Emergency scheduling adaptive control method based on dynamic feedback

Abstract

The invention relates to the technical field of emergency scheduling, in particular to an emergency scheduling adaptive control method based on dynamic feedback, which comprises the following steps: acquiring data in real time; determining a flood risk index; dividing a priority area; calculating an execution efficiency index; calculating an information reliability index; calculating a scheduling feedback index; determining an emergency area; correcting the pump distribution or threshold; and rescheduling. Multi-source data such as ponding depth, drainage pump power, road traffic rate, personnel load rate, task completion rate, unmanned aerial vehicle image frame rate and communication signal strength are collected in real time, flood risk, execution efficiency and information reliability are quantitatively analyzed, a scheduling feedback coefficient is formed, and the scheduling feedback coefficient is calculated according to dispersion and change characteristics of scheduling area distribution. The pump distribution amount or the scheduling threshold value is adjusted in a self-adaptive mode, and the problems that due to the single scheduling index and dynamic feedback lagging, emergency scheduling response lagging is caused, and resource distribution is uneven are effectively solved.

Description

Technical Field

[0001] This invention relates to the field of emergency dispatching technology, and in particular to an adaptive control method for emergency dispatching based on dynamic feedback. Background Technology

[0002] With the acceleration of urbanization and the frequent occurrence of extreme weather events, urban drainage systems, road capacity, and emergency resources are facing increasingly complex scheduling pressures. Traditional emergency management methods are unable to achieve rapid and accurate resource allocation and regional response. How to achieve efficient and coordinated emergency scheduling under the interaction of multiple regions, tasks, and factors has become an urgent challenge to be solved.

[0003] Chinese Patent Application Publication No. CN112200378A discloses a method and system for emergency dispatch and command of underground complexes under a dynamic feedback mechanism. The method includes: S1, constructing an emergency plan organization database based on disaster and emergency event analysis and emergency response capability assessment of the underground complex; S2, real-time monitoring of the underground complex's operating environment and uploading monitoring data to a diagnostic platform; S3, the diagnostic platform determines whether a disaster or emergency has occurred on-site; if the determination result is no, monitoring continues; if the determination result is yes, proceed to the next step; S4, using the monitoring data of the disaster or emergency collected in step S3 as initial response conditions input into the emergency plan organization database, the database pushes the optimal route and emergency plan, and issues emergency dispatch and command instructions. On the one hand, personnel are evacuated, and on the other hand, disasters or emergencies are dealt with; S5, the dynamic evolution of disasters or emergencies and the results of disaster or emergency response are dynamically fed back, and the reconfirmation of the disaster or emergency situation is used as the first dynamic response condition and is again used as the input in step S4; S6, the changes in emergency resources as the dispatch and command work proceeds are dynamically fed back, and the reconfirmation of emergency resource conditions is used as the second dynamic response condition and is again used as the input in step S4; S7, after dynamic feedback, steps S4-S6 are repeated; S8, the diagnostic platform determines whether the disaster or emergency has been resolved. If the dynamic evolution of the disaster or emergency tends to improve and it is finally determined that the disaster or emergency has been resolved, then the emergency dispatch and command of the underground complex is completed.

[0004] Therefore, the emergency dispatch and command method for underground complexes under the dynamic feedback mechanism has the following problems: the method mainly relies on the occurrence of disasters or emergencies and the status of emergency resources to make dispatch decisions, and the dispatch indicators are singular and lack quantitative analysis; the dynamic feedback of the method mainly targets the overall reconfirmation of the disaster status and emergency resource status, and lacks continuous monitoring and rapid response to micro-changes in various areas, which can easily lead to delayed dispatch response and uneven resource allocation. Summary of the Invention

[0005] To address this, the present invention provides an emergency dispatch adaptive control method based on dynamic feedback, which overcomes the problems of delayed emergency dispatch response and uneven resource allocation in the prior art due to the single dispatch index and the lag in dynamic feedback by acquiring multi-dimensional parameters in real time and dynamically adjusting the dispatch list.

[0006] To achieve the above objectives, the present invention provides an emergency dispatch adaptive control method based on dynamic feedback, comprising: Real-time acquisition of water depth in each area to be treated during the handling of urban flooding incidents, drainage power of drainage pumps operating based on preset pump allocation, road traffic rate, personnel load rate, task completion rate, image transmission frame rate of drones in each area to be treated, and communication signal strength. The flood risk index is determined based on the changing characteristics of the water depth and the road traffic rate. Based on the flood risk index and preset risk thresholds, several priority treatment areas are determined; The execution efficiency index is determined based on the synchronous change characteristics of the drainage power, personnel load rate, and task completion rate of the priority treatment area. The information reliability index is determined based on the fluctuation range of the image return frame rate and the communication signal strength. Calculate the scheduling feedback coefficient based on the execution efficiency index and the information reliability index; Several emergency dispatch areas are determined based on the dispatch feedback coefficient, the preset dispatch feedback threshold, the water depth, the drainage power, the personnel load rate, and the task completion rate. The preset pump allocation of the drainage pumps in the priority disposal area is adjusted according to the changes in the distribution characteristics of all the emergency dispatch areas within the preset adjustment time period, or the preset dispatch feedback threshold is adjusted. Scheduling is performed based on the scheduling feedback coefficients of all emergency scheduling areas, which are re-determined after correcting the preset pump allocation or the preset scheduling feedback threshold.

[0007] Furthermore, the process of determining the flood risk index based on the changing characteristics of the water depth and the road traffic capacity includes: Calculate the rate of change of the water depth from the initial time to each time within the preset first monitoring period to obtain several depth change rates; Calculate the rate of change of the road traffic rate from the initial time to each time within the same preset first monitoring period to obtain several road traffic rate changes; The flood risk index is determined based on all the aforementioned depth change rates and all the aforementioned road traffic change rates.

[0008] Furthermore, the process of determining the flood risk index based on all the aforementioned depth change rates and all the aforementioned road traffic change rates includes: The Pearson correlation coefficients of all the aforementioned depth change rates and all the aforementioned road traffic change rates are calculated to obtain the flood risk index.

[0009] Furthermore, the process of determining several priority treatment areas based on the flood risk index and preset risk thresholds includes: When the flood risk index is greater than the preset risk threshold, the corresponding area to be treated is determined to be the priority treatment area.

[0010] Furthermore, the process of determining the execution efficiency index based on the synchronous change characteristics of the drainage power, personnel load rate, and task completion rate of the priority treatment area includes: The drainage power of each priority treatment area is normalized by minimum-maximum value to obtain a normalized power value. The load rate of each person is normalized by minimum-maximum based on the load rate of all the priority handling areas to obtain a normalized load rate value. The completion rates of each task are normalized by minimum-maximum based on the completion rates of all the priority processing areas to obtain a normalized completion rate value. The execution efficiency index is determined based on the power normalization value, the load rate normalization value, and the completion rate normalization value within the preset second monitoring period.

[0011] Furthermore, the process of determining the execution efficiency index based on the power normalization value, the load rate normalization value, and the completion rate normalization value within the preset second monitoring period includes: Calculate the average value of all power normalization values, the average value of all load rate normalization values, and the average value of all completion rate normalization values ​​within the preset second monitoring period to obtain the power index value, load index value, and completion index value, respectively. The power index value, the load index value, and the completion index value are weighted and summed to obtain the index level index. The rate of change of all the power normalization values, all the load rate normalization values, and all the completion rate normalization values ​​from the initial time to each time within the preset second monitoring period is calculated respectively to obtain the power normalization speed change sequence, the load normalization speed change sequence, and the completion normalization speed change sequence. The Pearson correlation coefficients of the power normalized speed change sequence and the load normalized speed change sequence, the Pearson correlation coefficients of the power normalized speed change sequence and the completed normalized speed change sequence, and the Pearson correlation coefficients of the load normalized speed change sequence and the completed normalized speed change sequence are calculated respectively to obtain the first synchronization coefficient, the second synchronization coefficient, and the third synchronization coefficient. Calculate the average of the first synchronization coefficient, the second synchronization coefficient, and the third synchronization coefficient to obtain the synchronization index; The execution efficiency index is obtained by weighted summation of the indicator level index and the synchronicity index.

[0012] Furthermore, the process of determining the information reliability index based on the fluctuation range of the image return frame rate and the communication signal strength includes: The image transmission frame rate of each priority processing area within the same preset second monitoring period is normalized by minimum-maximum value to obtain several frame rate normalization values. The communication signal strength of each priority handling area within the same preset second monitoring period is subjected to minimum-maximum normalization processing to obtain several signal normalization values; Calculate the standard deviation of all the frame rate normalization values ​​to obtain the frame rate fluctuation value; Calculate the standard deviation of all the normalized values ​​of the signals to obtain the signal fluctuation value; The information reliability index is obtained by weighted summation of the frame rate fluctuation value and the signal fluctuation value.

[0013] Furthermore, the process of calculating the scheduling feedback coefficient based on the execution efficiency index and the information reliability index includes: The execution efficiency index and the information reliability index are weighted and summed to obtain the scheduling feedback coefficient.

[0014] Furthermore, the process of determining several emergency dispatch areas based on the dispatch feedback coefficient, the preset dispatch feedback threshold, the water depth, the drainage power, the personnel load rate, and the task completion rate includes: When the scheduling feedback coefficient is greater than the preset scheduling feedback threshold, the priority processing area is determined to be a candidate area; The ratio of the water depth to the drainage power in the candidate region is calculated to obtain the drainage pressure index; When the drainage pressure index is greater than a preset pressure index threshold, the candidate region is determined to be the first priority region; When the drainage pressure index is less than or equal to a preset pressure index threshold, the candidate region is determined to be the second priority region; The feasibility index is determined based on the synchronous change characteristics of the personnel load rate and the task completion rate in the second priority area; When the feasibility index is greater than the preset feasible threshold, both the first priority area and the second priority area are determined to be the emergency dispatch area; When the feasibility index is less than or equal to the preset feasibility threshold, the first priority area is determined to be the emergency dispatch area.

[0015] Furthermore, the process of adjusting the preset pump allocation of the drainage pumps in the priority handling area, or adjusting the preset dispatch feedback threshold, based on the changes in the distribution characteristics of all the emergency dispatch areas within the preset adjustment period includes: Obtain the Euclidean distance between the two-dimensional coordinates of all the emergency dispatch areas and the preset reference coordinates at each time within the preset correction time period to obtain several distribution distances; Calculate the standard deviation of all the distribution distances to obtain the distribution dispersion; Count the timestamps where the distribution dispersion is greater than a preset dispersion threshold within the preset correction time period to obtain several distribution times. Calculate the difference between each of the distribution times and the initial time of the preset correction time to obtain several distribution times; Calculate the standard deviation of all the distribution durations to obtain the dispersion of variation; When the variation dispersion is greater than the maximum value of the preset variation dispersion range, the preset pump allocation of the drainage pump in the priority treatment area is increased according to the relative deviation between the variation dispersion and the maximum value of the preset variation dispersion range. When the variation dispersion is less than the minimum value of the preset variation dispersion range, the preset scheduling feedback threshold is reduced according to the relative deviation of the variation dispersion being less than the minimum value of the preset variation dispersion range.

[0016] Compared with existing technologies, the beneficial effects of this invention lie in its ability to quantify and analyze flood risk, execution efficiency, and information reliability by collecting multi-source data in real time, including water depth, drainage pump power, road traffic capacity, personnel load rate, task completion rate, UAV image frame rate, and communication signal strength. This generates a scheduling feedback coefficient, thereby dynamically determining emergency dispatch areas and priority response areas. Based on the dispersion and variation characteristics of the dispatch area distribution, the system adaptively adjusts the pump allocation or scheduling threshold to ensure that drainage capacity is coordinated and matched with regional water accumulation, road traffic conditions, and personnel load, while simultaneously guaranteeing task completion efficiency and information transmission stability. Through comprehensive evaluation and feedback correction of multiple indicators, this method can achieve rational resource allocation between regions, balanced task execution, and reliable information pathways, significantly improving the response speed and efficiency of urban flood emergency response. It also avoids scheduling imbalances caused by abnormal single parameters, ensuring the stability and efficiency of the system in complex dynamic environments. This effectively solves the problems of delayed emergency dispatch response and uneven resource allocation caused by single scheduling indicators and lagging dynamic feedback.

[0017] Furthermore, by calculating the rate of change of water depth and road traffic capacity in each area to be treated within a preset monitoring period, the dynamic trend of water situation changes and traffic conditions can be quantified, thereby generating a flood risk index. This index combines the relationship between changes in water depth and changes in road traffic capacity, reflecting the degree of impact of water conditions on traffic flow. This enables the system to accurately identify high-risk areas, prioritize the allocation of drainage resources and dispatch personnel, achieve scientific and dynamic optimization of emergency response in each area, and improve the overall efficiency and effectiveness of flood event response.

[0018] Furthermore, by calculating the Pearson correlation coefficient between the rate of change of water depth and the rate of change of road traffic capacity in each area to be treated, the dynamic relationship between water situation changes and traffic flow is quantified into a flood risk index. This not only reflects the direct impact of water accumulation on road traffic, but also reflects the synchronous changes in water conditions and traffic conditions between different areas. This enables the system to scientifically identify high-risk areas, prioritize their handling, and optimize resource allocation, thereby improving the accuracy and efficiency of emergency response to urban flooding events.

[0019] Furthermore, by comparing the flood risk index of each area to be addressed with a preset risk threshold, high-risk areas can be scientifically distinguished from general areas. When the flood risk index exceeds the threshold, the system automatically identifies the area as a priority area for handling, enabling rapid identification and priority dispatch of areas with severe waterlogging, traffic disruptions, or urgent tasks. This method quantifies the combined impact of water situation changes and traffic conditions into actionable indicators, making resource allocation more rational and improving the efficiency and response speed of overall emergency response.

[0020] Furthermore, by performing minimum-maximum normalization on the drainage capacity, personnel load rate, and task completion rate of priority response areas, and combining this with data analysis over a preset monitoring period, the matching degree between resource input and execution results in each area can be quantified, generating an execution efficiency index. This index reflects the synchronous changes in drainage equipment capacity, personnel distribution, and task completion status, enabling the system to scientifically assess the overall efficiency of emergency response, optimize resource scheduling and operational coordination, thereby improving the response speed and overall effectiveness in each area.

[0021] Furthermore, by calculating the average values ​​of drainage power, personnel load rate, and task completion rate within a preset second monitoring period and weighting them according to their respective weights, an indicator level index is obtained. Simultaneously, the rate of change of each normalized value is analyzed, and their synchronicity is calculated to obtain a synchronicity index. Finally, these two are combined to generate an execution efficiency index. This index comprehensively reflects the overall level and coordination of resource input and task completion in each region, enabling the system to quantify the execution effect in different regions, accurately identify the level of resource utilization efficiency and operational coordination, thereby guiding priority scheduling and dynamic optimization, and improving the scientific nature and response speed of flood event handling.

[0022] Furthermore, by normalizing the image transmission frame rate and communication signal strength of priority areas and calculating their fluctuation amplitude, and then weighting and summing them to generate an information reliability index, the stability and continuity of information transmission in each area can be quantified. This index reflects the temporal consistency and fluctuation of image and communication data, enabling the system to identify areas where information transmission is interfered with, ensuring the reliability of the data foundation for UAV monitoring and command decision-making, thereby improving the accuracy and response efficiency of emergency dispatch.

[0023] Furthermore, by generating a scheduling feedback coefficient through a weighted summation of the execution efficiency index and the information reliability index, the actual efficiency of drainage pump operations and the stability of information transmission can be comprehensively reflected in a single indicator. This coefficient can measure the overall scheduling status of each priority response area in real time, enabling the system to consider both task execution capability and information reliability when making scheduling decisions, thereby optimizing the emergency response sequence and improving the overall efficiency and accuracy of flood event handling.

[0024] Furthermore, by comprehensively analyzing various parameters such as dispatch feedback coefficient, water depth, drainage power, personnel load rate, and task completion rate, emergency dispatch areas can be scientifically divided. Candidate areas are differentiated into different priorities based on the drainage pressure index. The feasibility index is then calculated by combining the synchronous changes in personnel load rate and task completion rate, thereby rationally determining the dispatch sequence and resource allocation for each area. This method can balance drainage efficiency, personnel carrying capacity, and task completion status in dynamic flood scenarios, achieving coordinated optimization of drainage pump dispatch and personnel task execution. This ensures that the dispatch plan is both timely and robust, adaptable to real-time changes in water accumulation and resource load in each area, and guarantees a scientific and orderly emergency response process.

[0025] Furthermore, by quantitatively analyzing the changes in the distribution characteristics of each emergency dispatch area within a preset correction period, dynamic adjustments can be made to the allocation of drainage pumps and the dispatch feedback threshold. When the dispersion of regional distribution and the dispersion of changes are high, the preset pump allocation of drainage pumps is increased to improve drainage capacity and ensure that the water accumulation is quickly eliminated; when the dispersion is low, the dispatch feedback threshold is appropriately reduced to avoid excessive concentration of resources or excessive dispatching. This achieves a coordinated dynamic balance between water depth, drainage power, personnel load rate, and task completion rate, realizing mutual constraints and compensation among various parameters, and ensuring the efficiency and reliability of emergency dispatch. Attached Figure Description

[0026] Figure 1 This is a flowchart of the emergency dispatch adaptive control method based on dynamic feedback in this embodiment; Figure 2 This embodiment defines the logic diagram for determining the priority processing area; Figure 3 This embodiment defines the logic diagram for determining the emergency dispatch area; Figure 4 The logic diagram for adjusting the preset pump allocation in this embodiment is shown. Detailed Implementation

[0027] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0028] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0029] Please see Figure 1The diagram shown is a flowchart of the emergency dispatch adaptive control method based on dynamic feedback in this embodiment. This embodiment provides an emergency dispatch adaptive control method based on dynamic feedback, including: Real-time acquisition of water depth in each area to be treated during the handling of urban flooding incidents, drainage power of drainage pumps operating based on preset pump allocation, road traffic rate, personnel load rate, task completion rate, image transmission frame rate of drones in each area to be treated, and communication signal strength. The flood risk index is determined based on the changing characteristics of the water depth and the road traffic rate. Based on the flood risk index and preset risk thresholds, several priority treatment areas are determined; The execution efficiency index is determined based on the synchronous change characteristics of the drainage power, personnel load rate, and task completion rate of the priority treatment area. The information reliability index is determined based on the fluctuation range of the image return frame rate and the communication signal strength. Calculate the scheduling feedback coefficient based on the execution efficiency index and the information reliability index; Several emergency dispatch areas are determined based on the dispatch feedback coefficient, the preset dispatch feedback threshold, the water depth, the drainage power, the personnel load rate, and the task completion rate. The preset pump allocation of the drainage pumps in the priority disposal area is adjusted according to the changes in the distribution characteristics of all the emergency dispatch areas within the preset adjustment time period, or the preset dispatch feedback threshold is adjusted. Scheduling is performed based on the scheduling feedback coefficients of all emergency scheduling areas, which are re-determined after correcting the preset pump allocation or the preset scheduling feedback threshold.

[0030] In this embodiment, in response to sudden urban flooding events, the city is divided into multiple areas to be dealt with. Each area may have complex situations such as water accumulation, traffic congestion, and uneven distribution of people. To achieve dynamic scheduling, the system acquires key parameters of each area to be treated in real time: For each area, the water depth is measured in real time by level sensors, ultrasonic sensors, or IoT water level monitoring devices deployed at low-lying or flood-prone areas to reflect changes in water conditions; the drainage power of the drainage pumps is obtained through the flow meters, current sensors, and pump controllers on the pumps within the area, and the actual drainage capacity is calculated in conjunction with the preset pump allocation; the road traffic rate is collected by traffic cameras, geomagnetic induction devices, or intelligent traffic monitors installed on roads in each area to collect traffic flow, speed, and congestion status, and the traffic efficiency is calculated in conjunction with the traffic management system algorithm; the personnel load rate is obtained through data from positioning terminals, access control systems, mobile devices, and inspection records within the area to reflect the density and distribution of personnel participating in emergency response; the task completion rate is analyzed through the on-site operation record system or drone patrol data to quantify the progress of each task; the image transmission frame rate of drones in each area is monitored in real time through drone communication modules and wireless networks, and the information acquisition speed is evaluated by combining frame rate changes; the communication signal strength is measured through wireless receivers, base stations, or drone communication modules deployed in each area to evaluate the stability and reliability of information transmission. By collecting and monitoring real-time, multi-source data from multiple areas to be addressed, the system can comprehensively grasp the dynamic situation of flooding events throughout the city, providing accurate basis for subsequent flood risk assessment, priority area determination, and adaptive allocation of emergency resources, thereby achieving coordinated handling in various areas and improving overall response efficiency.

[0031] In this embodiment, the scheduling feedback coefficient of each emergency scheduling area is recalculated based on the corrected preset pump allocation or preset scheduling feedback threshold, and all emergency scheduling areas are sorted from largest to smallest according to the scheduling feedback coefficient to form a scheduling priority list; drainage pump resources, personnel and drone patrol tasks are allocated in sequence according to this list, thereby achieving priority handling of high-risk areas, maximizing resource utilization and improving task completion efficiency.

[0032] The preset pump allocation refers to the initial drainage capacity allocated to each drainage pump by the system. It depends on the historical water accumulation in each area to be treated, the number of drainage pumps, and the rated power of the pumps. It is usually set between 50% and 100% of the rated flow rate of the pumps. In this embodiment, it is set to 80% of the rated flow rate, which can ensure the basic drainage capacity while leaving room for subsequent dynamic adjustments. The preset risk threshold is the critical value of the flood risk index for determining whether an area is a priority treatment area. It depends on the historical changes in the water depth and road traffic rate of the area and the city's drainage capacity. It is usually set between 0.6 and 0.9. In this embodiment, it is set to 0.75, which can distinguish between high-risk areas and general areas and achieve priority scheduling. The preset scheduling feedback threshold is the critical value of the scheduling feedback coefficient that triggers the correction of emergency scheduling areas. It depends on the historical distribution of the execution efficiency index and the information reliability index. It is usually set between 0.5 and 0.8. In this embodiment, it is set to 0.65, which can be adjusted in a timely manner when the system scheduling efficiency decreases or the information reliability is insufficient.

[0033] By collecting multi-source data in real time, including water depth, drainage pump power, road traffic capacity, personnel load rate, task completion rate, UAV image frame rate, and communication signal strength, the system quantifies and analyzes flood risk, execution efficiency, and information reliability to form a dispatch feedback coefficient, thereby dynamically determining emergency dispatch areas and priority response areas. Based on the dispersion and variation characteristics of the dispatch area distribution, the system adaptively adjusts pump allocation or dispatch thresholds to coordinate drainage capacity with regional water accumulation, road traffic conditions, and personnel load, while ensuring task completion efficiency and information transmission stability. Through comprehensive evaluation and feedback correction of multiple indicators, this method can achieve rational resource allocation between regions, balanced task execution, and reliable information channels, significantly improving the response speed and efficiency of urban flood emergency response. It also avoids dispatch imbalances caused by abnormal single parameters, ensuring the system's stability and efficiency in complex dynamic environments. This effectively solves the problems of delayed emergency dispatch response and uneven resource allocation caused by single dispatch indicators and lagging dynamic feedback.

[0034] Specifically, the process of determining the flood risk index based on the changing characteristics of the water depth and the road traffic capacity includes: Calculate the rate of change of the water depth from the initial time to each time within the preset first monitoring period to obtain several depth change rates; Calculate the rate of change of the road traffic rate from the initial time to each time within the same preset first monitoring period to obtain several road traffic rate changes; The flood risk index is determined based on all the aforementioned depth change rates and all the aforementioned road traffic change rates.

[0035] The preset first monitoring cycle refers to the time interval used to calculate the rate of change of water depth and road traffic capacity. It depends on the development speed of urban flooding events and the frequency of data collection. It is usually set between 5 minutes and 30 minutes. In this embodiment, it is set to 10 minutes, which can capture the trend of water conditions and traffic changes in a timely manner and provide an accurate basis for the calculation of flood risk index.

[0036] By calculating the rate of change of water depth and road traffic capacity in each area to be treated within a preset monitoring period, the dynamic trends of water situation changes and traffic conditions can be quantified, thereby generating a flood risk index. This index combines the relationship between changes in water depth and changes in road traffic capacity, reflecting the degree of impact of water conditions on traffic flow. This enables the system to accurately identify high-risk areas, prioritize the allocation of drainage resources and dispatch personnel, achieve scientific and dynamic optimization of emergency response in each area, and improve the overall efficiency and effectiveness of flood event response.

[0037] Specifically, the process of determining the flood risk index based on all the aforementioned depth change rates and all the aforementioned road traffic change rates includes: The Pearson correlation coefficients of all the aforementioned depth change rates and all the aforementioned road traffic change rates are calculated to obtain the flood risk index.

[0038] By calculating the Pearson correlation coefficient between the rate of change of water depth and the rate of change of road traffic capacity in each area to be treated, the dynamic relationship between water situation changes and traffic flow is quantified into a flood risk index. This not only reflects the direct impact of water accumulation on road traffic, but also reflects the synchronous changes in water conditions and traffic conditions between different areas. This enables the system to scientifically identify high-risk areas, prioritize their handling, and optimize resource allocation, thereby improving the accuracy and efficiency of emergency response to urban flooding events.

[0039] Please see Figure 2 As shown, this is a logic diagram for determining priority treatment areas in this embodiment. In this embodiment, the process of determining several priority treatment areas based on the flood risk index and preset risk threshold includes: When the flood risk index is greater than the preset risk threshold, the corresponding area to be treated is determined to be the priority treatment area.

[0040] By comparing the flood risk index of each area to be addressed with a preset risk threshold, high-risk areas can be scientifically distinguished from general areas. When the flood risk index exceeds the threshold, the system automatically identifies the area as a priority area for handling, enabling rapid identification and priority dispatch of areas with severe waterlogging, traffic disruptions, or urgent tasks. This method quantifies the combined impact of water situation changes and traffic conditions into actionable indicators, making resource allocation more rational and improving the efficiency and response speed of overall emergency response.

[0041] Specifically, the process of determining the execution efficiency index based on the synchronous change characteristics of the drainage power, personnel load rate, and task completion rate of the priority treatment area includes: The drainage power of each priority treatment area is normalized by minimum-maximum value to obtain a normalized power value. The load rate of each person is normalized by minimum-maximum based on the load rate of all the priority handling areas to obtain a normalized load rate value. The completion rates of each task are normalized by minimum-maximum based on the completion rates of all the priority processing areas to obtain a normalized completion rate value. The execution efficiency index is determined based on the power normalization value, the load rate normalization value, and the completion rate normalization value within the preset second monitoring period.

[0042] The preset second monitoring cycle refers to the time interval used to monitor changes in drainage power, personnel load rate, and task completion rate. It depends on the duration of the emergency response task and the data update frequency, and is usually set between 5 minutes and 20 minutes. In this embodiment, it is set to 10 minutes, which can reflect the dynamic changes in the execution efficiency of each area in a timely manner and provide an accurate basis for scheduling optimization.

[0043] By performing minimum-maximum normalization on the drainage capacity, personnel load rate, and task completion rate of priority response areas, and combining this with data analysis over a preset monitoring period, the matching degree between resource input and execution results in each area can be quantified, generating an execution efficiency index. This index reflects the synchronous changes in drainage equipment capacity, personnel distribution, and task completion status, enabling the system to scientifically assess the overall efficiency of emergency response, optimize resource scheduling and operational coordination, thereby improving the response speed and overall effectiveness in each area.

[0044] Specifically, the process of determining the execution efficiency index based on the power normalization value, the load rate normalization value, and the completion rate normalization value within a preset second monitoring period includes: Calculate the average value of all power normalization values, the average value of all load rate normalization values, and the average value of all completion rate normalization values ​​within the preset second monitoring period to obtain the power index value, load index value, and completion index value, respectively. The power index value, the load index value, and the completion index value are weighted and summed to obtain the index level index. The rate of change of all the power normalization values, all the load rate normalization values, and all the completion rate normalization values ​​from the initial time to each time within the preset second monitoring period is calculated respectively to obtain the power normalization speed change sequence, the load normalization speed change sequence, and the completion normalization speed change sequence. The Pearson correlation coefficients of the power normalized speed change sequence and the load normalized speed change sequence, the Pearson correlation coefficients of the power normalized speed change sequence and the completed normalized speed change sequence, and the Pearson correlation coefficients of the load normalized speed change sequence and the completed normalized speed change sequence are calculated respectively to obtain the first synchronization coefficient, the second synchronization coefficient, and the third synchronization coefficient. Calculate the average of the first synchronization coefficient, the second synchronization coefficient, and the third synchronization coefficient to obtain the synchronization index; The execution efficiency index is obtained by weighted summation of the indicator level index and the synchronization index, wherein the weight corresponding to the indicator level index is a preset indicator level weight, and the weight corresponding to the synchronization index is a preset synchronization weight.

[0045] The power index value, the load index value, and the completion index value are weighted and summed to obtain the index level index, wherein the weight corresponding to the power index value is a preset power index weight, the weight corresponding to the load index value is a preset load index weight, and the weight corresponding to the completion index value is a preset completion index weight. The preset indicator level weight refers to the weight used to measure the importance of the indicator level index when calculating the execution efficiency index. It depends on the degree of influence of drainage power, personnel load rate, and task completion rate on the overall emergency efficiency. It is usually set between 0.4 and 0.7. In this embodiment, it is set to 0.6, which can reasonably reflect the contribution of resource input and task completion level to the overall efficiency. The preset synchronicity weight refers to the weight used to measure the importance of the synchronicity index when calculating the execution efficiency index. It depends on the influence of the consistency of drainage, personnel, and task coordination in each area on the emergency response effect. It is usually set between 0.3 and 0.6. In this embodiment, it is set to 0.4, which can highlight the impact of work coordination on the overall scheduling efficiency.

[0046] The preset power index weight refers to the weight used to measure the importance of drainage power when calculating the index level. It depends on the degree of impact of drainage capacity on the overall emergency response efficiency and is usually set between 0.3 and 0.5. In this embodiment, it is set to 0.4, which can reasonably reflect the contribution of drainage resources to task completion. The preset load index weight refers to the weight used to measure the importance of personnel load rate when calculating the index level. It depends on the impact of personnel allocation on execution efficiency and is usually set between 0.2 and 0.4. In this embodiment, it is set to 0.3, which can reflect the role of personnel scheduling in operational efficiency. The preset completion index weight refers to the weight used to measure the importance of task completion rate when calculating the index level. It depends on the impact of task completion on the overall emergency response efficiency and is usually set between 0.3 and 0.5. In this embodiment, it is set to 0.3, which can highlight the contribution of actual task completion to efficiency assessment.

[0047] By calculating the average values ​​of drainage power, personnel load rate, and task completion rate within a preset second monitoring period and weighting them accordingly, an indicator level index is obtained. Simultaneously, the rate of change of each normalized value is analyzed, and their synchronicity is calculated to obtain a synchronicity index. Finally, these two indices are combined to generate an execution efficiency index. This index comprehensively reflects the overall level and coordination of resource input and task completion in each region, enabling the system to quantify the execution effect in different regions, accurately identify the level of resource utilization efficiency and operational collaboration, thereby guiding priority scheduling and dynamic optimization, and improving the scientific nature and response speed of flood event handling.

[0048] Specifically, the process of determining the information reliability index based on the fluctuation range of the image return frame rate and the communication signal strength includes: The image transmission frame rate of each priority processing area within the same preset second monitoring period is normalized by minimum-maximum value to obtain several frame rate normalization values. The communication signal strength of each priority handling area within the same preset second monitoring period is subjected to minimum-maximum normalization processing to obtain several signal normalization values; Calculate the standard deviation of all the frame rate normalization values ​​to obtain the frame rate fluctuation value; Calculate the standard deviation of all the normalized values ​​of the signals to obtain the signal fluctuation value; The information reliability index is obtained by weighted summation of the frame rate fluctuation value and the signal fluctuation value, wherein the weight corresponding to the frame rate fluctuation value is a preset frame rate fluctuation weight, and the weight corresponding to the signal fluctuation value is a preset signal fluctuation weight.

[0049] The preset frame rate fluctuation weight is used to measure the importance of image transmission frame rate fluctuation when calculating the information reliability index. It depends on the impact of image data stability on monitoring accuracy and is usually set between 0.4 and 0.6. In this embodiment, it is set to 0.5, which can reasonably reflect the contribution of image transmission to information reliability. The preset signal fluctuation weight is used to measure the importance of communication signal strength fluctuation when calculating the information reliability index. It depends on the impact of communication stability on data transmission continuity and is usually set between 0.4 and 0.6. In this embodiment, it is set to 0.5, which can highlight the role of communication quality in overall information reliability.

[0050] By normalizing the image transmission frame rate and communication signal strength of priority areas and calculating their fluctuation amplitude, and then weighting and summing them to generate an information reliability index, the stability and continuity of information transmission in each area can be quantified. This index reflects the temporal consistency and fluctuation of image and communication data, enabling the system to identify areas where information transmission is interfered with, ensuring the reliability of the data foundation for UAV monitoring and command decision-making, thereby improving the accuracy and response efficiency of emergency dispatch.

[0051] Specifically, the process of calculating the scheduling feedback coefficient based on the execution efficiency index and the information reliability index includes: The execution efficiency index and the information reliability index are weighted and summed to obtain the scheduling feedback coefficient, wherein the weight corresponding to the execution efficiency index is a preset execution efficiency index weight, and the weight corresponding to the information reliability index is a preset reliability index weight.

[0052] The preset execution efficiency index weight refers to the weight used to measure the importance of drainage pump operation efficiency to the overall scheduling feedback when calculating the scheduling feedback coefficient. It depends on the impact of task execution efficiency on the emergency response effect and is usually set between 0.4 and 0.6. In this embodiment, it is set to 0.5, which can reasonably reflect the contribution of efficiency to scheduling decisions. The preset reliability index weight refers to the weight used to measure the importance of information transmission stability to the overall scheduling feedback when calculating the scheduling feedback coefficient. It depends on the impact of data reliability on emergency scheduling decisions and is usually set between 0.4 and 0.6. In this embodiment, it is set to 0.5, which can highlight the role of information reliability in scheduling accuracy.

[0053] By generating a scheduling feedback coefficient through a weighted summation of the execution efficiency index and the information reliability index, the actual efficiency of drainage pump operations and the stability of information transmission can be comprehensively reflected in a single indicator. This coefficient can measure the overall scheduling status of each priority response area in real time, enabling the system to consider both task execution capability and information reliability when making scheduling decisions, thereby optimizing the emergency response sequence and improving the overall efficiency and accuracy of flood event handling.

[0054] Please see Figure 3 As shown, this is a logic diagram for determining emergency dispatch areas in this embodiment. In this embodiment, the process of determining several emergency dispatch areas based on the dispatch feedback coefficient, the preset dispatch feedback threshold, the water depth, the drainage power, the personnel load rate, and the task completion rate includes: When the scheduling feedback coefficient is greater than the preset scheduling feedback threshold, the priority processing area is determined to be a candidate area; The ratio of the water depth to the drainage power in the candidate region is calculated to obtain the drainage pressure index; When the drainage pressure index is greater than a preset pressure index threshold, the candidate region is determined to be the first priority region; When the drainage pressure index is less than or equal to a preset pressure index threshold, the candidate region is determined to be the second priority region; The feasibility index is determined based on the synchronous change characteristics of the personnel load rate and the task completion rate in the second priority area; When the feasibility index is greater than the preset feasible threshold, both the first priority area and the second priority area are determined to be the emergency dispatch area; When the feasibility index is less than or equal to the preset feasibility threshold, the first priority area is determined to be the emergency dispatch area; The process of determining the feasibility index based on the synchronous change characteristics of the personnel load rate and the task completion rate in the second priority area includes: Calculate the average value of all load rate normalization values ​​and the average value of all completion rate normalization values ​​within the preset third monitoring period to obtain the load feasible value and the completion feasible value, respectively. The load feasibility value and the completion feasibility value are weighted and summed to obtain the indicator feasibility index; The rate of change of all the normalized load rate values ​​and all the normalized completion rate values ​​from the initial time to each time within the preset third monitoring period are calculated respectively to obtain the feasible load speed change sequence and the feasible completion speed change sequence. Calculate the Pearson correlation coefficient between the load feasible speed change sequence and the completed feasible speed change sequence to obtain the feasible synchronization index; The feasibility index is obtained by weighted summation of the indicator feasibility index and the feasible synchronization index, wherein the weight corresponding to the indicator feasibility index is a preset indicator feasibility weight, and the weight corresponding to the feasible synchronization index is a preset feasible synchronization weight.

[0055] The load feasibility value and the completion feasibility value are weighted and summed to obtain the index feasibility index, wherein the weight corresponding to the load feasibility value is a preset load feasibility weight, and the weight corresponding to the completion feasibility value is a preset completion feasibility weight.

[0056] The preset pressure index threshold is a critical value for measuring the water pressure per unit drainage power. It depends on the matching relationship between the urban drainage system capacity, water depth, and drainage pump power, and is usually set between 0.5 s / m and 2.0 s / m. In this embodiment, it is set to 1.2 s / m, which can effectively distinguish urgent drainage areas and guide the priority of emergency dispatch. The preset feasibility threshold is a reference value for determining whether the second priority area can be used together with the first priority area as an emergency dispatch area. It depends on the personnel load capacity and task completion rate, and is usually set between 0.6 and 0.9. In this embodiment, it is set to 0.75, which can reasonably judge the feasibility of execution in each area.

[0057] The preset feasibility weight is used to calculate the feasibility index of the indicator. It depends on the importance allocation between the load feasibility value and the completion feasibility value, and is usually set between 0.3 and 0.7. In this embodiment, it is set to 0.5, which can balance the contribution of load and completion status in the feasibility assessment. The preset feasibility synchronization weight is used to calculate the feasibility synchronization index in the feasibility index. It depends on the priority of task synchronization and is usually set between 0.3 and 0.7. In this embodiment, it is set to 0.5, which can comprehensively reflect the coordination level of load and task completion.

[0058] The preset load feasibility weight is the weight of the load feasibility value in the feasibility index, which depends on the impact of personnel load on task execution. It is usually set between 0.4 and 0.6. In this embodiment, it is set to 0.5, which can appropriately reflect the role of personnel load in feasibility judgment. The preset completion feasibility weight is the weight of the completion feasibility value in the feasibility index, which depends on the impact of task completion rate on overall scheduling feasibility. It is usually set between 0.4 and 0.6. In this embodiment, it is set to 0.5, which can reasonably reflect the importance of task completion rate in feasibility assessment.

[0059] By comprehensively analyzing various parameters such as dispatch feedback coefficient, water depth, drainage power, personnel load rate, and task completion rate, emergency dispatch areas can be scientifically divided. Candidate areas are differentiated into different priorities based on the drainage pressure index. Furthermore, the feasibility index is calculated by combining the synchronous changes in personnel load rate and task completion rate, thereby rationally determining the dispatch sequence and resource allocation for each area. This method can balance drainage efficiency, personnel carrying capacity, and task completion status in dynamic flood scenarios, achieving coordinated optimization of drainage pump dispatch and personnel task execution. This ensures that the dispatch plan is both timely and robust, adaptable to real-time changes in water accumulation and resource load in each area, and guarantees a scientific and orderly emergency response process.

[0060] Please see Figure 4 As shown, this is a logic diagram for adjusting the preset pump allocation in this embodiment. In this embodiment, the process of adjusting the preset pump allocation of the drainage pumps in the priority handling area, or adjusting the preset scheduling feedback threshold, based on the distribution characteristics changes of all the emergency dispatch areas within the preset correction time, includes: Obtain the Euclidean distance between the two-dimensional coordinates of all the emergency dispatch areas and the preset reference coordinates at each time within the preset correction time period to obtain several distribution distances; Calculate the standard deviation of all the distribution distances to obtain the distribution dispersion; Count the timestamps where the distribution dispersion is greater than a preset dispersion threshold within the preset correction time period to obtain several distribution times. Calculate the difference between each of the distribution times and the initial time of the preset correction time to obtain several distribution times; Calculate the standard deviation of all the distribution durations to obtain the dispersion of variation; When the variation dispersion is greater than the maximum value of the preset variation dispersion range, the preset pump allocation of the drainage pump in the priority treatment area is increased according to the relative deviation between the variation dispersion and the maximum value of the preset variation dispersion range, where Y'=Y×(1+k1×︱U-Umax︱ / Umax), Y' is the increased preset pump allocation, Y is the original preset pump allocation, k1 is the preset allocation correction coefficient, Umax is the maximum value of the preset variation dispersion range, and U is the variation dispersion. When the variation dispersion is less than the minimum value of the preset variation dispersion range, the preset scheduling feedback threshold is reduced according to the relative deviation of the variation dispersion being less than the minimum value of the preset variation dispersion range, where W'=W×(1-k2×︱U-Umin︱ / Umin), W' is the preset scheduling feedback threshold after reduction, W is the preset scheduling feedback threshold before reduction, k2 is the preset threshold correction coefficient, and Umin is the minimum value of the preset variation dispersion range.

[0061] The preset reference coordinates are benchmark coordinates used to measure the degree of deviation of the location of each emergency dispatch area. They depend on urban area planning, road layout, and historical dispatch data, and are usually set near the center of each area to be treated. In this embodiment, they are set to the geometric center of each priority disposal area, which can provide a stable spatial reference to assess the balance of dispatch distribution. The preset dispersion threshold is a standard value for judging whether the distribution of emergency dispatch areas is too scattered. It depends on the number of dispatch areas, city size, and drainage pump coverage capacity, and is usually set between 0.5 and 5 meters. In this embodiment, it is set to 2 meters, which can promptly identify distribution anomalies and trigger dispatch adjustments. The preset variation dispersion range is used to determine whether the distribution dispersion fluctuation exceeds the acceptable range. It depends on historical distribution fluctuations, event patterns, and other factors. The model and response requirements are typically set between [1 meter, 10 meters], and in this embodiment, they are set to [3 meters, 8 meters], which can guide the reasonable correction of pump allocation or scheduling threshold. The preset allocation correction coefficient is used to adjust the increase ratio of the preset pump allocation of the drainage pump. It depends on the maximum flow capacity of the pump, the number of pumping stations, and the urgency of the event. It is typically set between 0.01 and 0.5, and in this embodiment, it is set to 0.1, which can flexibly increase or decrease the pump allocation according to the distribution deviation. The preset threshold correction coefficient is used to adjust the reduction ratio of the scheduling feedback threshold. It depends on the scheduling sensitivity, resource carrying capacity, and risk tolerance. It is typically set between 0.01 and 0.5, and in this embodiment, it is set to 0.1, which can appropriately reduce the threshold to optimize resource allocation when the distribution is too concentrated.

[0062] By quantitatively analyzing the changes in the distribution characteristics of each emergency dispatch area within a preset correction period, dynamic adjustments are made to the allocation of drainage pumps and the dispatch feedback threshold. When the dispersion of regional distribution and changes is high, the preset pump allocation of drainage pumps is increased to enhance drainage capacity and ensure rapid elimination of accumulated water. When the dispersion is low, the dispatch feedback threshold is appropriately lowered to avoid excessive concentration of resources or excessive dispatching. This achieves a coordinated dynamic balance between water depth, drainage power, personnel load rate, and task completion rate, realizing mutual constraints and compensation among various parameters, and ensuring the efficiency and reliability of emergency dispatch.

[0063] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. An adaptive control method for emergency dispatching based on dynamic feedback, characterized in that, include: Real-time acquisition of water depth in each area to be treated during the handling of urban flooding incidents, drainage power of drainage pumps operating based on preset pump allocation, road traffic rate, personnel load rate, task completion rate, image transmission frame rate of drones in each area to be treated, and communication signal strength. The flood risk index is determined based on the changing characteristics of the water depth and the road traffic rate. Based on the flood risk index and preset risk thresholds, several priority treatment areas are determined; The execution efficiency index is determined based on the synchronous change characteristics of the drainage power, personnel load rate, and task completion rate of the priority treatment area. The information reliability index is determined based on the fluctuation range of the image return frame rate and the communication signal strength. Calculate the scheduling feedback coefficient based on the execution efficiency index and the information reliability index; Several emergency dispatch areas are determined based on the dispatch feedback coefficient, the preset dispatch feedback threshold, the water depth, the drainage power, the personnel load rate, and the task completion rate. The preset pump allocation of the drainage pumps in the priority disposal area is adjusted according to the changes in the distribution characteristics of all the emergency dispatch areas within the preset adjustment time period, or the preset dispatch feedback threshold is adjusted. Scheduling is performed based on the scheduling feedback coefficients of all emergency scheduling areas after the preset pump allocation or the preset scheduling feedback threshold is corrected.

2. The emergency dispatch adaptive control method based on dynamic feedback according to claim 1, characterized in that, The process of determining the flood risk index based on the changing characteristics of the water depth and the road traffic capacity includes: Calculate the rate of change of the water depth from the initial time to each time within the preset first monitoring period to obtain several depth change rates; Calculate the rate of change of the road traffic rate from the initial time to each time within the same preset first monitoring period to obtain several road traffic rate changes; The flood risk index is determined based on all the aforementioned depth change rates and all the aforementioned road traffic change rates.

3. The emergency dispatch adaptive control method based on dynamic feedback according to claim 2, characterized in that, The process of determining the flood risk index based on all the aforementioned depth change rates and all the aforementioned road traffic change rates includes: The Pearson correlation coefficients of all the aforementioned depth change rates and all the aforementioned road traffic change rates are calculated to obtain the flood risk index.

4. The emergency dispatch adaptive control method based on dynamic feedback according to claim 3, characterized in that, The process of determining several priority treatment areas based on the flood risk index and preset risk thresholds includes: When the flood risk index is greater than the preset risk threshold, the corresponding area to be treated is determined to be the priority treatment area.

5. The emergency dispatch adaptive control method based on dynamic feedback according to claim 4, characterized in that, The process of determining the execution efficiency index based on the synchronous changes in the drainage power, personnel load rate, and task completion rate of the priority treatment area includes: The drainage power of each priority treatment area is normalized by minimum-maximum value to obtain a normalized power value. The load rate of each person is normalized by minimum-maximum based on the load rate of all the priority handling areas to obtain a normalized load rate value. The completion rates of each task are normalized by minimum-maximum based on the completion rates of all the priority processing areas to obtain a normalized completion rate value. The execution efficiency index is determined based on the power normalization value, the load rate normalization value, and the completion rate normalization value within the preset second monitoring period.

6. The emergency dispatch adaptive control method based on dynamic feedback according to claim 5, characterized in that, The process of determining the execution efficiency index based on the power normalization value, the load rate normalization value, and the completion rate normalization value within the preset second monitoring period includes: Calculate the average value of all power normalization values, the average value of all load rate normalization values, and the average value of all completion rate normalization values ​​within the preset second monitoring period to obtain the power index value, load index value, and completion index value, respectively. The power index value, the load index value, and the completion index value are weighted and summed to obtain the index level index. The rate of change of all the power normalization values, all the load rate normalization values, and all the completion rate normalization values ​​from the initial time to each time within the preset second monitoring period is calculated respectively to obtain the power normalization speed change sequence, the load normalization speed change sequence, and the completion normalization speed change sequence. The Pearson correlation coefficients of the power normalized speed change sequence and the load normalized speed change sequence, the Pearson correlation coefficients of the power normalized speed change sequence and the completed normalized speed change sequence, and the Pearson correlation coefficients of the load normalized speed change sequence and the completed normalized speed change sequence are calculated respectively to obtain the first synchronization coefficient, the second synchronization coefficient, and the third synchronization coefficient. Calculate the average of the first synchronization coefficient, the second synchronization coefficient, and the third synchronization coefficient to obtain the synchronization index; The execution efficiency index is obtained by weighted summation of the indicator level index and the synchronicity index.

7. The emergency dispatch adaptive control method based on dynamic feedback according to claim 6, characterized in that, The process of determining the information reliability index based on the fluctuation range of the image return frame rate and the communication signal strength includes: The image transmission frame rate of each priority processing area within the same preset second monitoring period is normalized by minimum-maximum value to obtain several frame rate normalization values. The communication signal strength of each priority handling area within the same preset second monitoring period is subjected to minimum-maximum normalization processing to obtain several signal normalization values; Calculate the standard deviation of all the frame rate normalization values ​​to obtain the frame rate fluctuation value; Calculate the standard deviation of all the normalized values ​​of the signals to obtain the signal fluctuation value; The information reliability index is obtained by weighted summation of the frame rate fluctuation value and the signal fluctuation value.

8. The emergency dispatch adaptive control method based on dynamic feedback according to claim 7, characterized in that, The process of calculating the scheduling feedback coefficient based on the execution efficiency index and the information reliability index includes: The execution efficiency index and the information reliability index are weighted and summed to obtain the scheduling feedback coefficient.

9. The emergency dispatch adaptive control method based on dynamic feedback according to claim 8, characterized in that, The process of determining several emergency dispatch areas based on the dispatch feedback coefficient, the preset dispatch feedback threshold, the water depth, the drainage power, the personnel load rate, and the task completion rate includes: When the scheduling feedback coefficient is greater than the preset scheduling feedback threshold, the priority processing area is determined to be a candidate area; The ratio of the water depth to the drainage power in the candidate region is calculated to obtain the drainage pressure index; When the drainage pressure index is greater than a preset pressure index threshold, the candidate region is determined to be the first priority region; When the drainage pressure index is less than or equal to a preset pressure index threshold, the candidate region is determined to be the second priority region; The feasibility index is determined based on the synchronous change characteristics of the personnel load rate and the task completion rate in the second priority area; When the feasibility index is greater than the preset feasible threshold, both the first priority area and the second priority area are determined to be the emergency dispatch area; When the feasibility index is less than or equal to the preset feasibility threshold, the first priority area is determined to be the emergency dispatch area.

10. The emergency dispatch adaptive control method based on dynamic feedback according to claim 9, characterized in that, The process of adjusting the preset pump allocation of the drainage pumps in the priority handling area, or adjusting the preset dispatch feedback threshold, based on the distribution characteristics changes of all the emergency dispatch areas within a preset adjustment period includes: Obtain the Euclidean distance between the two-dimensional coordinates of all the emergency dispatch areas and the preset reference coordinates at each time within the preset correction time period to obtain several distribution distances; Calculate the standard deviation of all the distribution distances to obtain the distribution dispersion; Count the timestamps where the distribution dispersion is greater than a preset dispersion threshold within the preset correction time period to obtain several distribution times. Calculate the difference between each of the distribution times and the initial time of the preset correction time to obtain several distribution times; Calculate the standard deviation of all the distribution durations to obtain the dispersion of variation; When the variation dispersion is greater than the maximum value of the preset variation dispersion range, the preset pump allocation of the drainage pump in the priority treatment area is increased according to the relative deviation between the variation dispersion and the maximum value of the preset variation dispersion range. When the variation dispersion is less than the minimum value of the preset variation dispersion range, the preset scheduling feedback threshold is reduced according to the relative deviation of the variation dispersion being less than the minimum value of the preset variation dispersion range.

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