Intelligent pipe network pressure control method and system

Abstract

The present invention discloses an intelligent pipe network pressure control method and system, which relates to the technical field of pipe network pressure control. Based on the fitting results of historical water supply data of the water supply pipe network in the target area and the corresponding influencing factor data, the functional relationship between water supply pressure and water consumption is obtained, the influencing factor data and water consumption are correlated and analyzed, the influencing factor types with high correlation with water consumption are screened, and a neural network model is constructed with the highly correlated influencing factor data as input and the corresponding water consumption data as output. The trained neural network model and the functional relationship are used to obtain the fitting value of the water supply pressure, and the pressure of the water supply pipe network in the target area is adjusted. The method can fully consider the synergistic influence of multiple factors on water demand, thereby improving the intelligence and refinement of the control effect of the time-divided pressure control strategy and meeting the current intelligent control requirements of pipe network pressure.

Description

Technical Field

[0001] The present invention relates to the technical field of pipe network pressure control, and in particular to an intelligent pipe network pressure control method and system. Background Art

[0002] With the acceleration of urbanization and population growth, the scale and complexity of water supply network systems are constantly increasing. Traditional water supply network pressure control methods mainly rely on manual adjustment and simple pressure regulation devices, such as manual pressure regulating valves and timed pressure control devices. These methods can meet basic water supply needs to a certain extent, but they have significant shortcomings in terms of refined management and intelligent control. In recent years, with the rapid development of sensor technology, automated control technology, and communication technology, intelligent pipeline network pressure control technology has gradually emerged. By installing monitoring equipment such as pressure sensors and flow meters in the pipeline network, real-time pipeline operation data is collected. Combined with automated control systems and intelligent algorithms, real-time monitoring and dynamic adjustment of pipeline pressure are achieved. However, existing intelligent pressure control technology still has some problems, which limits its widespread application in practical water supply systems.

[0003] At present, dynamic pressure control mainly includes the following strategies:

[0004] Time-of-day pressure control: Set different pressure target values ​​based on the diurnal changes in water demand. For example, maintain higher pressure during the day and lower pressure at night;

[0005] Zoned pressure control: Divide the pipe network into multiple independent metering areas (DMAs) and implement independent pressure management in each area;

[0006] Flow rate pressure control: Dynamically adjust the pressure according to flow rate changes to ensure that the pipe network pressure matches the water demand;

[0007] Intelligent algorithm control: Utilize intelligent algorithms (such as the FCM algorithm based on elevation neighborhood information) to adjust the opening of the control valve in real time and optimize the pressure distribution in the pipe network.

[0008] Among them, time-based pressure control is widely adopted as an effective control strategy. Although it can meet water demand to a certain extent, existing control strategies generally only consider the diurnal variation in water demand and adjust the pipe network pressure by setting fixed pressure target values ​​for each time period. Water demand is an important basis for the design and operation of water supply systems and is affected by various factors, such as seasonal changes, climatic conditions, and regional functions. Existing time-based pressure control strategies fail to fully consider the synergistic effects of various other factors on water demand, resulting in a low level of intelligence and refinement in the control effect. To this end, an intelligent pipe network pressure control method and system are proposed. Summary of the Invention

[0009] The main purpose of the present invention is to provide an intelligent pipe network pressure control method and system, which can effectively solve the problems in the background technology.

[0010] In order to achieve the above purpose, the technical solution adopted by the present invention is:

[0011] An intelligent pipe network pressure control method, comprising:

[0012] Obtaining historical water supply data and corresponding influencing factor data of a water supply network within a target area, wherein the water supply data includes water consumption within the target area and water supply pressure of the water supply network, and the influencing factor data is data that affects the water consumption; fitting the water supply pressure and the water consumption to obtain a functional relationship between the water supply pressure and the water consumption;

[0013] performing a correlation analysis on the influencing factor data and the water consumption, screening the types of influencing factors with high correlation with the water consumption, constructing a neural network model with the highly correlated influencing factor data as input and the corresponding water consumption data as output, training the neural network model using the acquired data, and adjusting the model parameters of the neural network model so that the expected value of its performance index is not less than a set threshold, thereby obtaining the trained neural network model;

[0014] Acquire real-time data of the influencing factors in the target area, use the real-time data of the influencing factors as input, use the trained neural network model to obtain a predicted value of the water consumption, and substitute the obtained predicted value of the water consumption into the functional relationship to obtain a fitting value of the water supply pressure of the water supply network in the target area;

[0015] The water supply network pressure in the target area is adjusted according to the obtained water supply pressure fitting value.

[0016] An intelligent pipe network pressure control system, comprising:

[0017] a data acquisition module, configured to acquire historical water supply data of the water supply network in a target area and corresponding influencing factor data, wherein the water supply data includes water consumption in the target area and water supply pressure of the water supply network, and the influencing factor data is data that affects the water consumption;

[0018] a data fitting module, configured to fit the water supply pressure and the water consumption to obtain a functional relationship between the water supply pressure and the water consumption;

[0019] A data analysis module, configured to perform correlation analysis on the influencing factor data and the water consumption, and screen the types of influencing factors that have a high correlation with the water consumption;

[0020] A neural network module is used to construct a neural network model that uses highly correlated influencing factor data as input and the corresponding water consumption data as output, trains the neural network model using the acquired data, and adjusts model parameters of the neural network model until the expected value of its performance index is not less than a set threshold, thereby obtaining the trained neural network model;

[0021] A real-time data acquisition module, used to obtain real-time data of the influencing factors in the target area;

[0022] a predicted pressure acquisition module, configured to use the real-time data of the influencing factors as input, obtain a predicted value of the water consumption using the trained neural network model, and substitute the obtained predicted value of the water consumption into the functional relationship to obtain a fitted value of the water supply pressure of the water supply network in the target area;

[0023] The pipe network pressure regulating module is used to adjust the water supply pipe network pressure in the target area according to the obtained water supply pressure fitting value.

[0024] The system further includes a memory, a processor, and an electronic program stored in the memory and capable of running on the processor.

[0025] Furthermore, the screening process for the types of influencing factors that have a high correlation with the water consumption includes the following steps:

[0026] Perform correlation analysis on the influencing factor data and the water consumption to obtain the Correlation coefficient between the influencing factors and the water consumption ;

[0027] The correlation coefficient Perform normalization processing to obtain the normalized correlation coefficient ;

[0028] The normalized correlation coefficient Arrange in descending order, select the The correlation coefficient of the sum of the values ​​is not less than 0.85 The corresponding influencing factors are used as the influencing factor types that have a high correlation with the water consumption.

[0029] Furthermore, the correlation coefficient The calculation method is:

[0030]

[0031] Where, For the The first influencing factor Subsampling value; The first Subsampling value; is the total amount of sampled data; is the resolution coefficient, which is taken as 0.5; is the minimum difference between the two levels of sampling data; is the maximum difference between the two levels of sampling data;

[0032] ;

[0033] ;

[0034] Indicates first take =1,2,..., hour, The minimum value of all calculation results is taken =1,2,..., When , get the minimum value of all minimum values;

[0035] Indicates first take =1,2,..., hour, The maximum value among all the calculated results is taken. =1,2,..., When , the maximum value of all maximum values ​​is obtained; The types of influencing factors.

[0036] Furthermore, the number of neurons in the input layer of the neural network model is the same as the type of influencing factors that have a high correlation with the water consumption, and the number of neurons in the output layer is 1.

[0037] Furthermore, the performance index of the neural network model is used to measure the difference between the predicted value and the sampled value of the water consumption, including one or more combinations of the first performance index, the second performance index, the third performance index and the fourth performance index; the calculation methods of the performance index are respectively:

[0038] First performance indicator = ;

[0039] Second performance indicator = ;

[0040] The third performance indicator = ;

[0041] Fourth performance indicator = ;

[0042] in, The first Subsampling value; is the predicted value of water consumption obtained using the neural network model; is the total amount of sampled data.

[0043] Furthermore, the expected value of the performance index of the neural network model is calculated as follows:

[0044]

[0045] in, Indicates the The expected value of a performance indicator; Indicates the The performance index Sub-calculated value; =1,2,3,4.

[0046] Furthermore, the specific process of adjusting the water supply network pressure in the target area according to the obtained water supply pressure fitting value is as follows:

[0047] Get the water supply pressure fitting value and the current water supply network pressure in the target area ;

[0048] According to the water supply pressure fitting value and the current water supply network pressure in the target area The numerical relationship of determines the adjustment strategy, including:

[0049] when The first adjustment strategy when

[0050] when The second adjustment strategy when

[0051] when The third adjustment strategy when

[0052] in, is the allowed pipe network pressure threshold;

[0053] When the first adjustment strategy is adopted, the water supply network pressure in the current target area is maintained constant;

[0054] When the second adjustment strategy is adopted, the water supply network pressure in the current target area is gradually reduced with an adjustment step of λ. to ;

[0055] When the third adjustment strategy is adopted, the water supply network pressure in the current target area is gradually increased with an adjustment step size of λ. to .

[0056] Furthermore, the adjustment step size satisfies the following relationship:

[0057]

[0058] Where, is the maximum adjustment step length, ; To obtain and The minimum value in .

[0059] The present invention has the following beneficial effects:

[0060] Compared with the existing technology, this solution obtains the functional relationship between water supply pressure and water consumption based on the fitting results of the historical water supply data of the water supply network in the target area and the corresponding influencing factor data, performs correlation analysis on the influencing factor data and water consumption, screens the types of influencing factors with high correlation with water consumption, constructs a neural network model with high-correlation influencing factor data as input and corresponding water consumption data as output, uses the trained neural network model to obtain the predicted value of water consumption, and brings the obtained predicted value of water consumption into the functional relationship to obtain the fitting value of water supply pressure of the water supply network in the target area, adjusts the pressure of the water supply network in the target area according to the obtained fitting value of water supply pressure, and can fully consider the synergistic influence of various factors such as seasonal changes, climatic conditions, and regional functions on water demand, thereby improving the intelligence and refinement of the control effect of the time-divided pressure control strategy and meeting the current demand for intelligent control of pipe network pressure. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 This is a flow chart of an intelligent pipe network pressure control method according to the present invention;

[0062] Figure 2 This is a structural block diagram of an intelligent pipe network pressure control system of the present invention;

[0063] Figure 3 Schematic diagram of the structure of one of the neural networks constructed in the embodiment of the solution of the present invention. DETAILED DESCRIPTION

[0064] The present invention will be further described below in conjunction with specific embodiments. The accompanying drawings are for illustrative purposes only and represent only schematic diagrams rather than actual drawings. They should not be understood as limiting the present invention. In order to better illustrate the specific embodiments of the present invention, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the size of the actual product.

[0065] The specific implementation process of the technical solution of the present invention includes the following steps:

[0066] Step 1: Obtain historical water supply data and corresponding influencing factor data of the water supply network in the target area. The water supply data includes water consumption in the target area and the water supply pressure of the water supply network. The influencing factor data is data that affects water consumption.

[0067] It should be noted that water demand is an important basis for the design and operation of water supply systems and is affected by many factors. The following are the main factors affecting water demand:

[0068] Time Factor

[0069] Day and night changes:

[0070] Peak hours: Usually in the morning (6:00-8:00) and evening (18:00-20:00), residents' water demand is concentrated, mainly for washing, cooking, bathing, etc.

[0071] Low-peak period: Water demand is lower at night (0:00-6:00), and the pipe network pressure can be reduced at this time to save energy.

[0072] Seasonal changes:

[0073] Summer: The temperature is high, and residents’ water demand increases, mainly for showering, air conditioning cooling, irrigation, etc. In addition, the summer tourist season and outdoor activities also increase water demand.

[0074] Winter: The temperature is low and residents’ water demand is relatively reduced, but heating systems (such as heaters) may require a certain amount of water.

[0075] Geographical factors

[0076] Terrain Elevation:

[0077] High-rise buildings or areas located at higher elevations require higher water supply pressure to ensure that water reaches the top floors.

[0078] Lower-lying areas may require less pressure, but need to be protected from excessive water pressure that could cause pipe ruptures.

[0079] Regional functions:

[0080] Residential areas: Water demand is mainly concentrated in domestic use, such as drinking, washing, and laundry.

[0081] Commercial area: Water demand is concentrated during business hours, mainly for catering, cleaning, air conditioning and cooling, etc.

[0082] Industrial areas: Water demand may be very high, and water use is relatively concentrated, mainly for production processes, cooling, cleaning, etc.

[0083] Agricultural areas: Water demand is mainly concentrated in the irrigation season and is greatly affected by the season and weather.

[0084] Climate factors

[0085] Temperature:

[0086] Hot weather increases residential and commercial water demand, primarily for showering, air conditioning cooling, and irrigation.

[0087] Cold weather may cause water pipes to freeze, requiring anti-freeze measures, and residents may need to reduce outdoor water use.

[0088] Rainfall:

[0089] When rainfall is adequate, residents may reduce irrigation use, but heavy rains can put increased pressure on drainage systems.

[0090] In arid regions or during dry seasons, water demand may increase, especially for agricultural irrigation and landscaping.

[0091] humidity:

[0092] In areas with high humidity, residential and commercial water demand may increase, mainly for air conditioning, dehumidification and cleaning.

[0093] By analyzing water demand, we can determine the type of data that needs to be collected. For categorical data, we can process it in the form of data coding to facilitate subsequent data analysis.

[0094] Step 2: Fit the water supply pressure and water consumption to obtain the functional relationship between the water supply pressure and water consumption.

[0095] It should be noted that in order to quantitatively analyze the relationship between water demand and water supply pressure, the following methods can be used:

[0096] In one possible embodiment, a relationship model between water demand and water supply pressure can be established through experiments or actual monitoring data. For example, water consumption and water supply pressure data can be collected in different time periods and different areas, and a mathematical model can be established through regression analysis: Q = a + b × P + c × P 2 ; where a, b, and c are regression coefficients, which can be solved by methods such as the least squares method.

[0097] In one possible embodiment, a hydraulic model (such as EPANET) can be used to simulate and analyze the water supply network. By inputting parameters such as the network topology, pipe diameter, and user water consumption, water demand under different pressures can be simulated. The simulation model can consider factors such as network leakage and user behavior, providing more accurate analysis results.

[0098] In one possible embodiment, machine learning algorithms (such as neural networks and support vector machines) can be used to model the relationship between water demand and water supply pressure. Using training data sets, the algorithm can learn the complex, nonlinear relationship between water demand and water supply pressure, and use it to predict and optimize control strategies.

[0099] Step 3: Conduct correlation analysis between influencing factor data and water consumption, and screen the types of influencing factors with high correlation with water consumption; specifically:

[0100] Conduct correlation analysis on influencing factor data and water consumption to obtain the Correlation coefficient between influencing factors and water consumption ;

[0101] The correlation coefficient Perform normalization processing to obtain the normalized correlation coefficient ;

[0102] The normalized correlation coefficient Arrange in descending order, select the The correlation coefficient of the sum of the values ​​is not less than 0.85 The corresponding influencing factors are the types of influencing factors with high correlation with water consumption.

[0103] Among them, the correlation coefficient The calculation method is:

[0104]

[0105] Where, For the The first influencing factor Subsampling value; The first Subsampling value; is the total amount of sampled data; is the resolution coefficient, which is taken as 0.5; is the minimum difference between the two levels of sampling data; is the maximum difference between the two levels of sampling data;

[0106] ;

[0107] ;

[0108] Indicates first take =1,2,..., hour, The minimum value of all calculation results is taken =1,2,..., When , get the minimum value of all minimum values;

[0109] Indicates first take =1,2,..., hour, The maximum value among all the calculated results is taken. =1,2,..., When , the maximum value of all maximum values ​​is obtained; Types of influencing factors.

[0110] Step 4: Construct a neural network model with highly correlated influencing factor data as input and corresponding water consumption data as output. Use the acquired data to train the neural network model, and adjust the model parameters of the neural network model until the expected value of its performance index is not less than the set threshold, thereby obtaining a trained neural network model.

[0111] Among them, the number of neurons in the input layer of the neural network model is the same as the types of influencing factors that have a high correlation with water consumption, and the number of neurons in the output layer is 1.

[0112] The performance index of the neural network model is used to measure the difference between the predicted value and the sampled value of water consumption, including one or more combinations of the first performance index, the second performance index, the third performance index and the fourth performance index; the calculation methods of the performance index are as follows:

[0113] First performance indicator = ;

[0114] Second performance indicator = ;

[0115] The third performance indicator = ;

[0116] Fourth performance indicator = ;

[0117] in, The first Subsampling value; is the predicted value of water consumption obtained using the neural network model; is the total amount of sampled data.

[0118] The expected value of the performance indicator of the neural network model is calculated as follows:

[0119]

[0120] in, Indicates the The expected value of a performance indicator; Indicates the The performance index Sub-calculated value; =1,2,3,4.

[0121] In a possible embodiment, an artificial neural network model may be used, wherein the artificial neural network model is as follows: Figure 3 The three-layer structure shown is the input layer, the middle layer and the output layer. The adjustable model parameter is the number of neurons in the middle layer.

[0122] Step 5: Obtain real-time data of influencing factors in the target area. Using the real-time data of influencing factors as input, use the trained neural network model to obtain the predicted value of water consumption, and then bring the obtained predicted value of water consumption into the functional relationship to obtain the fitting value of water supply pressure of the water supply network in the target area.

[0123] Step 6: Adjust the water supply network pressure in the target area based on the obtained water supply pressure fitting value. The specific process is as follows:

[0124] Get the fitted value of water supply pressure and the current water supply network pressure in the target area ;

[0125] According to the water supply pressure fitting value and the current water supply network pressure in the target area The numerical relationship of determines the adjustment strategy, including:

[0126] when The first adjustment strategy when

[0127] when The second adjustment strategy when

[0128] when The third adjustment strategy when

[0129] in, is the allowed pipe network pressure threshold;

[0130] When the first adjustment strategy is adopted, the water supply network pressure in the current target area is maintained constant;

[0131] When the second adjustment strategy is adopted, the water supply network pressure in the current target area is gradually reduced with an adjustment step of λ. to ;

[0132] When the third adjustment strategy is adopted, the water supply network pressure in the current target area is gradually increased with an adjustment step size of λ. to .

[0133] The adjustment step size satisfies the following relationship:

[0134]

[0135] Where, is the maximum adjustment step length, ; To obtain and The minimum value in .

[0136] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for controlling water supply pressure in an intelligent pipe network, characterized in that: include: Obtaining historical water supply data and corresponding influencing factor data of a water supply network within a target area, wherein the water supply data includes water consumption within the target area and water supply pressure of the water supply network, and the influencing factor data is data that affects the water consumption; fitting the water supply pressure and the water consumption to obtain a functional relationship between the water supply pressure and the water consumption; performing a correlation analysis on the influencing factor data and the water consumption, screening the types of influencing factors with high correlation with the water consumption, constructing a neural network model with the highly correlated influencing factor data as input and the corresponding water consumption data as output, training the neural network model using the acquired data, and adjusting the model parameters of the neural network model so that the expected value of its performance index is not less than a set threshold, thereby obtaining the trained neural network model; Acquire real-time data of the influencing factors in the target area, use the real-time data of the influencing factors as input, use the trained neural network model to obtain a predicted value of the water consumption, and substitute the obtained predicted value of the water consumption into the functional relationship to obtain a fitting value of the water supply pressure of the water supply network in the target area; Adjusting the water supply pressure of the pipe network in the target area according to the obtained water supply pressure fitting value; The screening process for the types of influencing factors that have a high correlation with the water consumption includes the following steps: Perform correlation analysis on the influencing factor data and the water consumption to obtain the correlation coefficient R between the jth influencing factor and the water consumption j ; The correlation coefficient R j Perform normalization processing to obtain the normalized correlation coefficient The normalized correlation coefficient Arrange them in descending order and select the first q correlation coefficients whose sum is not less than 0.

85. The corresponding influencing factors are types of influencing factors that have a high correlation with the water consumption; Correlation coefficient R j The calculation method is: Where x jk is the kth sampling value of the jth influencing factor; y k is the kth sampling value of water consumption; n is the total amount of sampling data; ρ is the resolution coefficient, which is 0.5; Δmin is the minimum difference between the two levels of sampling data; Δmax is the maximum difference between the two levels of sampling data; where, Δmin=min j my k |x jk -y k |; Δmax=max j max k |x jk -y k |; k=1,2,...,n; j=1,2,...,m; m is the type of influencing factor.

2. The intelligent pipe network water supply pressure control method according to claim 1, characterized in that: The number of neurons in the input layer of the neural network model is the same as the type of influencing factors that have a high correlation with the water consumption, and the number of neurons in the output layer is 1.

3. The intelligent pipe network water supply pressure control method according to claim 1, characterized in that: The performance index of the neural network model is used to measure the difference between the predicted value and the sampled value of the water consumption, including one or more combinations of the first performance index, the second performance index, the third performance index and the fourth performance index; the calculation methods of the performance index are respectively: Among them, y k is the kth sampling value of water consumption; is the predicted value of water consumption obtained by using the neural network model; n is the total amount of sampled data.

4. The intelligent pipe network water supply pressure control method according to claim 3, characterized in that: The expected value of the performance index of the neural network model is calculated as follows: Among them, E(Y) r represents the expected value of the rth performance indicator; Pi rk Represents the kth calculated value of the rth performance indicator; r = 1, 2, 3, 4.

5. The intelligent pipe network water supply pressure control method according to claim 1, characterized in that: The specific process of adjusting the water supply pressure of the pipe network in the target area according to the obtained water supply pressure fitting value is as follows: Get the water supply pressure fitting value And the pipe network water supply pressure p in the current target area; According to the water supply pressure fitting value The adjustment strategy is determined based on the numerical relationship between the water supply pressure p of the pipe network in the current target area, including: when The first adjustment strategy when when The second adjustment strategy when when The third adjustment strategy when Among them, p a is the permitted water supply pressure threshold of the pipe network; When the first adjustment strategy is adopted, the water supply pressure p of the pipe network in the current target area is kept unchanged; When the second adjustment strategy is adopted, the water supply pressure p of the pipe network in the current target area is gradually reduced to When the third adjustment strategy is adopted, the water supply pressure p of the pipe network in the current target area is gradually increased to 6. The intelligent pipe network water supply pressure control method according to claim 5, characterized in that: The adjustment step size satisfies the following relationship: Where λ max is the maximum adjustment step length, To obtain and p a The minimum value in .

7. An intelligent pipe network water supply pressure control system, characterized in that: include: a data acquisition module, configured to acquire historical water supply data of the water supply network in a target area and corresponding influencing factor data, wherein the water supply data includes water consumption in the target area and water supply pressure of the water supply network, and the influencing factor data is data that affects the water consumption; a data fitting module, configured to fit the water supply pressure and the water consumption to obtain a functional relationship between the water supply pressure and the water consumption; A data analysis module, configured to perform correlation analysis on the influencing factor data and the water consumption, and screen the types of influencing factors that have a high correlation with the water consumption; A neural network module is used to construct a neural network model that uses highly correlated influencing factor data as input and the corresponding water consumption data as output, trains the neural network model using the acquired data, and adjusts model parameters of the neural network model until the expected value of its performance index is not less than a set threshold, thereby obtaining the trained neural network model; A real-time data acquisition module, used to obtain real-time data of the influencing factors in the target area; a predicted water supply pressure acquisition module, configured to use the real-time data of the influencing factors as input, obtain a predicted value of the water consumption using the trained neural network model, and substitute the obtained predicted value of the water consumption into the functional relationship to obtain a fitted value of the water supply pressure of the water supply network in the target area; a pipe network water supply pressure regulating module, configured to adjust the pipe network water supply pressure in the target area according to the obtained water supply pressure fitting value; The screening process for the types of influencing factors that have a high correlation with the water consumption includes the following steps: Perform correlation analysis on the influencing factor data and the water consumption to obtain the correlation coefficient R between the jth influencing factor and the water consumption j ; The correlation coefficient R j Perform normalization processing to obtain the normalized correlation coefficient The normalized correlation coefficient Arrange them in descending order and select the first q correlation coefficients whose sum is not less than 0.

85. The corresponding influencing factors are types of influencing factors that have a high correlation with the water consumption; Correlation coefficient R j The calculation method is: Where x jk is the kth sampling value of the jth influencing factor; y k is the kth sampling value of water consumption; n is the total amount of sampling data; ρ is the resolution coefficient, which is 0.5; Δmin is the minimum difference between the two levels of sampling data; Δmax is the maximum difference between the two levels of sampling data; where, Δmin=min j my k |x jk -y k |; Δmax=max j max k |x jk -y k |; k=1,2,...,n; j=1,2,...,m; m is the type of influencing factor.

8. The intelligent pipe network water supply pressure control system according to claim 7, characterized in that: The system also includes a memory, a processor, and an electronic program stored in the memory and capable of running on the processor, wherein the processor can implement the steps of an intelligent pipe network water supply pressure control method described in any one of claims 1-6 when running the electronic program.

Images