Water pump state adjusting method, device, equipment and medium

Abstract

The invention relates to a water pump state adjustment method, device and equipment and a medium, and is applied to the technical field of water pump state adjustment. Analyzing the historical monitoring data to obtain data association between various types of monitoring data and preset parameters, the preset parameters being parameters for determining whether the state of the water pump needs to be adjusted; predicting the preset parameters based on the data association and the current monitoring data to obtain a prediction result of each preset parameter; determining abnormal information of the water pump based on the current monitoring data; and determining an adjustment strategy based on the prediction result and the abnormal information of the water pump, wherein the adjustment strategy comprises a state adjustment strategy and an abnormal adjustment strategy. The method has the effect of improving the water pump state adjusting efficiency.

Description

Technical Field

[0001] The present application relates to the technical field of water pump state adjustment, and in particular to a water pump state adjustment method, device, equipment and medium. Background Art

[0002] Water pumps are important equipment in modern industry and life, and are widely used in water supply, drainage, irrigation and other fields. With the development of social economy and technological progress, the application scope of water pumps has been continuously expanded, and higher requirements have been put forward for the performance and reliability of water pumps.

[0003] At present, pumping stations are usually equipped with frequency converters and sensors that can detect data such as water level and flow changes. However, the pump control system of the pump station mostly adopts the traditional manual control method. The operator needs to manually adjust the working state of the water pump according to the real-time monitoring data to optimize the efficiency. The pump station system that relies on manual control has problems such as slow response, cumbersome operation, and high labor costs. It cannot operate effectively when unattended, making it difficult to achieve efficient energy-saving management of the pump station. Summary of the invention

[0004] In order to improve the efficiency of water pump state adjustment, the present application provides a water pump state adjustment method, device, equipment and medium.

[0005] In a first aspect, the present application provides a method for adjusting the state of a water pump, which adopts the following technical solution: A method for adjusting a water pump state, comprising: Get current monitoring data and historical monitoring data; Analyzing the historical monitoring data to obtain data associations between various types of monitoring data and preset parameters, wherein the preset parameters are parameters for determining whether a water pump state adjustment is required; Predicting the preset parameters based on the data association and the current monitoring data to obtain a prediction result for each of the preset parameters; Determine water pump abnormality information based on the current monitoring data; An adjustment strategy is determined based on the prediction result and the water pump abnormality information, and the adjustment strategy includes a state adjustment strategy and an abnormality adjustment strategy.

[0006] By adopting the above technical solution, historical monitoring data is analyzed to obtain monitoring data that has data association with preset parameters, and the preset parameters are predicted based on the data association and current monitoring data, so as to obtain more accurate prediction results. According to the prediction results, the state of the water pump can be adjusted in advance, and no human intervention is required throughout the process. While improving the efficiency of water pump state adjustment, it also reduces labor costs.

[0007] Optionally, the analyzing the historical monitoring data to obtain data associations between various types of monitoring data and preset parameters includes: Calculate a first correlation coefficient between each monitoring data type and each preset parameter based on the historical monitoring data; Determining the monitoring data type whose first correlation coefficient meets a first preset threshold as a first associated data type of the preset parameter; Calculate the mutual information value of each two types of the first associated data corresponding to each of the preset parameters based on the mutual information method; If the mutual information value is greater than a preset mutual information value, determining the first associated data type having a smaller first correlation coefficient corresponding to the mutual information value as a second associated data type; The remaining associations between the first associated data types and the preset parameters are determined as the data associations.

[0008] By adopting the above technical solution, the first correlation coefficient between each monitoring data type and each preset parameter is calculated to obtain the first associated data type of each preset parameter, and the redundant first associated data type is screened out through the mutual information method, thereby obtaining the data association between various types of monitoring data and the preset parameters, and predicting the preset parameters based on the data association, so that more accurate preset parameters can be predicted with less data, thereby improving the efficiency and accuracy of the prediction.

[0009] Optionally, the predicting the preset parameter based on the data association and the current monitoring data includes: Building a first prediction model for each of the preset parameters based on the first associated data type corresponding to the data association and the historical monitoring data; The preset parameters are predicted based on the first prediction model and the current monitoring data to obtain prediction results for each of the preset parameters.

[0010] By adopting the above technical solution, corresponding first prediction models are constructed for various preset parameters, thereby improving the accuracy of prediction of the first prediction model.

[0011] Optionally, determining the abnormal information of the water pump based on the current monitoring data includes: Get the target operating environment and target life stage of the current water pump; Determine a water pump whose operating environment is the target operating environment and whose life stage is the target life stage as a reference water pump; Acquire historical abnormal information of the current water pump and the reference water pump; Analyze the historical abnormal information to obtain an abnormal threshold; The current monitoring data is compared with the abnormal threshold to obtain water pump abnormality information.

[0012] By adopting the above technical solution, different abnormal thresholds are determined according to the current target operating environment and target life stage of the water pump. Compared with using a fixed abnormal threshold, the reliability of the abnormal threshold is improved, thereby improving the reliability of the water pump abnormal information.

[0013] Optionally, determining an adjustment strategy based on the prediction result and the water pump abnormality information includes: Acquire historical water pump operation data, wherein the historical water pump operation data includes operation frequency and detection data; The preset algorithm is trained by using the historical water pump operation data to obtain a state adjustment model; Determine the adjustment frequency and adjustment time based on the prediction result and the state adjustment model; A state adjustment strategy is determined based on the adjustment frequency and the adjustment time.

[0014] Optionally, determining an adjustment strategy based on the prediction result and the water pump abnormality information includes: Determine the type and degree of abnormality based on the abnormal information of the water pump; Determining a maintenance strategy based on the abnormality type and the abnormality degree; Obtain the current life stage, historical life stage, and historical inspection cycle of the water pump; determining a first coefficient based on the current life stage and the historical life stage; Count the fault frequencies when performing inspections according to the historical inspection cycle; determining a second coefficient based on the fault frequency; Calculate a new inspection cycle based on the historical inspection cycle, the first coefficient, and the second coefficient; An abnormal adjustment strategy is determined based on the maintenance strategy and the new inspection cycle.

[0015] By adopting the above technical solution, when determining the inspection cycle of the water pump, not only the life stage of the water pump is taken into consideration, but also the failure frequency when inspecting according to the historical inspection cycle is taken into consideration, thereby improving the reliability of the inspection cycle.

[0016] Optionally, the method further includes: Get the importance level and failure frequency of water pumps at each location; The location where the importance level exceeds a preset level and / or the fault occurrence frequency is greater than a preset frequency is determined as the standby water pump installation location.

[0017] In a second aspect, the present application provides a water pump state adjustment device, which adopts the following technical solution: A water pump state adjustment device, comprising: Data acquisition module, used to obtain current monitoring data and historical monitoring data; An association determination module, used to analyze the historical monitoring data to obtain data associations between various types of monitoring data and preset parameters, wherein the preset parameters are parameters for determining whether a water pump state adjustment is required; A parameter prediction module, used to predict the preset parameters based on the data association and the current monitoring data, and obtain a prediction result of each preset parameter; An abnormality determination module, used to determine abnormal information of the water pump based on the current monitoring data; A strategy determination module is used to determine an adjustment strategy based on the prediction result and the water pump abnormality information, wherein the adjustment strategy includes a state adjustment strategy and an abnormality adjustment strategy.

[0018] By adopting the above technical solution, historical monitoring data is analyzed to obtain monitoring data that has data association with preset parameters, and the preset parameters are predicted based on the data association and current monitoring data, so as to obtain more accurate prediction results. According to the prediction results, the state of the water pump can be adjusted in advance, and no human intervention is required throughout the process. While improving the efficiency of water pump state adjustment, it also reduces labor costs.

[0019] In a third aspect, the present application provides an electronic device, which adopts the following technical solution: An electronic device comprises a processor, wherein the processor is coupled to a memory; The memory stores a computer program that can be loaded by a processor and executes the water pump state adjustment method described in any one of the first aspects.

[0020] In a fourth aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution: A computer-readable storage medium stores a computer program that can be loaded by a processor and execute the water pump state adjustment method described in any one of the first aspects. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a flow chart of a water pump state adjustment method provided in an embodiment of the present application.

[0022] Figure 2 It is a structural block diagram of a water pump state adjustment device provided in an embodiment of the present application.

[0023] Figure 3It is a structural block diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0024] The present application is further described in detail below in conjunction with the accompanying drawings.

[0025] The embodiment of the present application provides a method for adjusting the state of a water pump, which can be executed by an electronic device, which can be a server or a terminal device, wherein the server can be an independent physical server, a server cluster or a distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. The terminal device can be a smart phone, a tablet computer, a desktop computer, etc., but is not limited thereto.

[0026] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0027] In addition, the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article, unless otherwise specified, generally means that the associated objects before and after are in an "or" relationship.

[0028] like Figure 1 As shown, a method for adjusting the state of a water pump is provided, and the main process of the method is described as follows (steps S101 to S105): Step S101: Acquire current monitoring data and historical monitoring data.

[0029] Among them, the current monitoring data includes water pump working parameters, water quality monitoring data and environmental monitoring data. Water pump working parameters include: water pump speed, current, voltage, power, water pump inlet and outlet pressure, flow, water level, etc. Water quality monitoring data include: water temperature, pH value, turbidity, etc. Environmental monitoring data include: temperature, humidity, etc. Similarly, historical monitoring data also includes water pump working parameters, water quality monitoring data and environmental monitoring data. A variety of monitoring equipment is installed on the water pump, and the current monitoring data is obtained from the monitoring equipment, and the historical monitoring data is obtained from the database.

[0030] Step S102: Analyze the historical monitoring data to obtain data associations between various types of monitoring data and preset parameters.

[0031] Among them, the preset parameters are parameters for determining whether the water pump state (frequency) needs to be adjusted. The preset parameters include: flow rate, water pump inlet and outlet pressures, and water level. The flow rate directly reflects the delivery capacity of the water pump. By adjusting the frequency of the water pump by the flow rate, the water pump can operate stably as required; changes in the inlet and outlet pressures of the water pump can reflect changes in the resistance and demand of the water pump. By adjusting the water pump frequency according to the inlet and outlet pressures of the water pump, the pressure of the water pump can be more stable; for scenarios where a specific water level needs to be maintained, the frequency of the water pump can be adjusted by the water level to stabilize the water level within the set range. The types and quantities of preset parameters are pre-set by the staff according to actual conditions and are not specifically limited here.

[0032] Specifically, historical monitoring data are analyzed to obtain data associations between various types of monitoring data and preset parameters, including: calculating a first correlation coefficient between each type of monitoring data and each preset parameter based on the historical monitoring data; determining a monitoring data type whose first correlation coefficient satisfies a first preset threshold as a first associated data type of the preset parameter; calculating the mutual information value of each two first associated data types corresponding to each preset parameter based on a mutual information method; if the mutual information value is greater than a preset mutual information value, determining the first associated data type whose first correlation coefficient corresponding to the mutual information value is smaller as the second associated data type; and determining the association between the remaining first associated data types and the preset parameters as a data association.

[0033] In this embodiment, a data analysis tool is used to calculate the numerical values ​​corresponding to each monitoring data type in the historical monitoring data and each preset parameter to obtain a first correlation coefficient. The calculation method may be to calculate using the Pearson correlation coefficient, that is, the first correlation coefficient is the absolute value of the Pearson correlation coefficient between each monitoring data type and each preset parameter, that is, the larger the first correlation coefficient, the stronger the correlation between the monitoring data type and the preset parameter, and the smaller the first correlation coefficient, the weaker the correlation between the monitoring data type and the preset parameter. The monitoring data type whose first correlation coefficient is greater than a first preset threshold (pre-set, not specifically limited here) is determined as the first associated data type of the preset parameter, wherein the data analysis tool may be Excel, Python, or SQL.

[0034] Mutual information is a measure of the mutual dependence between two random variables. Mutual information represents the amount of information contained in one random variable about another random variable. For feature selection, if the mutual information between two features is very high, the two features may contain a large amount of overlapping information. The mutual information value of each two first associated data types corresponding to each preset parameter is calculated by the mutual information method. If the mutual information value is greater than the preset mutual information value (preset, not specifically limited here), it means that the two first associated data types contain a large amount of overlapping information. The first associated data type with a small first correlation coefficient among the two first associated data types corresponding to the mutual information value greater than the preset mutual information value is determined as the second associated data type. For example, if there are three first associated data types corresponding to mutual information values ​​greater than the preset mutual information value, the three groups of first associated data types are (A, B), (A, C), (C, B), and the magnitude relationship of the first correlation coefficient is A>B>C, then B and C are both determined as the second associated data type, and the remaining first associated data type is A; the association between the remaining first associated data type and the preset parameter is determined as data association.

[0035] Step S103: predicting the preset parameters based on data association and current monitoring data to obtain prediction results for each preset parameter.

[0036] Specifically, the preset parameters are predicted based on data association and current monitoring data, including: constructing a first prediction model for each preset parameter based on a first associated data type corresponding to the data association and historical monitoring data; predicting the preset parameters based on the first prediction model and current monitoring data to obtain a prediction result for each preset parameter.

[0037] In this embodiment, since the first associated data type corresponding to each preset parameter may be different, a corresponding first prediction model is constructed for each preset parameter. The first prediction model is implemented based on a prediction algorithm, and the prediction algorithm includes but is not limited to a linear regression algorithm, a decision tree and a random forest algorithm, a support vector machine algorithm, and a neural network and a deep learning algorithm. The first prediction model of each preset parameter is trained through the historical monitoring data corresponding to the first associated data type in the data association, and the current monitoring data is input into the first prediction model to predict the preset parameter to obtain a prediction result for each preset parameter, and the prediction result includes the predicted value of the preset parameter and the corresponding time.

[0038] Step S104: Determine water pump abnormality information based on current monitoring data.

[0039] Specifically, determining the abnormal information of the water pump based on the current monitoring data includes: obtaining the target operating environment and target life stage of the current water pump; determining a water pump whose operating environment is the target operating environment and whose life stage is the target life stage as a reference water pump; obtaining historical abnormal information of the current water pump and the reference water pump; analyzing the historical abnormal information to obtain an abnormal threshold; comparing the current monitoring data with the abnormal threshold to obtain the abnormal information of the water pump.

[0040] In this embodiment, the target operating environment and target life stage of the current water pump are obtained from the database or the staff, and the water pump with the same operating environment and life stage as the current water pump is determined as the reference water pump. The historical abnormal information of the current water pump and the reference water pump is obtained from the database; the historical abnormal information is analyzed by a data analysis tool to obtain an abnormal threshold value for each monitoring data. The abnormal threshold value for each monitoring data may include multiple data intervals, and different data intervals correspond to different degrees of abnormality. The current monitoring data is compared with the abnormal threshold value, and the current monitoring data in the abnormal interval of the abnormal threshold value is determined as abnormal monitoring data. The abnormal monitoring data, the corresponding abnormal type, and the degree of abnormality are determined as water pump abnormality information.

[0041] Step S105: determining an adjustment strategy based on the prediction result and the water pump abnormality information.

[0042] The adjustment strategies include status adjustment strategies and abnormality adjustment strategies.

[0043] Specifically, an adjustment strategy is determined based on the prediction results and the water pump abnormality information, including: obtaining historical water pump operation data, the historical water pump operation data including operation frequency and detection data; training a preset algorithm through the historical water pump operation data to obtain a state adjustment model; determining the adjustment frequency and adjustment time based on the prediction results and the state adjustment model; and determining the state adjustment strategy based on the adjustment frequency and adjustment time.

[0044] In this embodiment, historical water pump operation data is obtained from a database, and a preset algorithm is trained using the historical water pump operation data to obtain a state adjustment model. The state adjustment model can obtain the most suitable water pump frequency at each moment based on the prediction results. The preset algorithm is a deep learning neural network algorithm. The specific type of the algorithm is not specifically limited here. The prediction results are input into the state adjustment model to obtain the adjustment frequency and adjustment time. The state adjustment strategy is to adjust the working state of the water pump according to the adjustment frequency and adjustment time.

[0045] Specifically, an adjustment strategy is determined based on the prediction results and the water pump abnormality information, including: determining the abnormality type and the degree of abnormality based on the water pump abnormality information; determining a maintenance strategy based on the abnormality type and the degree of abnormality; obtaining the current life stage, historical life stage and historical inspection cycle of the water pump; determining a first coefficient based on the current life stage and the historical life stage; counting the failure frequency when inspecting according to the historical inspection cycle; determining a second coefficient based on the failure frequency; calculating a new inspection cycle based on the historical inspection cycle, the first coefficient and the second coefficient; and determining an abnormal adjustment strategy based on the maintenance strategy and the new inspection cycle.

[0046] In this embodiment, the abnormality type and abnormality degree are searched from the water pump abnormality information. The database stores the corresponding relationship between the abnormality type, abnormality degree and maintenance strategy. The maintenance strategy is searched from the database according to the abnormality type and abnormality degree.

[0047] The current life stage, historical life stage (the life stage of the water pump when determining the historical inspection cycle) and historical inspection cycle of the water pump are obtained from the staff or the database. The database stores the corresponding relationship between the current life stage, the historical life stage and the first coefficient. The first coefficient is obtained from the database according to the current life stage and the historical life stage. The fault frequency when inspected according to the historical inspection cycle is statistically analyzed through a data analysis tool. The database stores the corresponding relationship between the fault frequency and the second coefficient. The second coefficient is obtained from the database according to the fault frequency. The new inspection cycle = historical inspection cycle × first coefficient × second coefficient. The abnormal adjustment strategy is to perform equipment maintenance according to the maintenance strategy and to perform equipment inspection according to the new inspection cycle.

[0048] Specifically, the method also includes: obtaining the importance level and fault frequency of water pumps at each location; determining the location where the importance level exceeds the preset level and / or the fault frequency is greater than the preset frequency as the installation location of the standby water pump.

[0049] In this embodiment, the importance level and failure frequency of the water pumps at each location are obtained from the database or the staff. If the importance level of a location exceeds the preset level (pre-set) and / or the failure frequency is greater than the preset frequency (pre-set), it means that a backup water pump needs to be installed here to improve the emergency response speed.

[0050] Figure 2 This is a structural block diagram of a water pump state adjustment device 200 provided in an embodiment of the present application.

[0051] like Figure 2 As shown, the water pump state adjustment device 200 mainly includes: Data acquisition module 201, used to acquire current monitoring data and historical monitoring data; The association determination module 202 is used to analyze the historical monitoring data to obtain data associations between various types of monitoring data and preset parameters, where the preset parameters are parameters for determining whether a water pump state adjustment is required; The parameter prediction module 203 is used to predict the preset parameters based on data association and current monitoring data to obtain the prediction results of each preset parameter; An abnormality determination module 204, used to determine abnormal information of the water pump based on current monitoring data; The strategy determination module 205 is used to determine the adjustment strategy based on the prediction result and the abnormal information of the water pump, and the adjustment strategy includes a state adjustment strategy and an abnormal adjustment strategy.

[0052] As an optional implementation of this embodiment, the association determination module 202 is also specifically used to analyze historical monitoring data to obtain data associations between various types of monitoring data and preset parameters, including: calculating the first correlation coefficient between each type of monitoring data and each preset parameter based on the historical monitoring data; determining the monitoring data type whose first correlation coefficient meets the first preset threshold as the first associated data type of the preset parameter; calculating the mutual information value of each two first associated data types corresponding to each preset parameter based on the mutual information method; if the mutual information value is greater than the preset mutual information value, determining the first associated data type with a smaller first correlation coefficient corresponding to the mutual information value as the second associated data type; there is a data association between the remaining first associated data types and the preset parameters.

[0053] As an optional implementation of this embodiment, the parameter prediction module 203 is also specifically used to predict the preset parameters based on data association and current monitoring data, including: constructing a first prediction model for each preset parameter based on the first associated data type corresponding to the data association and the historical monitoring data; predicting the preset parameters based on the first prediction model and the current monitoring data to obtain the prediction results for each preset parameter.

[0054] As an optional implementation of this embodiment, the abnormality determination module 204 is also specifically used to determine the water pump abnormality information based on the current monitoring data, including: obtaining the target operating environment and target life stage of the current water pump; determining a water pump whose operating environment is the target operating environment and whose life stage is the target life stage as a reference water pump; obtaining historical abnormality information of the current water pump and the reference water pump; analyzing the historical abnormality information to obtain an abnormality threshold; comparing the current monitoring data with the abnormality threshold to obtain water pump abnormality information.

[0055] As an optional implementation of this embodiment, the strategy determination module 205 is also specifically used to determine the adjustment strategy based on the prediction results and the water pump abnormality information, including: obtaining historical water pump operation data, the historical water pump operation data including the operation frequency and detection data; training the preset algorithm through the historical water pump operation data to obtain a state adjustment model; determining the adjustment frequency and adjustment time based on the prediction results and the state adjustment model; determining the state adjustment strategy based on the adjustment frequency and adjustment time.

[0056] As an optional implementation of this embodiment, the strategy determination module 205 is also specifically used to determine the adjustment strategy based on the prediction results and the water pump abnormality information, including: determining the abnormality type and the degree of abnormality based on the water pump abnormality information; determining the maintenance strategy based on the abnormality type and the degree of abnormality; obtaining the current life stage, historical life stage and historical inspection cycle of the water pump; determining the first coefficient based on the current life stage and the historical life stage; counting the fault frequency when inspecting according to the historical inspection cycle; determining the second coefficient based on the fault frequency; calculating the new inspection cycle based on the historical inspection cycle, the first coefficient and the second coefficient; determining the abnormal adjustment strategy based on the maintenance strategy and the new inspection cycle.

[0057] As an optional implementation of this embodiment, the water pump state adjustment device 200 is also specifically used to: obtain the importance level and fault frequency of water pumps at each position; determine the position where the importance level exceeds the preset level and / or the fault frequency is greater than the preset frequency as the installation position of the standby water pump.

[0058] In one example, the module in any of the above devices can be one or more integrated circuits configured to implement the above methods, such as: one or more application specific integrated circuits (ASICs), or, one or more digital signal processors (DSPs), or, one or more field programmable gate arrays (FPGAs), or a combination of at least two of these integrated circuit forms.

[0059] For another example, when the modules in the device can be implemented in the form of a processing element scheduling program, the processing element can be a general-purpose processor, such as a central processing unit (CPU) or other processor that can call a program. For another example, these modules can be integrated together and implemented in the form of a system-on-a-chip (SOC).

[0060] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described devices and modules can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0061] Figure 3 A structural block diagram of an electronic device 300 provided in an embodiment of the present application.

[0062] like Figure 3 As shown, the electronic device 300 includes a processor 301 and a memory 302 , and may further include an information input / information output (I / O) interface 303 , one or more of a communication component 304 , and a communication bus 305 .

[0063] The processor 301 is used to control the overall operation of the electronic device 300 to complete all or part of the steps of the above-mentioned water pump state adjustment method; the memory 302 is used to store various types of data to support the operation of the electronic device 300, and these data may include, for example, instructions for any application or method used to operate on the electronic device 300, and data related to the application. The memory 302 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, EEPROM), erasable programmable read-only memory (Erasable Programmable Read-Only Memory, EPROM), programmable read-only memory (Programmable Read-Only Memory, PROM), read-only memory (Read-Only Memory, ROM), magnetic memory, flash memory, magnetic disk or optical disk. One or more.

[0064] The I / O interface 303 provides an interface between the processor 301 and other interface modules, and the above-mentioned other interface modules can be keyboards, mice, buttons, etc. These buttons can be virtual buttons or physical buttons. The communication component 304 is used for wired or wireless communication between the electronic device 300 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G or 4G, or a combination of one or more of them, so the corresponding communication component 304 can include: Wi-Fi components, Bluetooth components, NFC components.

[0065] The electronic device 300 can be implemented by one or more application specific integrated circuits (ASIC), digital signal processors (DSP), digital signal processing devices (DSPD), programmable logic devices (PLD), field programmable gate arrays (FPGA), controllers, microcontrollers, microprocessors or other electronic components to execute the water pump state adjustment method given in the above embodiment.

[0066] The communication bus 305 may include a path to transmit information between the above components. The communication bus 305 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The communication bus 305 may be divided into an address bus, a data bus, a control bus, etc.

[0067] The electronic device 300 may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc., and may also be servers, etc.

[0068] The present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned water pump state adjustment method are implemented.

[0069] The computer-readable storage medium may include: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0070] The terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not expressly listed, or also includes elements inherent to such process, method, article, or apparatus.

[0071] The above description is only a preferred embodiment of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of application involved in the present application is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the aforementioned application concept. For example, the above features are replaced with (but not limited to) technical features with similar functions applied in the present application.

Claims

1. A method for adjusting a water pump state, characterized in that: include: Get current monitoring data and historical monitoring data; Analyzing the historical monitoring data to obtain data associations between various types of monitoring data and preset parameters, wherein the preset parameters are parameters for determining whether a water pump state adjustment is required; Predicting the preset parameters based on the data association and the current monitoring data to obtain a prediction result for each of the preset parameters; Determine water pump abnormality information based on the current monitoring data; An adjustment strategy is determined based on the prediction result and the water pump abnormality information, and the adjustment strategy includes a state adjustment strategy and an abnormality adjustment strategy.

2. The method according to claim 1, characterized in that The historical monitoring data is analyzed to obtain data associations between various types of monitoring data and preset parameters, including: Calculate a first correlation coefficient between each monitoring data type and each preset parameter based on the historical monitoring data; Determining the monitoring data type whose first correlation coefficient meets a first preset threshold as a first associated data type of the preset parameter; Calculate the mutual information value of each two types of the first associated data corresponding to each of the preset parameters based on the mutual information method; If the mutual information value is greater than a preset mutual information value, determining the first associated data type having a smaller first correlation coefficient corresponding to the mutual information value as a second associated data type; The remaining associations between the first associated data types and the preset parameters are determined as the data associations.

3. The method according to claim 2, characterized in that The predicting the preset parameter based on the data association and the current monitoring data includes: Building a first prediction model for each of the preset parameters based on the first associated data type corresponding to the data association and the historical monitoring data; The preset parameters are predicted based on the first prediction model and the current monitoring data to obtain prediction results for each of the preset parameters.

4. The method according to claim 1, characterized in that: The determining of water pump abnormality information based on the current monitoring data includes: Get the target operating environment and target life stage of the current water pump; Determine a water pump whose operating environment is the target operating environment and whose life stage is the target life stage as a reference water pump; Acquire historical abnormal information of the current water pump and the reference water pump; Analyze the historical abnormal information to obtain an abnormal threshold; The current monitoring data is compared with the abnormal threshold to obtain water pump abnormality information.

5. The method according to claim 1, characterized in that The determining of the adjustment strategy based on the prediction result and the water pump abnormality information includes: Acquire historical water pump operation data, wherein the historical water pump operation data includes operation frequency and detection data; The preset algorithm is trained by using the historical water pump operation data to obtain a state adjustment model; Determine the adjustment frequency and adjustment time based on the prediction result and the state adjustment model; A state adjustment strategy is determined based on the adjustment frequency and the adjustment time.

6. The method according to claim 1, characterized in that The determining of the adjustment strategy based on the prediction result and the water pump abnormality information includes: Determine the type and degree of abnormality based on the abnormal information of the water pump; Determining a maintenance strategy based on the abnormality type and the abnormality degree; Obtain the current life stage, historical life stage, and historical inspection cycle of the water pump; determining a first coefficient based on the current life stage and the historical life stage; Count the fault frequencies when performing inspections according to the historical inspection cycle; determining a second coefficient based on the fault frequency; Calculate a new inspection cycle based on the historical inspection cycle, the first coefficient, and the second coefficient; An abnormal adjustment strategy is determined based on the maintenance strategy and the new inspection cycle.

7. The method according to claim 1, characterized in that The method further comprises: Get the importance level and failure frequency of water pumps at each location; The location where the importance level exceeds a preset level and / or the fault occurrence frequency is greater than a preset frequency is determined as the standby water pump installation location.

8. A water pump state adjustment device, characterized in that: include: Data acquisition module, used to obtain current monitoring data and historical monitoring data; An association determination module, used to analyze the historical monitoring data to obtain data associations between various types of monitoring data and preset parameters, wherein the preset parameters are parameters for determining whether a water pump state adjustment is required; A parameter prediction module, used to predict the preset parameters based on the data association and the current monitoring data, and obtain a prediction result of each preset parameter; An abnormality determination module, used to determine abnormal information of the water pump based on the current monitoring data; A strategy determination module is used to determine an adjustment strategy based on the prediction result and the water pump abnormality information, wherein the adjustment strategy includes a state adjustment strategy and an abnormality adjustment strategy.

9. An electronic device, characterized in that: comprising a processor coupled to a memory; The processor is configured to execute a computer program stored in the memory, so that the electronic device executes the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The method comprises a computer program or an instruction, which, when executed on a computer, causes the computer to execute the method according to any one of claims 1 to 7.

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