A method for monitoring and managing operation of a secondary water supply pump station

Abstract

The present invention relates to the field of resource management technology, specifically a secondary water supply pump station operation monitoring and management method, comprising the following steps: collecting performance records, pump speed and water pressure readings through water quality monitoring sensors and flow meters, performing data cleaning and format unification to obtain standardized data, applying regression analysis to the standardized data to predict the probability of risk events, and combining environmental monitoring data to generate regression analysis risk prediction results. The present invention uses regression analysis and variance analysis to conduct in-depth analysis of the collected pump speed, water pressure and environmental data. This strategy not only makes risk prediction more accurate, but also can evaluate in detail the specific impact of various operating variables on pump station performance. Through this method, managers can generate a list of risk points based on real-time data and dynamically adjust operating parameters, thereby achieving real-time optimization of the operating status of the pump station.

Description

Technical Field

[0001] The present invention relates to the technical field of resource management, and in particular to a secondary water supply pump station operation monitoring and management method. Background Art

[0002] The operation monitoring and management method of water supply pumping stations is generally to optimize the allocation and use of water resources by monitoring and managing the operation of water pumping stations. Sensors and data acquisition equipment are used to monitor the operating status of water pumps in real time, such as pump speed, water pressure, water quality and other parameters. By collecting and processing these data, the operation strategy can be adjusted in time to improve the efficiency and reliability of the water pumping station.

[0003] Existing technologies use sensors to monitor the basic operating status of water pumps in real time, but they often do not respond quickly enough when dealing with emergencies and lack sufficient data support to make scientific adjustment decisions. In addition, existing technologies usually adopt relatively static strategies in resource allocation and risk management. Once non-standard operating conditions are encountered, their adjustment mechanisms become clumsy and difficult to effectively adapt to environmental changes, which may lead to reduced efficiency and waste of resources. Summary of the invention

[0004] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a secondary water supply pump station operation monitoring and management method.

[0005] In order to achieve the above object, the present invention adopts the following technical solution, a secondary water supply pump station operation monitoring and management method, comprising the following steps:

[0006] Collect performance records, pump speed and water pressure readings through water quality monitoring sensors and flow meters, clean the data and unify the format to obtain standardized data, apply regression analysis to the standardized data to predict the probability of risk events, and combine environmental monitoring data to generate regression analysis risk prediction results;

[0007] Based on the risk prediction results of the regression analysis, variance analysis is performed to analyze the impact of pump speed changes and water quality fluctuations on pump station performance, and risk factor impact results are generated. According to the risk factor impact results, risk points are identified to form a risk point list;

[0008] Applying linear programming technology, adjusting pump speed and water pressure parameters based on the risk point list, obtaining pump speed and water pressure parameter configurations through calculation, and generating optimized operation parameter configuration results;

[0009] According to the optimized operation parameter configuration results, real-time monitoring is implemented, the operation status of the pump station is continuously analyzed, the risk management strategy is adjusted according to the real-time data, and the dynamic management strategy adjustment results are generated.

[0010] Preferably, the steps of obtaining the standardized data are:

[0011] Collect pump speed and water pressure readings during pump station operation through water quality monitoring sensors and flow meters and record them as raw performance records;

[0012] Based on the original performance records, data cleaning is performed to remove errors and outliers, and format unification is performed to obtain cleaned and format-unified performance records;

[0013] Based on the cleaned and formatted performance records, standardization processing is performed to convert the data into a unified analysis format to obtain standardized data.

[0014] Preferably, the steps for obtaining the risk prediction results of the regression analysis are:

[0015] Collecting environmental monitoring data, wherein the environmental monitoring data and the standardized data form integrated data;

[0016] Based on the comprehensive data, the probability of risk events is calculated using the following formula:

[0017]

[0018] in, is the predicted probability of risk events, is the intercept term, and is the regression coefficient of the impact of performance and environmental factors on risk probability, and represent standardized performance indicators and environmental variables, respectively;

[0019] Based on the risk event probability, a regression analysis risk prediction result is obtained.

[0020] Preferably, the steps for obtaining the risk factor impact results are:

[0021] Based on the risk prediction results of the regression analysis, the pump speed change data and the water quality fluctuation data are sorted and extracted to obtain the pump speed and water quality data;

[0022] Based on the pump speed and water quality data, the variance analysis results of the effects of pump speed changes and water quality fluctuations on pump station performance are calculated using the following formula:

[0023]

[0024] in, is the variance ratio, For the The average value of the group data, is the overall average value, For the Group Observations, For the The number of observations in the group, is the number of groups, is the total number of observations;

[0025] Based on the variance ratio, the impact of pump speed and water quality changes on the performance of the pumping station is determined, and risk factor impact results are generated.

[0026] Preferably, the steps of obtaining the risk point list are:

[0027] Based on the risk factor impact results, extract the impact value of each risk factor on the performance to obtain impact value data;

[0028] Based on the impact value data, the probability of each impact factor exceeding the risk threshold is calculated, and the calculation formula is:

[0029]

[0030] in, Indicates The probability of a factor becoming a risk point, Influencing factors The sensitivity coefficient of Indication factors The impact value of

[0031] Based on the risk probability of each influencing factor, factors with probabilities higher than the set threshold are screened to form a list of risk points.

[0032] Preferably, the steps of obtaining the optimization operation parameter configuration result are:

[0033] Based on the risk point list, collect pump speed and water pressure parameters that need to be adjusted;

[0034] A linear programming model was established, and the targets of pump speed and water pressure parameters were set to the sum of the probabilities of reducing risk points. The calculation formula was:

[0035]

[0036] in, represents the total risk amount, For the The influencing factors of each risk point are and Pump speed and water pressure The linear adjustment factor for risk, is the number of risk points;

[0037] Solve the linear programming model, calculate the pump speed and water pressure parameters, and form the optimized operation parameter configuration results.

[0038] Preferably, the steps of obtaining the dynamic management strategy adjustment result are:

[0039] Based on the optimized operation parameter configuration result, the parameters of pump speed and water pressure are configured to obtain a configured monitoring strategy;

[0040] Initiate the configured monitoring strategy, collect the pump speed, water pressure and related operation data of the pump station in real time, continuously record the data, and obtain a real-time operation data set;

[0041] Based on the real-time operation data set, the performance and potential risk points of the pump station are regularly analyzed. If the operation data deviates from the configuration, the associated parameters are immediately adjusted to generate an adjusted risk management strategy.

[0042] Preferably, the step of obtaining the dynamic management strategy adjustment result further comprises: forming the dynamic management strategy adjustment result according to the adjusted risk management strategy, updated alarm thresholds and operation guidelines.

[0043] Compared with the prior art, the advantages and positive effects of the present invention are:

[0044] The present invention uses regression analysis and variance analysis to conduct in-depth analysis of the collected pump speed, water pressure and environmental data. This strategy not only makes risk prediction more accurate, but also can evaluate in detail the specific impact of various operating variables on pump station performance. Through this method, managers can generate a list of risk points based on real-time data and dynamically adjust operating parameters to achieve real-time optimization of the operating status of the pump station. In addition, the use of linear programming technology to optimize parameter configuration provides a more flexible and scientific management strategy for pump station operations, reducing resource waste and potential failure risks caused by improper operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 It is a schematic diagram of the steps of the present invention. DETAILED DESCRIPTION

[0046] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0047] See also Figure 1 The present invention provides a technical solution, a secondary water supply pump station operation monitoring and management method, comprising the following steps:

[0048] Collect performance records, pump speed and water pressure readings through water quality monitoring sensors and flow meters, clean the data and unify the format to obtain standardized data, apply regression analysis to the standardized data to predict the probability of risk events, and combine it with environmental monitoring data to generate regression analysis risk prediction results;

[0049] Based on the risk prediction results of regression analysis, variance analysis is performed to analyze the impact of pump speed changes and water quality fluctuations on pump station performance, generate risk factor impact results, identify risk points based on risk factor impact results, and form a risk point list;

[0050] Apply linear programming technology to adjust pump speed and water pressure parameters based on the risk point list, obtain pump speed and water pressure parameter configuration through calculation, and generate optimized operation parameter configuration results;

[0051] Based on the results of optimizing the operating parameter configuration, real-time monitoring is implemented, the operating status of the pump station is continuously analyzed, the risk management strategy is adjusted according to the real-time data, and dynamic management strategy adjustment results are generated.

[0052] The steps to obtain standardized data are:

[0053] Collect pump speed and water pressure readings during pump station operation through water quality monitoring sensors and flow meters and record them as raw performance records;

[0054] Based on the original performance records, perform data cleaning, remove errors and outliers, and unify the format to obtain cleaned and unified performance records;

[0055] Based on the cleaned and formatted performance records, standardization processing is performed to convert the data into a unified analysis format to obtain standardized data.

[0056] Specifically, the pump speed and water pressure readings during the operation of the pump station are collected through water quality monitoring sensors and flow meters. First, the data collection frequency is set in the monitoring system to ensure that data is collected once a minute. The collected data includes the pump speed in revolutions per minute (RPM) and the water pressure value (PSI). Then, the integrity and correctness of the data are verified, including checking the integrity, verification and inspection of the data packet. These steps ensure that the collected data meets the preset standards in terms of quantity and quality. The raw data is temporarily stored in the data buffer, awaiting further processing and analysis to obtain the original performance record.

[0057] Based on the original performance records, data cleaning is performed. First, data records with incorrect or missing timestamps are screened out, and the pump speed and water pressure data are checked to see if they are within the normal working range. Abnormal pump speed data, such as readings that exceed the design limit of the equipment, and water pressure jumps that do not conform to physical possibilities, will be marked and eliminated. In addition, data format unification involves converting the data formats in different sensors and recording devices into a unified database acceptance format, which includes a unified time format, the number of decimal places for numerical values, etc. Through these processes, the accuracy and consistency of the data are ensured, thus forming a performance record after cleaning and format unification.

[0058] Based on the performance records after cleaning and formatting, standardization processing is performed. First, all data are quantified and standardized, such as converting pump speed into the standard unit RPM and water pressure into PSI. Then, statistical methods are used to normalize the data, including centering and scaling the data so that the data distribution conforms to the standard normal distribution. The processed data is more suitable for subsequent analysis and a standardized performance record is obtained.

[0059] The steps to obtain the risk prediction results of regression analysis are as follows:

[0060] Collect environmental monitoring data, and form comprehensive data with standardized data;

[0061] Based on the comprehensive data, the probability of risk events is calculated using the following formula:

[0062]

[0063] in, is the predicted probability of risk events, is the intercept term, and is the regression coefficient of the impact of performance and environmental factors on risk probability, and represent standardized performance indicators and environmental variables, respectively;

[0064] Based on the probability of risk events, the regression analysis risk prediction results are obtained.

[0065] Specifically, through water quality monitoring sensors and flow meters, the pump speed and water pressure readings during the operation of the pump station are collected, and these data are recorded as original performance records. This process ensures the real-time and accuracy of the data. The original performance records are the basis for the analysis of the pump station operation efficiency, providing the necessary data support, laying the foundation for subsequent data processing and analysis, ensuring the reliability and effectiveness of the processed data, and providing an accurate input source for risk prediction.

[0066] The formula is beneficial in that it allows a composite risk probability to be calculated by combining the quantified impacts of environmental and performance data, providing a mathematical basis to support more targeted decision making.

[0067] The acquisition steps are as follows: by fitting the environmental monitoring data for 180 consecutive days with the pump station performance records, the statistical and The intercept term is obtained by first extracting the average performance index and average environmental variables recorded daily, and then using the least squares regression fitting method to process these means and intercept the position of the regression curve on the vertical axis to determine The value of

[0068] The acquisition steps are as follows: select the high and low values ​​of certain stages from the performance indicator sequence obtained above, obtain the risk data of the corresponding time period, and then use the segmented processing method of multiple linear regression to calculate the influence coefficient of the performance indicator on the risk probability, and weight the regression coefficients of different stages according to the correction ratio of time and environmental factors to finally determine ;

[0069] The steps to obtain θ are to statistically analyze the risk distribution under different air pressure and humidity environments, and combine the corresponding performance indicators to fit the impact of environmental variables on risk probability using multiple sets of data. After analysis, the results are averaged and the final coefficient is obtained.

[0070] Calculation process:

[0071] , , ,By standardizing the average value of daily performance indicators, we can get , after standardizing the environment variables in the same way, we get ;

[0072] The first step of operation:

[0073]

[0074] The second operation:

[0075]

[0076] The third step of operation:

[0077]

[0078] The result shows that the current probability of a risk event is approximately 0.278. If subsequent monitoring shows that this value continues to rise, it means that more attention needs to be paid to changes in performance indicators and environmental factors and necessary operational management strategies need to be adopted. If it is lower than 0.1, it means that the overall risk trend is low.

[0079] Based on the obtained risk event probability, the safety performance of the pump station is further analyzed to generate risk prediction results. These results will be used to guide the actual pump station operation and maintenance decisions. Through real-time monitoring and feedback adjustments, the pump station can be kept within a safe operating range, potential risks can be avoided, and the long-term stability and safe operation of the pump station can be ensured.

[0080] The steps to obtain the risk factor impact results are:

[0081] Based on the risk prediction results of regression analysis, the pump speed change data and water quality fluctuation data were sorted and extracted to obtain the pump speed and water quality data.

[0082] Based on the pump speed and water quality data, the variance analysis results of the impact of pump speed changes and water quality fluctuations on pump station performance are calculated using the following formula:

[0083]

[0084] in, is the variance ratio, For the The average value of the group data, is the overall average value, For the Group Observations, For the The number of observations in the group, is the number of groups, is the total number of observations.

[0085] Based on the variance ratio, the impact of pump speed and water quality changes on pump station performance is determined, and risk factor impact results are generated.

[0086] Specifically, based on the risk prediction results of the regression analysis obtained previously, the process of collating and extracting the pump speed change data and water quality fluctuation data includes using real-time monitoring tools to record the pump speed and water quality indicators at each time point. These data are automatically collected by sensors and transmitted to the database. Each data point is timestamped to ensure that it can be accurately tracked to the specific monitoring moment. Data collation involves checking the integrity and consistency of each data point and eliminating any obvious outliers or damaged data. The processed data set provides a high-quality and reliable input source for subsequent analysis, forming a pump speed and water quality data set.

[0087] The formula is useful in determining the statistical significance of pump rate changes and water quality fluctuations by calculating the ratio of variance between groups to within groups, which helps identify which changes are most likely to affect the performance of the pumping station.

[0088] The benefit of the formula is that it measures whether the differences in pump speed and water quality in multiple sets of observation data are significant in the form of variance ratios, so that the impact of these two changes on pump station performance can be evaluated under diverse operating conditions.

[0089] The steps to obtain are: when collecting the same set of observations, first add up all the values ​​in the group and divide by the number of observations in the group , for example, in When recording speed in a group, add up all rpm values ​​in the group and divide the result by the number of records to get the corresponding value. ;

[0090] The steps to obtain are to aggregate the observations of all groups together and divide by the total number of observations , that is, first combine the speed or water quality data of each group, then do an overall sum, and finally use Perform the division operation to form the overall average;

[0091] The steps to obtain are as follows, in the Group At each observation value, read the speed or water quality value at that moment, unify it to the same dimension such as rpm or NTU according to the situation, and include it in the data set of this group, and record its original timestamp and observation order at any time;

[0092] The steps to obtain are: Count all the records in the group, for example When the group contains 15 records, is 15;

[0093] The acquisition step is to count the number of groups according to the previous scheme of grouping according to water quality fluctuations and speed ranges. For example, the speed is divided into 0 to 1000, 1001 to 2000, and 2001 to 3000, and the water quality is combined into 0NTU to 2NTU, 2NTU to 5NTU, and 5NTU to 10NTU. If it is finally combined into 4 groups, then ;

[0094] The steps to obtain is to add up the number of records in all groups to get the total number of observations. For example, if there are 10, 12, 14, and 9 records in the 4 groups, then .

[0095] Calculation process:

[0096] make , let group 1 , Group 2 , Group 3 , Group 4 , let the total number of observations in all groups ;

[0097] In group 1, , Group 2 , Group 3 , Group 4 , let the overall mean ;

[0098] Calculate the numerator separately:

[0099]

[0100] Substitute the above differences into numerical calculations, add them up and divide by 3 to get the numerator result;

[0101] Calculate the denominator again:

[0102]

[0103] Here, the sum of the squared differences of each observation in each group compared to the group mean is combined and the result is divided by Finally, divide the numerator result by the denominator result to get value.

[0104] Based on the ANOVA results, the detailed process of evaluating the specific effects of pumping speed and water quality changes on pumping station performance includes The value is compared with the preset critical value. If the value exceeds this critical value, it indicates that the change has a significant impact on performance. This assessment relies on precise statistical analysis and threshold setting. This threshold is set based on historical data and industry standards to ensure the reliability and applicability of the assessment results. These assessment results will be used to optimize the operating parameters of the pumping station to reduce risks and improve efficiency, and generate risk factor impact results.

[0105] The steps to obtain the risk point list are:

[0106] Based on the risk factor impact results, the impact value of each risk factor on the performance is extracted to obtain the impact value data.

[0107] Based on the impact value data, the probability of each influencing factor exceeding the risk threshold is calculated using the following formula:

[0108]

[0109] in, Indicates The probability of a factor becoming a risk point, Influencing factors The sensitivity coefficient of Indication factors The impact value of .

[0110] Based on the risk probability of each influencing factor, factors with probabilities higher than the set threshold are screened to form a list of risk points.

[0111] Specifically, based on the impact results of risk factors, data on the impact of each risk factor on the performance of the pumping station are collected, the degree of impact of each factor is recorded in detail, and these data are integrated into a comprehensive impact value data set, which includes but is not limited to the frequency of changes in pump speed, the fluctuation range of water quality and its specific impact on the overall performance of the pumping station. It ensures that each data point has a clear source and recording method, and the data collection process strictly adheres to the principles of real-time monitoring and recording. Each data point is strictly verified and screened to ensure the authenticity and accuracy of the data.

[0112] The benefit of the formula is that it can directly calculate the probability of risk occurrence based on the sensitivity and actual impact value of each factor, providing an intuitive way to evaluate and identify possible risk points, making risk management more accurate and targeted.

[0113] The acquisition steps are as follows: by counting the frequency of the same factor triggering anomalies in different time periods, and then combining the reference indicators provided by the equipment manufacturer and the distribution of anomalies during 20 consecutive days of testing, the sensitivity coefficient is obtained by weighted average of multiple data. For example, when counting the frequency of the impact of risk factors on performance fluctuations within each hour, the trigger records of multiple time periods are merged and normalized to obtain The value of

[0114] The acquisition steps are as follows: extract the impact value on performance from each risk factor and ensure the uniformity of units. For example, water quality parameters are recorded in NTU and pump speed is recorded in rpm. The average impact value representing the factor is obtained by accumulating and summing multiple observations of the same factor and dividing by the number of observations. In the calculation process, the dimensions of different data sources are normalized and mapped to form the final .

[0115] Calculation process:

[0116] make ,make , first calculate :

[0117]

[0118] Then bring in the index term :

[0119]

[0120] Then perform the main calculation ;

[0121] The result shows that for the current factor, the risk probability is about 0.6321. If it is greater than 0.5, it means that the factor has a high possibility of becoming a risk point. If it is lower than 0.3, it means that its impact is relatively small.

[0122] Based on the calculated risk probability, the risk factors are screened and the factors with probabilities exceeding the preset threshold are selected. For example, the threshold is set to 70%. By comparing the probability values ​​of each risk factor, the risk points exceeding this threshold are screened out to form a detailed list of risk points. The list lists in detail all factors that may cause serious performance problems and provides a basis for future risk management decision-making processes, making the entire risk management process more orderly and controllable.

[0123] The steps to obtain the optimized operation parameter configuration results are as follows:

[0124] Based on the risk point list, collect the pump speed and water pressure parameters that need to be adjusted.

[0125] A linear programming model was established, and the targets of pump speed and water pressure parameters were set to the sum of the probabilities of reducing risk points. The calculation formula was:

[0126]

[0127] in, represents the total risk amount, For the The influencing factors of each risk point are and Pump speed and water pressure The linear adjustment factor for risk, is the number of risk points.

[0128] Solve the linear programming model, calculate the pump speed and water pressure parameters, and form the optimized operation parameter configuration results.

[0129] Specifically, based on the list of risk points, necessary data for adjusting pump speed and water pressure parameters are collected. This step includes extracting relevant operating data from monitoring equipment and system logs, where each data point is directly associated with a specific risk point. This collection process ensures the real-time and relevance of the data. Through data collection, we can obtain a data set containing necessary adjustment parameters, which will be used in subsequent linear programming models to provide a basis for the final operation optimization.

[0130] The benefit of the formula is that it directly links the adjustment of pump speed and water pressure to the probability reduction of risk points. By calculating the influencing factors of each risk point and the linear adjustment coefficients of pump speed and water pressure, a quantitative assessment of the total risk can be obtained, which helps to accurately control the operation of the pump station to achieve the optimal safety and efficiency standards.

[0131] The steps to obtain are: Collect the frequency, duration, and corresponding water quality and operating status of abnormalities from all monitoring records, combine the water pressure and pump speed fluctuations obtained previously, and use the impact degree of multiple abnormalities to weight them, and obtain the impact factor value through normalized weighting;

[0132] The acquisition steps are as follows: monitor the pump speed s in rpm and count the occurrence rate of abnormal triggering at a certain risk point z, so as to deduce the correlation between the risk point and the pump speed change, and obtain the linear adjustment coefficient at this risk point through inductive calculation. ;

[0133] The steps for obtaining the water pressure p are as follows: measure the water pressure p in MPa and record the proportion of abnormalities in each interval. Use linear fitting to present the influence of water pressure changes on the corresponding risk points as coefficients , the mapping relationship between actual measurement values ​​and abnormal distribution is summarized into a set of linear functions to express;

[0134] The acquisition steps are: when the risk point list obtained above contains multiple records, all risk points that meet the screening conditions are combined and the total number is counted as The value of .

[0135] Calculation process:

[0136] , , , , , , , , , , pump speed rpm, water pressure MPa.

[0137] Calculate the partial value of the 1st risk point:

[0138]

[0139]

[0140] Calculate the partial value of the second risk point:

[0141]

[0142]

[0143] Calculate the partial value of the 3rd risk point:

[0144]

[0145]

[0146] Finally, add up the three values:

[0147]

[0148] The results show that when the pump speed is 1200rpm and the water pressure is 0.3MPa, the total risk of the three risk points is about 34.8111. If the value is higher than 50, it means that the parameters need to be further optimized in combination with actual monitoring. If it is lower than 20, it means that most risk points are at a low impact level.

[0149] Through the established linear programming model, the optimal configuration of pump speed and water pressure can be calculated. This process involves weight calculation and parameter optimization of multiple risk points, setting of constraints and minimization of objective functions. Through these technical means, the optimized operating parameter configuration is finally generated. These configurations can reduce risks in actual applications and improve the operating efficiency and safety of the pumping station.

[0150] The steps to obtain the dynamic management policy adjustment results are as follows:

[0151] Based on the optimized operation parameter configuration results, the parameters of pump speed and water pressure are configured to obtain the configured monitoring strategy.

[0152] Start the configured monitoring strategy to collect the pump speed, water pressure and related operation data of the pump station in real time, continuously record the data and obtain the real-time operation data set.

[0153] Based on real-time operation data sets, the performance and potential risk points of the pumping station are analyzed regularly. If the operation data deviates from the configuration, the associated parameters are adjusted immediately to generate an adjusted risk management strategy.

[0154] Based on the adjusted risk management strategy, updated alarm thresholds and operation guidelines, dynamic management strategy adjustment results are formed.

[0155] Specifically, to collect real-time operating data, the initial pump speed and water pressure parameter configuration is first performed. Through the built-in sensors and data acquisition modules, the key operating parameters of the pump station, such as pump speed and water pressure readings, are continuously monitored. The data output by these sensors is first marked with a timestamp, and then organized into a structured format and stored in the database for subsequent data analysis and historical backtracking. The data organization process includes standardization and cleaning to remove outliers introduced by sensor failure or external interference to ensure the quality and accuracy of the collected data. This series of operations is completed automatically without human intervention, and the data display can be updated in real time, providing an intuitive operation interface for operators to monitor the status of the pump station, thereby obtaining a real-time operation data set.

[0156] Real-time operational data sets are used to continuously analyze the operating status of the pumping station. The latest operational data, including pump speed, water pressure and other key operational indicators, are regularly extracted from the database. In-depth trend analysis and pattern recognition are performed on these data to identify any possible operational deviations or potential risk factors. For example, if the pump speed is continuously lower or higher than the preset optimal parameter range, this situation will be marked as a potential maintenance warning. The water pressure data will also be evaluated to check whether there are pressure anomalies caused by valve failure or pipeline leakage. These analyses help the operation team identify problems that may lead to reduced efficiency or equipment damage in advance, so that preventive measures can be taken to ensure that the pumping station continues to operate in the best condition, thereby obtaining an adjusted risk management strategy.

[0157] According to the adjusted risk management strategy, the operation interface and alarm settings of the monitoring system are updated. The administrator of the monitoring system adjusts the alarm thresholds according to the latest risk management strategy. This operation usually involves modifying the parameter settings in the system configuration file, such as increasing or decreasing the alarm trigger thresholds for pump speed and water pressure to adapt to the latest operational risk assessment. In addition, the operation interface will also be updated to reflect the latest operating guidelines and alarm status to ensure that all operators can receive any important alarm information in a timely manner and operate in accordance with the updated guidelines. These changes are also recorded in the system's operation log for future audits or troubleshooting. Through these continuous updates and adjustments, the monitoring system can maintain its responsiveness and accuracy, and ultimately form a dynamic management strategy adjustment result.

[0158] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A secondary water supply pump station operation monitoring and management method, characterized in that: The following steps are involved: Collect performance records, pump speed and water pressure readings through water quality monitoring sensors and flow meters, clean the data and unify the format to obtain standardized data, apply regression analysis to the standardized data to predict the probability of risk events, and combine environmental monitoring data to generate regression analysis risk prediction results; Based on the risk prediction results of the regression analysis, variance analysis is performed to analyze the impact of pump speed changes and water quality fluctuations on pump station performance, and risk factor impact results are generated. According to the risk factor impact results, risk points are identified to form a risk point list; Applying linear programming technology, adjusting pump speed and water pressure parameters based on the risk point list, obtaining pump speed and water pressure parameter configurations through calculation, and generating optimized operation parameter configuration results; According to the optimized operation parameter configuration result, real-time monitoring is implemented to continuously analyze the operation status of the pump station, and the risk management strategy is adjusted according to the real-time data to generate dynamic management strategy adjustment results; The steps for obtaining the risk factor impact results are: Based on the risk prediction results of the regression analysis, the pump speed change data and the water quality fluctuation data are sorted and extracted to obtain the pump speed and water quality data; Based on the pump speed and water quality data, the variance analysis results of the effects of pump speed changes and water quality fluctuations on pump station performance are calculated using the following formula: in, is the variance ratio, For the The average value of the group data, is the overall average value, For the Group Observations, For the The number of observations in the group, is the number of groups, is the total number of observations; Based on the variance ratio, determine the impact of pump speed and water quality changes on pump station performance and generate risk factor impact results; The steps for obtaining the risk point list are as follows: Based on the risk factor impact results, extract the impact value of each risk factor on the performance to obtain impact value data; Based on the impact value data, the probability of each impact factor exceeding the risk threshold is calculated, and the calculation formula is: in, Indicates The probability of a factor becoming a risk point, Influencing factors The sensitivity coefficient of Indication factors The impact value of Based on the risk probability of each influencing factor, filter out factors with a probability higher than the set threshold to form a list of risk points; The steps for obtaining the dynamic management strategy adjustment result are: Based on the optimized operation parameter configuration result, the parameters of pump speed and water pressure are configured to obtain a configured monitoring strategy; Initiate the configured monitoring strategy, collect the pump speed, water pressure and related operation data of the pump station in real time, continuously record the data, and obtain a real-time operation data set; Based on the real-time operation data set, the performance and potential risk points of the pump station are regularly analyzed. If the operation data deviates from the configuration, the associated parameters are immediately adjusted to generate an adjusted risk management strategy.

2. The secondary water supply pump station operation monitoring and management method according to claim 1, characterized in that: The steps for obtaining the standardized data are: Collect pump speed and water pressure readings during pump station operation through water quality monitoring sensors and flow meters and record them as raw performance records; Based on the original performance records, data cleaning is performed to remove errors and outliers, and format unification is performed to obtain cleaned and format-unified performance records; Based on the cleaned and formatted performance records, standardization processing is performed to convert the data into a unified analysis format to obtain standardized data.

3. The secondary water supply pump station operation monitoring and management method according to claim 1, characterized in that: The steps for obtaining the risk prediction results of the regression analysis are as follows: Collecting environmental monitoring data, wherein the environmental monitoring data and the standardized data form integrated data; Based on the comprehensive data, the probability of risk events is calculated using the following formula: in, is the predicted probability of risk events, is the intercept term, and is the regression coefficient of the impact of performance and environmental factors on risk probability, and represent standardized performance indicators and environmental variables, respectively; Based on the risk event probability, a regression analysis risk prediction result is obtained.

4. The secondary water supply pump station operation monitoring and management method according to claim 1, characterized in that: The steps for obtaining the optimization operation parameter configuration result are: Based on the risk point list, collect pump speed and water pressure parameters that need to be adjusted; A linear programming model was established, and the targets of pump speed and water pressure parameters were set to the sum of the probabilities of reducing risk points. The calculation formula was: in, represents the total risk amount, For the The influencing factors of each risk point are and Pump speed and water pressure The linear adjustment factor for risk, is the number of risk points; Solve the linear programming model, calculate the pump speed and water pressure parameters, and form the optimized operation parameter configuration results.

5. The secondary water supply pump station operation monitoring and management method according to claim 1, characterized in that: The step of acquiring the dynamic management strategy adjustment result also includes: forming the dynamic management strategy adjustment result according to the adjusted risk management strategy, updated alarm threshold and operation guide.

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