A smart water supply device control method and system based on the Internet of Things

Abstract

The present invention discloses a control method for an intelligent water supply device based on the Internet of Things, and the method comprises: obtaining a water level height analog quantity, real-time status data of a water-using device, and environmental data; performing regression analysis on the water level height analog quantity to obtain a predicted water level height, and performing weighted calculation on the water level characteristics; performing analysis on the real-time status data to obtain real-time status characteristics, and performing regression analysis on the environmental data to obtain an environmental impact function value; performing weighted calculation on the water level characteristics and the real-time status characteristics to obtain an initial decision score, and then optimizing the initial decision according to the environmental impact function value to obtain an optimized decision score; judging according to the optimized decision score and a preset water supply time trigger threshold, determining the water supply time and generating a water supply instruction to send to the intelligent water supply device to start the water supply operation; after the water-using device stops pumping water, monitoring the water level, and sending a stop water supply instruction when the preset water level height threshold is reached. The method can reduce energy waste.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent water filler control, and in particular to an intelligent water filler control method and system based on the Internet of Things. Background Art

[0002] At present, the water pump system in residential water equipment mostly adopts the traditional water tank and water supply device structure. In these systems, the water tank is usually not equipped with a metering device, and the water consumption of the water pump cannot be directly measured, resulting in a waste of water resources and not conducive to improving energy efficiency. In the water supply device, most water pumps are multi-stage pumps, which achieve single-stage operation through a fixed water level to reduce energy consumption. However, the control system of most household water supply devices is not perfect, and it is impossible to reasonably monitor and control the operating status of the water pump, and lacks automatic monitoring functions, which affects the energy saving effect and water use efficiency.

[0003] In an existing intelligent water pump control solution, by building an intelligent water pump control test bench, the operation of the water pump is optimized by using the dual-effect energy-saving mode, the water pump soft start mode and the water level optimization mode, which can achieve energy-saving effects. However, although these applications effectively optimize the control of the water pump, they cannot achieve single-stage water supply operation according to water level changes. Most systems fail to consider the combination of water level control and water use plan, resulting in overload operation of the water pump or frequent water supply, which still generates unnecessary energy waste.

[0004] In summary, the existing technology cannot realize single-stage water supply according to the water level, and the control of the water supply device will cause unnecessary energy waste. Summary of the invention

[0005] The present invention provides an intelligent water supply device control method and system based on the Internet of Things to reduce energy waste and improve the efficiency and effect of water resource management.

[0006] In the first aspect, in order to solve the above problems, the present invention provides a smart water supply device control method based on the Internet of Things, comprising: Obtain the water level analog value output by the sensor of the water supply device, the real-time status data of the water-using equipment, the environmental data and the current water level; Performing regression analysis on the water level height simulation to obtain a predicted water level height, and performing weighted calculation on the predicted water level height to obtain water level characteristics; Analyze the real-time status data to obtain real-time status characteristics, and perform regression analysis on the environmental data to obtain an environmental impact function value; Performing weighted calculation on the water level characteristics and the real-time state characteristics to obtain an initial decision score, and optimizing the initial decision score according to the environmental impact function value to obtain an optimized decision score; The optimization decision score is judged against a preset water supply time trigger threshold, and the water supply time is determined according to the judgment result and the real-time status data; Generate a water supply instruction according to the water supply time, and send the water supply instruction to the intelligent water supply device so that the intelligent water supply device starts the water supply operation; After the water-using equipment stops pumping water, when the current water level is greater than or equal to a preset water level threshold, a stop water supply instruction is sent.

[0007] As an optional implementation, performing regression analysis on the water level simulation to obtain a predicted water level includes: Set the initial time, calculate the water level difference between the water level simulation value at the initial time and the water level simulation value after the predetermined time interval; Obtaining the arrival time when the water level difference reaches a preset difference value, and subtracting the arrival time from the initial time to obtain the water level difference arrival time; Perform regression analysis based on the water level difference and the time when the water level difference is reached to obtain the predicted water level height; The predicted water level is calculated by the following formula: In the formula, To predict the water level, h for The water level analog value at the moment, h for The water level simulation value at the moment, is the water level difference, is the water level difference reaching time, is the arrival time, is the initial time; is the error amount, which represents the error between the predicted water level and the actual water level , is the preset regression coefficient.

[0008] As an optional implementation, weighted calculation is performed on the predicted water level height to obtain water level characteristics, including: Calculate the water level change rate by calculating the ratio of the predicted water level height to the water level difference arrival time; Obtain the maximum water level and the minimum water level between the initial time and the arrival time, and obtain the fluctuation amplitude by subtracting the maximum water level from the minimum water level; Performing weighted calculation on the water level difference, the water level change rate and the fluctuation amplitude to obtain water level characteristics; The water level characteristic is calculated by the following formula: + In the formula, , and is the weighting coefficient; is the water level difference, is the fluctuation range; is the water level change rate; The water level characteristic.

[0009] As an optional implementation, weighted calculation is performed on the water level characteristics and the real-time state characteristics to obtain an initial decision score, and the initial decision score is optimized according to the environmental impact function value to obtain an optimized decision score, including: Among them, the initial decision score is calculated by the following formula: The optimization decision score is calculated using the following formula: In the formula, Score the initial decision, is the water level characteristic, For real-time status features, is the weight of the water level feature, is the weight of the real-time status feature, To optimize decision scoring, is the environmental impact function value, The weights used to score the initial decision, is the weight of the environmental impact function value.

[0010] As an optional implementation, the optimization decision score is judged against a preset water supply time trigger threshold, and the water supply time is determined according to the judgment result and the real-time status data, including: It is determined whether the optimization decision score exceeds the water supply time trigger threshold. If so, the water supply time is determined according to the real-time status data; if not, the status quo is maintained and the water supply time is delayed.

[0011] As an optional implementation manner, performing regression analysis on the environmental data to obtain an environmental impact function value includes: Standardizing the environmental data to obtain standard environmental data; The standard environmental data is subjected to regression analysis according to a linear regression model to calculate the environmental impact function value.

[0012] As an optional implementation manner, determining the water supply time according to the real-time status data includes: The water filling time is calculated by the following formula: In the formula, It's time to go on the water. is the rated power of the first water pump, is the working time of the first water pump, is the number of electrical equipment, is the average power of a single electrical device, is the real-time water pressure output by the first water pump, is the water flow rate, is the ambient temperature.

[0013] In a second aspect, the present invention provides an intelligent water supply device control system based on the Internet of Things, comprising: A data acquisition module is used to obtain the water level analog output by the sensor of the water supply device, the real-time status data of the water-using equipment and the environmental data; A feature extraction module performs regression analysis on the water level simulation to obtain a predicted water level, performs weighted calculation on the predicted water level to obtain water level features, and analyzes the real-time status data to obtain real-time status features; An environmental impact function value acquisition module is used to perform regression analysis on the environmental data to obtain an environmental impact function value; A decision score optimization module performs weighted calculation on the water level characteristics and the real-time state characteristics to obtain an initial decision score, and optimizes the initial decision score according to the environmental impact function value to obtain an optimized decision score; A water supply time determination module judges the optimization decision score and a preset water supply time trigger threshold, and determines the water supply time according to the judgment result and the real-time status data; A water supply instruction generating module generates a water supply instruction according to the water supply time, and sends the water supply instruction to the intelligent water supply device so that the intelligent water supply device starts the water supply operation; The water supply stop instruction sending module obtains the current water level height output by the sensor of the water supply device again after the water-using equipment stops pumping water. When the current water level height is greater than or equal to the preset water level height threshold, the water supply stop instruction is sent.

[0014] In a third aspect, the present invention further provides an electronic device comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements any one of the above-mentioned methods for controlling an intelligent water filler based on the Internet of Things.

[0015] In a fourth aspect, the present invention further provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute any one of the above-mentioned methods for controlling an intelligent water supply device based on the Internet of Things.

[0016] Compared with the prior art, the present invention has the following beneficial effects: the present invention realizes intelligent control of the water supply process through a smart water supply device control method and system based on the Internet of Things, thereby reducing energy waste. First, the water level height analog quantity, the real-time status data of the water-using equipment and the environmental data are collected in real time by the sensor, the feature extraction module performs regression analysis on the water level height analog quantity to obtain the predicted water level height, and then the water level characteristics are obtained by the weighted calculation method, and the real-time status data is analyzed to obtain the real-time status characteristics. At the same time, the environmental impact function value acquisition module performs standardized processing and regression analysis on the environmental data to calculate the environmental impact function value. In the decision scoring optimization module, the initial decision score is obtained by weighted calculation in combination with the water level characteristics and the real-time status characteristics, and the score is further optimized according to the environmental impact function value. The water supply time determination module compares the optimized decision score with the preset water supply time trigger threshold to determine the water supply time, thereby reducing the delay and error of human operation and improving the response speed and accuracy of the water supply operation. The water supply instruction generation module generates a water supply instruction according to the determined water supply time and sends it to the intelligent water supply device to accurately control the start of the water supply operation, avoiding the waste of water resources caused by excessive water supply. The water supply stop instruction sending module starts immediately after the water-using equipment stops pumping water. It monitors the current water level in real time through the sensor connected to the water supply device and compares it with the preset water level threshold. When the water level reaches or exceeds this safety threshold, the module quickly generates and sends a water supply stop instruction to cut off the water source in time to prevent excessive water supply.

[0017] In summary, the present invention provides an intelligent water supply device control method and system based on the Internet of Things, which realizes the precise management of water supply operation, not only saves energy, but also improves the efficiency and reliability of the system. In long-term operation, this intelligent control method can significantly reduce energy consumption, reduce equipment loss, reduce operating costs, and improve the efficiency and effect of water resource management, with significant economic and environmental benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a flow chart of a method for controlling an intelligent water dispenser based on the Internet of Things provided by an embodiment of the present invention; Figure 2 It is a structural schematic diagram of an intelligent water supply device control system based on the Internet of Things provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] Reference Figure 1 The first embodiment of the present invention provides a method for controlling an intelligent water supply device based on the Internet of Things, comprising the following steps: S11, obtaining the water level analog value output by the sensor of the water supply device, the real-time status data of the water-using equipment, the environmental data and the current water level; S12, performing regression analysis on the water level simulation to obtain a predicted water level, and performing weighted calculation on the predicted water level to obtain a water level characteristic; S13, analyzing the real-time status data to obtain real-time status characteristics, and performing regression analysis on the environmental data to obtain an environmental impact function value; S14, performing weighted calculation on the water level characteristics and the real-time state characteristics to obtain an initial decision score, and optimizing the initial decision score according to the environmental impact function value to obtain an optimized decision score; S15, judging the optimization decision score and a preset water supply time trigger threshold, and determining the water supply time according to the judgment result and the real-time status data; S16, generating a water supply instruction according to the water supply time, and sending the water supply instruction to the intelligent water supply device, so that the intelligent water supply device starts a water supply operation; S17, after the water-using equipment stops pumping water, when the current water level is greater than or equal to a preset water level threshold, a stop water supply instruction is sent.

[0021] It should be noted that S11 to S17 constitute a complete intelligent water supply control process. Data is collected through sensors, including the water level height simulation of the water supply device, the real-time status data of the water-using equipment, and environmental data, to provide a data basis for subsequent analysis. The water level height simulation is processed using regression analysis to predict future water level changes. The predicted water level height is weighted to extract water level characteristics, and the real-time status data is analyzed to extract real-time status characteristics. At the same time, regression analysis is performed on the environmental data to obtain the environmental impact function value. These two characteristics jointly reflect the impact of water-using equipment and the environment on the water supply operation. The initial decision score is obtained by combining the water level characteristics and the real-time status characteristics for weighted calculation, and then the initial decision score is optimized according to the environmental impact function value to obtain a more accurate optimized decision score. The optimized decision score is compared with the preset water supply time trigger threshold, and the specific water supply time is determined based on the comparison result and real-time status data. A water supply instruction is generated based on the determined water supply time and sent to the intelligent water supply device to start the water supply operation to ensure the timely supply of water resources. When the water-using equipment stops pumping water, the current water level height is monitored again. If the current water level height reaches or exceeds the preset water level height threshold, a stop water supply instruction will be sent to cut off the water source in time to prevent waste of water resources and potential overflow risks.

[0022] It is worth mentioning that the present invention demonstrates an efficient and accurate intelligent water supply control process. Through regression analysis and weighted calculation methods, it realizes precise control of water supply operations, improves the efficiency of water resource management, and reduces energy consumption, with significant economic and environmental benefits.

[0023] In step S11, the water level analog value output by the sensor of the water supply device, the real-time status data of the water-using equipment and the environmental data are obtained.

[0024] It should be noted that the intelligent sensors are deployed in the water supply device and water-using equipment to collect the water level analog value, the real-time status data of the water-using equipment and the environmental data in real time, and transmit these data to the data processing center. The water level analog value is the water level data obtained by real-time monitoring by the sensor of the water supply device. It reflects the current height of the water in the water supply device and is a key indicator for judging the water supply operation. The real-time status data of the water-using equipment includes the operating parameters, power and energy consumption of the equipment. These data are used to evaluate the operating efficiency and health of the equipment. Environmental data includes temperature and humidity. These data are used to analyze the impact of the environment on the water supply operation.

[0025] In step S12, regression analysis is performed on the water level simulation to obtain a predicted water level, and weighted calculation is performed on the predicted water level to obtain water level characteristics, including: Set the initial time, calculate the water level difference between the water level simulation value at the initial time and the water level simulation value after the predetermined time interval; Obtaining the arrival time when the water level difference reaches a preset difference value, and subtracting the arrival time from the initial time to obtain the water level difference arrival time; Perform regression analysis based on the water level difference and the time when the water level difference is reached to obtain the predicted water level height; The predicted water level is calculated by the following formula: In the formula, To predict the water level, h for The water level analog value at the moment, h for The water level simulation value at the moment, is the water level difference, is the water level difference reaching time, is the arrival time, is the initial time; is the error amount, which represents the error between the predicted water level and the actual water level , is the preset regression coefficient.

[0026] Calculate the water level change rate by calculating the ratio of the predicted water level height to the water level difference arrival time; Obtain the maximum water level and the minimum water level between the initial time and the arrival time, and obtain the fluctuation amplitude by subtracting the maximum water level from the minimum water level; Performing weighted calculation on the water level difference, the water level change rate and the fluctuation amplitude to obtain water level characteristics; The water level characteristic is calculated by the following formula: + In the formula, , and is the weighting coefficient; is the water level difference, is the fluctuation range; is the water level change rate; The water level characteristic.

[0027] It should be noted that, first, an initial time point is set, and the difference between the water level simulation at that moment and the water level simulation after the predetermined time interval is calculated. This difference is called the water level. Then, the time required for the water level difference to reach the preset difference is determined, and the difference between this time and the initial time, that is, the water level difference arrival time, is calculated. Using these data, regression analysis is performed to obtain the predicted water level height. The predicted water level height is calculated by a formula, which includes the water level simulation at different time points, the water level difference, the water level difference arrival time, the initial time, the error amount and the preset regression coefficient. Further, the predicted water level height is calculated by the ratio of the water level difference arrival time to obtain the water level change rate, which reflects the speed of the water level change. At the same time, the maximum and minimum values ​​of the water level between the initial time and the arrival time are also calculated, and their difference, that is, the fluctuation amplitude, is calculated. This amplitude reflects the severity of the water level change. Finally, the three parameters of water level difference, water level change rate and fluctuation amplitude are weighted to obtain water level characteristics, which integrate the speed and intensity of water level changes and provide key data support for the system's intelligent water supply decision. The calculation formula of water level characteristics includes weighted coefficients, which are preset according to the system design and actual needs to ensure the accuracy and effectiveness of characteristic calculation. Through such step-by-step calculation, the changing trend of water level can be accurately captured, and intelligent water supply decisions can be made accordingly.

[0028] In step S13, the real-time state data is analyzed to obtain real-time state characteristics, and the environmental data is subjected to regression analysis to obtain an environmental impact function value, including: Standardizing the environmental data to obtain standard environmental data; Performing regression analysis on the standard environmental data according to a linear regression model to calculate an environmental impact function value; It should be noted that the real-time status data is collected through sensors, which include the real-time operating parameters, power and energy consumption of water-using equipment. The collected data will be preprocessed, including cleaning, denoising and formatting, to ensure the quality and consistency of the data. Subsequently, the key real-time status features are extracted from the preprocessed data through feature extraction technology, which can accurately reflect the current working status of the water-using equipment. For environmental data, preprocessing is also performed first to ensure the accuracy of the analysis. Then, the environmental data is analyzed using regression analysis methods to obtain environmental impact function values, which quantify the impact of environmental factors such as temperature and humidity on water supply operations. Through these analyses, more accurate water supply decisions can be made based on the real-time status of the equipment and environmental conditions, thereby improving the efficiency and responsiveness of water resource management.

[0029] In step S14, weighted calculation is performed on the water level characteristics and the real-time state characteristics to obtain an initial decision score, and the initial decision score is optimized according to the environmental impact function value to obtain an optimized decision score, including: Among them, the initial decision score is calculated by the following formula: The optimization decision score is calculated using the following formula: In the formula, Score the initial decision, is the water level characteristic, For real-time status features, is the weight of the water level feature, is the weight of the real-time status feature, To optimize decision scoring, is the environmental impact function value, The weights used to score the initial decision, is the weight of the environmental impact function value.

[0030] It should be noted that the initial decision score is obtained by weighted calculation of water level characteristics and real-time status characteristics. The water level characteristics and real-time status characteristics represent the water level of the water supply device and the current working status of the water-using equipment, respectively. These characteristics have different weights in the decision-making process, and the size of the weight reflects their importance in the decision-making. The optimized decision score is based on the initial decision score and further considers the influence of the environmental impact function value. The environmental impact function value quantifies the impact of environmental factors such as temperature and humidity on the water supply operation. By incorporating these impacts into the decision-making process, a more accurate and adaptable decision score can be obtained.

[0031] It is worth noting that through this weighted calculation and optimization process, the water level, equipment status and environmental factors can be comprehensively considered to obtain an optimized decision score. This decision score calculation method improves the decision quality of the system and makes water supply operations more precise and efficient, thereby achieving optimized management of water resources and saving energy consumption.

[0032] In step S15, the optimization decision score is judged against a preset water supply time trigger threshold, and the water supply time is determined according to the judgment result and the real-time status data, including: The water filling time is calculated by the following formula: In the formula, It's time to go on the water. is the rated power of the first water pump, is the working time of the first water pump, is the number of electrical equipment, is the average power of a single electrical device, is the real-time water pressure output by the first water pump, is the water flow rate, is the ambient temperature.

[0033] It is determined whether the optimization decision score exceeds the water supply time trigger threshold. If so, the water supply time is determined according to the real-time status data; if not, the status quo is maintained and the water supply time is delayed.

[0034] It should be noted that the calculation formula for water supply time takes into account the rated power of the first water pump. , Working time of the first water pump , the number of electrical devices n, the average power of a single electrical device , Real-time water pressure output by the first water pump , water flow and ambient temperature , this formula uses these parameters to calculate the water time , ensuring that the water supply operation meets the water demand while taking into account energy efficiency and equipment workload. In this formula, the rated power of the first water pump and working hours Determines the working capacity of the water pump, the number of electrical equipment n and the average power of a single electrical equipment Reflects the power demand of the system, while the real-time water pressure output by the first water pump and water flow It is directly related to water supply efficiency and ambient temperature. It is also an important factor because it may affect the physical properties of water and the working efficiency of equipment. The setting of the water supply time trigger threshold is based on the predicted water level height simulation. At the same time, the system response time and operation efficiency are considered to avoid energy waste and equipment loss caused by frequent start and stop. The impact of environmental data and real-time status data of water-using equipment is also taken into consideration to adapt to water resource management under different conditions. First, the calculated optimization decision score is compared with the preset water supply time trigger threshold. If the optimization decision score exceeds the preset water supply time trigger threshold, it will be considered that the current time is appropriate for water supply operation. In this case, the specific water supply time will be determined based on real-time status data, such as the current working status of the equipment and environmental conditions. This ensures that the water supply operation can respond to water demand in a timely manner while avoiding unnecessary energy waste. If the optimization decision score does not exceed the preset threshold, the status quo will be maintained and the water supply time will be delayed.

[0035] It is worth noting that this decision takes into account the current water level, equipment status and environmental conditions to ensure efficiency and savings in water resource management. Through this scoring and threshold-based judgment mechanism, the water supply time can be flexibly adjusted to meet water demand and optimize energy use. This approach improves the automation level of the system, reduces human intervention, and makes water resource management more efficient and precise.

[0036] In step S16, a water filling instruction is generated according to the water filling time, and the water filling instruction is sent to the intelligent water filling device so that the intelligent water filling device starts the water filling operation.

[0037] It should be noted that this step will be entered after the calculated water supply time determines the specific water supply time. At this time, a clear water supply instruction will be generated. This instruction contains all the information required to start the water supply operation, such as the duration and flow rate of water supply. After the water supply instruction is generated, the instruction will be sent to the smart water supply device through the Internet of Things communication network, so that it can perform the water supply operation according to the predetermined parameters. After receiving the instruction, the smart water supply device will start the water pump and supply water according to the specified parameters in the instruction to ensure the timely supply of water resources.

[0038] It is worth noting that this process reflects the automation and intelligence of the intelligent water supply control system. Through the precise command generation and sending mechanism, the precise control of water supply operation is achieved. Such a design not only improves the efficiency of water resource management, but also ensures the reliability and safety of water supply operation, avoiding the risk of excessive water supply or water shortage. In this way, the intelligent water supply control system can respond to real-time water demand, optimize energy use, and achieve efficient management of water resources.

[0039] In step S17, after the water-using equipment stops pumping water, the current water level height output by the sensor of the water supply device is obtained again, and when the current water level height is greater than or equal to the preset water level height threshold, a stop water supply instruction is sent.

[0040] It should be noted that a safety and efficiency check process is performed. After the water-using equipment completes the pumping operation, it will not immediately enter the dormant state, but continue to perform the monitoring task. It will use the sensor installed in the water supply device to obtain the current water level height data again. This is to ensure that the result of the water supply operation meets expectations and the water level is maintained within a safe and effective range. The sensor monitors and outputs the current water level height information in real time, and compares these data with the preset water level height threshold to ensure that the water level is neither too high to cause overflow nor too low to affect the next water demand. If the current water level reaches or exceeds the preset water level threshold, it will be judged that the water supply operation is sufficient, and there is no need to continue water supply. After confirming that the water level has reached an appropriate level, a stop water supply instruction will be sent to the smart water supply device. This instruction will be executed immediately, thereby shutting down the water pump and stopping the water supply operation. This step not only prevents the waste of water resources, but also avoids equipment damage or safety risks that may be caused by excessively high water levels.

[0041] It is worth noting that through this sophisticated monitoring and response mechanism, closed-loop control of water supply operations is achieved, ensuring the effective management of water resources while also improving the reliability and safety of the system. This intelligent water level management strategy makes water supply operations more precise and efficient, providing strong support for the sustainable use of water resources.

[0042] In order to facilitate the understanding of the present invention, some preferred embodiments of the present invention are further described below.

[0043] In this embodiment, water supply needs to be provided to a residential area.

[0044] The working process is as follows: Step 1: Data collection. Collect water level simulation, real-time status data of water-using equipment, and environmental data from the water level sensor of the community water tower, the monitoring system of water-using equipment, and environmental monitoring equipment.

[0045] Step 2: Feature extraction. Perform regression analysis on water level data, predict water level change trends, and calculate water level features. At the same time, analyze the real-time status and environmental data of water-using equipment to extract real-time status features and environmental impact function values.

[0046] Step 3: Calculation of decision score. Using the extracted features, the initial decision score is calculated. This score is based on the comprehensive consideration of the water level simulation, the real-time status data of the water-using equipment, and the environmental data. Then, the initial score is optimized to obtain the optimized decision score.

[0047] Step 4: Determine the water time. Compare the optimization decision score with the preset water time trigger threshold to determine whether the water time has been reached. If the score exceeds the threshold, the optimal water time will be calculated based on the real-time status data.

[0048] Step 5: Generate water supply instruction. Generate water supply instruction and send it to the smart water supply device.

[0049] Step 6: The water supply operation is executed. The water supply instruction is sent to the intelligent water supply device, which starts the water pump and supplies water according to the preset parameters.

[0050] Step 7: Monitor and stop water supply. After the water-using equipment stops pumping water, continue to monitor the current water level. When the water level reaches or exceeds the preset water level height threshold, immediately send a stop water supply instruction to shut down the water pump to prevent water waste and water tower overflow, ensuring the safety and efficiency of the water supply system.

[0051] In summary, the present invention provides an intelligent water supply device control method and system based on the Internet of Things, which optimizes water resource management and reduces energy waste through a series of steps. First, key data, water level height simulation, real-time status data of water-using equipment and environmental data are collected. These data provide a basis for subsequent intelligent analysis and decision-making. Then, the water level height simulation is regressed and analyzed to predict the trend of water level changes and calculate water level characteristics. At the same time, real-time status characteristics and environmental impact function values ​​are also extracted. These characteristics reflect the operating efficiency of the equipment and the impact of the environment on the water supply system. Subsequently, these characteristics are combined to calculate the initial decision score, and it is optimized to obtain the optimized decision score. This score is compared with the preset water supply time trigger threshold to determine whether it is necessary to start the water supply operation. If the score exceeds the threshold, the optimal water supply time will be calculated based on the real-time status data, and a water supply instruction will be generated and sent to the intelligent water supply device to start the water pump for water supply. After the water-using equipment stops pumping water, the current water level will continue to be monitored to ensure that the water level will not exceed the preset water level height threshold due to excessive water supply. Once the water level reaches or exceeds the preset water level height threshold, a stop water supply instruction will be sent immediately to shut down the water pump to prevent water resource waste and water tower overflow, thereby ensuring the safety and efficiency of the water supply system. In general, through this series of intelligent operations, the present invention can not only flexibly respond to residents' water needs, but also optimize energy consumption, reduce operating costs, and improve the reliability and safety of the water supply system, thereby ensuring the continuity and efficiency of water supply.

[0052] Reference Figure 2 The present invention provides an intelligent water supply device control system based on the Internet of Things, comprising: A data acquisition module is used to obtain the water level analog output by the sensor of the water supply device, the real-time status data of the water-using equipment and the environmental data; A feature extraction module performs regression analysis on the water level simulation to obtain a predicted water level, performs weighted calculation on the predicted water level to obtain water level features, and analyzes the real-time status data to obtain real-time status features; An environmental impact function value acquisition module is used to analyze the real-time state data to obtain real-time state characteristics, and to perform regression analysis on the environmental data to obtain an environmental impact function value; A decision score optimization module performs weighted calculation on the water level characteristics and the real-time state characteristics to obtain an initial decision score, and optimizes the initial decision score according to the environmental impact function value to obtain an optimized decision score; A water supply time determination module judges the optimization decision score and a preset water supply time trigger threshold, and determines the water supply time according to the judgment result and the real-time status data; A water supply instruction generating module generates a water supply instruction according to the water supply time, and sends the water supply instruction to the intelligent water supply device so that the intelligent water supply device starts the water supply operation; The water supply stop instruction sending module obtains the current water level height output by the sensor of the water supply device again after the water-using equipment stops pumping water. When the current water level height is greater than or equal to the preset water level height threshold, the water supply stop instruction is sent.

[0053] In summary, the present invention provides an intelligent water supply control system based on the Internet of Things, which optimizes water resource management and reduces energy waste through a series of steps. First, key data, water level height simulation, real-time status data of water-using equipment and environmental data are collected. These data provide a basis for subsequent intelligent analysis and decision-making. Then, the water level height simulation is regressed and analyzed to predict the trend of water level changes and calculate water level characteristics. At the same time, real-time status characteristics and environmental impact function values ​​are also extracted. These characteristics reflect the operating efficiency of the equipment and the impact of the environment on the water supply system. Subsequently, these characteristics are combined to calculate the initial decision score, and it is optimized to obtain the optimized decision score. This score is compared with the preset water supply time trigger threshold to determine whether it is necessary to start the water supply operation. If the score exceeds the threshold, the optimal water supply time will be calculated based on the real-time status data, and a water supply instruction will be generated and sent to the intelligent water supply device to start the water pump for water supply. After the water-using equipment stops pumping water, the current water level will continue to be monitored to ensure that the water level will not exceed the preset water level height threshold due to excessive water supply. Once the water level reaches or exceeds the preset water level height threshold, a stop water supply instruction will be sent immediately to shut down the water pump to prevent water resource waste and water tower overflow, thereby ensuring the safety and efficiency of the water supply system. In general, through this series of intelligent operations, the present invention can not only flexibly respond to residents' water needs, but also optimize energy consumption, reduce operating costs, and improve the reliability and safety of the water supply system, thereby ensuring the continuity and efficiency of water supply.

[0054] It should be noted that the smart water filler control system based on the Internet of Things provided in an embodiment of the present invention is used to execute all the process steps of the smart water filler control method based on the Internet of Things in the above-mentioned embodiment. The working principles and beneficial effects of the two correspond one to one, and therefore will not be repeated here.

[0055] The embodiment of the present invention further provides an electronic device. The electronic device includes: a processor, a memory, and a computer program stored in the memory and executable on the processor, such as an intelligent water supply device control program based on the Internet of Things. When the processor executes the computer program, the steps in the above-mentioned intelligent water supply device control method embodiments based on the Internet of Things are implemented, such as Figure 1 Alternatively, when the processor executes the computer program, the functions of each module / unit in the above-mentioned device embodiments are implemented, such as the feature extraction module.

[0056] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program in the electronic device.

[0057] The electronic device may be a computing device such as a desktop computer, a notebook, a PDA, and a smart tablet. The electronic device may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that the above components are merely examples of electronic devices and do not constitute a limitation on the electronic device. The electronic device may include more or fewer components than the above components, or may combine certain components, or different components. For example, the electronic device may also include input and output devices, network access devices, buses, etc.

[0058] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the electronic device, and uses various interfaces and lines to connect various parts of the entire electronic device.

[0059] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the electronic device by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0060] Wherein, if the module / unit integrated in the electronic device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0061] It should be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art may understand and implement it without paying any creative effort.

[0062] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. A smart water supply device control method based on the Internet of Things, characterized in that: Executed by the Control Center, including: Obtain the water level analog value output by the sensor of the water supply device, the real-time status data of the water-using equipment, and the environmental data; Performing regression analysis on the water level height simulation to obtain a predicted water level height, and performing weighted calculation on the predicted water level height to obtain water level characteristics; Analyze the real-time status data to obtain real-time status characteristics, and perform regression analysis on the environmental data to obtain an environmental impact function value; Performing weighted calculation on the water level characteristics and the real-time state characteristics to obtain an initial decision score, and optimizing the initial decision score according to the environmental impact function value to obtain an optimized decision score; The optimization decision score is judged against a preset water supply time trigger threshold, and the water supply time is determined according to the judgment result and the real-time status data; Generate a water supply instruction according to the water supply time, and send the water supply instruction to the intelligent water supply device so that the intelligent water supply device starts the water supply operation; After the water-using equipment stops pumping water, the current water level height output by the sensor of the water supply device is obtained again. When the current water level height is greater than or equal to the preset water level height threshold, a stop water supply instruction is sent.

2. The method for controlling an intelligent water supply device based on the Internet of Things according to claim 1 is characterized in that: The water level simulation is subjected to regression analysis to obtain the predicted water level, including: Set the initial time, calculate the water level difference between the water level simulation value at the initial time and the water level simulation value after the predetermined time interval; Obtaining the arrival time when the water level difference reaches a preset difference value, and subtracting the arrival time from the initial time to obtain the water level difference arrival time; Perform regression analysis based on the water level difference and the time when the water level difference is reached to obtain the predicted water level height; The predicted water level is calculated by the following formula: In the formula, To predict the water level, h for The water level analog value at the moment, h for The water level simulation value at the moment, is the water level difference, is the water level difference reaching time, is the arrival time, is the initial time; is the error amount, which represents the error between the predicted water level and the actual water level , is the preset regression coefficient.

3. The method for controlling an intelligent water supply device based on the Internet of Things according to claim 2 is characterized in that: The predicted water level height is weightedly calculated to obtain water level characteristics, including: Calculate the water level change rate by calculating the ratio of the predicted water level height to the water level difference arrival time; Obtain the maximum water level and the minimum water level between the initial time and the arrival time, and obtain the fluctuation amplitude by subtracting the maximum water level from the minimum water level; Performing weighted calculation on the water level difference, the water level change rate and the fluctuation amplitude to obtain water level characteristics; The water level characteristic is calculated by the following formula: In the formula, , and is the weighting coefficient; is the water level difference, is the fluctuation range; is the water level change rate; The water level characteristic.

4. The method for controlling an intelligent water supply device based on the Internet of Things according to claim 1 is characterized in that: The water level characteristics and the real-time state characteristics are weighted to obtain an initial decision score, and the initial decision score is optimized according to the environmental impact function value to obtain an optimized decision score. include: Among them, the initial decision score is calculated by the following formula: The optimization decision score is calculated using the following formula: In the formula, Score the initial decision, is the water level characteristic, For real-time status features, is the weight of the water level feature, is the weight of the real-time status feature, To optimize decision scoring, is the environmental impact function value, The weights used to score the initial decision, is the weight of the environmental impact function value.

5. The method for controlling an intelligent water supply device based on the Internet of Things according to claim 4 is characterized in that The optimization decision score is judged against a preset water supply time trigger threshold, and the water supply time is determined according to the judgment result and the real-time status data, including: It is determined whether the optimization decision score exceeds the water supply time trigger threshold. If so, the water supply time is determined according to the real-time status data; if not, the status quo is maintained and the water supply time is delayed.

6. The method for controlling an intelligent water supply device based on the Internet of Things according to claim 4 is characterized in that: Performing regression analysis on the environmental data to obtain an environmental impact function value includes: Standardizing the environmental data to obtain standard environmental data; The standard environmental data is subjected to regression analysis according to a linear regression model to calculate the environmental impact function value.

7. The method for controlling an intelligent water supply device based on the Internet of Things according to claim 5 is characterized in that Determining the water supply time according to the real-time status data includes: The water filling time is calculated by the following formula: In the formula, It's time to go on the water. is the rated power of the first water pump, is the working time of the first water pump, is the number of electrical equipment, is the average power of a single electrical device, is the real-time water pressure output by the first water pump, is the water flow rate, is the ambient temperature.

8. An intelligent water supply device control system based on the Internet of Things, characterized in that: include: A data acquisition module is used to obtain the water level analog output by the sensor of the water supply device, the real-time status data of the water-using equipment and the environmental data; A feature extraction module performs regression analysis on the water level simulation to obtain a predicted water level, performs weighted calculation on the predicted water level to obtain water level features, and analyzes the real-time status data to obtain real-time status features; An environmental impact function value acquisition module is used to analyze the real-time state data to obtain real-time state characteristics, and to perform regression analysis on the environmental data to obtain an environmental impact function value; A decision score optimization module performs weighted calculation on the water level characteristics and the real-time state characteristics to obtain an initial decision score, and optimizes the initial decision score according to the environmental impact function value to obtain an optimized decision score; A water supply time determination module judges the optimization decision score and a preset water supply time trigger threshold, and determines the water supply time according to the judgment result and the real-time status data; A water supply instruction generating module generates a water supply instruction according to the water supply time, and sends the water supply instruction to the intelligent water supply device so that the intelligent water supply device starts the water supply operation; The water supply stop instruction sending module obtains the current water level height output by the sensor of the water supply device again after the water-using equipment stops pumping water. When the current water level height is greater than or equal to the preset water level height threshold, the water supply stop instruction is sent.

9. An electronic device, characterized in that: It comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, it implements an intelligent water supply device control based on the Internet of Things as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The control center readable storage medium includes a stored control program, wherein when the control program is running, the device where the control center readable storage medium is located is controlled to execute an intelligent water supply device control based on the Internet of Things as described in any one of claims 1 to 7.

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