Water outlet pump liquid level prediction system and method

Abstract

The invention discloses a water outlet pump liquid level prediction system and method, and the system comprises an original data obtaining module which obtains original data and completes the data cleaning work; the classification module is used for classifying the data into first training set data and first test set data; the prediction model construction module is used for constructing a time sequence and constructing a regression prediction model when the water outlet pump is not started and a regression prediction model when the water outlet pump is started in combination with a business rule; the training module is used for training the first training set data through the regression prediction model when the water outlet pump is not started and generating a first training result, and training the first training set data through the regression prediction model when the water outlet pump is started and generating a second training result; the judgment module judges whether the first training result meets the first requirement or not and then judges whether the second training result meets the second requirement or not. Accurate prediction of the liquid level of the water outlet pump is achieved through the prediction model building module, the training module and the like, and accurate utilization of the water outlet pump is achieved.

Description

Technical Field

[0001] The invention relates to a liquid level prediction system and method, and in particular to a water outlet pump liquid level prediction system and method. Background Art

[0002] As the construction of underground transportation projects in major cities in China continues to expand, the importance of drainage systems has become increasingly prominent. The underground engineering drainage system is composed of a multi-level liquid pump group. The number of pumps to be started is determined by the pre-set multi-level liquid level value, and the started pumps work together to complete the drainage work.

[0003] Now many enterprises and municipalities require the realization of automated control for water pump water supply and drainage systems, and realize remote monitoring, automatic control, unmanned operation and regular inspection of pump stations. For example, a Chinese patent with the patent publication number "CN103277322A" discloses a high-precision water pump body liquid level detection method and detection device. The existing technologies are all focused on the monitoring and detection of the outlet pump liquid level, but there is no technology for predicting the outlet pump liquid level. Summary of the invention

[0004] In view of the above situation, in order to solve the technical problems of the prior art, the present invention provides a water outlet pump liquid level prediction system and method.

[0005] The present invention solves the above technical problems through the following technical solutions: a water outlet pump liquid level prediction system, characterized in that it includes: The raw data acquisition module acquires the raw data and completes data cleaning; A classification module classifies the data into first training set data and first test set data; The prediction model building module builds time series and combines business rules to build regression prediction models when the water outlet pump is not turned on and regression prediction models when the water outlet pump is turned on; A training module, which trains the first training set data by using a regression prediction model when the water outlet pump is not turned on and generates a first training result, and trains the first training set data by using a regression prediction model when the water outlet pump is turned on and generates a second training result; The judgment module first judges whether the first training result meets the first requirement, and then judges whether the second training result meets the second requirement.

[0006] Preferably, the judgment module judges that the first training result meets the first requirement, and then predicts the liquid level data when the water pump is not turned on by using the regression prediction model when the water outlet pump is not turned on.

[0007] Preferably, if the judgment module judges that the first training result does not meet the first requirement, the model coefficient and lag time of the regression prediction model when the water outlet pump is not turned on are adjusted until the first requirement is met.

[0008] Preferably, if the judgment module judges that the second training result meets the second requirement, the liquid level data when the water pump is turned on is predicted by the regression prediction model when the water outlet pump is turned on.

[0009] Preferably, if the judgment module judges that the second training result does not meet the first requirement, the model coefficient and lag time of the regression prediction model when the water outlet pump is turned on are adjusted until the second requirement is met.

[0010] Preferably, the raw data acquisition module includes: An extraction module extracts the water pump level data and water inlet flow data from the database; The cleaning module supplements and optimizes missing values ​​and outliers in data through partitioning, thus improving the accuracy and reliability of subsequent processing; The normalization module removes noise and standardizes the data format.

[0011] Preferably, the amount of the first training set data is nine times the amount of the first test set data.

[0012] Preferably, the first requirement and the second requirement use real-time data of the water outlet pump, and classify the real-time data into second training set data and second test set data, respectively, as a basis for judgment.

[0013] Preferably, the first test set data uses WAPE and RMSE evaluation indicators, adjusts parameters and model structure according to the first training results and the second training results, and repeats the test to further optimize the regression prediction model when the water outlet pump is not turned on and the regression prediction model when the water outlet pump is turned on.

[0014] The present invention also provides a method for predicting the liquid level of a water outlet pump, characterized in that it comprises the following steps: Step 1: Obtain the original data and complete data cleaning; Step 2: Classify the data into first training set data and first test set data respectively; Step 3: Build a time series and combine it with business rules to build a regression prediction model when the water outlet pump is not turned on and a regression prediction model when the water outlet pump is turned on; Step 4: training the first training set data by the regression prediction model when the water outlet pump is not turned on and generating a first training result, and training the first training set data by the regression prediction model when the water outlet pump is turned on and generating a second training result; Step 5: first determine whether the first training result meets the first requirement, and then determine whether the second training result meets the second requirement.

[0015] The positive and progressive effect of the present invention is that the present invention realizes accurate prediction of the liquid level of the water outlet pump through a prediction model construction module, a training module, etc., and realizes accurate utilization of the water outlet pump. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a structural block diagram of the water outlet pump liquid level prediction system of the present invention.

[0017] Figure 2 It is a structural block diagram of the original data acquisition module in the present invention.

[0018] Figure 3 The present invention is a flow chart of the method for predicting the liquid level of a water outlet pump. DETAILED DESCRIPTION

[0019] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0020] Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0021] In the description of the present invention, it is necessary to understand that the terms "upper", "lower", "front", "back", "left", "right", "top", "bottom", "inside", "outside", etc., indicating the orientation or position relationship are based on the orientation or position relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention.

[0022] like Figure 1 As shown, the water outlet pump liquid level prediction system of the present invention includes: The raw data acquisition module acquires the raw data and completes data cleaning; A classification module classifies the data into first training set data and first test set data; The prediction model building module builds time series and combines business rules to build regression prediction models when the water outlet pump is not turned on and regression prediction models when the water outlet pump is turned on; A training module, which trains the first training set data by using a regression prediction model when the water outlet pump is not turned on and generates a first training result, and trains the first training set data by using a regression prediction model when the water outlet pump is turned on and generates a second training result; The judgment module first judges whether the first training result meets the first requirement, and then judges whether the second training result meets the second requirement, thereby eliminating the influence of the water pump start-up state on the model training result.

[0023] In this embodiment, if the judgment module determines that the first training result meets the first requirement, the liquid level data when the water pump is not turned on is predicted by the regression prediction model when the water pump is not turned on. Otherwise, if the judgment module determines that the first training result does not meet the first requirement, the model coefficient and lag time of the regression prediction model when the water pump is not turned on are adjusted until the first requirement is met.

[0024] In addition, if the judgment module determines that the second training result meets the second requirement, the liquid level data when the water pump is turned on is predicted by the regression prediction model when the water pump is turned on. Otherwise, if the judgment module determines that the second training result does not meet the first requirement, the model coefficient and lag time of the regression prediction model when the water pump is turned on are adjusted until the second requirement is met.

[0025] When the regression prediction model reaches a reasonable accuracy, the regression prediction model can be applied to the water outlet pump level prediction, that is, the water outlet pump level is accurately predicted, and the prediction cycle can be selected within thirty minutes. The present invention determines the opening state of the water outlet pump through the regression prediction model to realize automatic switching of the water outlet pump.

[0026] like Figure 2 As shown, the original data acquisition module includes: An extraction module extracts the water pump level data and water inlet flow data from the database; The cleaning module supplements and optimizes missing values ​​and outliers in data through partitioning, thus improving the accuracy and reliability of subsequent processing; The normalization module removes noise and standardizes the data format.

[0027] The amount of the first training set data is nine times that of the first test set data, which improves the training effect. The first requirement and the second requirement use the real-time data of the water outlet pump, and classify the real-time data into the second training set data and the second test set data respectively as the basis for judgment.

[0028] Furthermore, the first test set data used WAPE (Weighted Absolute Percentage Error) and RMSE (Root Mean Square Error) evaluation indicators, adjusted the parameters and model structure according to the first training results and the second training results, and repeated tests further optimized the regression prediction model when the water outlet pump was not turned on and the regression prediction model when the water outlet pump was turned on, so as to improve the prediction accuracy of the model.

[0029] like Figure 3 As shown, the method for predicting the liquid level of a water outlet pump of the present invention comprises the following steps: Step 1: Obtain the original data and complete data cleaning; Step 2: Classify the data into first training set data and first test set data respectively; Step 3: Build a time series and combine it with business rules to build a regression prediction model when the water outlet pump is not turned on and a regression prediction model when the water outlet pump is turned on; Step 4: training the first training set data by the regression prediction model when the water outlet pump is not turned on and generating a first training result, and training the first training set data by the regression prediction model when the water outlet pump is turned on and generating a second training result; Step 5: first determine whether the first training result meets the first requirement, and then determine whether the second training result meets the second requirement.

[0030] In addition, an embodiment of the present invention provides an electronic device, which includes: a memory and a processor; The memory is used to store computer programs; The processor is used to implement the method in the above embodiment when executing the computer program.

[0031] In addition, the present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the method in the above embodiment is implemented.

[0032] Based on the method in the above embodiment, an embodiment of the present invention provides a computer program product. When the computer program product runs on a processor, the processor executes the method in the above embodiment.

[0033] Based on the method in the above embodiment, the embodiment of the present invention further provides a chip, including one or more processors and an interface circuit. Optionally, the chip may also include a bus. Among them: The processor may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in the processor or instructions in the form of software. The above processor can be a general-purpose processor, a digital communicator (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods and steps disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc.

[0034] The interface circuit can be used to send or receive data, instructions or information. The processor can use the data, instructions or other information received by the interface circuit to process it, and can send the processing completion information through the interface circuit.

[0035] Optionally, the chip further includes a memory, which may include a read-only memory and a random access memory, and provides operation instructions and data to the processor. A portion of the memory may also include a non-volatile random access memory (NVRAM).

[0036] Optionally, the memory stores an executable software module or a data structure, and the processor can perform corresponding operations by calling an operation instruction stored in the memory (the operation instruction may be stored in an operating system).

[0037] Optionally, the interface circuit can be used to output the execution result of the processor.

[0038] It should be noted that the functions corresponding to the processor and the interface circuit can be implemented through hardware design, software design, or a combination of hardware and software, and there is no limitation here.

[0039] It should be understood that each step of the above method embodiment can be completed by a hardware-based logic circuit or a software-based instruction in a processor.

[0040] It is understandable that the size of the sequence number of each step in the above embodiment does not mean the order of execution, and the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present application. In addition, in some possible implementations, each step in the above embodiment can be selectively executed according to actual conditions, and can be partially executed or fully executed, which is not limited here.

[0041] It is understandable that the processor in the embodiments of the present application may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. The general-purpose processor may be a microprocessor or any conventional processor.

[0042] The method steps in the embodiments of the present application can be implemented by hardware or by a processor executing software instructions. The software instructions can be composed of corresponding software modules, and the software modules can be stored in random access memory (RAM), flash memory, read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), registers, hard disks, mobile hard disks, CD-ROMs, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can be located in an ASIC.

[0043] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented by software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted through the computer-readable storage medium. The computer instructions may be transmitted from a website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid state disk (SSD)), etc.

[0044] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A water outlet pump liquid level prediction system, characterized in that: It includes: The raw data acquisition module acquires the raw data and completes data cleaning; A classification module classifies the data into first training set data and first test set data; The prediction model building module builds time series and combines business rules to build regression prediction models when the water outlet pump is not turned on and regression prediction models when the water outlet pump is turned on; A training module, which trains the first training set data by using a regression prediction model when the water outlet pump is not turned on and generates a first training result, and trains the first training set data by using a regression prediction model when the water outlet pump is turned on and generates a second training result; The judgment module first judges whether the first training result meets the first requirement, and then judges whether the second training result meets the second requirement.

2. The water outlet pump liquid level prediction system according to claim 1, characterized in that: If the judgment module judges that the first training result meets the first requirement, the liquid level data when the water pump is not turned on is predicted by the regression prediction model when the water outlet pump is not turned on.

3. The water outlet pump liquid level prediction system according to claim 1, characterized in that: If the judgment module judges that the first training result does not meet the first requirement, the model coefficient and lag time of the regression prediction model when the water outlet pump is not turned on are adjusted until the first requirement is met.

4. The water outlet pump liquid level prediction system according to claim 1, characterized in that: If the judgment module judges that the second training result meets the second requirement, the liquid level data when the water pump is turned on is predicted by the regression prediction model when the water outlet pump is turned on.

5. The water outlet pump liquid level prediction system according to claim 1, characterized in that: If the judgment module judges that the second training result does not meet the first requirement, the model coefficient and lag time of the regression prediction model when the water outlet pump is turned on are adjusted until the second requirement is met.

6. The water outlet pump liquid level prediction system according to claim 1, characterized in that: The original data acquisition module comprises: An extraction module extracts the water pump level data and water inlet flow data from the database; The cleaning module supplements and optimizes missing values ​​and outliers in data through partitioning, thus improving the accuracy and reliability of subsequent processing; The normalization module removes noise and standardizes the data format.

7. The water outlet pump liquid level prediction system according to claim 1, characterized in that: The amount of the first training set data is nine times the amount of the first test set data.

8. The water outlet pump liquid level prediction system according to claim 1, characterized in that: The first requirement and the second requirement use the real-time data of the water outlet pump, and classify the real-time data into the second training set data and the second test set data respectively as the basis for judgment.

9. The water outlet pump liquid level prediction system according to claim 1, characterized in that: The first test set data uses WAPE and RMSE evaluation indicators, adjusts parameters and model structure according to the first training results and the second training results, and repeats the test to further optimize the regression prediction model when the water outlet pump is not turned on and the regression prediction model when the water outlet pump is turned on.

10. A method for predicting the liquid level of a water outlet pump, characterized in that: It includes the following steps: Step 1: Obtain the original data and complete data cleaning; Step 2: Classify the data into first training set data and first test set data respectively; Step 3: Build a time series and combine it with business rules to build a regression prediction model when the water outlet pump is not turned on and a regression prediction model when the water outlet pump is turned on; Step 4: training the first training set data by the regression prediction model when the water outlet pump is not turned on and generating a first training result, and training the first training set data by the regression prediction model when the water outlet pump is turned on and generating a second training result; Step 5: first determine whether the first training result meets the first requirement, and then determine whether the second training result meets the second requirement.

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