A rainwater grate regulation method and system

Abstract

This invention relates to the field of stormwater management technology and discloses a method and system for regulating storm drain grates. The method includes: real-time acquisition of environmental data, including rainfall intensity, soil moisture, surface water depth, and reservoir water level; constructing a short-term prediction model based on the acquired environmental data, including a rainfall intensity prediction model, a soil moisture prediction model, and a surface water depth prediction model; predicting rainfall intensity, soil moisture, and surface water depth for a set period based on the short-term prediction model, obtaining corresponding prediction results; determining the drainage mode and the opening / closing degree of the storm drain grates based on the prediction results; and driving the mechanical structure of the storm drain grates to regulate their opening and closing according to the determined drainage mode and the opening / closing degree. This invention achieves intelligent adjustment of the opening / closing degree of storm drain grates, reducing the time of surface water accumulation and significantly improving urban flood control capabilities.

Description

Technical Field

[0001] This invention relates to the field of rainwater management technology, and in particular to a method and system for regulating rainwater grates. Background Technology

[0002] There is a significant contradiction in the coordinated operation of current urban drainage systems and sponge city facilities. Traditional drainage systems prioritize rapid discharge, relying on the gravity flow of pipe networks and storm drains to quickly guide surface runoff into downstream water bodies. In contrast, sponge city facilities emphasize slow discharge and release, achieving rainwater resource utilization through infiltration, retention, and purification. The design logics of the two are fundamentally conflicting.

[0003] In heavy rainfall scenarios, when traditional rain grates are fully open, a large amount of rainwater is drained away without being fully infiltrated by the sponge city infrastructure, resulting in a waste of the infrastructure's storage capacity. This also exacerbates the load on downstream pipe networks and may even cause urban flooding. In light rainfall scenarios, if rain grates are forcibly closed to prioritize infiltration, the soil infiltration rate and infrastructure capacity may limit the infiltration capacity, leading to long-term surface water accumulation, breeding mosquitoes and flies, and affecting traffic.

[0004] Existing storm drain grates are mostly fixed-opening structures or rely on manual adjustment, lacking the ability to dynamically respond to real-time rainfall intensity, soil moisture, and the saturation of sponge city infrastructure. Sponge city infrastructure and drainage networks often operate independently, lacking a coordinated control mechanism. This fragmented management of drainage systems and sponge city infrastructure leads to the dual challenges of urban flooding prevention and rainwater utilization. There is an urgent need for a storm drain grate control method and system capable of dynamically balancing infiltration and drainage needs in real time. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for regulating rainwater grates to solve the aforementioned technical problems in the prior art.

[0006] According to a first aspect of the present invention, a method for regulating rainwater grates is provided.

[0007] The rainwater grate regulation method includes: Real-time collection of environmental data, including rainfall intensity, soil moisture, surface water depth, and reservoir water level; Based on the collected environmental data, a short-term prediction model is constructed, which includes: a rainfall intensity prediction model, a soil moisture prediction model, and a surface water depth prediction model. Based on the aforementioned short-term prediction model, the rainfall intensity, soil moisture, and surface water depth for a set period are predicted, and the corresponding prediction results are obtained. Based on the prediction results, the drainage mode and the opening and closing degree of the rain grate are determined; and based on the determined drainage mode and the opening and closing degree of the rain grate, the mechanical structure of the rain grate is driven to control the opening and closing.

[0008] In addition, the rain grate control method further includes: before constructing a short-term prediction model, using the moving average method to perform noise reduction processing on the collected environmental data.

[0009] In addition, based on the collected environmental data, the short-term prediction model is constructed by: constructing a rainfall intensity prediction model based on a dynamic weighted fusion model using the collected environmental data; wherein the formula for the rainfall intensity prediction model is: ; ; In the formula, for Real-time rainfall intensity forecast results; This is the weighting coefficient for rainfall intensity; The rainfall intensity is collected in real time; Linear interpolation for rainfall intensity forecast; , Adjacent forecast times and Corresponding rainfall intensity forecast values; It is a time variable.

[0010] Furthermore, based on the collected environmental data, the short-term prediction model is constructed as follows: A soil moisture prediction model is constructed based on the collected environmental data and a physics-data hybrid model; wherein the formula for the soil moisture prediction model is: ; ; In the formula, for Predicted soil moisture at any given time; This refers to the real-time collected soil moisture. For a moment Soil permeability coefficient; for Real-time rainfall intensity forecast results; The soil saturation moisture threshold; This is the coefficient for natural evaporation / infiltration rate; This is the baseline value for soil drying conditions; The initial permeability coefficient of the soil; This is the initial time. The decay rate constant; It is a time variable.

[0011] In addition, based on the collected environmental data, the short-term prediction model is constructed as follows: based on the collected environmental data and a dynamic drainage balance model, a surface water depth prediction model is constructed; wherein, the formula for the surface water depth prediction model is: ; ; ; In the formula, for Predicted results of surface water depth at any given time; This refers to the real-time collected depth of surface water. for Real-time rainfall intensity forecast results; for The maximum permeability of the sponge system at all times; This represents the drainage rate at the current opening of the storm drain grate; It is a time variable; This indicates the current opening and closing position of the storm drain grate; For flow coefficient; This refers to the maximum opening area of ​​the rain grate; It is the acceleration due to gravity; For a moment Soil permeability coefficient; The effective infiltration area of ​​the sponge city facility; The water level in the reservoir is collected in real time. This refers to the length of the infiltration path of the sponge city facility; for Predicted soil moisture at any given time; This represents the soil saturation moisture threshold.

[0012] In addition, based on the prediction results, determining the drainage pattern and the opening and closing degree of the storm drain grates includes: comparing the prediction results with the maximum infiltration capacity of the sponge city infrastructure, and when the comparison results are... Rainfall intensity prediction results at any time Less than or equal to Maximum permeability of sponge facilities at all times In such cases, the sponge system infiltration mode should be used for drainage as a priority. The degree of opening and closing of the rain grate at all times The calculation is as follows: ; In the formula, To ensure the minimum opening and closing degree of basic drainage; This refers to the critical opening degree for the saturation of the sponge city facility.

[0013] In addition, based on the prediction results, determining the drainage pattern and the opening and closing degree of the storm drain grates includes: comparing the prediction results with the maximum infiltration capacity of the sponge city infrastructure, and when the comparison results are... Rainfall intensity prediction results at any time Greater than Maximum permeability of sponge facilities at all times In such cases, the rainwater grate drainage mode should be used as the priority for drainage; The degree of opening and closing of the rain grate at all times The calculation is as follows: ; In the formula, The critical opening degree for the saturation of the sponge city facility; This represents the maximum opening and closing degree of the intelligent rain grate.

[0014] In addition, based on the forecast results, determining the drainage pattern and the degree of opening and closing of the storm drain grates includes: Soil moisture prediction results at any time greater than 95% soil saturation moisture threshold In this situation, the emergency drainage mode is activated, and the storm drain grates are fully opened; Predicted Surface Water Depth at Any Time If the water level exceeds 10cm, the emergency drainage mode will be activated, and the rainwater grate will be fully opened.

[0015] Furthermore, the aforementioned rain grate control method is characterized by further comprising: during the opening and closing control, real-time monitoring of the actual surface water depth and actual soil moisture, and calculating the actual drainage effect based on the actual surface water depth and actual soil moisture; comparing the actual drainage effect with the predicted result to determine the error between the actual drainage effect and the predicted result; continuously monitoring the error, and if the error continues to exceed the limit, adjusting the permeability coefficient and flow coefficient according to the gradient descent method, and applying the adjusted permeability coefficient and flow coefficient to the short-term prediction model for the next cycle.

[0016] According to another aspect of the present invention, a rain grate control system is provided.

[0017] The rainwater grate control system includes: The data acquisition module is used to collect environmental data in real time, including rainfall intensity, soil moisture, surface water depth, and reservoir water level. The model building module is used to build short-term prediction models based on the collected environmental data. The short-term prediction models include: a rainfall intensity prediction model, a soil moisture prediction model, and a surface water depth prediction model. The model prediction module is used to predict the rainfall intensity, soil moisture and surface water depth for a set period based on the short-term prediction model, and obtain the corresponding prediction results. The opening and closing control module is used to determine the drainage mode and the opening and closing degree of the rain grate based on the prediction results; and to drive the mechanical structure of the rain grate to open and close according to the determined drainage mode and the opening and closing degree of the rain grate.

[0018] In addition, the rain grate control system also includes a noise cancellation module, which is used to perform noise cancellation processing on the collected environmental data using the moving average method before constructing a short-term prediction model.

[0019] In addition, when constructing a short-term prediction model based on the collected environmental data, the model building module can construct a rainfall intensity prediction model based on a dynamic weighted fusion model, wherein the formula for the rainfall intensity prediction model is: ; ; In the formula, for Real-time rainfall intensity forecast results; This is the weighting coefficient for rainfall intensity; The rainfall intensity is collected in real time; Linear interpolation for rainfall intensity forecast; , Adjacent forecast times and Corresponding rainfall intensity forecast values; It is a time variable.

[0020] Furthermore, when constructing a short-term prediction model based on the collected environmental data, the model building module can also construct a soil moisture prediction model based on a physics-data hybrid model, using the collected environmental data; wherein the formula for the soil moisture prediction model is: ; ; In the formula, for Predicted soil moisture at any given time; This refers to the real-time collected soil moisture. For a moment Soil permeability coefficient; for Real-time rainfall intensity forecast results; The soil saturation moisture threshold; This is the coefficient for natural evaporation / infiltration rate; This is the baseline value for soil drying conditions; The initial permeability coefficient of the soil; This is the initial time. The decay rate constant; It is a time variable.

[0021] In addition, when constructing a short-term prediction model based on the collected environmental data, the model building module can also construct a surface water depth prediction model based on the dynamic drainage balance model, using the collected environmental data; wherein the formula for the surface water depth prediction model is: ; ; ; In the formula, for Predicted results of surface water depth at any given time; This refers to the real-time collected depth of surface water. for Real-time rainfall intensity forecast results; for The maximum permeability of the sponge system at all times; This represents the drainage rate at the current opening of the storm drain grate; It is a time variable; This indicates the current opening and closing position of the storm drain grate; For flow coefficient; This refers to the maximum opening area of ​​the rain grate; It is the acceleration due to gravity; For a moment Soil permeability coefficient; The effective infiltration area of ​​the sponge city facility; The water level in the reservoir is collected in real time. This refers to the length of the infiltration path of the sponge city facility; for Predicted soil moisture at any given time; This represents the soil saturation moisture threshold.

[0022] Furthermore, when determining the drainage mode and the opening / closing degree of the rain grate based on the prediction results, the opening / closing control module compares the prediction results with the maximum infiltration capacity of the sponge city infrastructure. The comparison result is... Rainfall intensity prediction results at any time Less than or equal to Maximum permeability of sponge facilities at all times In such cases, the sponge system infiltration mode should be used for drainage as a priority. The degree of opening and closing of the rain grate at all times The calculation is as follows: ; In the formula, To ensure the minimum opening and closing degree of basic drainage; This refers to the critical opening degree for the saturation of the sponge city facility.

[0023] In addition, when the opening and closing control module determines the drainage mode and the opening and closing degree of the rain grate based on the prediction results, it compares the prediction results with the maximum infiltration capacity of the sponge city infrastructure. Rainfall intensity prediction results at any time Greater than Maximum permeability of sponge facilities at all times In such cases, the rainwater grate drainage mode should be used as the priority for drainage; The degree of opening and closing of the rain grate at all times The calculation is as follows: ; In the formula, The critical opening degree for the saturation of the sponge city facility; This represents the maximum opening and closing degree of the intelligent rain grate.

[0024] Furthermore, when the opening and closing control module determines the drainage mode and the degree of opening and closing of the rain grate based on the prediction results, Soil moisture prediction results at any time greater than 95% soil saturation moisture threshold In this situation, the emergency drainage mode is activated, and the storm drain grates are fully opened; Predicted Surface Water Depth at Any Time If the water level exceeds 10cm, the emergency drainage mode will be activated, and the rainwater grate will be fully opened.

[0025] In addition, the aforementioned rain grate control system is characterized by further comprising: an effect monitoring module, used to monitor the actual surface water depth and actual soil moisture in real time during opening and closing control, and calculate the actual drainage effect based on the actual surface water depth and actual soil moisture; a dynamic correction module, used to compare the actual drainage effect with the predicted result, determine the error between the actual drainage effect and the predicted result; continuously monitor the error, and if the error continues to exceed the limit, adjust the permeability coefficient and flow coefficient according to the gradient descent method, and apply the adjusted permeability coefficient and flow coefficient to the short-term prediction model of the next cycle.

[0026] The technical solution provided by this invention may include the following beneficial effects: This invention achieves intelligent adjustment of the opening and closing degree of rainwater grates by real-time monitoring of rainfall intensity, soil moisture, and the infiltration capacity of sponge city facilities, employing a dynamic infiltration model and a multi-parameter collaborative decision-making algorithm. The system can automatically switch between three modes—infiltration priority, drainage priority, and emergency discharge—based on actual hydrological conditions, thereby improving rainwater resource utilization and reducing surface waterlogging time. Through adaptive infiltration coefficient correction and predictive feedback mechanisms, it effectively extends the service life of sponge city facilities and reduces maintenance costs. It significantly enhances urban flood control capabilities, reduces non-point source pollution, and can seamlessly integrate with smart city management platforms, providing efficient and reliable technical support for sponge city construction.

[0027] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the invention. Attached Figure Description

[0028] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0029] Figure 1 This is a schematic flowchart illustrating a rain grate control method according to an exemplary embodiment; Figure 2 This is a structural block diagram of a rain grate control system according to an exemplary embodiment; Figure 3 This is a schematic diagram of the structure of a computer device according to an exemplary embodiment. Detailed Implementation

[0030] Figure 1 An embodiment of a rain grate control method according to the present invention is shown.

[0031] In this optional embodiment, the rain grate control method includes: Step S101: Collect environmental data in real time, including rainfall intensity, soil moisture, surface water depth and reservoir water level. Step S102: Based on the collected environmental data, construct a short-term prediction model, which includes: a rainfall intensity prediction model, a soil moisture prediction model, and a surface water depth prediction model. Step S103: Based on the short-term prediction model, the rainfall intensity, soil moisture and surface water depth of the set period are predicted to obtain the corresponding prediction results. Step S104: Based on the prediction results, determine the drainage mode and the opening and closing degree of the rain grate; and based on the determined drainage mode and the opening and closing degree of the rain grate, drive the mechanical structure of the rain grate to control its opening and closing.

[0032] Figure 2 An embodiment of a rain grate control system according to the present invention is shown.

[0033] In this optional embodiment, the rain grate control system includes: Data acquisition module 201 is used to collect environmental data in real time, including rainfall intensity, soil moisture, surface water depth and reservoir water level. The model building module 202 is used to build a short-term prediction model based on the collected environmental data. The short-term prediction model includes: a rainfall intensity prediction model, a soil moisture prediction model, and a surface water depth prediction model. The model prediction module 203 is used to predict the rainfall intensity, soil moisture and surface water depth for a set period based on the short-term prediction model, and obtain the corresponding prediction results. The opening and closing control module 204 is used to determine the drainage mode and the opening and closing degree of the rain grate based on the prediction results; and to drive the mechanical structure of the rain grate to perform opening and closing control according to the determined drainage mode and the opening and closing degree of the rain grate.

[0034] In the above optional embodiments, when collecting environmental data in real time, rainfall intensity can be collected in real time by using a pre-set rain gauge, a capacitive sensor buried in the sponge city facility, an ultrasonic sensor, and a water level sensor, respectively. Soil moisture Surface water depth Water level in the reservoir Before constructing a short-term prediction model, noise reduction processing can be performed on the collected environmental data using the moving average method, as follows: Set the data collection and upload frequency to Fixed time window is Every minute, the system maintains a storage record containing the most recent data. The buffer queue for each sampling point's data is configured such that, upon receiving a new data point, the oldest data point in the queue is deleted, and the new data point is added to the end of the queue, ensuring that the queue always retains the most recent data point. Data within minutes; for fixed time windows The arithmetic mean of the data is used to eliminate short-term noise and random fluctuations while preserving the long-term trend of the data. The formula is as follows: ; In the formula, For the first in the window Data from each sampling point, including rainfall intensity Soil moisture Surface water depth , water level in the reservoir For the current moment Smoothed data, including rainfall intensity Soil moisture Surface water depth Water level in the reservoir .

[0035] In the above optional embodiments, when constructing a short-term prediction model based on the collected environmental data, a rainfall intensity prediction model can be constructed based on a dynamic weighted fusion model using the collected environmental data; wherein, the formula for the rainfall intensity prediction model is: ; ; In the formula, for Real-time rainfall intensity forecast results; This is the weighting coefficient for rainfall intensity; The rainfall intensity is collected in real time; Linear interpolation for rainfall intensity forecast; , Adjacent forecast times and Corresponding rainfall intensity forecast values; The variable is time. A soil moisture prediction model can also be constructed based on the collected environmental data and a physical-data hybrid model; the formula for the soil moisture prediction model is: ; ; In the formula, for Predicted soil moisture at any given time; This refers to the real-time collected soil moisture. For a moment Soil permeability coefficient; for Real-time rainfall intensity forecast results; The soil saturation moisture threshold; This is the coefficient for natural evaporation / infiltration rate; This is the baseline value for soil drying conditions; The initial permeability coefficient of the soil; This is the initial time. The decay rate constant; It is a time variable.

[0036] Simultaneously, based on the collected environmental data and a dynamic drainage balance model, a surface water depth prediction model can be constructed; wherein the formula for the surface water depth prediction model is: ; ; ; In the formula, for Predicted results of surface water depth at any given time; This refers to the real-time collected depth of surface water. for Real-time rainfall intensity forecast results; for The maximum permeability of the sponge system at all times; This represents the drainage rate at the current opening of the storm drain grate; It is a time variable; This indicates the current opening and closing position of the storm drain grate; For flow coefficient; This refers to the maximum opening area of ​​the rain grate; It is the acceleration due to gravity; For a moment Soil permeability coefficient; The effective infiltration area of ​​the sponge city facility; The water level in the reservoir is collected in real time. This refers to the length of the infiltration path of the sponge city facility; for Predicted soil moisture at any given time; This represents the soil saturation moisture threshold.

[0037] In the above optional embodiments, when determining the drainage pattern and the opening and closing degree of the rain grate based on the prediction results, the prediction results can be compared with the maximum infiltration capacity of the sponge city infrastructure. The comparison result is... The predicted rainfall intensity at any given time is less than or equal to Maximum permeability of sponge facilities at all times In such cases, the sponge system infiltration mode should be used for drainage as a priority. The degree of opening and closing of the rain grate at all times The calculation is as follows: ; In the formula, To ensure the minimum opening and closing degree of basic drainage; This refers to the critical opening degree for the saturation of the sponge city facility.

[0038] And the comparison results are Rainfall intensity prediction results at any time Greater than Maximum permeability of sponge facilities at all times In such cases, the rainwater grate drainage mode should be used as the priority for drainage; The degree of opening and closing of the rain grate at all times The calculation is as follows: ; In the formula, The critical opening degree for the saturation of the sponge city facility; This represents the maximum opening and closing degree of the intelligent rain grate.

[0039] exist Soil moisture prediction results at any time greater than 95% soil saturation moisture threshold In this situation, the emergency drainage mode is activated, and the storm drain grates are fully opened; Predicted Surface Water Depth at Any Time If the water level exceeds 10cm, the emergency drainage mode will be activated, and the rainwater grate will be fully opened.

[0040] In the above optional embodiments, the actual surface water depth and actual soil moisture can be monitored in real time during the opening and closing control, and the actual drainage effect can be calculated based on the actual surface water depth and actual soil moisture. The actual drainage effect is compared with the predicted result to determine the error between the actual drainage effect and the predicted result. This error is continuously monitored, and if the error continues to exceed the limit, the permeability coefficient and flow coefficient are adjusted according to the gradient descent method, and the adjusted permeability coefficient and flow coefficient are applied to the short-term prediction model for the next cycle. Specifically: Real-time monitoring of actual surface water depth during implementation and actual soil moisture If within 3 consecutive cycles Then the flow coefficient is updated based on the actual monitored surface water depth. Compared with the predicted value Deviation determination error function : ； Drainage rate at current storm drain grate opening It is the flow coefficient The function of error on the flow coefficient The partial derivatives are: ； in, Derived from the surface water depth prediction model: ； Update the flow coefficients using gradient descent. The learning rate of traffic , For the updated flow coefficient, Original flow coefficient: ； If within 3 consecutive cycles Then the permeability coefficient is updated, and the permeability coefficient error is... The biases in soil moisture prediction or water depth prediction are combined and optimized. ； Sponge infrastructure permeability It is a moment Soil permeability coefficient The function, error with respect to time Soil permeability coefficient The partial derivatives are: ； in: ; ; according to Correcting the initial permeability coefficient and decay rate constant : ； ； In the formula, The corrected initial permeability coefficient; This is the corrected decay rate constant; The initial permeability coefficient before correction; For penetration learning rate; The decay rate constant before correction; The decay rate is the learning rate.

[0041] To better understand the above-mentioned technical solution of the present invention, the following uses simulated rainfall process data to further illustrate the above-mentioned technology of the present invention, as detailed below: Set the data collection and upload frequency to 5. The system maintains a 75-minute time window and a 75-length buffer queue to store the data from the 75 most recent sampling points. When a new data point is received, the oldest data point in the queue is deleted, and the new data point is added to the end of the queue. The queue always retains the most recent data. Data within minutes; For a fixed time window The data over the past 15 minutes is taken as the arithmetic mean, eliminating short-term noise and random fluctuations while preserving the long-term trend. This indicates the rainfall intensity over the past 15 minutes. Soil moisture Surface water depth Water level in the reservoir ; A rainfall intensity prediction model is constructed by using a dynamic weighted fusion model that combines real-time data with weather forecasts. ； ； in, For linear interpolation of rainfall intensity forecast, The weighting factor for real-time data is set to 0.7. , The forecast rainfall intensity values ​​for the adjacent forecast times of 14:05 and 14:20 are 0.6 mm / h and 1.2 mm / h, respectively. The predicted rainfall intensity at 14:10 is 0.94 mm / h. Based on the physics-data hybrid model, a soil moisture prediction model is constructed: ； ； Based on field survey data, the soil saturation moisture threshold The value is 45%, representing the natural evaporation / infiltration rate coefficient. The value is 0.005, which is the baseline value for soil drying conditions. The soil permeability coefficient was 18%, and it decreased rapidly due to the gradual blockage of soil pores, then tended to stabilize in the later stages, with a decay rate constant of 18%. The initial soil permeability coefficient is 0.02 / min. Soil permeability coefficient at 0.6 mm / s and time 14:10 The predicted soil moisture rate at 14:10 is 0.18 mm / s. It was 19.8%; Based on a dynamic drainage balance model, and combining rainfall input, sponge city infrastructure infiltration, and the drainage capacity of smart rain grates, a surface water depth prediction model is constructed: ； ； ； Among them, the flow coefficient The maximum opening area of ​​the intelligent rain grate is 0.65. 0.5m 2 , The opening and closing degree of current smart rain grate is determined by gravitational acceleration. The drainage rate is 5% at the current opening degree of the smart rain grate. The effective permeability area of ​​the sponge city facility is 0 mm / h. 150m 2 Length of the infiltration path of sponge city facilities It is 1.5m. Maximum permeability of sponge facilities at all times It is 83.59 mm / h. Current surface water depth It is 0cm; In this embodiment It adopts a permeation-first mode to ensure the minimum opening and closing degree of basic drainage. The critical opening degree for saturation of sponge city facilities is 5%. The value is 35%. The calculated opening and closing degree of the intelligent rain grate is 5.33%. The optimized opening command is sent to the stepper motor, driving the mechanical structure of the rain grate to achieve the opening and closing control for this cycle. During execution, the actual surface water depth is monitored in real time. and actual soil moisture The simulation process calculates the deviation between the actual drainage effect and the predicted value. =0cm, =20.1%; In this embodiment No need to correct the permeability coefficient.

[0042] Figure 3 An embodiment of a computer device according to the present invention is shown. The computer device may be a server, and includes a processor, memory, and a network interface connected via a system bus. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database of the computer device stores static and dynamic information data. The network interface of the computer device is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements the steps in the above-described method embodiment.

[0043] Those skilled in the art will understand that Figure 3The structure shown is merely a block diagram of a portion of the structure related to the present invention and does not constitute a limitation on the computer device to which the present invention is applied. A specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0044] In addition, the present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0045] In addition, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the above method embodiments.

[0046] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. Any references to memory, storage, databases, or other media used in the embodiments provided by this invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

Claims

1. A method for regulating rainwater grates, characterized in that, include: Real-time collection of environmental data, including rainfall intensity, soil moisture, surface water depth, and reservoir water level; Based on the collected environmental data, a short-term prediction model is constructed, which includes: a rainfall intensity prediction model, a soil moisture prediction model, and a surface water depth prediction model. Based on the aforementioned short-term prediction model, the rainfall intensity, soil moisture, and surface water depth for a set period are predicted, and the corresponding prediction results are obtained. Based on the prediction results, the drainage mode and the opening and closing degree of the rain grate are determined; and based on the determined drainage mode and the opening and closing degree of the rain grate, the mechanical structure of the rain grate is driven to control the opening and closing. The short-term prediction model, based on the collected environmental data, includes: Based on the collected environmental data, a rainfall intensity prediction model is constructed using a dynamic weighted fusion model. Based on the collected environmental data, a soil moisture prediction model is constructed using a physical-data hybrid model. Based on the collected environmental data, a surface water depth prediction model is constructed using a dynamic drainage balance model. The determination of drainage patterns and the degree of opening and closing of storm drain grates based on the prediction results includes: The predicted results were compared with the maximum permeability of the sponge city infrastructure. The comparison results were... Rainfall intensity prediction results at any time Less than or equal to Maximum permeability of sponge facilities In such cases, the sponge system infiltration mode should be used for drainage as a priority. The predicted results were compared with the maximum permeability of the sponge city infrastructure. The comparison results were... Rainfall intensity prediction results at any time Greater than Maximum permeability of sponge facilities In such cases, the rainwater grate drainage mode should be used as the priority for drainage; exist Soil moisture prediction results at any time greater than 95% soil saturation moisture threshold In such cases, the emergency drainage mode is activated, and the rainwater grates are fully opened; exist Predicted Surface Water Depth at Any Time If the water level exceeds 10cm, the emergency drainage mode will be activated, and the rainwater grate will be fully opened.

2. The rainwater grate regulation method according to claim 1, characterized in that, Also includes: Before constructing a short-term prediction model, noise reduction processing was performed on the collected environmental data using the moving average method.

3. The rainwater grate regulation method according to claim 1, characterized in that, The formula for the rainfall intensity prediction model is: ； ； In the formula, for Real-time rainfall intensity forecast results; This is the weighting coefficient for rainfall intensity; This refers to the rainfall intensity collected in real time. Linear interpolation for rainfall intensity forecast; , Adjacent forecast times and Corresponding rainfall intensity forecast values; It is a time variable.

4. The rainwater grate regulation method according to claim 3, characterized in that, The formula for the soil moisture prediction model is: ； ； In the formula, for Predicted soil moisture at any given time; This refers to the real-time collected soil moisture. For a moment Soil permeability coefficient; for Real-time rainfall intensity forecast results; The soil saturation moisture threshold; This is the coefficient for natural evaporation / infiltration rate; This is the baseline value for soil drying conditions; The initial permeability coefficient of the soil; This is the initial time. The decay rate constant; It is a time variable.

5. The rainwater grate regulation method according to claim 4, characterized in that, The formula for the surface water depth prediction model is as follows: ； ； ； In the formula, for Predicted results of surface water depth at any given time; This refers to the real-time collected depth of surface water. for Real-time rainfall intensity forecast results; for The maximum permeability of the sponge system at all times; This represents the drainage rate at the current opening of the storm drain grate; It is a time variable; This indicates the current opening and closing position of the storm drain grate; For flow coefficient; This refers to the maximum opening area of ​​the rain grate; It is the acceleration due to gravity; For a moment Soil permeability coefficient; The effective infiltration area of ​​the sponge city facility; The water level in the reservoir is collected in real time. This refers to the length of the infiltration path of the sponge city facility; for Predicted soil moisture at any given time; This represents the soil saturation moisture threshold.

6. The rainwater grate regulation method according to claim 5, characterized in that, The degree of opening and closing of the rain grate at all times The calculation is as follows: ； In the formula, To ensure the minimum opening and closing degree of basic drainage; This refers to the critical opening degree for the saturation of the sponge city facility.

7. The rainwater grate regulation method according to claim 5, characterized in that, The degree of opening and closing of the rain grate at all times The calculation is as follows: ； In the formula, The critical opening degree for the saturation of the sponge city facility; This represents the maximum opening and closing degree of the intelligent rain grate.

8. The rainwater grate regulation method according to claim 1, characterized in that, Also includes: During the opening and closing control, the actual surface water depth and actual soil moisture are monitored in real time, and the actual drainage effect is calculated based on the actual surface water depth and actual soil moisture. The actual drainage effect is compared with the predicted result to determine the error between the actual drainage effect and the predicted result; The error is continuously monitored. If the error continues to exceed the limit, the permeability coefficient and flow coefficient are adjusted according to the gradient descent method, and the adjusted permeability coefficient and flow coefficient are applied to the short-term prediction model for the next cycle.

9. A rainwater grate control system, characterized in that, include: The data acquisition module is used to collect environmental data in real time, including rainfall intensity, soil moisture, surface water depth, and reservoir water level. The model building module is used to build short-term prediction models based on the collected environmental data. The short-term prediction models include: a rainfall intensity prediction model, a soil moisture prediction model, and a surface water depth prediction model. The model prediction module is used to predict the rainfall intensity, soil moisture and surface water depth for a set period based on the short-term prediction model, and obtain the corresponding prediction results. The opening and closing control module is used to determine the drainage mode and the opening and closing degree of the rain grate based on the prediction results; and to drive the mechanical structure of the rain grate to control the opening and closing according to the determined drainage mode and the opening and closing degree of the rain grate. The short-term prediction model, based on the collected environmental data, includes: Based on the collected environmental data, a rainfall intensity prediction model is constructed using a dynamic weighted fusion model. Based on the collected environmental data, a soil moisture prediction model is constructed using a physical-data hybrid model. Based on the collected environmental data, a surface water depth prediction model is constructed using a dynamic drainage balance model. The determination of drainage patterns and the degree of opening and closing of storm drain grates based on the prediction results includes: The predicted results were compared with the maximum permeability of the sponge city infrastructure. The comparison results were... Rainfall intensity prediction results at any time Less than or equal to Maximum permeability of sponge facilities In such cases, the sponge system infiltration mode should be used for drainage as a priority. The predicted results were compared with the maximum permeability of the sponge city infrastructure. The comparison results were... Rainfall intensity prediction results at any time Greater than Maximum permeability of sponge facilities In such cases, the rainwater grate drainage mode should be used as the priority for drainage; exist Soil moisture prediction results at any time greater than 95% soil saturation moisture threshold In such cases, the emergency drainage mode is activated, and the rainwater grates are fully opened; exist Predicted Surface Water Depth at Any Time If the water level exceeds 10cm, the emergency drainage mode will be activated, and the rainwater grate will be fully opened.

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