Intelligent rainfall control method and system and intelligent terminal
By using intelligent precipitation control methods and analyzing real-time and historical water level changes, the system automatically controls the start and stop of water pumps, solving the problem of unstable precipitation effects during foundation pit excavation. This achieves unattended and stable precipitation control, improving construction safety.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-31
- Publication Date
- 2026-04-07
AI Technical Summary
In existing technologies, during the excavation of foundation pits, relying on manual judgment of groundwater levels to control the start and stop of water pumps makes it difficult to achieve on-demand dewatering, which leads to unresolved contradictions such as instability at the bottom of the foundation pit or settlement of the surrounding environment, and the dewatering effect is unstable.
An intelligent precipitation control method is adopted. By collecting real-time and historical water level change characteristics of precipitation wells, analyzing the similarity and trend of water level changes, and automatically controlling the start and stop of water pumps, the stability of precipitation equipment is ensured.
It has enabled unattended intelligent precipitation control, which has improved the stability and safety of precipitation effects and reduced the cost and risk of manual inspection.
Smart Images
Figure CN121807018A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the technical field of precipitation control, and in particular to an intelligent precipitation control method, system and intelligent terminal. Background Technology
[0002] Dewatering control refers to a measure taken during earthwork excavation to reduce soil moisture and lower groundwater levels. Specifically, it addresses the depressurization of slightly confined and confined aquifers. Calculations show that as the weight of the overlying soil decreases during excavation, the distance between the pit bottom and the excavation surface or the top of the confined aquifer decreases. When the excavation reaches a critical depth, the weight of the overlying soil may fall below the safety factor, making it prone to sudden seepage and instability at the pit bottom, seriously jeopardizing pit safety. To prevent piping of confined aquifers, which could affect construction safety and project quality, dewatering is a technique used to lower the groundwater level or confined aquifer depth to a safe depth.
[0003] In related technologies, groundwater or confined water should be treated before excavation of the foundation pit. When the pressure from the confined water reaches the critical excavation depth, the confined water needs to be depressurized. Typically, water pumps are used for manual pumping. Workers place the pump inside the dewatering well pipe, connect the pump outlet to the drainage pipeline, and then start the pump to extract water from the dewatering well. When the workers observe insufficient water flow, they control the power to stop the pump, waiting for groundwater or confined water to seep into the dewatering well. After a certain period, the workers restart the pump to circulate the water.
[0004] Regarding the aforementioned technologies, during the dewatering phase, relying on manual judgment and monitoring of the water level to determine if it meets requirements presents challenges. When the pressurized head is too high, a sudden surge can easily occur in the foundation pit, leading to instability at the bottom. Conversely, if the pressurized head is too low, it can easily cause settlement in the surrounding environment. Preventing sudden surges within the pit and controlling settlement in the external environment are two conflicting measures. When choosing between these two options, manual dewatering often results in workers keeping the pumps running continuously to prevent sudden surges, making it extremely difficult to achieve on-demand dewatering. Relying on manual observation of water levels and manual control of pump start-up and shutdown is susceptible to variations in worker reaction speed and execution, easily leading to pressurized water exceeding the safe level and causing sudden surges, or excessive pressurized water drop causing settlement in the surrounding environment. This results in a contradiction and instability between pressurization and on-demand dewatering, indicating room for improvement. Summary of the Invention
[0005] To improve the stability of precipitation effects, this application provides an intelligent precipitation control method, system, and intelligent terminal.
[0006] Firstly, this application provides an intelligent precipitation control method, which adopts the following technical solution: A smart precipitation control method includes: Real-time precipitation data is collected from pre-set precipitation equipment. The corresponding state transition threshold is found in the preset precipitation stage threshold relationship based on the real-time precipitation stage. Collect real-time water levels from pre-set precipitation wells; Analyze the real-time detected water level and state transition threshold to determine the current state transition signal of the precipitation equipment; The precipitation equipment is controlled to switch states based on the current state transition signal.
[0007] Optionally, the step of analyzing real-time detected water levels and state transition thresholds to determine the current state transition signal of the precipitation equipment includes: Time of water level detection in precipitation wells; The image is plotted based on the water level detection time and real-time water level to generate a current water level change map; Feature values are extracted and integrated based on the current water level change map to generate a current water level change feature matrix; Based on the historical water level change feature matrix and corresponding historical state transition signals collected during real-time precipitation stages; Analyze the similarity between the current water level change feature matrix and the historical water level change feature matrix to generate water level change similarity; The current state transition signal is determined by analyzing the state transition threshold, water level change similarity, and corresponding historical state transition signals.
[0008] Optionally, the step of analyzing the similarity between the current water level change feature matrix and the historical water level change feature matrix to generate water level change similarity includes: Calculate the cosine similarity between the current water level change feature matrix and the historical water level change feature matrix to generate water level change trend similarity; Calculate the Euclidean distance between the current water level change feature matrix and the historical water level change feature matrix to generate the water level change feature distance; The similarity of water level change characteristics is determined by analyzing the distance between water level change features and the preset maximum distance threshold. The water level change trend similarity and water level change feature similarity are weighted and summed according to the preset similarity weights to generate water level change similarity.
[0009] Optionally, the step of analyzing the state transition threshold, water level change similarity, and corresponding historical state transition signals to determine the current state transition signal includes: Determine whether the similarity of water level changes meets the preset similarity threshold requirements; If the conditions are not met, the state transition threshold and the current water level change characteristic matrix are analyzed to determine the current state transition signal. If the conditions are met, the corresponding time correction coefficient will be found in the preset precipitation correction relationship based on the real-time precipitation stage. The time correction coefficient, the current water level change characteristic matrix, the historical water level change characteristic matrix, and the historical state transition signal are analyzed to determine the current state transition signal.
[0010] Optionally, the step of analyzing the state transition threshold and the current water level change feature matrix to determine the current state transition signal includes: Determine the water level change trend characteristics, water level change rate characteristics, and current water level characteristics based on the current water level change characteristic matrix; Determine whether the water level change trend characteristics meet the preset requirements for accelerated change trend characteristics; If not, the state transition threshold, water level change rate characteristics, and current water level characteristics are fitted according to the preset linear fitting model to determine the current state transition signal; If the conditions are met, the water level change trend characteristics, water level change rate characteristics, and current water level characteristics are substituted into the preset nonlinear fitting model to determine the nonlinear water level prediction function. The state transition threshold is substituted into the nonlinear water level prediction function for calculation to generate the current state transition signal.
[0011] Optionally, the step of fitting the state transition threshold, water level change rate characteristics, and current water level characteristics according to a preset linear fitting model to determine the current state transition signal includes: Substitute the characteristics of water level change rate and current water level into the linear fitting model to determine the linear water level prediction function; The state transition threshold is substituted into the linear water level prediction function for calculation to generate the current state transition signal.
[0012] Optionally, the steps of analyzing the time correction coefficient, the current water level change characteristic matrix, the historical water level change characteristic matrix, and the historical state transition signal to determine the current state transition signal include: Determine the average water level change velocity deviation, water level threshold deviation, and trend duration deviation based on the current water level change characteristic matrix and the historical water level change characteristic matrix; Determine whether the real-time precipitation stage is a preset pumping stage or a preset water storage stage; If it is the water storage stage, the deviation of average water velocity and water level threshold are corrected according to the time correction coefficient to generate the basic correction time. If it is the pumping stage, the deviation of average water velocity and the deviation of trend duration are corrected according to the time correction coefficient to generate the basic correction time. The historical state transition signals are corrected based on the baseline correction time to generate the current state transition signal.
[0013] Optionally, it also includes a verification step for the current state transition signal, the specific steps of which include: The current state transition signal and the preset review pre-processing time are analyzed to determine the review start time; The water level change map was collected based on the start time of the review; Determine the standard deviation of water level change and water level deviation based on the reviewed water level change map and the current water level change map; Determine whether the standard deviation of water level change and the water level deviation meet the requirements of the preset verification standard conditions; If the conditions are met, the current state transition signal is maintained; If it does not meet the requirements, the current state transition signal will be determined based on the verified water level change diagram.
[0014] Secondly, this application provides an intelligent precipitation control system, which adopts the following technical solution: A smart precipitation control system includes: The data acquisition module is used to collect real-time precipitation data and monitor water levels in real time. A memory for storing a program for an intelligent precipitation control method as described in any of the preceding claims; The processor and the program in the memory can be loaded and executed by the processor to implement an intelligent precipitation control method as described in any of the above.
[0015] Thirdly, this application provides a smart terminal, which adopts the following technical solution: A smart terminal includes a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and executed as described in any of the preceding claims.
[0016] In summary, this application includes at least one of the following beneficial technical effects: 1. By collecting real-time water level data from rainwater wells, the current state transition signal can be obtained by analyzing the real-time water level and state transition threshold, eliminating the need for operators to inspect the water pump output and water level in the rainwater wells in real time, thus ensuring the stability of the rainwater effect. 2. By determining that the similarity of water level changes meets the similarity threshold, the historical state transition signal is used as a benchmark. The historical state transition signal is then corrected based on the deviation between the current water level change feature matrix and the historical water level change feature matrix to obtain the current state transition signal, thereby improving the accuracy of determining the current state transition signal. 3. By collecting the water level change map during the review start time, the standard deviation of water level change and water level deviation are calculated based on the characteristics of the review water level change map and the current water level change map. When the standard deviation of water level change and water level deviation meet the requirements of the preset review standard conditions, the current state transition signal is maintained, thereby ensuring the accuracy of the current state transition signal. Attached Figure Description
[0017] Figure 1 This is a flowchart of an intelligent precipitation control method in an embodiment of this application.
[0018] Figure 2 This is a flowchart of the steps in this application embodiment to analyze the real-time detected water level and state transition threshold to determine the current state transition signal of the precipitation equipment.
[0019] Figure 3 This is a flowchart of the steps in this application embodiment to analyze the similarity between the current water level change feature matrix and the historical water level change feature matrix to generate water level change similarity.
[0020] Figure 4 This is a flowchart of the steps in this application embodiment to analyze the state transition threshold, water level change similarity, and corresponding historical state transition signals to determine the current state transition signal.
[0021] Figure 5 This is a flowchart of the steps in this application embodiment to analyze the state transition threshold and the current water level change feature matrix to determine the current state transition signal.
[0022] Figure 6 This is a flowchart of the steps in this application embodiment to fit the state transition threshold, water level change rate characteristics, and current water level characteristics according to a preset linear fitting model in order to determine the current state transition signal.
[0023] Figure 7 This is a flowchart of the steps in this application embodiment to analyze the time correction coefficient, the current water level change feature matrix, the historical water level change feature matrix, and the historical state transition signal to determine the current state transition signal.
[0024] Figure 8 This is a flowchart of the verification steps for the current state transition signal in the embodiments of this application. Detailed Implementation
[0025] To make the purpose, technical solution, and advantages of this application clearer, the following description is provided in conjunction with the appendix. Figures 1 to 8 The present application will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the application.
[0026] Reference Figure 1 This application discloses an intelligent precipitation control method, which includes the following steps: Step S100: Real-time precipitation data collection from preset precipitation equipment.
[0027] The dewatering equipment refers to dewatering or depressurization equipment used to extract groundwater or confined water from dewatering wells. It can consist of a deep well pump and a pumping pipeline. The pipeline connects the pump's inlet and outlet to the dewatering well, pumping out the groundwater accumulated in the well. The pipeline then connects the pump's outlet to a drainage pipe, discharging the extracted groundwater through the drainage pipe. By controlling the well depth and pumping, a dewatering funnel curve is formed, thereby reducing the groundwater depth or pressure. This application's embodiment uses a novel intelligent semiconductor and terminal control system combined with an adjustable-speed dedicated deep well pump for dewatering and depressurization. This enables the dewatering equipment to become a new type of intelligent, information-based, convenient, and efficient pumping system with adjustable pump motor speed control for pumping flow, maintaining pit and environmental stability, safety and reliability, automatic control, no need for continuous manual pumping, and on-demand dewatering based on the groundwater inflow and drawdown requirements. The main function and purpose is to ensure that when the foundation pit is excavated to a certain depth, dewatering or pressure reduction is necessary, while preventing excessive water level drop from causing settlement in the surrounding environment. This effectively protects the safety of the foundation pit and surrounding environment for underground structures such as underground tunnels, municipal utility tunnels, underground pipe networks, and various underground working wells. The entire system's workflow can be summarized as a closed-loop control of "monitoring-judgment-execution-recording". In one embodiment, the initialization and parameter setting of the dewatering equipment includes: 1. Pump start-up water level (high water level or set meter): When the water level in the well rises to this elevation, the system automatically starts the pump. This water level should be set at the critical point where pumping needs to begin to reduce water pressure. 2. Pump stop water level (low water level or set meter): When the water level in the well drops to this elevation, the system automatically stops the pump. This water level should be set at a safe position that has achieved the pressure reduction target and is slightly higher than the pump suction inlet to prevent the pump from running dry. 3. Water pump alarm level (excessive water level or set meter): When the water level rises abnormally to this level, it indicates a possible system malfunction (such as pump damage or insufficient flow), triggering an audible and visual alarm or remote notification. The inspection and commissioning of the dewatering equipment includes: 4. Water level sensor: Calibrate pressure-type or float-type water level sensors to ensure accurate readings. 5. Water pump: Check the performance of the submersible pump to ensure its head and flow rate meet requirements. 6. Control system (dedicated semiconductor controller, semiconductor control panel): Check the power supply, communication module (such as 4G / IoT), and control logic program. 7. Backup power supply: Ensure the UPS or generator can automatically switch in case of mains power failure. After initialization, the dewatering equipment enters a continuous automatic operation state, recording data and performing remote monitoring, including: 1. Data recording: The control system records water level, pump runtime, start / stop count, current / voltage, and other data at set time intervals (such as every 5 minutes). 2. Remote monitoring: Data is transmitted in real-time to the central monitoring center or cloud platform via 4G, Ethernet, or IoT technology, allowing staff to view the data anytime via computer or mobile app. 3. Real-time status: Current water level and pump operating status.4. Historical Curves: Water level change trend chart, pump operation records. 5. Alarm Information: Receives and promptly handles alarms such as exceeding limits and equipment malfunctions. Alarm and fault handling includes: 1. Water Level Exceedance Alarm: Water level continuously exceeds the alarm level (set meter value). 2. Equipment Fault Alarm: Pump overload, phase loss, abnormal current, or sensor failure. 3. Communication Interruption Alarm: Loss of remote monitoring data. 4. When an alarm occurs, the system will execute according to preset strategies, such as starting a backup pump and immediately notifying maintenance personnel for intervention. Core Advantages of Dewatering Equipment: 1. Precise Control: Pumping water on demand, avoiding blind and intermittent pumping, resulting in more stable dewatering effects. 2. High Efficiency and Energy Saving: Operates only when needed, significantly saving power consumption and equipment wear. 3. Unattended Operation: Reduces manual inspection costs and safety risks, especially suitable for remote or dangerous areas. 4. Early Warning Function: Promptly detects potential risks (such as a sudden increase in water inflow), gaining time to take countermeasures. 5. Data-Driven: Accumulated hydrological data provides a basis for analyzing groundwater dynamics and optimizing dewatering schemes.
[0028] The real-time precipitation phase refers to the current precipitation stage, which includes the pumping phase and the water storage phase. During the pumping phase, the precipitation equipment is activated to extract water from the wells; during the water storage phase, the equipment stops, waiting for groundwater to accumulate in the wells. Upon initial activation, the processing terminal automatically identifies the pumping phase as the real-time precipitation phase. Subsequently, the processing terminal identifies the on / off status of the precipitation equipment. If the equipment is activated, the real-time precipitation phase is the pumping phase; if it is deactivated, the real-time precipitation phase is the water storage phase. Determining the real-time precipitation phase provides data support for subsequently determining the water level threshold for the current phase.
[0029] Step S101: Find the corresponding state transition threshold in the preset precipitation stage threshold relationship based on the real-time precipitation stage.
[0030] The precipitation stage threshold relationship refers to the correspondence between different precipitation stages and water level thresholds. During the pumping stage, the lowest water level in the precipitation well is used as the water level threshold to ensure that the groundwater in the precipitation well can be effectively pumped out. During the water storage stage, the highest water level in the precipitation well is used as the water level threshold to ensure that the groundwater in the precipitation well can be pumped out in a timely manner. The specific values are determined by the operators based on the actual situation of the precipitation well, and a mapping table is formed to correspond the precipitation stage with the water level threshold.
[0031] The state transition threshold refers to the water level threshold at which the current precipitation stage transitions to another precipitation stage. It is obtained by the processing terminal by looking up the threshold in a mapping table corresponding to the real-time precipitation stage. By determining the state transition threshold, the benchmark for subsequent state transitions of precipitation equipment is clarified, thereby controlling the timely start and stop of precipitation equipment and ensuring the stability of precipitation.
[0032] Step S102: Collect the real-time water level of the preset precipitation well.
[0033] Among them, dewatering wells refer to wells used to extract groundwater. They are dug by operators at the construction site. The specific number and size parameters are determined by the operators according to actual needs, and will not be elaborated here.
[0034] Real-time water level monitoring refers to the current water level within the dewatering well, detected by a water level sensor within the well and transmitted to the processing terminal. The water level sensor can be a calibrated pressure type or a float-type sensor to ensure accurate readings. Real-time water level monitoring provides comparative data for determining whether the dewatering equipment needs to undergo a status switch.
[0035] Step S103: Analyze the real-time detected water level and state transition threshold to determine the current state transition signal of the precipitation equipment.
[0036] The current state transition signal refers to the signal that controls the precipitation equipment to transition between states. It is determined by the processing terminal after analyzing the real-time detected water level and the state transition threshold. In one embodiment, the current state transition signal is output by simply comparing the real-time detected water level and the state transition threshold; if they are equal, the current state transition signal is output. However, this method has a control delay, which can easily lead to over-pumping or over-storage. Therefore, this application proposes another embodiment that predicts the state transition time in advance by detecting the real-time water level and the state transition threshold, thereby determining the state transition time as the current state transition signal. The specific method is described in [reference needed]. Figure 2 These steps ensure that the precipitation equipment can switch states in a timely manner, preventing over-pumping or over-storage, thereby improving the stability of precipitation.
[0037] Step S104: Control the precipitation equipment to switch states according to the current state switching signal.
[0038] After receiving the current state transition signal, the processing terminal starts timing according to the time and instruction corresponding to the current state transition signal. When the time reaches the target, the precipitation equipment is controlled to switch states according to the instruction, ensuring that the precipitation equipment can switch states instantly when the water level reaches the threshold, avoiding over-pumping or over-storage, thereby improving the stability of precipitation.
[0039] Reference Figure 2 The steps for analyzing real-time detected water levels and state transition thresholds to determine the current state transition signal of the precipitation equipment include: Step S200: Collect the water level detection time of the precipitation well.
[0040] The water level detection time refers to the time when the water level is detected in real time. A unique timestamp is generated immediately upon collecting the water level data; this is the water level detection time. Determining the water level detection time provides data support for subsequent water level change mapping.
[0041] Step S201: Draw an image based on the water level detection time and real-time water level detection to generate a current water level change map.
[0042] The current water level change map refers to the image of the water level changing over time at the current moment. The processing terminal first uses Kalman filtering and sliding window denoising to reduce the noise of the water level detection time and the real-time detected water level. Then, it removes abrupt changes and values that exceed the range in the real-time detected water level and their corresponding water level detection times. Finally, it is plotted with the water level detection time as the horizontal axis and the real-time detected water level as the vertical axis. By using the current water level change map, the pattern of water level change can be determined, and the time when the water level reaches the threshold can be determined based on the pattern of water level change.
[0043] Step S202: Extract and integrate feature values based on the current water level change map to generate a current water level change feature matrix.
[0044] The current water level change feature matrix refers to a matrix containing water level change features, including the instantaneous slope of the curve, the average rate of water level change, the duration of trend maintenance, the rate of change of slope, the curve curvature, the standard deviation of slope, the periodic water level range, the average water level, the threshold deviation, the mutation value, the number of mutations, and the data stability coefficient.
[0045] The instantaneous slope reflects the rate of water level change. It is obtained by extracting the water levels at the current and previous moments from the current water level change map, subtracting the water levels, and then dividing by the time. The average rate of water level change refers to the rate of water level change within the current period, reflecting the strength of the water level change trend within the period. It is obtained by extracting the water levels at the start and current moments from the current water level change map, subtracting the water levels, and then dividing by the total time. The duration of trend maintenance refers to the length of time that water level changes follow the same trend. It is obtained by statistically analyzing the duration of consistent instantaneous slopes in the current water level change map. The rate of change of slope refers to the rate of change of the slope, reflecting the changing trend of the water level change speed. It is obtained by extracting the instantaneous slopes of adjacent moments from the current water level change map and then calculating the quotient of the difference in instantaneous slopes and the time difference. The curvature of the curve refers to the degree of curvature of the water level change curve in the current water level change map, reflecting the strength of the water level change trend. It is obtained by extracting the slopes of different sampling points from the current water level change map and calculating the slope based on the calculus curvature formula. The slope standard deviation refers to the standard deviation of the slope, reflecting the stability of the water level change trend. It is calculated by extracting the slope of all points in the current water level change map from the treatment terminal and then calculating the slope standard deviation using the standard deviation formula. The periodic water level range refers to the maximum range of water level differences within a period. It is obtained by subtracting the maximum and minimum water level values extracted from the current water level change map from the treatment terminal. The average water level refers to the average water level within a period. It is obtained by averaging all water levels extracted from the current water level change map from the treatment terminal. The threshold deviation refers to the difference between the current period threshold and the previous period threshold, which needs to be compared with the threshold of the same precipitation stage. The abrupt change value refers to the water level that changes abruptly. It is obtained by subtracting adjacent water levels in the current water level change map from the treatment terminal. When the water level change value is greater than the threshold, it is determined to be an abrupt change value. The number of abrupt changes refers to the number of abrupt changes, which is obtained by statistically analyzing the number of abrupt changes from the treatment terminal. The data stability coefficient refers to the average value of the water level difference, which reflects the intensity of water level changes. It is obtained by extracting the water level difference from the current water level change map by the processing terminal and then averaging the water level difference.
[0046] After determining the characteristic parameters, the characteristic parameters are normalized and then arranged in the characteristic order to form the current water level change characteristic matrix.
[0047] Step S203: Collect historical water level change feature matrix and corresponding historical state transition signals based on real-time precipitation stages.
[0048] Among them, the historical water level change feature matrix refers to the water level change feature matrix of the same precipitation stage in the previous cycle, and the historical state transition signal refers to the state transition signal of the same precipitation stage in the previous cycle. The processing terminal obtains the water level change feature matrix and the state transition signal by calling the corresponding stage of the real-time precipitation stage from the backup database.
[0049] Step S204: Analyze the similarity between the current water level change feature matrix and the historical water level change feature matrix to generate water level change similarity.
[0050] Among them, water level change similarity refers to the quantified similarity between the water level change pattern of the current period and the water level change pattern of the previous period. It is obtained by the processing terminal after analyzing the similarity between the current water level change feature matrix and the historical water level change feature matrix. The specific method is described in [reference needed]. Figure 3 The steps.
[0051] Step S205: Analyze the state transition threshold, water level change similarity, and corresponding historical state transition signals to determine the current state transition signal.
[0052] The current state transition signal in this step is consistent with the current state transition signal in step S103. It is obtained by the processing terminal after analyzing the state transition threshold, water level change similarity, and corresponding historical state transition signals. The specific method is as follows: Figure 4 The steps.
[0053] Reference Figure 3 The steps for analyzing the similarity between the current water level change feature matrix and the historical water level change feature matrix to generate water level change similarity include: Step S300: Calculate the cosine similarity between the current water level change feature matrix and the historical water level change feature matrix to generate water level change trend similarity.
[0054] Among them, the water level change trend similarity refers to the quantitative value of the similarity between the water level change trends of the current cycle and the previous cycle. It is obtained by the processing terminal calculating the cosine similarity between the current water level change feature matrix and the historical water level change feature matrix, that is, by multiplying the two matrices by the product of their moduli.
[0055] Step S301: Calculate the Euclidean distance between the current water level change feature matrix and the historical water level change feature matrix to generate the water level change feature distance.
[0056] Among them, the water level change characteristic distance refers to the Euclidean distance between the water level characteristic parameters of the current cycle and the water level characteristic parameters of the previous cycle, which is calculated by the processing terminal based on the Euclidean distance formula from the current water level change characteristic matrix and the historical water level change characteristic matrix.
[0057] Step S302: Analyze the water level change feature distance and the preset maximum distance threshold to determine the similarity of water level change features.
[0058] The maximum distance threshold refers to the maximum value of the Euclidean distance, which is used to normalize the calculated Euclidean distance. The specific value is determined by the operator based on the actual situation.
[0059] Water level change feature similarity refers to the similarity of water level change feature parameters. The normalized Euclidean distance is obtained by calculating the quotient of the water level change feature distance and the maximum distance threshold by the processing terminal. Then, the difference between 1 and the normalized Euclidean distance is calculated to obtain the water level change feature similarity.
[0060] Step S303: The water level change trend similarity and water level change feature similarity are weighted and summed according to the preset similarity weights to generate water level change similarity.
[0061] The similarity weight refers to the weight of the water level change trend and the water level change characteristics in the water level change similarity. In this embodiment, the weight of the water level change trend is 0.7 and the weight of the water level change characteristics is 0.3.
[0062] The water level change similarity in this step is consistent with the water level change similarity in step S204. It is obtained by the processing terminal by weighting and summing the water level change trend similarity and water level change feature similarity according to the similarity weight.
[0063] Reference Figure 4 The steps for determining the current state transition signal by analyzing the state transition threshold, water level change similarity, and corresponding historical state transition signals include: Step S400: Determine whether the similarity of water level changes meets the requirements of the preset similarity threshold.
[0064] The similarity threshold refers to the minimum similarity between the current cycle water level change pattern and the previous cycle water level change pattern. The specific value is determined by the operator based on the actual situation. The requirement for the similarity threshold is that it should not be less than the similarity threshold.
[0065] By processing the terminal to determine whether the similarity of water level changes is not less than the similarity threshold, it can be determined whether the current state transition signal can be obtained after correction based on the historical state transition signal.
[0066] Step S401: If it does not meet the requirements, analyze the state transition threshold and the current water level change feature matrix to determine the current state transition signal.
[0067] If the processing terminal determines that the water level change similarity is less than the similarity threshold, it indicates that the water level change pattern of the previous cycle differs significantly from that of the current cycle. Therefore, the current state transition signal cannot be obtained by correcting the historical state transition signal. Thus, the state transition threshold and the current water level change feature matrix are analyzed to determine the current state transition signal. The specific method is described in [reference needed]. Figure 5 The steps.
[0068] Step S402: If the conditions are met, the corresponding time correction coefficient is found in the preset precipitation correction relationship according to the real-time precipitation stage.
[0069] If the processing terminal determines that the water level change similarity is not less than the similarity threshold, it indicates that the water level change pattern of the previous cycle is highly similar to the water level change pattern of the current cycle. Therefore, the corresponding time correction coefficient is found in the precipitation correction relationship based on the real-time precipitation stage, providing data support for subsequent correction of historical state transition signals.
[0070] The precipitation correction relationship refers to the correspondence between different precipitation stages and time correction coefficients. The pumping stage includes correction coefficients for the deviation of the average water velocity and the deviation of the trend maintenance time. In this embodiment, 0.18 and 0.2 are used as examples. The water storage stage includes correction coefficients for the deviation of the average water velocity and the deviation of the water level threshold. In this embodiment, 0.2 and 0.15 are used as examples.
[0071] The time correction coefficient refers to the influence coefficient of water level change characteristic deviation on state transition time, which is obtained by the processing terminal from the mapping table corresponding to the precipitation correction relationship based on the real-time precipitation stage.
[0072] Step S4021: Analyze the time correction coefficient, the current water level change feature matrix, the historical water level change feature matrix, and the historical state transition signal to determine the current state transition signal.
[0073] The current state transition signal in this step is the same as the current state transition signal in step S205. It is obtained by the processing terminal after analyzing the time correction coefficient, the current water level change feature matrix, the historical water level change feature matrix, and the historical state transition signal. The specific method is as follows: Figure 7 The steps.
[0074] Reference Figure 5 The steps for analyzing the state transition threshold and the current water level change feature matrix to determine the current state transition signal include: Step S500: Determine the water level change trend characteristics, water level change rate characteristics, and current water level characteristics based on the current water level change characteristic matrix.
[0075] Among them, the water level change trend feature refers to the trend of water level change, which is the slope rate of change in the current water level change feature matrix. The water level change velocity feature refers to the instantaneous rate of change of water level, which is the instantaneous slope in the current water level change feature matrix. The current water level feature refers to the current water level value, which is the real-time detected water level.
[0076] Step S501: Determine whether the water level change trend characteristics meet the preset requirements of the accelerated change trend characteristics.
[0077] Among them, the accelerated change trend characteristic refers to the minimum slope change rate of the water level change trend, which is 0. The requirement for the accelerated change trend characteristic is that it is not less than the accelerated change trend characteristic.
[0078] By processing the terminal to determine whether the water level change trend is not less than the accelerated change trend, the current state transition signal can be determined by linear or nonlinear fitting.
[0079] Step S5011: If it does not meet the requirements, then the state transition threshold, water level change rate characteristics and current water level characteristics are fitted according to the preset linear fitting model to determine the current state transition signal.
[0080] If the processing terminal determines that the water level change trend characteristic is less than the accelerating change trend characteristic, it indicates that the water level change trend is in a decelerating state. At this time, the water level change trend tends to be stable, and the water level change trend characteristic is small. The error and computational load of using linear fitting are small. Therefore, a linear fitting model is used to fit the state transition threshold, water level change rate characteristic, and current water level characteristic to determine the current state transition signal. The specific method is described in [reference needed]. Figure 6 The steps.
[0081] A linear fitting model is a model that determines the linear relationship between water level and time. In this embodiment, water level is the dependent variable, time is the independent variable, the water level change rate characteristic is the coefficient, and the current water level characteristic is the constant term to obtain the linear fitting model.
[0082] Step S5012: If the conditions are met, the water level change trend characteristics, water level change rate characteristics, and current water level characteristics are substituted into the preset nonlinear fitting model to determine the nonlinear water level prediction function.
[0083] If the processing terminal determines that the water level change trend feature is not less than the accelerated change trend feature, it indicates that the water level change trend is in an accelerated change state. At this time, linear fitting cannot capture the accelerated change trend. Therefore, the water level change trend feature, water level change rate feature and current water level feature are substituted into the nonlinear fitting model to determine the nonlinear water level prediction function.
[0084] A nonlinear fitting model refers to a pattern for determining the nonlinear relationship between water level and time. In this embodiment, water level is used as the dependent variable, time is used as the independent variable, the water level change trend characteristics are used as the quadratic term coefficients, the water level change rate characteristics are used as the linear term coefficients, and the current water level characteristics are used as the constant term to obtain the nonlinear fitting model.
[0085] The nonlinear water level prediction function refers to the function of the current water level changing over time. The processing terminal substitutes the water level change trend characteristics, water level change rate characteristics, and current water level characteristics into the nonlinear fitting model to obtain a function that retains only the two unknowns: water level and time.
[0086] Step S50121: Substitute the state transition threshold into the nonlinear water level prediction function for calculation to generate the current state transition signal.
[0087] In this step, the current state transition signal is the same as the current state transition signal in step S401. The processing terminal uses the state transition threshold as the dependent variable and substitutes it into the nonlinear water level prediction function to solve for the corresponding time value. The solved time value is added to the real-time water level detection time to finally determine the current state transition signal.
[0088] Reference Figure 6 The steps for determining the current state transition signal by fitting the state transition threshold, water level change rate characteristics, and current water level characteristics to a preset linear fitting model include: Step S600: Substitute the water level change rate characteristics and the current water level characteristics into the linear fitting model to determine the linear water level prediction function.
[0089] The linear water level prediction function refers to the function of the current water level changing over time. The processing terminal substitutes the water level change rate characteristics and the current water level characteristics into the linear fitting model to obtain a function that retains only the two unknowns: water level and time.
[0090] Step S601: Substitute the state transition threshold into the linear water level prediction function for calculation to generate the current state transition signal.
[0091] In this step, the current state transition signal is the same as the current state transition signal in step S401. The processing terminal uses the state transition threshold as the dependent variable and substitutes it into the linear water level prediction function to solve for the corresponding time value. The solved time value is added to the real-time water level detection time to finally determine the current state transition signal.
[0092] Reference Figure 7 The steps for determining the current state transition signal by analyzing the time correction coefficient, the current water level change characteristic matrix, the historical water level change characteristic matrix, and the historical state transition signals include: Step S700: Determine the average water level change velocity deviation, water level threshold deviation, and trend duration deviation based on the current water level change feature matrix and the historical water level change feature matrix.
[0093] The average water level change rate deviation refers to the deviation between the average water level change rate of the current cycle and the previous cycle. It is obtained by subtracting the average water level change rate from the current water level change feature matrix and the historical water level change feature matrix in the processing terminal.
[0094] Water level threshold deviation refers to the deviation between the water level threshold of the current period and the previous period. It is obtained by subtracting the water level thresholds in the current water level change feature matrix and the historical water level change feature matrix from the processing terminal.
[0095] Trend duration deviation refers to the deviation between the trend duration of the current period and the previous period. It is obtained by subtracting the trend duration from the current water level change feature matrix and the historical water level change feature matrix in the processing terminal.
[0096] Step S701: Determine whether the real-time precipitation stage is a preset pumping stage or a preset water storage stage.
[0097] The pumping stage refers to the stage where the rainwater equipment is pumping water, while the water storage stage refers to the stage where the rainwater equipment is storing water.
[0098] By processing the terminal to determine whether the real-time precipitation stage is the pumping stage or the water storage stage, the system can determine how to correct the historical time based on the deviation of water level changes.
[0099] Step S7011: If it is the water storage stage, the deviation of average water velocity and water level threshold are corrected according to the time correction coefficient to generate the basic correction time.
[0100] If the processing terminal determines that the real-time precipitation stage is the water storage stage, it indicates that the deviation of the water level change rate and the deviation of the water level threshold have a significant impact on the water level reaching the current threshold. Therefore, the deviation of the average water change rate and the deviation of the water level threshold are corrected according to the time correction coefficient to generate the basic correction time.
[0101] The basic correction time refers to the correction value of the water level arrival time of the previous cycle based on the deviation of water level changes in adjacent cycles. The water velocity change influence time is obtained by multiplying the correction coefficient for the deviation of average water velocity change in the time correction coefficient calculated by the treatment terminal and the average water velocity change deviation. Then, the water level threshold influence time is obtained by multiplying the correction coefficient for the deviation of water level threshold change in the time correction coefficient and the water level threshold deviation. Finally, the difference between the water level threshold influence time and the water velocity change influence time is calculated to obtain the basic correction time.
[0102] Step S7012: If it is the pumping stage, the deviation of average water velocity and the deviation of trend duration are corrected according to the time correction coefficient to generate the basic correction time.
[0103] If the processing terminal determines that the real-time precipitation stage is the pumping stage, it indicates that the deviation of the water level change rate and the duration of the water level change trend have a significant impact on the water level reaching the current threshold. Therefore, the deviation of the average water change rate and the deviation of the trend duration are corrected according to the time correction coefficient to generate the basic correction time.
[0104] The basic correction time in this step is the same as the basic correction time in step S7011. The difference is that the basic correction time in this step is obtained by multiplying the correction coefficient for the deviation of the average water velocity in the time correction coefficient of the processing terminal calculation to obtain the water velocity change influence time. Then, the correction coefficient for the trend maintenance duration deviation in the time correction coefficient is calculated by multiplying the trend duration deviation to obtain the trend influence time. Finally, the difference between the trend influence time and the water velocity change influence time is calculated to obtain the basic correction time.
[0105] Step S702: Correct the historical state transition signal according to the basic correction time to generate the current state transition signal.
[0106] In this step, the current state transition signal is the same as the current state transition signal in step S4021. The processing terminal calculates the sum of the water level arrival time and the basic correction time corresponding to the historical state transition signal to obtain the current water level arrival time, thereby determining the current state transition signal.
[0107] Reference Figure 8 It also includes a verification step for the current state transition signal, the specific steps of which include: Step S800: Analyze the current state transition signal and the preset review pre-sequence time to determine the review start time.
[0108] The pre-verification time refers to the time required to verify the current state transition signal before the water level reaches a certain time. In this embodiment, 60 seconds is used as an example.
[0109] The review start time refers to the time when the review of the current state transition signal begins. The review start time is obtained by the processing terminal based on the time of the current state transition signal and after reviewing the preceding time.
[0110] Step S801: Collect and verify the water level change map based on the verification start time.
[0111] Among them, the water level change map for review refers to the water level change map at the time of review start-up. It is obtained by calling the water level change map at this time after the processing terminal determines that the time of review start-up has arrived.
[0112] Step S802: Determine the standard deviation of water level change and water level deviation based on the reviewed water level change map and the current water level change map.
[0113] Among them, the standard deviation of water level change refers to the standard deviation of water level velocity change. The processing terminal calculates the instantaneous slope of all sampling points from the review water level change map and the current water level change map, then calculates the average slope by averaging the instantaneous slope, and then calculates the standard deviation of water level change by using the standard deviation formula to calculate the instantaneous slope and the average slope.
[0114] Water level deviation refers to the difference between the water level at the time of verification and the water level at the predicted time of verification. The processing terminal retrieves the water level at the time of verification from the water level change map, and then predicts the water level at the time of verification based on the water level change pattern in the current water level change map. The difference is then used to obtain the water level deviation.
[0115] Step S803: Determine whether the standard deviation of water level change and the water level deviation meet the requirements of the preset verification standard conditions; The verification standard conditions refer to the verification standards when the prediction accuracy meets the requirements. These include the verification standard for the standard deviation of water level change, which is taken as not greater than 0.0005 in this embodiment; and the verification standard for water level deviation, which is taken as not greater than 0.02 in this embodiment. The requirement of the verification standard conditions means that the verification standard conditions are met.
[0116] By determining whether the standard deviation of water level change and the water level deviation both meet the verification criteria, it can be determined whether the prediction accuracy of the water level arrival time meets the usage requirements.
[0117] Step S8031: If the condition is met, then maintain the current state transition signal.
[0118] If the processing terminal determines that both the standard deviation of water level change and the water level deviation meet the verification standard conditions, it indicates that the prediction accuracy of the water level arrival time meets the usage requirements, and therefore the current state transition signal can be maintained.
[0119] Step S8032: If it does not meet the requirements, determine the current state transition signal based on the verified water level change diagram.
[0120] If the processing terminal determines that the standard deviation of water level change and the water level deviation do not meet the verification criteria, it indicates that the prediction accuracy of the water level arrival time is low. Therefore, the current state transition signal is re-determined based on the verified water level change diagram. The specific logic is... Figure 2 The logic of the intermediate steps is consistent and will not be elaborated here.
[0121] Based on the same inventive concept, embodiments of this application provide an intelligent precipitation control system, including: The data acquisition module is used to collect real-time precipitation data, monitor water levels in real time, record water level monitoring time, and verify water level change maps. A memory used to store the program for an intelligent precipitation control method; The processor is a program in memory that can be loaded and executed by the processor to implement an intelligent precipitation control method.
[0122] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device, and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.
[0123] This application provides a computer-readable storage medium storing a computer program that can be loaded by a processor and executed to provide an intelligent precipitation control method.
[0124] Computer storage media include, for example, USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media that can store program code.
[0125] Based on the same inventive concept, this application provides a smart terminal, including a memory and a processor, wherein the memory stores a computer program that can be loaded and executed by the processor to provide a smart precipitation control method.
[0126] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device, and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.
[0127] The above are all preferred embodiments of this application and are not intended to limit the scope of protection of this application. Any feature disclosed in this specification (including the abstract and drawings) may be replaced by other equivalent or similar features unless specifically stated otherwise. That is, unless specifically stated otherwise, each feature is only one example of a series of equivalent or similar features.
Claims
1. A smart precipitation control method, characterized in that, include: Real-time precipitation data is collected from pre-set precipitation equipment. The corresponding state transition threshold is found in the preset precipitation stage threshold relationship based on the real-time precipitation stage. Collect real-time water levels from pre-set precipitation wells; Analyze the real-time detected water level and state transition threshold to determine the current state transition signal of the precipitation equipment; The precipitation equipment is controlled to switch states based on the current state transition signal.
2. The intelligent precipitation control method according to claim 1, characterized in that, The steps for analyzing real-time water level and state transition thresholds to determine the current state transition signal of the precipitation equipment include: Time of water level detection in precipitation wells; The image is plotted based on the water level detection time and real-time water level to generate a current water level change map; Feature values are extracted and integrated based on the current water level change map to generate a current water level change feature matrix; Based on the historical water level change feature matrix and corresponding historical state transition signals collected during real-time precipitation stages; Analyze the similarity between the current water level change feature matrix and the historical water level change feature matrix to generate water level change similarity; The current state transition signal is determined by analyzing the state transition threshold, water level change similarity, and corresponding historical state transition signals.
3. The intelligent precipitation control method according to claim 2, characterized in that, The steps to analyze the similarity between the current water level change feature matrix and the historical water level change feature matrix to generate water level change similarity include: Calculate the cosine similarity between the current water level change feature matrix and the historical water level change feature matrix to generate water level change trend similarity; Calculate the Euclidean distance between the current water level change feature matrix and the historical water level change feature matrix to generate the water level change feature distance; The similarity of water level change characteristics is determined by analyzing the distance between water level change features and the preset maximum distance threshold. The water level change trend similarity and water level change feature similarity are weighted and summed according to the preset similarity weights to generate water level change similarity.
4. The intelligent precipitation control method according to claim 2, characterized in that, The steps to determine the current state transition signal by analyzing the state transition threshold, water level change similarity, and corresponding historical state transition signals include: Determine whether the similarity of water level changes meets the preset similarity threshold requirements; If the conditions are not met, the state transition threshold and the current water level change characteristic matrix are analyzed to determine the current state transition signal. If the conditions are met, the corresponding time correction coefficient will be found in the preset precipitation correction relationship based on the real-time precipitation stage. The time correction coefficient, the current water level change characteristic matrix, the historical water level change characteristic matrix, and the historical state transition signal are analyzed to determine the current state transition signal.
5. The intelligent precipitation control method according to claim 4, characterized in that, The steps for analyzing the state transition threshold and the current water level change feature matrix to determine the current state transition signal include: Determine the water level change trend characteristics, water level change rate characteristics, and current water level characteristics based on the current water level change characteristic matrix; Determine whether the water level change trend characteristics meet the preset requirements for accelerated change trend characteristics; If the condition is not met, the state transition threshold, water level change rate characteristics, and current water level characteristics are fitted according to the preset linear fitting model to determine the current state transition signal. If the conditions are met, the water level change trend characteristics, water level change rate characteristics, and current water level characteristics are substituted into the preset nonlinear fitting model to determine the nonlinear water level prediction function. The state transition threshold is substituted into the nonlinear water level prediction function for calculation to generate the current state transition signal.
6. The intelligent precipitation control method according to claim 5, characterized in that, The steps for determining the current state transition signal by fitting the state transition threshold, water level change rate characteristics, and current water level characteristics to a pre-defined linear fitting model include: Substitute the characteristics of water level change rate and current water level into the linear fitting model to determine the linear water level prediction function; The state transition threshold is substituted into the linear water level prediction function for calculation to generate the current state transition signal.
7. The intelligent precipitation control method according to claim 4, characterized in that, The steps for analyzing the time correction factor, the current water level change characteristic matrix, the historical water level change characteristic matrix, and the historical state transition signal to determine the current state transition signal include: Determine the average water level change velocity deviation, water level threshold deviation, and trend duration deviation based on the current water level change characteristic matrix and the historical water level change characteristic matrix; Determine whether the real-time precipitation stage is a preset pumping stage or a preset water storage stage; If it is the water storage stage, the deviation of average water velocity and water level threshold are corrected according to the time correction coefficient to generate the basic correction time. If it is the pumping stage, the deviation of average water velocity and the deviation of trend duration are corrected according to the time correction coefficient to generate the basic correction time. The historical state transition signals are corrected based on the baseline correction time to generate the current state transition signal.
8. The intelligent precipitation control method according to claim 2, characterized in that, It also includes a verification step for the current state transition signal, the specific steps of which include: The current state transition signal and the preset review pre-processing time are analyzed to determine the review start time; The water level change map was collected based on the start time of the review; Determine the standard deviation of water level change and water level deviation based on the reviewed water level change map and the current water level change map; Determine whether the standard deviation of water level change and the water level deviation meet the requirements of the preset verification standard conditions; If the conditions are met, the current state transition signal is maintained; If it does not meet the requirements, the current state transition signal will be determined based on the verified water level change diagram.
9. An intelligent precipitation control system, characterized in that, include: The data acquisition module is used to collect real-time precipitation data and monitor water levels in real time. A memory for storing a program of an intelligent precipitation control method as described in any one of claims 1 to 8; The processor and the program in the memory can be loaded and executed by the processor to implement the intelligent precipitation control method as described in any one of claims 1 to 8.
10. A smart terminal, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and executed as described in any one of claims 1 to 8.