site response results. The execution status of the structured processing work order is updated in real time based on the returned data.

[0010] Furthermore, the method also includes: The actual processing data corresponding to the structured disposal work order is fed back to the drainage knowledge base so that the AI ​​model can retrieve and optimize subsequent abnormal event prediction and structured disposal work order generation. A multi-dimensional comprehensive review and analysis of multiple completed structured disposal work orders is conducted, and at least one analysis result is output, including work order duration statistics, work order type distribution, and regional flood-prone heat map.

[0011] Secondly, this disclosure provides a drainage and flood control emergency response device, the device comprising: The monitoring data acquisition module is used to collect drainage and flood control monitoring data obtained in real time from various terminal sensing devices. The disposal work order generation module is connected to the monitoring data acquisition module. It is used to predict drainage and flood control abnormal events by using a preset artificial intelligence (AI) model and drainage knowledge base, combined with the monitoring data, and automatically generate a structured disposal work order of the corresponding type. The structured disposal work order carries the disposal responsible person and supporting disposal plan intelligently recommended by the AI ​​model based on historical data in the drainage knowledge base. The hierarchical review and control mechanism, connected to the disposal work order generation module, is used to conduct hierarchical review and control of the generation, execution, and archiving stages of the structured disposal work order based on a multi-level work order control strategy, so as to complete the corresponding drainage emergency disposal task.

[0012] Thirdly, this disclosure provides an electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores one or more computer programs executable by the at least one processor, and the one or more computer programs are executed by the at least one processor to enable the at least one processor to perform the drainage and flood control emergency response method described in the first aspect above.

[0013] Fourthly, this disclosure provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the drainage and flood control emergency response method described in the first aspect.

[0014] The drainage and flood control emergency response methods, devices, equipment, and media provided in this disclosure collect real-time drainage and flood control monitoring data from various terminal sensing devices. Using a pre-set AI model and drainage knowledge base, they automatically generate structured response work orders with intelligent recommendations for responsible personnel and corresponding response plans. A multi-level work order control strategy is implemented to hierarchically review and control the generation, execution, and archiving stages of the work orders. This achieves intelligent replacement of manual experience in drainage and flood control emergency response and the structured accumulation and reuse of historical experience, improving the response efficiency and accuracy of task assignment, avoiding the risk of omissions in manual assignment, and providing timely and appropriate rapid response plans for different abnormal events through structured response work orders, ensuring the controllability and standardization of the entire emergency response process. This solves the problems of existing urban drainage and flood control emergency response methods that rely on manual experience for assignment, have slow response times and are prone to omissions, cannot accumulate and reuse historical experience, and lack targeted, structured, rapid response plans for emergency situations. Attached Figure Description

[0015] The accompanying drawings are provided to further illustrate the present disclosure and form part of the specification. They are used together with the embodiments of the present disclosure to explain the disclosure and do not constitute a limitation thereof. In the drawings: Figure 1 A flowchart of a drainage and flood control emergency response method provided in this embodiment of the disclosure; Figure 2 This is a schematic diagram of the structure of the drainage and flood control emergency response system provided in the embodiments of this disclosure; Figure 3 A block diagram of a drainage and flood control emergency response device provided in an embodiment of this disclosure; Figure 4 This is a block diagram of an electronic device provided in an embodiment of the present disclosure. Detailed Implementation

[0016] To enable those skilled in the art to better understand the technical solutions of this disclosure, exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments of this disclosure to aid understanding. These should be considered merely exemplary. Therefore, those skilled in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0017] Where there is no conflict, the various embodiments of this disclosure and the features thereof in the embodiments may be combined with each other.

[0018] As used herein, the term “and / or” includes any and all combinations of one or more related enumerated entries.

[0019] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit this disclosure. As used herein, the singular forms “a” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that when the terms “comprising” and / or “made of” are used in this specification, the presence of the stated feature, integral, step, operation, element, and / or component is specified, but the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or groups thereof is not excluded. Words such as “connected” or “linked” are not limited to physical or mechanical connections but can include electrical connections, whether direct or indirect.

[0020] Unless otherwise specified, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art. It will also be understood that terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant art and this disclosure, and will not be interpreted as having an idealized or overly formal meaning, unless expressly so defined herein.

[0021] Figure 1 A flowchart illustrating an emergency response method for drainage and flood control provided in this embodiment of the disclosure. (Refer to...) Figure 1 The method includes: Step S101: Collect drainage and flood control monitoring data obtained in real time from the sensing devices at each terminal.

[0022] Specifically, the terminal sensing devices are various monitoring devices deployed at key locations in urban drainage and flood control facilities. For example, the terminal sensing devices may include at least two of the following: level detectors, flow detectors, manhole cover detectors, rainfall monitors, and video monitoring cameras. The drainage and flood control facilities include pumping stations, sluice gates, drainage outlets, inspection wells, storm drains, and their ancillary facilities, covering key locations such as urban flood-prone areas and important nodes in drainage pipe networks.

[0023] In some embodiments, the collection of drainage and flood control monitoring data obtained in real time from each terminal sensing device includes at least two of the following: Water level measurement data is collected using a pre-installed liquid level detector; Instantaneous flow rate, cumulative flow rate, and flow velocity monitoring data are collected using pre-installed flow meters. Data on manhole cover displacement and tilt status are collected using a pre-installed manhole cover detector. Rainfall monitoring data is collected using pre-installed rain gauges; Video image data of key locations in drainage facilities are collected through pre-installed video monitoring cameras.

[0024] Specifically, level detectors can be installed at locations such as underpasses, upstream and downstream inspection wells in flood-prone areas, pump station pipelines, inlets and outlets of storage facilities, and receiving water bodies, primarily for water level measurement; flow detectors can be installed at locations such as pump station pipelines, pipeline inspection wells, and straight sections of rainwater outfalls, primarily for real-time monitoring of instantaneous flow, cumulative flow, and flow velocity; manhole cover detectors can be installed at inspection wells around flood-prone areas to monitor indicators such as manhole cover displacement and tilt; rainfall monitors can reuse existing hydrological and meteorological stations, with additional stations added as needed, primarily for monitoring precipitation; and video monitoring cameras can be installed at pump stations, sluice gates, flood-prone areas, or reused road surveillance systems, primarily for collecting real-time dynamic video information.

[0025] Specifically, the terminal sensing device collects monitoring data at different monitoring frequencies. During non-rainy periods, the monitoring data is collected at a first monitoring frequency. When rainfall or an overflow event occurs, the monitoring data is collected at a second monitoring frequency higher than the first monitoring frequency. For example, the liquid level detector's monitoring interval during non-rainy periods is no more than 15 minutes, and the data transmission frequency is once every 60 minutes; when rainfall or an overflow event occurs, the monitoring interval is 1 minute, and the data transmission interval is no more than 5 minutes.

[0026] Step S102: Using a preset AI (Artificial Intelligence) model and drainage knowledge base, combined with the monitoring data, predict abnormal drainage and flood control events, and automatically generate a corresponding structured disposal work order. The structured disposal work order carries the disposal responsible person and supporting disposal plan intelligently recommended by the AI ​​model based on historical data in the drainage knowledge base.

[0027] Specifically, monitoring data collected by terminal sensing devices is used as input data to invoke a preset AI model and drainage knowledge base for anomaly event prediction. The AI ​​model predicts the types of possible drainage and flood control anomalies based on current monitoring data and automatically triggers corresponding structured response orders based on the prediction results. These structured response orders include a corresponding response plan generated after task orchestration based on historical event information in the drainage knowledge base, and a response leader intelligently recommended based on a personnel capability matching algorithm. The AI ​​model, through configuring intelligent agents and input training data, completes inference, model training, model verification, and prediction output functions.

[0028] In some embodiments, before using a preset artificial intelligence (AI) model and drainage knowledge base, combined with the monitoring data, to predict drainage and flood control anomalies and automatically generate corresponding structured response work orders, the method further includes: Collect at least one of the following information related to drainage and flood control: technical standards and specifications, engineering design information, construction technology information, pipeline and equipment information, pipeline and equipment operation and maintenance information, historical event process data, historical event analysis information, and business management workflows, and construct the drainage knowledge base based on the collected information.

[0029] Specifically, the drainage knowledge base stores knowledge and historical experience data related to drainage and flood control. The technical standards and specifications include industry and local relevant technical standards and specifications; the engineering design information includes urban drainage engineering design data; the pipe network and equipment information includes spatial data, attribute data, and topological relationships of drainage pipe networks and equipment; the pipe network and equipment operation and maintenance information includes daily inspection records, maintenance records, and operational status data; the historical event process data and historical event analysis information include the occurrence time, location, cause, handling process, and handling results of each drainage and flood control event; and the business management workflow is used to configure standardized paths for work order flow and task orchestration. The above information is imported into the drainage knowledge base and used as training data for the AI ​​model, providing knowledge support for the AI ​​model's anomaly prediction and work order generation.

[0030] In some embodiments, the AI ​​model includes a general basic model and a drainage and flood control-specific model; The general basic model includes at least one of a general large model and a visual large model; The drainage and flood control model includes at least one of the following: rainfall prediction model, SWMM (Storm Water Management Model), Naive Bayes classification model, and flood-prone area clustering model.

[0031] Specifically, the AI ​​model achieves multi-dimensional collaborative prediction by integrating multiple types of sub-models. Among them, a general large model provides natural language understanding and generation capabilities, such as generating disposal suggestion text; the visual large model identifies information such as water accumulation area, water depth, and manhole cover status in video images captured by video monitoring cameras. The rainfall prediction model predicts future rainfall intensity and cumulative rainfall based on meteorological and historical rainfall data; the SWMM (Sudden Rainfall and Flood Management Model) simulates runoff processes and water level changes in urban drainage networks based on rainfall input, predicting flow rates and potential overflow risks at each network node; the Naive Bayes classification model categorizes current monitoring data into preset risk levels to determine the type of abnormal events; and the flood-prone zoning clustering model performs spatial clustering analysis on the geographical locations of historical flooding events to identify high-risk flooding areas. The AI ​​model uses an intelligent agent to uniformly schedule and manage these multiple sub-models. Based on the type of input data and prediction objectives, it selects one or more matching sub-models to perform inference and prediction, and merges the output results of each sub-model to generate complete prediction conclusions and work order information.

[0032] Step S103: Based on the multi-level work order control strategy, the generation, execution and archiving stages of the structured disposal work order are subject to hierarchical review and control to complete the corresponding drainage emergency disposal task.

[0033] Specifically, the multi-level work order control strategy includes first-level control, second-level control, and third-level control, corresponding to the generation, execution, and archiving stages of the structured processing work order, respectively. After the structured processing work order is automatically generated, first-level control is triggered, where a first-level control specialist confirms or corrects the generated content of the work order. After the work order enters the execution stage, second-level control is triggered, where a second-level control specialist tracks the work order processing status and performs verification and confirmation. After the work order processing is completed, third-level control is triggered, where a third-level control specialist evaluates, archives, or corrects the work order processing result. Control specialists at each level interact with the system through a control platform or terminal devices. The system responds to the input commands of each control specialist by executing corresponding work order updates, issuance, dispatch, closure, and archiving operations.

[0034] In some embodiments, the hierarchical review and control of the generation, execution, and archiving stages of the structured processing work order based on the multi-level work order control strategy specifically includes: After the structured processing work order is generated, the system receives a first confirmation or correction instruction from a Level 1 control specialist regarding the work order type, work order content, and the person responsible for processing the work order. In response to the first confirmation or correction instruction, the system updates the structured processing work order. During the execution phase of the structured handling work order, the work order path is calculated, personnel capability matching is verified, handling suggestions are verified, and processing time is estimated based on the work order content. The verification results are then pushed to the secondary control specialist. The system receives a second confirmation or correction instruction from the secondary control specialist regarding the person responsible for handling the structured handling work order, the handling method, and the processing time. In response to the second confirmation or correction instruction, the structured handling work order is updated and issued to the confirmed person responsible for handling the work order. After receiving feedback from the confirmed responsible party confirming the completion of the work order, the system obtains the evaluation input from the Level 3 control specialist regarding the handling result and quality of the structured work order, as well as the algorithm recommendation feedback input submitted by the Level 3 control specialist after comparing the work order path calculation result, personnel capability matching review result, handling suggestion review result, processing time estimation result with the actual execution of the work order. Based on the evaluation input and the algorithm recommendation feedback input, the system performs a re-dispatch operation for structured work orders that are not properly handled, closes and archives completed structured work orders, and evaluates and records the work capability of the confirmed responsible party.

[0035] Specifically, in the first-level control, the first-level control specialist mainly verifies the accuracy of work orders automatically generated by the AI ​​model, including confirming whether the work order type matches the predicted abnormal event type, whether the work order content is complete and accurate, and whether the recommended responsible person is reasonable and available. If deviations are found in the automatically generated content, the first-level control specialist corrects the work order type, work order content, or responsible person by inputting a correction command. The system responds to the confirmation or correction command to update the structured handling work order, and the updated work order enters the execution phase.

[0036] Specifically, in the second-level control, the system automatically performs the following based on the structured handling work order: work order path calculation (i.e., planning the optimal handling path based on the event location), personnel capability matching verification (i.e., checking whether the skills, qualifications, historical work evaluations, and current location of the AI-recommended handling personnel meet the requirements of this task), handling suggestion verification (i.e., checking whether the operational steps in the supporting handling plan are reasonable and feasible), and processing time estimation (i.e., estimating the completion time of this task based on the average processing time of similar work orders in the past and current traffic conditions). After completing the above calculations and verifications, the system pushes the verification results to the second-level control specialist, who then makes the final confirmation or correction of the handling personnel, handling method, and processing time. In response to the confirmation or correction instruction from the second-level control specialist, the system updates the structured handling work order and issues the updated work order to the confirmed handling personnel.

[0037] Specifically, in the third-level control, after the person responsible for handling the situation completes the on-site handling, they provide feedback on the completion of the work order through a mobile device or IoT wearable device. Upon receiving this feedback, the system obtains the evaluation input from the third-level control specialist regarding the work order handling result and quality. The third-level control specialist compares the system-recommended work order path, personnel capability matching scheme, handling suggestions, and estimated processing time with the actual execution of the work order, judging the rationality and accuracy of the recommendations, and submits algorithm recommendation feedback input accordingly. Based on the evaluation input and the algorithm recommendation feedback input, the system automatically determines whether the work order has been properly handled: if the evaluation is that it has not been properly handled, a re-dispatch operation is performed; if the evaluation is that it has been completed and is satisfactory, a closure and archiving operation is performed. Simultaneously, the system evaluates and updates the work capability of the person responsible for handling the situation based on the evaluation records of the third-level control specialist.

[0038] In some embodiments, the method further includes: Receive at least one type of data from the confirmed responsible party, collected and uploaded by the party in charge of drainage emergency response through IoT wearable devices and / or mobile terminals during the execution of the task; on-site images, on-site measured data, on-site response methods, and on-site response results. The execution status of the structured processing work order is updated in real time based on the returned data.

[0039] Specifically, after the confirmed person responsible for handling the situation arrives at the incident site, they will wear IoT wearable devices (such as smart helmets, smart bracelets, portable data acquisition terminals, etc.) and / or use a mobile application to perform the drainage emergency response task according to the corresponding response plan in the structured response work order. During the execution process, the person responsible for handling the situation will use the aforementioned devices to collect and report on-site images (such as on-site photos and short videos), on-site measured data (such as on-site water level measurements and flow rate detection values), on-site response methods (such as specific operational measures taken, such as pumping, dredging, and valve adjustment), and on-site response results (such as the receding of water and the recovery of equipment) to the system. After receiving the reported data, the system will update the execution status of the structured response work order in real time (such as pending response, in progress, completed, accepted, etc.) to achieve real-time tracking and visual monitoring of the work order execution process.

[0040] In some embodiments, the method further includes: The actual processing data corresponding to the structured disposal work order is fed back to the drainage knowledge base so that the AI ​​model can retrieve and optimize subsequent abnormal event prediction and structured disposal work order generation. A multi-dimensional comprehensive review and analysis of multiple completed structured disposal work orders is conducted, and at least one analysis result is output, including work order duration statistics, work order type distribution, and regional flood-prone heat map.

[0041] Specifically, after the structured disposal work order is closed and archived, the system automatically feeds back the actual processing data corresponding to the work order to the drainage knowledge base. This actual processing data includes all information generated throughout the entire work order process, such as: abnormal event prediction information, work order type and content, recommended and actual disposal personnel, recommended and implemented disposal plans, confirmation or correction records for each control stage, on-site feedback data, disposal results and quality evaluation, and evaluation of the disposal personnel's work capabilities. After this data is stored in the drainage knowledge base, it can be retrieved by the AI ​​model and used as sample data for subsequent model training and inference. This optimizes the accuracy of subsequent abnormal event predictions, the precision of matching disposal personnel, and the rationality of supporting disposal plans, thus forming a closed-loop iterative mechanism of "prediction-response-feedback-optimization." Simultaneously, the system conducts a multi-dimensional comprehensive review and analysis of multiple completed structured disposal work orders. For example, the system can statistically analyze the number and distribution of work orders based on their status; it can also statistically analyze the average processing time and distribution curve of each work order; it can statistically analyze the number and proportion of different types of work orders; it can statistically analyze the number of work orders, average processing time, and work evaluation by the handler; and it can perform spatial statistical analysis based on the location information of urban flooding events to generate regional flood-prone heat maps, visually displaying the distribution of urban flooding risks in various areas of the city. The results of these analyses can be used to guide subsequent drainage facility upgrades, resource allocation optimization, and emergency response plan improvements.

[0042] In one specific embodiment, the drainage and flood control emergency response method is applied to a drainage and flood control emergency response system, such as... Figure 2 As shown, the system includes: an IoT sensing module, an AI model and knowledge base module, an automated work order module, an auxiliary engine module, and a single-soldier execution module. The details of each component are as follows: 1. IoT Sensing Module: This module includes device management, access management, data acquisition, and data aggregation modules. It connects to deployed terminal sensing devices, including various liquid level detectors, flow detectors, manhole cover detectors, rainfall monitors, and video surveillance cameras, for dynamic monitoring of urban flood-prone areas, storm drains, inspection wells, key nodes in drainage pipes and channels, pumping stations, sluice gates, drainage outlets, and other ancillary facilities.

[0043] 2. AI Model and Knowledge Base Module: This module includes two parts: Drainage Knowledge Base Management and AI Model Algorithm Management. The Drainage Knowledge Base Management module includes industry and local technical standards and specifications, urban drainage engineering design information, drainage construction technology information, drainage pipe network and equipment information, drainage pipe network and equipment operation and maintenance information, historical event process data, historical event analysis information, and business management workflows. The AI ​​Model Algorithm Management module integrates general-purpose large models and visual large models, as well as machine learning algorithms such as rainfall prediction models, SWMM models, Naive Bayes classification models, and clustering models. By configuring intelligent agents and inputting training data, it completes inference, model training, model validation, and prediction output functions.

[0044] 3. Automated Work Order Module: Includes work order triggering model, three-level control and correction, work order processing, workflow management, risk prediction, anomaly warning, human resource efficiency monitoring, and comprehensive analysis, realizing full-process monitoring and management of drainage and flood control incidents.

[0045] Specifically, the work order triggering model in the automated work order module is implemented as follows: based on the factual scene data obtained by the IoT sensing module, the model capabilities of the AI ​​model and knowledge base module are called to train the work order model. Based on the current data, possible abnormal urban drainage and flood control events are predicted. Based on the prediction, the corresponding type of structured handling work order is automatically triggered. At the same time, combined with historical event information in the knowledge base, the task is arranged and the handling personnel are recommended.

[0046] The three-level control and correction in the automated work order module are as follows: (1) Level 1 intervention control. The management department's Level 1 control specialist confirms and corrects the automatically generated work orders, including the work order type, work order content, and the person responsible for handling the work order. This mainly realizes the accuracy verification of risk prediction and trigger response actions. (2) Level 2 intervention control. The management department's Level 2 control specialist tracks the work order processing status and corrects the errors. The automated work order module, based on the work order content and combined with the content constructed in steps one to three, completes the work order path calculation, personnel capability matching verification, disposal suggestion verification, and processing time estimation. The Level 2 control specialist confirms the person responsible for handling the work order, the disposal method, and the processing time. This realizes the accuracy verification of task arrangement during the handling of risk issues. (3) Level 3 intervention control. The management department's Level 3 control specialist evaluates, archives, or corrects the work order processing status. Specifically, this includes evaluating the handling results and quality of work orders, comparing recommended work order paths, personnel capability matching recommendations, handling suggestions, comparing estimated processing time with actual work order situations, submitting algorithm recommendation feedback, and providing materials for algorithm optimization. Work orders that are not properly handled can be reassigned. Completed work orders can be closed and archived. Personnel work capabilities are evaluated. Work orders with high reference value are tagged and recorded as high-value work orders. This allows for the confirmation of task completion.

[0047] The comprehensive analysis within the automated work order module specifically includes: outputting multi-dimensional drainage business work order analysis, reviewing and analyzing drainage-related events to accumulate historical experience, and providing two-way feedback for work order triggering and control. This includes statistics by work order status, average processing time, average processing time (month-on-month change), processing time distribution curve, statistics by work order type, statistics by organization distribution-work order status, statistics by equipment type-work order type, statistics by creator, statistics by receiving team-work order status, statistics by processing personnel's work order status, and statistics by the number of work orders, average processing time, and work evaluation. It also includes statistical analysis of regional flood-prone heat maps based on the location information of flooding events.

[0048] 4. Auxiliary Engine Modules, including a Big Data Service Engine, a Workflow Engine, and a GIS (Geographic Information System) Engine. The Big Data Service Engine, based on a distributed system framework, performs data extraction, cleaning, and transformation from large amounts of collected equipment data and historical ledger information. The Workflow Engine, considering the organizational structure of drainage and flood control management, work division, personnel qualifications, emergency response capabilities, and management systems, maintains business management workflows and operational standards. The GIS Engine provides map-related services for the entire system, including the geographical location, elevation, and burial depth information of pipeline equipment, as well as the deployment location information of IoT monitoring equipment.

[0049] 5. Individual soldier execution module, including mobile terminal handling service package, on-site handling, video communication, data collection, and decision support sub-modules. With the help of IoT wearable devices, it simplifies on-site operations and improves the execution efficiency of drainage and flood control emergency response actions.

[0050] Based on the above system, the drainage and flood control emergency response method may include the following steps: Step 1: Install terminal sensing devices and connect them to the IoT sensing module, including various liquid level detectors, flow detectors, manhole cover detectors, rainfall monitors, video detection cameras, etc., to dynamically monitor urban flood-prone areas, rainwater inlets, inspection wells, important nodes of drainage pipes and channels, pumping stations, sluice gates, drainage outlets and other ancillary facilities.

[0051] The liquid level detectors are installed in manholes within 100 meters upstream and downstream of underpasses and flood-prone areas, in the inlet and outlet pipes of pumping stations, in manholes of main stormwater and sewage pipes, inlet and outlet of stormwater storage facilities, outlets and overflow outlets, and in receiving water bodies. They are primarily used for water level measurement. The monitoring frequency should be no more than 5 minutes during the rainy season. During fixed monitoring, the monitoring interval should not exceed 15 minutes, and the data transmission frequency should be once every 60 minutes. During rainfall or overflow events, the monitoring interval should be 1 minute, and the data transmission interval should not exceed 5 minutes. The flow meter is installed in the inlet and outlet pipes of the storm and sewage pumping station, important storm and sewage pipe network inspection wells, storm drains, intercepting wells, overflow outlets, and inlet and outlet of receiving water bodies. It is mainly used for real-time monitoring of instantaneous flow, cumulative flow and flow velocity. The flow monitoring points in the pipes should be installed on straight pipe sections, and there should be straight pipe sections with sufficient length upstream and downstream.

[0052] The manhole cover detector is installed at the location of manhole covers in areas prone to flooding. Monitoring indicators for manhole covers should include manhole cover displacement and tilt.

[0053] Among them, the rainfall monitoring instrument utilizes the rainfall stations already deployed by the hydrological and meteorological departments. When the existing stations cannot meet the needs, additional stations should be added according to actual needs, and fixed monitoring of precipitation should be adopted.

[0054] Among them, video monitoring cameras are installed in key locations of drainage facilities such as pumping stations, sluice gates, and flood-prone areas, and existing road monitoring equipment can also be reused to collect real-time dynamic video information.

[0055] Step 2, AI Model and Knowledge Base Module Construction Steps: Collect and organize relevant industry and local technical standards and specifications, urban drainage engineering design information, drainage construction technology information, drainage pipe network and equipment information, drainage pipe network and equipment operation and maintenance information, historical event process data, historical event analysis information, etc., and import the above-mentioned data into the drainage knowledge base for storage. At the same time, the AI ​​model uses the stored data in the drainage knowledge base as training data to carry out model learning and training.

[0056] Step 3: Import business management workflows. Based on the organizational structure, division of labor, personnel qualifications, emergency response capabilities, and management systems of the drainage and flood control business management organization, maintain the business management workflows and operational standards within the workflow control engine.

[0057] Step 4, Work Order Trigger Model Training and Prediction: Based on the multi-source heterogeneous data such as weather forecasts, real-time water level monitoring, pipeline topology, historical work orders, and video surveillance integrated in Steps 1 to 3, the AI ​​model and knowledge base information are called to train the risk handling work order early warning model and the work order triggering model. This enables the prediction of possible abnormal urban drainage and flood control events based on current data, automatic triggering of corresponding types of handling work orders based on the prediction, and intelligent assignment of handling personnel.

[0058] Step 5: Level 1 Control of Work Orders. The management department's level 1 control specialist confirms and corrects automatically generated work orders, including work order type, content, and responsible party for processing. This primarily verifies the accuracy of risk predictions and triggered response actions.

[0059] Step Six: Secondary Control of Work Orders. The secondary control specialist in the management department tracks the processing status of work orders and corrects deviations. The intelligent work order module, based on the work order content and the content constructed in Steps One through Three, completes work order path calculation, personnel capability matching recommendations, handling suggestions, and processing time estimation. The secondary control specialist confirms the responsible person, handling method, and processing time for each work order. This ensures the accuracy of task scheduling during the handling of risk issues.

[0060] Step Seven, Work Order Processing. In the individual execution module, the personnel handling the incident go to the scene according to the work order details, and feed back the actual measurement data, image information, handling methods, and results to the system. The on-site data collection process utilizes IoT wearable devices to reduce the complexity of the personnel's operations and improve the efficiency of data entry. The information collected by the wearable devices is collected and processed by the IoT smart sensing module and automatically reported.

[0061] Step 8: Three-Tier Work Order Control. The management department's three-tier control specialist confirms, archives, or corrects work order processing. This includes evaluating the work order processing results and quality, comparing them with the intelligently recommended work order path, personnel capability matching recommendations, processing suggestions, and comparing the estimated processing time with the actual work order situation. Feedback is submitted to the algorithm to provide materials for algorithm optimization. Work orders that are not properly processed can be reassigned. Completed work orders can be closed and archived. Personnel work capabilities are evaluated. This confirms the degree of task completion.

[0062] Step Nine, Emergency Response Work Order Analysis. Review and analyze drainage-related events to accumulate historical experience. Output statistics by work order status, average processing time, average processing time (month-on-month change), processing time distribution curve, statistics by work order type, statistics by organization distribution-work order status, statistics by equipment type-work order type, statistics by creator, statistics by receiving team-work order status, statistics by handler's work order status, statistics by worker's work order status, and statistics by flood-prone area heat map based on flood event location information.

[0063] The drainage and flood control emergency response method provided in this disclosure collects real-time drainage and flood control monitoring data from various terminal sensing devices. It automatically generates structured response work orders with intelligent recommendations for responsible personnel and corresponding response plans using a pre-set AI model and drainage knowledge base. Based on a multi-level work order control strategy, the generation, execution, and archiving stages of the work orders are subject to hierarchical review and control. This achieves intelligent replacement of manual experience in the drainage and flood control emergency response process and the structured accumulation and reuse of historical experience, improving the response efficiency and accuracy of task assignment, avoiding the risk of omissions in manual assignment, and providing timely and appropriate rapid response plans for different abnormal events through structured response work orders, ensuring the controllability and standardization of the entire emergency response process. This solves the problems of existing urban drainage and flood control emergency response methods that rely on manual experience for assignment, have slow response times and are prone to omissions, cannot accumulate and reuse historical experience, and lack targeted structured rapid response plans for emergency situations.

[0064] It is understood that the various method embodiments mentioned above in this disclosure can be combined with each other to form combined embodiments without violating the principle and logic. Due to space limitations, this disclosure will not elaborate further. Those skilled in the art will understand that in the above methods of specific implementation, the specific execution order of each step should be determined by its function and possible internal logic.

[0065] Figure 3 This is a block diagram of a drainage and flood control emergency response device provided in an embodiment of the present disclosure.

[0066] Reference Figure 3This disclosure provides a drainage and flood control emergency response device for performing the above-described drainage and flood control emergency response method. The device includes: The monitoring data acquisition module 11 is used to collect drainage and flood control monitoring data obtained in real time from various terminal sensing devices; The disposal work order generation module 12 is connected to the monitoring data acquisition module 11. It is used to predict drainage and flood control abnormal events by using a preset artificial intelligence (AI) model and drainage knowledge base, combined with the monitoring data, and automatically generate a structured disposal work order of the corresponding type. The structured disposal work order carries the disposal responsible person and supporting disposal plan intelligently recommended by the AI ​​model based on historical data in the drainage knowledge base. The hierarchical review and control module 13 is connected to the disposal work order generation module 12 and is used to conduct hierarchical review and control on the generation, execution and archiving stages of the structured disposal work order based on a multi-level work order control strategy, so as to complete the corresponding drainage emergency disposal task.

[0067] Optionally, the monitoring data acquisition module 11 is used for at least two of the following: Water level measurement data is collected using a pre-installed liquid level detector; Instantaneous flow rate, cumulative flow rate, and flow velocity monitoring data are collected using pre-installed flow meters. Data on manhole cover displacement and tilt status are collected using a pre-installed manhole cover detector. Rainfall monitoring data is collected using pre-installed rain gauges; Video image data of key locations in drainage facilities are collected through pre-installed video monitoring cameras.

[0068] Optionally, the device further includes: The drainage knowledge base construction module is used to collect at least one of the following information related to drainage and flood control: technical standards and specifications, engineering design information, construction technology information, pipeline and equipment information, pipeline and equipment operation and maintenance information, historical event process data, historical event analysis information, and business management workflows, and to construct the drainage knowledge base based on the collected information.

[0069] Optionally, the AI ​​model includes a general basic model and a drainage and flood control-specific model; The general basic model includes at least one of a general large model and a visual large model; The drainage and flood control model includes at least one of the following: rainfall prediction model, rainstorm and flood management model (SWMM), Naive Bayes classification model, and flood-prone area clustering model.

[0070] Optionally, the hierarchical audit and control 13 includes: A primary control unit is used to receive a first confirmation or correction instruction from a primary control specialist regarding the work order type, work order content, and the person responsible for handling the structured processing work order after the structured processing work order is generated, and to update the structured processing work order in response to the first confirmation or correction instruction. The secondary control unit is used to perform work order path calculation, personnel capability matching verification, disposal suggestion verification, and processing time estimation based on the work order content during the execution phase of the structured disposal work order, and push the verification results to the secondary control specialist; receive the secondary control specialist's second confirmation or correction instruction regarding the disposal responsible person, disposal method, and processing time of the structured disposal work order; and, in response to the second confirmation or correction instruction, update the structured disposal work order and issue it to the confirmed disposal responsible person. The three-level control unit, upon receiving work order completion information from the confirmed responsible party, acquires the evaluation input of the three-level control specialist regarding the handling result and quality of the structured handling work order, as well as the algorithm recommendation feedback input submitted by the three-level control specialist after comparing the work order path calculation result, personnel capability matching review result, handling suggestion review result, processing time estimation result with the actual execution of the work order; based on the evaluation input and the algorithm recommendation feedback input, it performs a re-dispatch operation for structured handling work orders that are not properly handled, performs a closure and archiving operation for completed structured handling work orders, and evaluates and records the work capability of the confirmed responsible party.

[0071] Optionally, the device further includes: The data return receiving module is used to receive at least one type of data from the confirmed responsible person in the course of performing drainage emergency response tasks, including on-site images, on-site measured data, on-site response methods, and on-site response results, collected and uploaded by the person in charge through IoT wearable devices and / or mobile terminals. The execution status update module is used to update the execution status of the structured processing work order in real time based on the returned data.

[0072] Optionally, the device further includes: The feedback optimization module is used to feed back the actual processing data corresponding to the structured disposal work order to the drainage knowledge base, so that the AI ​​model can retrieve and optimize the subsequent abnormal event prediction and structured disposal work order generation. The comprehensive review module is used to perform multi-dimensional comprehensive review analysis on multiple completed structured disposal work orders, and output at least one analysis result, including work order duration statistics, work order type distribution, and regional flood-prone heat map.

[0073] Figure 4 This is a block diagram of an electronic device provided in an embodiment of the present disclosure.

[0074] Reference Figure 4 This disclosure provides an electronic device, which includes: at least one processor 701; at least one memory 702; and one or more I / O interfaces 703 connected between the processor 701 and the memory 702; wherein the memory 702 stores one or more computer programs that can be executed by the at least one processor 701, and the one or more computer programs are executed by the at least one processor 701 to enable the at least one processor 701 to perform the above-described drainage and flood control emergency response method.

[0075] This disclosure also provides a computer-readable storage medium storing a computer program thereon, wherein the computer program, when executed by a processor, implements the aforementioned drainage and flood control emergency response method. The computer-readable storage medium may be volatile or non-volatile.

[0076] In summary, the drainage and flood control emergency response method, apparatus, equipment, and medium provided in this disclosure collect real-time drainage and flood control monitoring data from various terminal sensing devices. Using a pre-set AI model and drainage knowledge base, they automatically generate structured response work orders containing intelligent recommendations for responsible personnel and corresponding response plans. Furthermore, based on a multi-level work order control strategy, the generation, execution, and archiving stages of the work orders are subject to hierarchical review and control. This achieves intelligent replacement of manual experience in the drainage and flood control emergency response process and the structured accumulation and reuse of historical experience. It improves the response efficiency and accuracy of task assignment, avoids the risk of omissions in manual assignment, and provides timely and appropriate rapid response plans for different abnormal events through structured response work orders, ensuring the controllability and standardization of the entire emergency response process. This solves the problems of existing urban drainage and flood control emergency response methods that rely on manual experience for assignment, have slow response times and are prone to omissions, cannot accumulate and reuse historical experience, and lack targeted structured rapid response plans for emergency situations.

[0077] Those skilled in the art will understand that all or some of the steps, systems, and apparatuses disclosed above, and their functional modules / units, can be implemented as software, firmware, hardware, or suitable combinations thereof. In hardware implementations, the division between functional modules / units mentioned above does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed collaboratively by several physical components. Some or all physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit (ASIC). Such software can be distributed on a computer-readable storage medium, which may include computer storage media (or non-transitory media) and communication media (or transient media).

[0078] As is known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information, such as computer-readable program instructions, data structures, program modules, or other data. Computer storage media includes, but is not limited to, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), static random access memory (SRAM), flash memory or other memory technologies, portable compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, it is known to those skilled in the art that communication media typically contain computer-readable program instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0079] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0080] Computer program instructions used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing the status information of the computer-readable program instructions to implement various aspects of this disclosure.

[0081] This disclosure has disclosed exemplary embodiments, and although specific terminology has been used, it is for general illustrative purposes only and should not be construed as limiting. In some instances, it will be apparent to those skilled in the art that features, characteristics, and / or elements described in conjunction with particular embodiments may be used alone, or in combination with features, characteristics, and / or elements described in conjunction with other embodiments, unless otherwise expressly indicated. Therefore, those skilled in the art will understand that various changes in form and detail may be made without departing from the scope of this disclosure as set forth by the appended claims.

Claims

1. A drainage and flood control emergency response method, characterized in that, The method includes: Collect drainage and flood control monitoring data obtained in real time from various terminal sensing devices; Using a pre-set artificial intelligence (AI) model and drainage knowledge base, combined with the monitoring data, abnormal drainage and flood control events are predicted, and a corresponding structured disposal work order is automatically generated. The structured disposal work order carries the disposal responsible person and supporting disposal plan intelligently recommended by the AI ​​model based on historical data in the drainage knowledge base. The structured disposal work order is subject to hierarchical review and control during its generation, execution, and archiving stages based on a multi-level work order control strategy, in order to complete the corresponding drainage emergency disposal tasks.

2. The method according to claim 1, characterized in that, The drainage and flood control monitoring data obtained in real time from the various terminal sensing devices includes at least two of the following: Water level measurement data is collected using a pre-installed liquid level detector; Instantaneous flow rate, cumulative flow rate, and flow velocity monitoring data are collected using pre-installed flow meters. Data on manhole cover displacement and tilt status are collected using a pre-installed manhole cover detector. Rainfall monitoring data is collected using pre-installed rain gauges; Video image data of key locations in drainage facilities are collected through pre-installed video monitoring cameras.

3. The method according to claim 1, characterized in that, Before automatically generating corresponding structured response work orders by using a preset artificial intelligence (AI) model and drainage knowledge base, combined with the monitoring data, to predict abnormal drainage and flood control events, the method further includes: Collect at least one of the following information related to drainage and flood control: technical standards and specifications, engineering design information, construction technology information, pipeline and equipment information, pipeline and equipment operation and maintenance information, historical event process data, historical event analysis information, and business management workflows, and construct the drainage knowledge base based on the collected information.

4. The method according to claim 1, characterized in that, The AI ​​model includes a general basic model and a drainage and flood control-specific model; The general basic model includes at least one of a general large model and a visual large model; The drainage and flood control model includes at least one of the following: rainfall prediction model, rainstorm and flood management model (SWMM), Naive Bayes classification model, and flood-prone area clustering model.

5. The method according to claim 1, characterized in that, The multi-level work order control strategy implements hierarchical review and control over the generation, execution, and archiving stages of the structured processing work orders, specifically including: After the structured processing work order is generated, the system receives a first confirmation or correction instruction from a Level 1 control specialist regarding the work order type, work order content, and the person responsible for processing the work order. In response to the first confirmation or correction instruction, the system updates the structured processing work order. During the execution phase of the structured handling work order, the work order path is calculated, personnel capability matching is verified, handling suggestions are verified, and processing time is estimated based on the work order content. The verification results are then pushed to the secondary control specialist. The system receives a second confirmation or correction instruction from the secondary control specialist regarding the person responsible for handling the structured handling work order, the handling method, and the processing time. In response to the second confirmation or correction instruction, the structured handling work order is updated and issued to the confirmed person responsible for handling the work order. After receiving feedback from the confirmed responsible party confirming the completion of the work order, the system obtains the evaluation input from the Level 3 control specialist regarding the handling result and quality of the structured work order, as well as the algorithm recommendation feedback input submitted by the Level 3 control specialist after comparing the work order path calculation result, personnel capability matching review result, handling suggestion review result, processing time estimation result with the actual execution of the work order. Based on the evaluation input and the algorithm recommendation feedback input, the system performs a re-dispatch operation for structured work orders that are not properly handled, closes and archives completed structured work orders, and evaluates and records the work capability of the confirmed responsible party.

6. The method according to claim 5, characterized in that, The method further includes: Receive at least one type of data from the confirmed responsible party, collected and uploaded by the party in charge of drainage emergency response through IoT wearable devices and / or mobile terminals during the execution of the task; on-site images, on-site measured data, on-site response methods, and on-site response results. The execution status of the structured processing work order is updated in real time based on the returned data.

7. The method according to claim 1, characterized in that, The method further includes: The actual processing data corresponding to the structured disposal work order is fed back to the drainage knowledge base so that the AI ​​model can retrieve and optimize subsequent abnormal event prediction and structured disposal work order generation. A multi-dimensional comprehensive review and analysis of multiple completed structured disposal work orders is conducted, and at least one analysis result is output, including work order duration statistics, work order type distribution, and regional flood-prone heat map.

8. A drainage and flood control emergency response device, characterized in that, The device includes: The monitoring data acquisition module is used to collect drainage and flood control monitoring data obtained in real time from various terminal sensing devices. The disposal work order generation module is connected to the monitoring data acquisition module. It is used to predict drainage and flood control abnormal events by using a preset artificial intelligence (AI) model and drainage knowledge base, combined with the monitoring data, and automatically generate a structured disposal work order of the corresponding type. The structured disposal work order carries the disposal responsible person and supporting disposal plan intelligently recommended by the AI ​​model based on historical data in the drainage knowledge base. The hierarchical review and control mechanism, connected to the disposal work order generation module, is used to conduct hierarchical review and control of the generation, execution, and archiving stages of the structured disposal work order based on a multi-level work order control strategy, so as to complete the corresponding drainage emergency disposal task.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores one or more computer programs that can be executed by the at least one processor, and the one or more computer programs are executed by the at least one processor to enable the at least one processor to perform the drainage and flood control emergency response method as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the drainage and flood control emergency response method as described in any one of claims 1-7.

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