Water pump operation data acquisition and analysis system based on cloud platform

Abstract

The invention discloses a water pump operation data acquisition and analysis system based on a cloud platform, and relates to the technical field of water pump monitoring, the system comprises a local layer and a cloud computing layer, the local layer comprises a water pump monitoring module and a group management module; the group management module divides the water pumps with the same working targets into water pump groups and sets water supply weight coefficients of the water pumps in the water pump groups; the water pump monitoring module comprises a plurality of operation monitoring units and a plurality of water outlet monitoring units; and each operation monitoring unit and each water outlet monitoring unit are respectively matched with each water pump and are used for acquiring working data and water outlet data of the corresponding water pump. Electrical parameters, mechanical parameters, water outlet parameters and water quality parameters of the water pump are collected through the local layer, comprehensive data dimensions are formed in combination with the environment temperature and the working noise of the water pump, the abnormal amplitude and frequency are calculated by the central processing module through comparison with standard parameters, and then a comprehensive abnormal score is constructed.

Description

Technical Field

[0001] The present invention relates to the technical field of water pump monitoring, and in particular to a water pump operation data acquisition and analysis system based on a cloud platform. Background Art

[0002] With the development of smart water services and industrial automation, water pumps, as key equipment in fluid delivery systems, are widely used in various scenarios such as municipal water supply, industrial cooling, agricultural irrigation, and sewage treatment. In order to ensure the operational stability and safety of the water pump system, a data acquisition system based on multiple sensors has been established in the existing technology to monitor the electrical parameters, mechanical parameters, and water discharge parameters of the water pump, and to evaluate and warn the operating status in combination with the data analysis platform; In actual applications, some technical solutions further introduce environmental monitoring methods to assist in analyzing the operating environment of the water pump by collecting data such as ambient temperature and humidity. In addition, some systems also support trend analysis and data visualization based on historical data, which improves operation and maintenance efficiency and management level.

[0003] After searching, a Chinese patent (publication number: CN119376279A) discloses a method, system and medium for real-time monitoring and data analysis of an intelligent pump room. The patent includes: extracting water pump operation characteristic data, motor operation characteristic data and valve operation characteristic data corresponding to multiple monitoring time points, processing according to the water pump operation characteristic data to obtain a water pump performance reliability index, processing according to the motor operation characteristic data to obtain a motor state stability index, processing according to the water pump operation characteristic data combined with the valve operation characteristic data to obtain a water pump regulation sensitivity index, obtaining environmental characteristic data of the area where the target pump room is located, processing to obtain an environmental interference suppression index, processing in combination with the water pump performance reliability index, the motor state stability index and the water pump regulation sensitivity index to obtain the equipment operation effectiveness index of the target pump room, and making corresponding adjustments and maintenance to the target pump room.

[0004] With the widespread application of water pump systems in complex environments, higher technical requirements are put forward for the system in terms of multi-dimensional data fusion, intelligent evaluation of operation anomalies, environmental factor response analysis, and high-density equipment interference stripping. How to achieve higher-precision anomaly judgment has become an important direction for improving the intelligence level of the system. Therefore, the present invention proposes a water pump operation data collection and analysis system based on a cloud platform. Summary of the invention

[0005] The purpose of the present invention is to provide a water pump operation data acquisition and analysis system based on a cloud platform to solve the problems mentioned in the above background technology.

[0006] The present invention can be implemented by the following technical solutions: a water pump operation data acquisition and analysis system based on a cloud platform, including a local layer and a cloud computing layer; The local layer includes an environment monitoring module, a water pump monitoring module and a group management module; The group management module divides each water pump into a plurality of water pump groups based on the working target of each water pump, and the working targets of the water pumps in each water pump group are the same. At the same time, the group management module sets a corresponding water supply weight coefficient for each water pump in the same water pump group based on a preset working target; The water pump monitoring module includes multiple operation monitoring units and multiple water discharge monitoring units; Each operation monitoring unit and each water discharge monitoring unit are matched with each water pump respectively, and are used to collect the working data and water discharge data of the corresponding water pump; Among them, the working data includes the electrical parameters of the water pump (voltage, current, power, power consumption), mechanical operating parameters (speed, operating time, number of starts and stops, operating temperature, vibration intensity); Outlet water data include hydraulic parameters (outlet flow, outlet pressure, head), water quality parameters (water temperature, dissolved oxygen, turbidity); The environmental monitoring module includes a temperature monitoring unit and multiple noise monitoring units; The temperature monitoring unit is used to monitor the ambient temperature of each water pump in the same area, and each noise monitoring unit monitors the working noise of the corresponding water pump respectively; The cloud computing layer includes a large database and a central processing module; The large database includes standard working data, standard water discharge data, standard ambient temperature, standard working noise and abnormal score mapping table of various types of water pumps based on different working requirements, and the large database transmits the standard working data, standard water discharge data, standard ambient temperature and standard working noise as standard parameters to the central processing module; The anomaly score mapping table converts different anomaly scores into Divided into multiple abnormal levels, and each abnormal level corresponds to a different water pump processing strategy; The central processing module is used to receive working data, water output data, ambient temperature and working noise as monitoring parameters, and the central processing module compares the monitoring parameters with the corresponding standard parameters. When the difference between the two exceeds the corresponding preset difference threshold, the central processing module marks the corresponding monitoring parameter as abnormal data, and the central processing module counts the average abnormal amplitude and abnormal frequency of the abnormal data, and calculates the abnormal score of the corresponding water pump through the formula , the formula used is: ; Where n is the total number of monitoring parameters; i is the index number of the i-th monitoring parameter; is the data type weight coefficient of the i-th monitoring parameter; is the average abnormal amplitude of the i-th monitoring parameter, indicating the average deviation of the monitoring parameter from the standard parameter; and ,in, is the abnormal amplitude of the i-th monitoring parameter at time point t, which is specifically the part of the monitoring parameter that exceeds the corresponding preset difference threshold; It is the set of all time points during the statistical period when the monitoring parameter is judged to be abnormal; is the total number of times the i-th monitoring parameter is judged to be abnormal during the statistical period; α is an index that controls the intensity of the influence of the average anomaly amplitude on the anomaly score; is the abnormal frequency of the ith monitoring parameter; and ,in, is the total number of times the i-th monitoring parameter is judged to be abnormal within the statistical period, is the total number of sampling times of the i-th monitoring parameter in the statistical period; β is an index that controls the intensity of the impact of abnormal frequency on the score; The central processing module will correspond to the abnormal score of the water pump Match with the abnormality score mapping table to obtain the abnormality level of the corresponding water pump, and control the water pump to execute the corresponding processing strategy; At the same time, the central processing module adjusts the working targets of the remaining water pumps in the same water pump group based on the water supply weight coefficients, and allocates the working targets of the water pumps determined to be abnormal.

[0007] A further technical improvement of the present invention is that the method for the central processing module to assign a working target to a water pump determined to be abnormal comprises: A1. The central processing module obtains the working target of the pump that is judged to be abnormal , and identify the remaining water pumps in the corresponding water pump group; A2. The central processing module calculates the remaining load H of the remaining water pumps. If the remaining load H≤0, the water pump does not participate in the allocation (indicating that the water pump is fully loaded). If the remaining load H>0, the water pump participates in the allocation. The central processing module counts the water pumps participating in the allocation as 1, 2, 3, ..., j. A3. The central processing module calculates the weighted capacity of all pumps j involved in the allocation ; , where is the water supply weight coefficient of the j-th water pump; is the current remaining load of the j-th water pump; A4. Central processing module calculates total weighted capacity And the distribution ratio ; ; A5. Central processing modules are allocated according to the proportion The working target of the abnormal pump Allocate to the participating pumps and revise the working target of the participating pumps to the final target ; ; In the formula, is the initial working target of the jth water pump.

[0008] A further technical improvement of the present invention is that: the water pump group is provided with a backup water pump; If the remaining allocatable work target of the participating pumps in the pump group is less than the work target of the abnormal pump , then start the standby water pump.

[0009] A further technical improvement of the present invention is that the central processing module collects the ambient temperature in real time through the temperature monitoring unit , record the temperature data at consecutive time points, and calculate the ambient temperature change rate through the temperature data ; ; In the formula, is the ambient temperature at time t , for Ambient temperature at the moment ; At the same time, the central processing module collects the operating temperature of each water pump , and calculate the temperature change rate of each water pump ;in, ; The central processing module then calculates the ambient temperature change rate The time point when the preset environmental change range is exceeded , and the central processing module detects the temperature change rate The time point when the preset pump change amplitude is exceeded ; Finally, the central processing module passes Calculate the water pump temperature delay time .

[0010] A further technical improvement of the present invention is that the central processing module is provided with a corresponding delay threshold based on the model, use environment and service life of the water pump. and amplitude threshold ; The central processing module delays the water pump temperature With delay threshold For comparison, the temperature change rate of the water pump With amplitude threshold Make a comparison; If the water pump temperature delay time > Delay threshold ,or >Amplitude threshold , then the central processing module determines the corresponding water pump as having an abnormal temperature response; The central processing module adds abnormal temperature response to the abnormal score Calculation of anomaly scores Make corrections.

[0011] A further technical improvement of the present invention is that when multiple water pumps are arranged within the collection range of a single noise monitoring unit, the central processing module establishes a neighboring water pump set for each water pump within the collection range of the noise monitoring unit. ; And for each water pump i, the central processing module records its location coordinates, the set of neighboring water pumps and each adjacent pump Physical distance to the noise monitoring unit corresponding to pump i , where physical distance Obtained through measurement when laying out water pumps or map settings.

[0012] A further technical improvement of the present invention is that the central processing module obtains the working noise and vibration intensity of the water pump i When the working noise is set to the original noise value ; And the central processing module obtains the neighboring water pumps of water pump i Vibration intensity ; The central processing module establishes the distance attenuation factor ,in ; The central processing module is used for each adjacent pump , calculate its noise interference, the formula used is: , regarded as the interference contribution of pump j to the noise monitoring unit of pump i; The central processing module then sends all adjacent pumps The noise interference of pump i is accumulated to obtain the total noise interference ; The central processing module converts the total amount of noise interference From the original noise value The measured noise of pump i is obtained by stripping , , measurement noise As anomaly score Monitoring parameters of the grey water pump.

[0013] Compared with the prior art, the present invention has the following beneficial effects: The present invention collects the electrical parameters, mechanical parameters, water output parameters and water quality parameters of the water pump at the local level, and combines the ambient temperature and the working noise of the water pump to form a comprehensive data dimension. The central processing module calculates the abnormal amplitude and frequency by comparing with the standard parameters, and then constructs a comprehensive abnormality score to achieve accurate monitoring and early warning of the operating status of the water pump; In addition, the present invention collects the ambient temperature change rate and the temperature response of the water pump body in real time, calculates the temperature change delay time and amplitude, and combines the preset delay threshold and amplitude threshold to intelligently judge the water pump heat dissipation abnormality or sensor failure, effectively improving the environmental adaptability and system intelligence level; At the same time, the central processing module automatically controls the water pump to enter load reduction, detection or shutdown state based on the abnormal score and level mapping, significantly improving the adaptive regulation capability of the system operation and reducing manual intervention. When the water pump is judged to be abnormal, the central processing module calculates the weighted capacity and proportionally distributes the work objectives of the abnormal water pump based on the water supply weight and residual load of each water pump in the water pump group, thereby achieving a smooth transition of task coordination within the group and the system's water supply capacity. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0015] Figure 1 It is a system block diagram of the present invention. DETAILED DESCRIPTION

[0016] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the specific implementation methods, structures, features and effects of the present invention are described in detail below in conjunction with the accompanying drawings and preferred embodiments.

[0017] See also Figure 1 As shown, the present invention provides a water pump operation data collection and analysis system based on a cloud platform, including a local layer and a cloud computing layer; The local layer includes an environmental monitoring module, a water pump monitoring module, and a group management module; The group management module divides each water pump into multiple water pump groups based on its working target, and the water pumps in each water pump group have the same working target. At the same time, the group management module sets the corresponding water supply weight coefficient for each water pump in the same water pump group based on the preset working target; The water pump monitoring module includes multiple operation monitoring units and multiple water discharge monitoring units; Each operation monitoring unit and each water discharge monitoring unit are matched with each water pump respectively, and are used to collect the working data and water discharge data of the corresponding water pump; Among them, the working data includes the electrical parameters of the water pump (voltage, current, power, power consumption), mechanical operating parameters (speed, operating time, number of starts and stops, operating temperature, vibration intensity); Outlet water data include hydraulic parameters (outlet flow, outlet pressure, head), water quality parameters (water temperature, dissolved oxygen, turbidity); The environmental monitoring module includes a temperature monitoring unit and multiple noise monitoring units; The temperature monitoring unit is used to monitor the ambient temperature of each water pump in the same area, and each noise monitoring unit monitors the working noise of the corresponding water pump respectively; The cloud computing layer includes a large database and a central processing module; The big database includes standard working data, standard water discharge data, standard ambient temperature, standard working noise and abnormal score mapping table of various types of water pumps based on different working requirements, and the big database transmits the standard working data, standard water discharge data, standard ambient temperature and standard working noise as standard parameters to the central processing module; The anomaly score mapping table converts different anomaly scores into Divided into multiple abnormal levels, and each abnormal level corresponds to a different water pump processing strategy; In this embodiment, the abnormal level is determined as follows:

[0018] The central processing module is used to receive working data, water output data, ambient temperature and working noise as monitoring parameters, and the central processing module compares the monitoring parameters with the corresponding standard parameters. When the difference between the two exceeds the corresponding preset difference threshold, the central processing module marks the corresponding monitoring parameter as abnormal data, and the central processing module counts the average abnormal amplitude and abnormal frequency of the abnormal data, and calculates the abnormal score of the corresponding water pump through the formula , the formula used is: ; Where n is the total number of monitoring parameters, such as (voltage, current, power, power consumption, water flow, water pressure, head, water temperature, dissolved oxygen, turbidity and working noise); i is the index number of the i-th monitoring parameter; is the data type weight coefficient of the i-th monitoring parameter; is the average abnormal amplitude of the i-th monitoring parameter, indicating the average deviation of the monitoring parameter from the standard parameter; and ,in, is the abnormal amplitude of the i-th monitoring parameter at time point t, which is specifically the part of the monitoring parameter that exceeds the corresponding preset difference threshold; It is the set of all time points during the statistical period when the monitoring parameter is judged to be abnormal; is the total number of times the i-th monitoring parameter is judged to be abnormal during the statistical period; α is an index that controls the intensity of the influence of the average anomaly amplitude on the anomaly score; is the abnormal frequency of the ith monitoring parameter; and ,in, is the total number of times the i-th monitoring parameter is judged to be abnormal within the statistical period, is the total number of sampling times of the i-th monitoring parameter in the statistical period; β is an index that controls the intensity of the impact of abnormal frequency on the score; Will and Substitute the formula into the anomaly score We can get: ; The central processing module will correspond to the abnormal score of the water pump Match with the abnormality score mapping table to obtain the abnormality level of the corresponding water pump, and control the water pump to execute the corresponding processing strategy; At the same time, the central processing module adjusts the working targets of the remaining water pumps in the same water pump group based on the water supply weight coefficients, and allocates the working targets of the water pumps determined to be abnormal.

[0019] The method for the central processing module to assign the working target of the water pump determined to be abnormal includes: A1. The central processing module obtains the working target of the pump that is judged to be abnormal , and identify the remaining water pumps in the corresponding water pump group; A2. The central processing module calculates the remaining load H of the remaining water pumps. If the remaining load H≤0, the water pump does not participate in the allocation (indicating that the water pump is fully loaded). If the remaining load H>0, the water pump participates in the allocation. The central processing module counts the water pumps participating in the allocation as 1, 2, 3, ..., j. A3. The central processing module calculates the weighted capacity of all pumps j involved in the allocation ; , where is the water supply weight coefficient of the j-th water pump; is the current remaining load of the j-th water pump; A4. Central processing module calculates total weighted capacity And the distribution ratio ; ; A5. Central processing modules are allocated according to the proportion The working target of the abnormal pump Allocate to the participating pumps and revise the working target of the participating pumps to the final target ; ; In the formula, is the initial working target of the j-th water pump; If the remaining allocatable work target of the participating pumps in the pump group is less than the work target of the abnormal pump , then start the standby water pump.

[0020] In addition, the central processing module collects the ambient temperature in real time through the temperature monitoring unit. , record the temperature data at consecutive time points, and calculate the ambient temperature change rate through the temperature data ; ; In the formula, is the ambient temperature at time t , for Ambient temperature at the moment ; At the same time, the central processing module collects the operating temperature of each water pump , and calculate the temperature change rate of each water pump ;in, ; The central processing module then calculates the ambient temperature change rate The time point when the preset environmental change range is exceeded , and the central processing module detects the temperature change rate The time point when the preset pump change amplitude is exceeded ; Finally, the central processing module passes Calculate the temperature delay time of the water pump .

[0021] The central processing module sets the corresponding delay threshold based on the model, use environment and service life of the water pump. and amplitude threshold ; The central processing module delays the water pump temperature With delay threshold For comparison, the temperature change rate of the water pump With amplitude threshold Make a comparison; If the water pump temperature delay time > Delay threshold ,or >Amplitude threshold , then the central processing module determines the corresponding water pump as having an abnormal temperature response; The central processing module adds abnormal temperature response to the abnormal score Calculation of anomaly scores Make corrections.

[0022] When multiple water pumps are set within the collection range of a single noise monitoring unit, the central processing module establishes a neighboring water pump set for each water pump within the collection range of the noise monitoring unit. ; And for each water pump i, the central processing module records its location coordinates, the set of neighboring water pumps and each adjacent pump Physical distance to the noise monitoring unit corresponding to pump i , where physical distance Obtained through measurement when laying out water pumps or map settings.

[0023] The central processing module obtains the working noise and vibration intensity of pump i When the working noise is set to the original noise value ; And the central processing module obtains the neighboring water pumps of water pump i Vibration intensity ; The central processing module establishes the distance attenuation factor ,in ; The greater the distance, the greater the distance attenuation factor The smaller it is, the weaker the impact of noise propagation is; The central processing module is used for each adjacent pump , calculate its noise interference, the formula used is: , regarded as the interference contribution of pump j to the noise monitoring unit of pump i; The central processing module then sends all adjacent pumps The noise interference of pump i is accumulated to obtain the total noise interference ; The central processing module converts the total amount of noise interference From the original noise value The measured noise of pump i is obtained by stripping , ; If the measurement noise If a negative value appears, it is treated as 0, indicating that there is no real noise or too much stripping; And the system constantly corrects the distance attenuation factor according to the actual monitoring results during periodic operation If the noise is too low or there is residual interference after stripping, the distance attenuation factor is corrected using linear regression or multivariate fitting algorithm. .

[0024] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment as above, it is not used to limit the present invention. Any technical personnel in this field can make some changes or modify the technical contents disclosed above into equivalent embodiments without departing from the scope of the technical solution of the present invention. However, any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present invention without departing from the content of the technical solution of the present invention still fall within the scope of the technical solution of the present invention.

Claims

1. The water pump operation data collection and analysis system based on the cloud platform includes a local layer and a cloud computing layer, and is characterized by: The local layer includes: The group management module divides the water pumps with the same working objectives into water pump groups and sets the water supply weight coefficient of each water pump; A water pump monitoring module, including multiple operation monitoring units and multiple water discharge monitoring units; Each operation monitoring unit and each water discharge monitoring unit are matched with each water pump, and collect the working data and water discharge data of the corresponding water pump; The cloud computing layer includes a large database and a central processing module; The large database includes the standard parameters of the corresponding water pumps and the abnormality score mapping table. The abnormality score mapping table converts different abnormality scores into Divided into multiple abnormal levels, and each abnormal level corresponds to a different water pump processing strategy; After receiving the working data, water discharge data and working noise, the central processing module compares them with the corresponding standard parameters as monitoring parameters. When the difference between the two exceeds the corresponding preset difference threshold, the central processing module marks the corresponding monitoring parameter as abnormal data; And the central processing module counts the average abnormal amplitude of abnormal data and abnormal frequency , and by the formula , calculate the abnormal score of the corresponding water pump ; The central processing module scores the anomaly Match it with the abnormal score mapping table to obtain the abnormal level of the corresponding water pump, and control the water pump to execute the corresponding processing strategy. The central processing module adjusts the working targets of the remaining water pumps based on the water supply weight coefficients of the remaining water pumps in the same water pump group, and allocates the working targets of the water pumps judged to be abnormal.

2. The water pump operation data acquisition and analysis system based on the cloud platform according to claim 1 is characterized in that: The working data of the pump include: Electrical parameters, specifically voltage, current, power, and power consumption; Mechanical operating parameters, specifically speed, operating time, number of starts and stops, operating temperature, and vibration intensity; The water output data of the pump includes: Hydraulic parameters, specifically water flow, water pressure, and head; Water quality parameters, specifically water temperature, dissolved oxygen, and turbidity.

3. The water pump operation data acquisition and analysis system based on the cloud platform according to claim 2 is characterized in that: Anomaly Scoring In the calculation formula, n is the total number of monitoring parameters; i is the index number of the i-th monitoring parameter; is the data type weight coefficient of the i-th monitoring parameter; is the average abnormal amplitude of the i-th monitoring parameter, and ,in, is the abnormal amplitude of the i-th monitoring parameter at time point t; It is the set of all time points during the statistical period when the monitoring parameter is judged to be abnormal; is the total number of times the i-th monitoring parameter is judged to be abnormal during the statistical period; α is an index that controls the intensity of the influence of the average anomaly amplitude on the anomaly score; is the abnormal frequency of the ith monitoring parameter; and ,in, is the total number of times the i-th monitoring parameter is judged to be abnormal within the statistical period, is the total number of sampling times of the ith monitoring parameter in the statistical period.

4. The water pump operation data acquisition and analysis system based on the cloud platform according to claim 3 is characterized in that: The local layer includes an environmental monitoring module, which includes a temperature monitoring unit and multiple noise monitoring units; The temperature monitoring unit is used to monitor the ambient temperature of each water pump in the same area, and each noise monitoring unit monitors the working noise of the corresponding water pump respectively.

5. The water pump operation data acquisition and analysis system based on the cloud platform according to claim 4 is characterized in that: The method for the central processing module to assign the working target of the water pump determined to be abnormal includes: A1. The central processing module obtains the working target of the pump that is judged to be abnormal , and identify the remaining water pumps in the corresponding water pump group; A2. The central processing module calculates the remaining load H of the remaining water pumps. If the remaining load H≤0, the water pump does not participate in the distribution. If the remaining load H>0, the water pump participates in the distribution. The central processing module counts the pumps involved in the allocation as 1, 2, 3, ..., j; A3. The central processing module calculates the weighted capacity of all pumps j involved in the allocation ; , where is the water supply weight coefficient of the j-th water pump; is the current remaining load of the j-th water pump; A4. Central processing module calculates total weighted capacity And the distribution ratio ; ； A5. Central processing modules are allocated according to the proportion The working target of the abnormal pump Allocate to the participating pumps and revise the working target of the participating pumps to the final target ; ； In the formula, is the initial working target of the jth water pump.

6. The water pump operation data acquisition and analysis system based on the cloud platform according to claim 5 is characterized in that: The water pump group is provided with a backup water pump; If the remaining allocatable work target of the participating pumps in the pump group is less than the work target of the abnormal pump , then start the standby water pump.

7. The water pump operation data acquisition and analysis system based on the cloud platform according to claim 4 is characterized in that: The central processing module collects the ambient temperature in real time through the temperature monitoring unit , record the temperature data at consecutive time points, and calculate the ambient temperature change rate through the temperature data ; At the same time, the central processing module collects the operating temperature of each water pump , and calculate the temperature change rate of each water pump ; The central processing module calculates the rate of change of ambient temperature The time point when the preset environmental change range is exceeded , and the central processing module detects the temperature change rate The time point when the preset pump change amplitude is exceeded ; Finally, the central processing module passes Calculate the water pump temperature delay time .

8. The water pump operation data acquisition and analysis system based on the cloud platform according to claim 7 is characterized in that: The central processing module is preset with a delay threshold corresponding to the water pump and amplitude threshold ; The central processing module delays the water pump temperature With delay threshold For comparison, the temperature change rate of the water pump With amplitude threshold Make a comparison; If the water pump temperature delay time > Delay threshold ,or >Amplitude threshold , then the central processing module determines the corresponding water pump as having an abnormal temperature response; The central processing module adds abnormal temperature response to the abnormal score Calculation of anomaly scores Make corrections.

9. The water pump operation data acquisition and analysis system based on the cloud platform according to claim 5 is characterized in that: When multiple water pumps are set within the collection range of a single noise monitoring unit, the central processing module establishes a neighboring water pump set for each water pump within the collection range of the noise monitoring unit. ; And for each water pump i, the central processing module records its location coordinates, the set of neighboring water pumps and each adjacent pump Physical distance to the noise monitoring unit corresponding to pump i .

10. The water pump operation data acquisition and analysis system based on the cloud platform according to claim 9 is characterized in that: The central processing module obtains the working noise and vibration intensity of pump i When the working noise is set to the original noise value ; And the central processing module obtains the neighboring water pumps of water pump i Vibration intensity ; The central processing module establishes the distance attenuation factor ,in ; The central processing module is used for each adjacent pump , through the formula Calculate the noise interference ; The central processing module then sends all adjacent pumps The noise interference of pump i is accumulated to obtain the total noise interference ; The central processing module converts the total amount of noise interference From the original noise value The measured noise of pump i is obtained by stripping , .

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