Water pump operation energy-saving control method and system based on multi-source data analysis

Abstract

The invention discloses a water pump operation energy-saving control method and system based on multi-source data analysis, and relates to the technical field of water pump energy saving. The method comprises the steps of obtaining state parameters actually measured at the same moment and corresponding water pump energy consumption value data, eliminating abnormal data points in sampling data, training a neural network by using the sampling data until a square error expected value of a prediction result is smaller than a set expected threshold value, and outputting a mapping relation between the state parameters and water pump energy consumption values. Obtaining the adjustable interval of the active state parameter and the interval of the water pump energy consumption value corresponding to the measured value of the passive state parameter at the current moment by utilizing the mapping relation, performing anomaly detection on each water pump energy consumption value in the interval by taking lambda as a detection step length, and obtaining the reasonable interval of the water pump energy consumption value at the current moment; and the active state parameter value corresponding to the reasonable interval upper limit of the water pump energy consumption value at the current moment is taken as the optimal state value of the state parameter, and the active state parameter is adjusted.

Description

Technical Field

[0001] The present invention relates to the technical field of water pump energy saving, and in particular to a water pump operation energy saving control method and system based on multi-source data analysis. Background Art

[0002] As one of the indispensable equipment in industrial and agricultural production, water pumps account for a large proportion of the total energy consumption in the entire production process. Traditional water pumps consume a lot of energy during long-term operation, which directly leads to energy waste and high operating costs. As energy prices continue to rise, enterprises and institutions are increasingly feeling the increasing pressure of energy costs, and there is an urgent need to find effective ways to reduce energy consumption. Energy-saving transformation of water pumps can optimize the production process while reducing energy consumption, and achieve effective cost control. In addition, the high energy consumption of traditional water pumps not only increases greenhouse gas emissions, but also has a negative impact on the rational use of water resources. Through energy-saving transformation of water pumps, energy consumption can be reduced, environmental pollution can be reduced, and the green development goals of enterprises can be achieved.

[0003] During the operation of the water pump, the heat generated by its internal components is one of the important factors affecting the increase in energy consumption. Generally, a matching cooling fan is configured for the water pump to reduce its operating temperature and achieve the purpose of energy saving and consumption reduction. On the one hand, the higher the operating frequency of the cooling fan of the water pump motor, the better the heat dissipation effect is generally, which is conducive to reducing energy consumption; on the other hand, the higher the operating frequency of the cooling fan of the water pump motor, the more power support is required, resulting in an increase in overall energy consumption. In addition, the higher the operating frequency, the more noise will increase, and too high a frequency will cause additional burden on the motor and shorten its service life.

[0004] At present, the control of the cooling fan operating frequency of the water pump motor is usually based on the single influencing factor of the cooling effect on energy consumption. It does not consider the impact between the cooling fan operating frequency and the comprehensive energy consumption. The precision and intelligence of the control process are low, resulting in poor control effect. It is often impossible to determine the optimal operating frequency of the cooling fan during the operation of the water pump. To this end, we propose a water pump operation energy-saving control method and system based on multi-source data analysis. Summary of the invention

[0005] The main purpose of the present invention is to provide a water pump operation energy-saving control method and system based on multi-source data analysis, which can effectively solve the problems in the background technology.

[0006] In order to achieve the above object, the technical solution adopted by the present invention is:

[0007] The water pump operation energy-saving control method based on multi-source data analysis includes:

[0008] Sample the state parameters of the water pump during operation to obtain the i-th state parameter x measured at the same time i And the corresponding pump energy consumption value Ef i The historical data of the pump energy consumption value Ef i Perform anomaly detection and remove the pump energy consumption value Ef in the sampled data i And the corresponding state parameter x i Abnormal data points, where i = 1, 2, ..., n; n is the type of state parameter;

[0009] The normalized state parameters As input parameter, the normalized pump energy consumption value A neural network model is constructed for the output parameters, and the neural network model is trained using the sampled data until the expected square error value E of its prediction result is p is less than the set expected threshold ε, and the trained neural network model is used to output the state parameter x i The water pump energy consumption value Ef i The mapping relationship f between them is expressed as: f:((x 1 ,x 1 ,...,x i ) T )→Ef i ,

[0010] The state parameters are classified into active state parameters and passive state parameters according to whether the object is controlled, and the adjustable range of the u-th active state parameter at the current time t is obtained. and the measured value of the passive state parameter described in item v in, is the lower limit of the adjustable interval of the u-th active state parameter at the current time t; is the upper limit of the adjustable interval of the u-th active state parameter at the current time t, where u, v∈n, and u+v=n;

[0011] The mapping relationship f is used to obtain the adjustable range of the active state parameter at the current time t. and the measured value of the passive state parameter The corresponding pump energy consumption value Ef t The initial interval is the minimum energy consumption of the water pump at the current time t, is the maximum energy consumption value of the water pump at the current time t;

[0012] Take λ as the detection step size for the initial interval The energy consumption value of each water pump in the test is detected abnormally, and the energy consumption value Ef of the water pump at the current time t is obtained according to the detection results. t Reasonable range in, is the energy consumption value of the water pump Ef t The lower limit of the reasonable range; is the energy consumption value of the water pump Ef t The upper limit of the reasonable range;

[0013] Take the energy consumption value of the water pump Ef at the current time t t The upper limit of the reasonable range The corresponding active state parameter value is used as the optimal state value of the state parameter, and the u-th active state parameter at the current time t is adjusted to the optimal state value.

[0014] The state parameters include the operating frequency of the cooling fan of the water pump motor, and the state parameters also include at least one of the ambient temperature, ambient humidity, the three-phase winding end temperature of the water pump motor, the front end bearing temperature of the water pump motor, and the rear end bearing temperature of the water pump motor. Among them, the operating frequency of the cooling fan of the water pump motor can only be used as an active state parameter, and there is at least one passive state parameter.

[0015] Expected value of square error E p The calculation formula is:

[0016]

[0017] In the formula, is the ideal output value of the qth prediction result in the neural network training sample; Y q is the actual output value of the qth prediction result in the neural network training sample; N is the total amount of data in the neural network training sample.

[0018] Status parameters The calculation formula is: In the formula, x ij is the jth sampling value of the i-th state parameter; Q is the total amount of data sampling; the energy consumption value of the water pump The calculation formula is: Where, Ef ij It is the energy consumption value of the water pump corresponding to the j-th sampling value of the i-th state parameter.

[0019] The anomaly detection process specifically includes the following steps:

[0020] Step S11: Assume that the sampled value of the water pump energy consumption value is the same as the rth sampling point The kth sampling point with the smallest distance is r∈Q, Q is the total amount of data sampling; according to the formula: Calculate the rth sampling point k nearest neighbor distance

[0021] Step S12: Calculate all remaining sampling points and the rth sampling point respectively The distance is calculated to be less than the k nearest neighbor distance The set of sampling points is taken as the rth sampling point k-distance neighborhood

[0022] Step S13: Get k nearest neighbor distances k-distance neighborhood Medium distance sampling point The maximum value of the distance to the nearest k-th sampling point is taken as the r-th sampling point and the kth sampling point is The kth reachable distance

[0023] Step S14: Calculate the rth sampling point Ef according to the acquired data i r The local reachable density ρ k (Ef i r ), where the calculation formula for local reachability density is:

[0024] Step S15: Calculate the rth sampling point Ef according to the obtained local reachability density i r The local outlier factor is calculated as: In the formula, ρ k (Ef i k ) is the kth sampling point Ef i k The local reachable density of

[0025] Step S16: Use the obtained local outlier factor to calculate the rth sampling point The abnormal situation is judged according to the following principles:

[0026] when Represents the rth sampling point It is abnormal data;

[0027] when Represents the rth sampling point It is normal data.

[0028] Pump energy consumption value Ef t The process of determining the reasonable range of includes the following steps:

[0029] Step S51: Use the abnormality detection process to detect the lower limit of the detection step length interval To detect and determine Whether it is normal data;

[0030] Step S52: When the lower limit is determined For normal data, increase A step size λ, to increase the data As the detected data, the current detected data Determine whether it is normal data;

[0031] Step S53: Repeat step S52. If the detected data is determined to be abnormal data, the interval in which the detected data is determined to be normal data is used as the water pump energy consumption value Ef. t reasonable range.

[0032] The water pump operation energy-saving control system based on multi-source data analysis includes a historical data acquisition module, a data processing module, a neural network construction module, a real-time data acquisition module, an energy consumption interval acquisition module, a reasonable adjustment interval acquisition module, and a state parameter adjustment module;

[0033] The historical data acquisition module is used to sample the state parameters of the water pump during operation, and obtain the i-th state parameter x measured at the same time. i And the corresponding pump energy consumption value Ef i Historical data;

[0034] The data processing module is used to obtain the water pump energy consumption value Ef i Perform anomaly detection and remove the pump energy consumption value Ef in the sampled data i And the corresponding state parameter x i Abnormal data points;

[0035] The neural network building module is used to normalize the state parameters As input parameter, the normalized pump energy consumption value A neural network model is constructed for the output parameters, and the neural network model is trained using the sampled data to the expected square error value E of its prediction results. p is less than the set expected threshold ε, and the trained neural network model is used to output the state parameter x i The water pump energy consumption value Ef i The mapping relationship f between them;

[0036] The real-time data acquisition module is used to obtain the adjustable range of the uth active state parameter at the current time t. and the measured value of the passive state parameter described in item v

[0037] The energy consumption interval acquisition module is used to obtain the adjustable interval of the active state parameter at the current time t by using the mapping relationship f. and the measured value of the passive state parameter The corresponding pump energy consumption value Ef t The initial interval

[0038] The reasonable adjustment interval acquisition module is used to detect the initial interval with λ as the detection step length. The energy consumption value of each water pump in the test is detected abnormally, and the energy consumption value Ef of the water pump at the current time t is obtained according to the detection results. t Reasonable range

[0039] The state parameter adjustment module is used to adjust the energy consumption value Ef of the water pump at the current time t. t The upper limit of the reasonable range The corresponding active state parameter value is used as the optimal state value of the state parameter, and the u-th active state parameter at the current time t is adjusted to the optimal state value.

[0040] The system includes a memory, a processor, and a computer program stored in the memory and executable on the processor.

[0041] The present invention has the following beneficial effects:

[0042] Compared with the prior art, by sampling the state parameters of the water pump during operation, the i-th state parameter x measured at the same time is obtained. i And the corresponding pump energy consumption value Ef i The historical data of the pump energy consumption value Ef i Perform anomaly detection and remove the pump energy consumption value Ef in the sampled data i And the corresponding state parameter x i The abnormal data points are normalized to the state parameters As input parameter, the normalized pump energy consumption value Construct a neural network model for the output parameters, and use the sampled data to train the neural network model to the expected square error value E of its prediction results. p is less than the set expected threshold ε, and the trained neural network model is used to output the state parameter x i And the energy consumption value of the pump Ef i The mapping relationship f between them is used to classify the state parameters into active state parameters and passive state parameters according to whether they are controlled objects, and obtain the adjustable range of the u-th active state parameter at the current time t. and the measured value of the vth passive state parameter Use the mapping relationship f to obtain the adjustable range of the active state parameter at the current time t and measured values ​​of passive state parameters The corresponding pump energy consumption value Ef t The initial interval Take λ as the detection step size for the initial interval The energy consumption value of each water pump in the test is detected abnormally, and the energy consumption value Ef of the water pump at the current time t is obtained according to the detection results. t Reasonable range Take the energy consumption value of the water pump Ef at the current time t t The upper limit of the reasonable range The corresponding active state parameter value is used as the optimal state value of the state parameter, and the u-th active state parameter at the current time t is adjusted to the optimal state value, thereby improving the precision and intelligence of the control process, keeping the cooling fan of the water pump in the optimal operating frequency range during operation, and improving the energy-saving effect of the water pump operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a flow chart of the water pump operation energy-saving control method based on multi-source data analysis of the present invention;

[0044] Figure 2 It is a structural block diagram of the water pump operation energy-saving control system based on multi-source data analysis of the present invention;

[0045] Figure 3 This is a structural diagram of the neural network constructed in the solution of the present invention. DETAILED DESCRIPTION

[0046] The present invention will be further described below in conjunction with specific implementation methods, wherein the accompanying drawings are only used for exemplary descriptions and represent only schematic diagrams rather than actual drawings, and should not be understood as limiting the present invention. In order to better illustrate the specific implementation methods of the present invention, some parts of the accompanying drawings may be omitted, enlarged or reduced, and do not represent the size of the actual product.

[0047] The specific implementation process of the technical solution of the present invention includes the following steps:

[0048] Step 1: Sample the state parameters of the water pump during operation and obtain the i-th state parameter x measured at the same time i And the corresponding pump energy consumption value Ef i Historical data of, where i = 1, 2, ..., n; n is the type of state parameter;

[0049] Among them, the state parameters include the operating frequency of the cooling fan of the water pump motor, and the state parameters also include at least one of the ambient temperature, ambient humidity, the three-phase winding end temperature of the water pump motor, the front end bearing temperature of the water pump motor, and the rear end bearing temperature of the water pump motor.

[0050] In this embodiment, the state parameters include the cooling fan operating frequency of the water pump motor, the ambient temperature, the ambient humidity, the three-phase winding end temperature of the water pump motor, the front end bearing temperature of the water pump motor, and the rear end bearing temperature of the water pump motor.

[0051] Step 2: Obtain the energy consumption value Ef of the water pump i Perform anomaly detection and remove the pump energy consumption value Ef in the sampled data i And the corresponding state parameter x i The specific process is as follows:

[0052] Step S21: Assume that the sampled value of the water pump energy consumption value is the same as the rth sampling point The kth sampling point with the smallest distance is r∈Q, Q is the total amount of data sampling; according to the formula: Calculate the rth sampling point k nearest neighbor distance

[0053] Step S22: Calculate all remaining sampling points and the rth sampling point respectively The distance is calculated to be less than the k nearest neighbor distance The set of sampling points is taken as the rth sampling point k-distance neighborhood

[0054] Step S23: Get k nearest neighbor distances k-distance neighborhood Medium distance sampling point The maximum value of the distance to the nearest k-th sampling point is taken as the r-th sampling point and the kth sampling point is The kth reachable distance

[0055] Step S24: Calculate the rth sampling point Ef according to the acquired data i r The local reachable density ρ k (Ef i r ), where the calculation formula for local reachability density is:

[0056] Step S25: Calculate the rth sampling point Ef according to the obtained local reachability density ir The local outlier factor is calculated as: In the formula, ρ k (Ef i k ) is the kth sampling point Ef i k The local reachable density of

[0057] Step S26: Use the obtained local outlier factor to calculate the rth sampling point The abnormal situation is judged according to the following principles:

[0058] when Represents the rth sampling point It is abnormal data;

[0059] when Represents the rth sampling point It is normal data.

[0060] Step 3: Normalized state parameters As input parameter, the normalized pump energy consumption value A neural network model is constructed for the output parameters, where the number of neurons in the input layer is equal to the type of input state parameters, the number of output neurons is 1, and the number of neurons in the intermediate hidden layer can be determined by an empirical formula, specifically: α and β are the number of neurons in the input layer and the output layer respectively. In this embodiment, α=7, β=1, a is an integer from 1 to 9, and the structure of the constructed neural network model is as follows: Figure 3 As shown;

[0061] Among them, the state parameters The calculation formula is: In the formula, x ij is the jth sampling value of the i-th state parameter; Q is the total amount of data sampling; the energy consumption value of the water pump The calculation formula is: Where, Ef ij It is the energy consumption value of the water pump corresponding to the j-th sampling value of the i-th state parameter.

[0062] Step 4: Use the sampled data to train the neural network model to the expected square error value E of its prediction results p is less than the expected threshold ε, and the trained neural network model is used to output the state parameter x i And the energy consumption value of the pump Ef i The mapping relationship f between them is expressed as: f:((x 1 ,x 1 ,...,x i ) T)→Ef i ; Among them, the expected value of the square error E p The calculation formula is:

[0063]

[0064] In the formula, is the ideal output value of the qth prediction result in the neural network training sample; Y q is the actual output value of the qth prediction result in the neural network training sample; N is the total amount of data in the neural network training sample.

[0065] Step 5: Classify the state parameters into active state parameters and passive state parameters according to whether they are controlled objects, and obtain the adjustable range of the u-th active state parameter at the current time t. and the measured value of the vth passive state parameter in, is the lower limit of the adjustable interval of the u-th active state parameter at the current time t; is the upper limit of the adjustable interval of the u-th active state parameter at the current time t, where u, v∈n, and u+v=n;

[0066] Among them, the cooling fan operating frequency of the water pump motor can only be used as an active state parameter, and there is at least one passive state parameter.

[0067] In this embodiment, the passive state parameters include ambient temperature, ambient humidity, three-phase winding end temperature of the water pump motor, front end bearing temperature of the water pump motor, and rear end bearing temperature of the water pump motor.

[0068] Step 6: Use the mapping relationship f to obtain the adjustable range of the active state parameter at the current time t and measured values ​​of passive state parameters The corresponding pump energy consumption value Ef t The initial interval In this embodiment, the adjustable range of the active state parameter is the adjustable range of the cooling fan operating frequency of the water pump motor. is the minimum energy consumption of the water pump at the current time t, is the maximum energy consumption value of the water pump at the current time t;

[0069] Step 7: Use λ as the detection step size for the initial interval The energy consumption value of each water pump in the test is detected abnormally, and the energy consumption value Ef of the water pump at the current time t is obtained according to the detection results. t Reasonable range in, is the energy consumption value of the water pump Ef tThe lower limit of the reasonable range; is the energy consumption value of the water pump Ef t The upper limit of the reasonable range;

[0070] Pump energy consumption value Ef t The process of determining the reasonable range of includes the following steps:

[0071] Step S71: Use the abnormality detection process to detect the lower limit of the detection step length interval To detect and determine Whether it is normal data;

[0072] Step S72: When the lower limit is determined For normal data, increase A step size λ, to increase the data As the detected data, the current detected data Determine whether it is normal data;

[0073] Step S73: Repeat step S52. If the detected data is determined to be abnormal data, the interval in which the detected data is determined to be normal data is used as the water pump energy consumption value Ef. t reasonable range.

[0074] It should be noted that when the interval When the energy consumption values ​​of each water pump are normal data, the water pump energy consumption value Ef t Reasonable range That is the interval

[0075] Step 8: Take the energy consumption value Ef of the water pump at the current time t t The upper limit of the reasonable range The corresponding active state parameter value is used as the optimal state value of the state parameter, and the u-th active state parameter at the current time t is adjusted to the optimal state value.

[0076] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A water pump operation energy-saving control method based on multi-source data analysis, characterized in that: include: Sample the state parameters of the water pump during operation to obtain the i-th state parameter x measured at the same time i And the corresponding pump energy consumption value Ef i The historical data of the pump energy consumption value Ef i Perform anomaly detection and remove the pump energy consumption value Ef in the sampled data i And the corresponding state parameter x i Abnormal data points, where i = 1, 2, ..., n; n is the type of state parameter; The normalized state parameters As input parameter, the normalized pump energy consumption value Ef i A neural network model is constructed for the output parameters, and the neural network model is trained using the sampled data until the expected square error value E of its prediction result is p is less than the set expected threshold ε, and the trained neural network model is used to output the state parameter x i The water pump energy consumption value Ef i The mapping relationship f between them is expressed as: f:((x1,x1,...,x i ) T )→Ef i , The state parameters are classified into active state parameters and passive state parameters according to whether the object is controlled, and the adjustable range of the u-th active state parameter at the current time t is obtained. and the measured value of the passive state parameter described in item v in, is the lower limit of the adjustable interval of the u-th active state parameter at the current time t; is the upper limit of the adjustable interval of the u-th active state parameter at the current time t, where u, v∈n, and u+v=n; The mapping relationship f is used to obtain the adjustable range of the active state parameter at the current time t. and the measured value of the passive state parameter The corresponding pump energy consumption value Ef t The initial interval is the minimum energy consumption of the water pump at the current time t, is the maximum energy consumption value of the water pump at the current time t; Take λ as the detection step size for the initial interval The energy consumption value of each water pump in the test is detected abnormally, and the energy consumption value Ef of the water pump at the current time t is obtained according to the detection results. t Reasonable range in, is the energy consumption value of the water pump Ef t The lower limit of the reasonable range; is the energy consumption value of the water pump Ef t The upper limit of the reasonable range; Take the energy consumption value of the water pump Ef at the current time t t The upper limit of the reasonable range The corresponding active state parameter value is used as the optimal state value of the state parameter, and the u-th active state parameter at the current time t is adjusted to the optimal state value.

2. The water pump operation energy-saving control method based on multi-source data analysis according to claim 1 is characterized in that: The state parameters include the operating frequency of the cooling fan of the water pump motor, and the state parameters also include at least one of the ambient temperature, ambient humidity, the three-phase winding end temperature of the water pump motor, the front end bearing temperature of the water pump motor, and the rear end bearing temperature of the water pump motor. Among them, the operating frequency of the cooling fan of the water pump motor can only be used as an active state parameter, and there is at least one passive state parameter.

3. The water pump operation energy-saving control method based on multi-source data analysis according to claim 1 is characterized in that: Expected value of square error E p The calculation formula is: In the formula, is the ideal output value of the qth prediction result in the neural network training sample; Y q is the actual output value of the qth prediction result in the neural network training sample; N is the total amount of data in the neural network training sample.

4. The water pump operation energy-saving control method based on multi-source data analysis according to claim 1 is characterized in that: Status parameters The calculation formula is: In the formula, x ij is the jth sampling value of the i-th state parameter; Q is the total amount of data sampling; the energy consumption value of the water pump The calculation formula is: Where, Ef ij It is the energy consumption value of the water pump corresponding to the j-th sampling value of the i-th state parameter.

5. The water pump operation energy-saving control method based on multi-source data analysis according to claim 1 is characterized in that: The anomaly detection process specifically includes the following steps: Step S11: Assume that the sampled value of the water pump energy consumption value is the same as the rth sampling point Ef i r The kth sampling point with the smallest distance is Ef i k , r∈Q, Q is the total amount of data sampling; according to the formula: Calculate the rth sampling point Ef i The distance between r's k neighbors dk(Efir); Step S12: Calculate all remaining sampling points and the rth sampling point Ef respectively i r The distance is calculated so that the distance value is less than the k nearest neighbor distance d k (Ef i r ) is taken as the rth sampling point Ef i r The k-distance neighborhood N k (Ef i r ); Step S13: Get k nearest neighbor distance d k (Ef i r ), k distance neighborhood N k (Ef i r )Medium distance sampling point Ef i k The maximum value of the distances to the nearest k-th sampling point is taken as the r-th sampling point Ef i r With the kth sampling point as Ef i k The k-th reachable distance reach_dist k (Ef i k , Ef i r ); Step S14: Calculate the rth sampling point Ef according to the acquired data i r The local reachable density ρ k (Ef i r ), where the calculation formula for local reachability density is: Step S15: Calculate the rth sampling point Ef according to the obtained local reachability density i r The local outlier factor is calculated as: In the formula, ρ k (Ef i k ) is the kth sampling point Ef i k The local reachable density of Step S16: Use the obtained local outlier factor to calculate the rth sampling point Ef i r The abnormal situation is judged according to the following principles: When LOF k (Ef i r )>1, indicating the rth sampling point Ef i r It is abnormal data; When LOF k (Ef i r )≤1, indicating the rth sampling point Ef i r It is normal data.

6. The water pump operation energy-saving control method based on multi-source data analysis according to claim 1 is characterized in that: Pump energy consumption value Ef t The process of determining the reasonable range of includes the following steps: Step S51: Use the abnormality detection process to detect the lower limit of the detection step length interval To detect and determine Whether it is normal data; Step S52: When the lower limit is determined For normal data, increase A step size λ, to increase the data +λ is used as the detected data, and the current detected data + Determine whether λ is normal data; Step S53: Repeat step S52. If the detected data is determined to be abnormal data, the interval in which the detected data is determined to be normal data is used as the water pump energy consumption value Ef. t reasonable range.

7. The water pump operation energy-saving control system based on multi-source data analysis is characterized by: The system is used to implement the steps of the water pump operation energy-saving control method based on multi-source data analysis according to any one of claims 1 to 6, including a historical data acquisition module, a data processing module, a neural network construction module, a real-time data acquisition module, an energy consumption interval acquisition module, a reasonable adjustment interval acquisition module, and a state parameter adjustment module; The historical data acquisition module is used to sample the state parameters of the water pump during operation, and obtain the i-th state parameter x measured at the same time. i And the corresponding pump energy consumption value Ef i Historical data; The data processing module is used to obtain the water pump energy consumption value Ef i Perform anomaly detection and remove the pump energy consumption value Ef in the sampled data i And the corresponding state parameter x i Abnormal data points; The neural network building module is used to normalize the state parameters As input parameter, the normalized pump energy consumption value A neural network model is constructed for the output parameters, and the neural network model is trained using the sampled data until the expected square error value E of its prediction result is p is less than the set expected threshold ε, and the trained neural network model is used to output the state parameter x i The water pump energy consumption value Ef i The mapping relationship f between them; The real-time data acquisition module is used to obtain the adjustable range of the uth active state parameter at the current time t. and the measured value of the passive state parameter described in item v The energy consumption interval acquisition module is used to obtain the adjustable interval of the active state parameter at the current time t by using the mapping relationship f. and the measured value of the passive state parameter The corresponding pump energy consumption value Ef t The initial interval The reasonable adjustment interval acquisition module is used to detect the initial interval with λ as the detection step length. The energy consumption value of each water pump in the test is detected abnormally, and the energy consumption value Ef of the water pump at the current time t is obtained according to the detection results. t Reasonable range The state parameter adjustment module is used to adjust the energy consumption value Ef of the water pump at the current time t. t The upper limit of the reasonable range The corresponding active state parameter value is used as the optimal state value of the state parameter, and the u-th active state parameter at the current time t is adjusted to the optimal state value.

8. The water pump operation energy-saving control system based on multi-source data analysis according to claim 7 is characterized in that: The system includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor can implement the steps of the water pump operation energy-saving control method based on multi-source data analysis described in any one of claims 1 to 6 when executing the program.

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