Intelligent pump station overload prevention control method

Abstract

The present invention belongs to the field of water management technology, and specifically relates to a smart pump station overload prevention control method, comprising the following steps: S1, the database converts the pump group operating temperature and power consumption into digital signals; S2, the data processing module extracts the temperature and power consumption data for preliminary judgment and identification; S3, the data processing module divides the temperature interval and calculates the corresponding power consumption variance; S4, judges whether the conditions are met to reach the preset power consumption variance threshold; S5, the data processing module calculates the power consumption increase and decrease, and generates a temperature and power consumption network model; S6, the instruction issuing module sends a pump stop instruction to the in-use pump and sends a pump start instruction to the standby pump; S7, judges whether the in-use pump temperature drops to normal temperature; S8, judges whether the pump group switching times reach 2 times; if so, the data statistics module generates a pump group fault alarm. The present invention can solve the problem of long-term disrepair of the pump station, insufficient informatization leading to inadequate management and low maintenance efficiency, and has a good market application prospect.

Description

Technical Field

[0001] The present invention belongs to the technical field of water management, and specifically relates to an intelligent pump station overload prevention control method. Background Art

[0002] With the increase of urbanization, the urban water supply network is complicated, and the lack of water pressure has prompted the water supply project to set up many small and medium-sized pumping stations according to the area. However, due to the wide geographical distribution and scattered distribution of pumping stations, the traditional operation mode is no longer sufficient to meet the growing water demand. In addition, after years of use, the pumping stations have low operating efficiency, high energy consumption, aging equipment, disrepair, water pollution and other problems. In addition, the lack of informatization has led to inadequate management and low maintenance efficiency, which are becoming increasingly prominent.

[0003] This situation highlights the limitations of traditional pumping stations and the urgency of improving informatization, especially the water supply industry needs to ensure water safety and timely water supply. Therefore, an anti-overload control method is introduced to more efficiently manage pump station operation, timely and accurate alarms, avoid false alarms, prevent pump group failures and water interruptions, so as to improve water supply efficiency and safety. In order to solve the above problems, the present invention proposes an intelligent pump station anti-overload control method.

[0004] The information disclosed in this background technology section is only intended to enhance the understanding of the overall background of the invention and should not be regarded as an acknowledgment or any form of suggestion that the information constitutes the prior art already known to a person skilled in the art. Summary of the invention

[0005] The purpose of the present invention is to provide a smart pump station anti-overload control method to solve the problem that the pump station is in disrepair for a long time and the degree of informatization is insufficient, resulting in inadequate management and low maintenance efficiency.

[0006] In order to achieve the above object, the present invention provides the following technical solutions:

[0007] The smart pump station overload prevention control method includes the following steps:

[0008] S1. The database obtains the analog data of the pump group operation through the data acquisition module, and converts it into digital signals and transmits it to the data transmission module; the analog data of the pump group operation includes temperature and power consumption;

[0009] S2, the data processing module extracts the temperature and power consumption data from the database for preliminary judgment and identification, and determines whether the conditions are met to reach the preset temperature threshold: if so, it is marked as abnormal data, and the data statistics module generates a pump group failure warning and executes S6; if not, executes S3;

[0010] S3, the data processing module inputs data, divides the temperature interval, and calculates the corresponding power consumption variance;

[0011] S4, by calculating the power consumption variance, determine whether the conditions are met to reach the preset power consumption variance threshold; if so, mark it as abnormal data, and the data statistics module generates an abnormal power consumption warning for the pump group; if not, execute S5;

[0012] S5, the data processing module calculates the increase or decrease of power consumption and generates a temperature and power consumption network model; sets the early warning increase or decrease threshold according to the predicted data, and sends a maintenance alarm to the management personnel through the instruction issuing module;

[0013] S6, sending a pump stop command to the in-use pump and a pump start command to the standby pump through the command issuing module;

[0014] S7, determining whether the temperature of the pump in use has dropped to normal temperature; if so, sending a pump stop command to the standby pump and a pump start command to the pump in use through the command issuing module;

[0015] S8. Determine whether the pump group switching times reaches 2 times; if so, the data statistics module generates a pump group failure alarm.

[0016] A system using a smart pump station overload prevention control method, comprising:

[0017] The data acquisition module is used to collect the analog data of the pump group operation, perform analog-to-digital conversion, and convert the analog signal into a digital signal;

[0018] The data transmission module is used to transmit the digital signal generated by the data acquisition module to the database through NB-IOT;

[0019] Database, used to store and send and receive data that various modules need to retrieve and process;

[0020] The data processing module retrieves the collected pump group operation data through the database, identifies the pump group with a temperature higher than 85°C, and marks its temperature and power consumption as abnormal data; divides the temperature of non-abnormal data into different intervals, and calculates the mathematical expectation of power consumption in the corresponding temperature intervals. By calculating the variance σ 2 Determine whether the collected power consumption E is abnormal data; variance σ 2 The calculation formula is:

[0021]

[0022] Among them, N is the total number of cases; after screening the abnormal data, the mean of the data for each day is calculated, and the variance σ is calculated 2 The difference between the daily data increase and decrease is used to determine the data increase and decrease trend, forming a forecast of the pump group temperature and power consumption;

[0023] The data statistics module generates a time series diagram of the temperature and power consumption of the pump group through the data collected or filtered by the database; the normal data and the increase and decrease difference form a temperature and power consumption network model for predicting the overload of the pump group;

[0024] The instruction issuing module presets the temperature warning threshold and the power consumption variance warning threshold. When the operating temperature and power consumption of the pump group reach the preset warning threshold, the instruction issuing module sends the pump stop and start instructions to the pump group.

[0025] Compared with the prior art, the present invention has the following beneficial effects:

[0026] (1) The intelligent pump station overload control method of the present invention can effectively prevent the pump group from operating at abnormally high temperatures and causing a pump failure by setting a temperature threshold; by automatically switching the pump group to cool down, it can eliminate the abnormal high temperature operation of the pump group due to accidental factors, prevent the system from false alarms, and help improve the accuracy of the alarm.

[0027] (2) The intelligent pump station overload prevention control method of the present invention obtains the pump group operation data through the database, and performs basic data modeling and anomaly detection, which helps to improve the effectiveness of pump group management; by establishing a time series database of basic data, the system can automatically identify and mark abnormal data, which is conducive to improving the accuracy of subsequent data analysis.

[0028] (3) The intelligent pump station overload prevention control method of the present invention constructs a temperature and power consumption network model for prediction, and comprehensively considers the historical power consumption data of each temperature range, including the pump group number identification, historical power consumption and corresponding date, which can improve the accuracy of pump group overload prediction and environmental adaptability.

[0029] (4) The intelligent pump station overload prevention control method of the present invention, by setting up early warning and alarm mechanisms, enables the equipment system to automatically eliminate pump group overload anomalies caused by accidental factors, reduces the possibility of false alarms, improves alarm accuracy, and improves the intelligence and adaptability of the system. It provides adaptive pump group operation monitoring services for pump station operation and maintenance management, which is conducive to improving the efficiency of pump station management. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 It is a system framework diagram of the present invention;

[0031] Figure 2 It is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0032] The following is a clear and complete description of the technical solution of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0033] See attached Figure 1-2 The intelligent pump station overload prevention control method of the present invention comprises the following steps:

[0034] Step 1: The data acquisition module collects the pump group operation data (temperature, power consumption), and sends the collected operation data to the data communication module, and the data communication module is used as a data communication node to send the collected temperature and power consumption data to the database;

[0035] Step 2: The data processing module extracts data from the database for data calculation and judgment, and analyzes the collected data; if the temperature is higher than 85°C, it is marked as abnormal data;

[0036] Step 3: Based on the collected pump group operation data (temperature, power consumption), divide the temperature into different intervals and calculate the mathematical expectation (i.e. mean) of the corresponding power consumption.

[0037] Step 4: By calculating the variance σ 2 To determine whether the collected power consumption E is abnormal data, the calculation formula is:

[0038]

[0039] Where N is the total number of cases;

[0040] Step 5: The non-abnormal data in the database is judged to establish a temperature and power consumption network model for predicting pump group overload. The data processing module calculates the daily data mean and calculates the variance σ 2 The difference between the increase and decrease of daily data is used to determine the increase and decrease trend of data, and a temperature and power consumption network model is formed to predict the overload of the pump group; when the increase and decrease exceed the normal value, an overload abnormality prediction and warning are generated;

[0041] Step 6: The data judged as abnormal is sent to the command issuing module, which initiates the pump stop command for the in-use pump. At the same time, the command issuing module initiates the standby pump start command and simultaneously sends the abnormal data to the database. The number of pump stop commands is entered into the database, and a pump stop fault warning is generated in the statistics module.

[0042] Step 7: When the temperature of the original pump (the pump in use) drops to the normal temperature T, the command issuing module initiates the start-up command of the original pump (the pump in use) and the stop-up command of the standby pump.

[0043] Step 8: If a pump generates a pump stop fault warning twice in a row in the statistical module, a maintenance alarm is generated, prompting maintenance personnel to go and check and repair it immediately.

[0044] In this embodiment, the operation process of the data acquisition module is as follows:

[0045] Set the timing task, and the data acquisition module collects the analog data of the pump group operation (temperature, power consumption), converts it into digital signals and transmits it to the data transmission module.

[0046] The operation process of the data transmission module is as follows:

[0047] The data transmission module sends the pump group operation data (temperature, power consumption) and transmits it to the database through narrowband Internet of Things technology (NB-IOT).

[0048] The operation process of the database is as follows:

[0049] The database standardizes and stores the collected data, and sends and receives the required data to various modules.

[0050] The operation process of the data processing module is as follows:

[0051] Retrieve data from the database to obtain the pump group operation data (temperature, power consumption), including the pump group number, historical power consumption and corresponding date; divide the temperature into different intervals to eliminate the temperature factor affecting the power consumption change; calculate the mathematical expectation (i.e., mean) of the corresponding power consumption respectively It can accurately identify and eliminate abnormal power consumption, and improve the accuracy and sensitivity of abnormal data identification during pump operation;

[0052] By calculating the variance σ 2 To determine whether the collected power consumption E is abnormal data, the deviation of the power consumption of the pump group can be evaluated based on the historical power consumption of the pump group, and which power consumption data deviates far from the normal power consumption pattern and is thus judged to be abnormal. The calculation method is:

[0053]

[0054] Where N is the total number of cases. The larger the variance, the greater the deviation of the pump group's operating power consumption from the normal operating power consumption law.

[0055] After determining the non-abnormal data, a temperature and power consumption network model for predicting pump group overload is established, and the data processing module calculates the daily data mean. Calculate the variance σ 2By comparing the difference between the daily data increase and decrease and judging the data increase and decrease trend, a temperature and power consumption network model for predicting pump group overload is formed. This can improve the accuracy of pump group overload prediction, provide more accurate pump group operation monitoring services for pump station owners, reduce the burden of operation and maintenance, and help improve pump station management efficiency.

[0056] The historical power consumption data provides the basis for the temperature and power consumption network model, and the introduction of variance calculation enables the model to adjust the prediction results more flexibly to adapt to the variability and suddenness of the pump group operating environment. By fitting historical data and using variance calculation to exclude abnormal data for calibration, an overload prediction that better matches the future pump group operation with environmental changes is achieved.

[0057] When the increase or decrease exceeds the normal value, an overload abnormality prediction and warning are generated. By setting up the warning and alarm mechanism, the equipment system can automatically eliminate the pump group overload abnormality caused by accidental factors, reduce the possibility of false alarms, and improve the accuracy of alarms.

[0058] The system of the present invention can extract and mark the abnormal data obtained by calculation, which is different from the normal data. The system sends out an abnormal signal to indicate the irregularity of the power consumption of the pump group operation, which helps to improve the efficiency of pump station management and enables the system to more intelligently respond to changes in temperature and power consumption caused by environmental factors, so as to improve the accuracy of the temperature and power consumption network model in predicting pump group overload. This process helps to intelligently monitor and manage the pump group and improves the accuracy and sensitivity of identifying abnormalities in the overload operation of the pump group.

[0059] The operation process of the instruction issuing module is as follows:

[0060] The data determined to be abnormal is sent to the command issuing module, which initiates the pump stop command for the in-use pump. At the same time, the command issuing module initiates the standby pump start command. When the temperature of the original pump (the in-use pump) drops to the normal temperature T, the command issuing module initiates the original pump (the in-use pump) start command and the standby pump stop command.

[0061] The operation process of the data statistics module is as follows:

[0062] The data statistics module obtains the pump group operation data (temperature, power consumption) through the database, generates a time series database of basic data, obtains non-abnormal data after judgment and cleaning, generates a temperature and power consumption network model to predict pump group overload, and generates overload abnormality prediction and warning by calculating the increase and decrease. The number of pump stop instructions generates a pump stop fault warning in the statistics module. If a pump generates a pump stop fault warning in the statistics module twice in a row, it will form a maintenance alarm, prompting maintenance personnel to go and check and repair immediately.

[0063] The foregoing description of specific exemplary embodiments of the present invention is for the purpose of illustration and demonstration. These descriptions are not intended to limit the present invention to the precise form disclosed, and it is clear that many changes and variations can be made based on the above teachings. The purpose of selecting and describing the exemplary embodiments is to explain the specific principles of the present invention and its practical application, so that those skilled in the art can realize and utilize various different exemplary embodiments of the present invention and various different selections and changes. The scope of the present invention is intended to be limited by the claims and their equivalents.

Claims

1. The intelligent pump station overload prevention control method is characterized by: The following steps are involved: S1. The database obtains the analog data of the pump group operation through the data acquisition module, and converts it into digital signals and transmits it to the data transmission module; the analog data of the pump group operation includes temperature and power consumption; S2, the data processing module extracts the temperature and power consumption data from the database for preliminary judgment and identification, and determines whether the conditions are met to reach the preset temperature threshold: if so, it is marked as abnormal data, and the data statistics module generates a pump group failure warning and executes S6; if not, executes S3; S3, the data processing module inputs data, divides the temperature interval, and calculates the corresponding power consumption variance; S4. By calculating the power consumption variance, determine whether the condition is met to reach the preset power consumption variance threshold; If yes, it is marked as abnormal data, and the data statistics module generates an abnormal warning of pump group power consumption; If not, execute S5; S5, the data processing module calculates the increase or decrease of power consumption and generates a temperature and power consumption network model; sets the early warning increase or decrease threshold according to the predicted data, and sends a maintenance alarm to the management personnel through the instruction issuing module; S6, sending a pump stop command to the in-use pump and a pump start command to the standby pump through the command issuing module; S7, determining whether the temperature of the pump in use has dropped to normal temperature; if so, sending a pump stop command to the standby pump and a pump start command to the pump in use through the command issuing module; S8. Determine whether the pump group switching times reaches 2 times; if so, the data statistics module generates a pump group failure alarm.

2. A system for the intelligent pump station overload prevention control method according to claim 1, characterized in that: include: The data acquisition module is used to collect the analog data of the pump group operation, perform analog-to-digital conversion, and convert the analog signal into a digital signal; The data transmission module is used to transmit the digital signal generated by the data acquisition module to the database through NB-IOT; Database, used to store and send and receive data that various modules need to retrieve and process; The data processing module retrieves the collected pump group operation data through the database, identifies the pump group with a temperature higher than 85°C, and marks its temperature and power consumption as abnormal data; divides the temperature of non-abnormal data into different intervals, and calculates the mathematical expectation of power consumption in the corresponding temperature intervals. By calculating the variance σ 2 Determine whether the collected power consumption E is abnormal data; variance σ 2 The calculation formula is: Among them, N is the total number of cases; after screening the abnormal data, the mean of the data for each day is calculated, and the variance σ is calculated 2 The difference between the daily data increase and decrease is used to determine the data increase and decrease trend, forming a forecast of the pump group temperature and power consumption; The data statistics module generates a time series diagram of the temperature and power consumption of the pump group through the data collected or filtered by the database; the normal data and the increase and decrease difference form a temperature and power consumption network model for predicting the overload of the pump group; The instruction issuing module presets the temperature warning threshold and the power consumption variance warning threshold. When the operating temperature and power consumption of the pump group reach the preset warning threshold, the instruction issuing module sends the pump stop and start instructions to the pump group.

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