Remote automatic monitoring system for secondary water supply

Abstract

The invention relates to the technical field of remote monitoring, in particular to a secondary water supply remote automatic monitoring system, which comprises a data collection module for recording network bandwidth, delay, packet loss rate and daily water consumption of a user through a sensor and a monitoring interface, and regularly uploading the network bandwidth, delay, packet loss rate and daily water consumption of the user to a central database to obtain network and user data records. According to the invention, comprehensive insight of the operation state of the water supply system is realized by monitoring the network bandwidth, delay, packet loss rate and user daily water consumption data, so that the adjustment of the water supply pump speed and the data transmission frequency can more accurately respond to the actual use condition. According to the data-driven optimization, the distribution efficiency of water resources is improved, the operation and maintenance cost is reduced, and particularly, real-time adjustment can be realized and the balance between supply and demand can be ensured by automatically analyzing user behavior characteristics in a water consumption peak period.

Description

Technical Field

[0001] The present invention relates to the technical field of remote monitoring, and in particular to a secondary water supply remote automatic monitoring system. Background Art

[0002] The secondary water supply remote automatic monitoring system is designed to use remote monitoring technology to monitor and manage the water supply system of urban buildings in real time. The system installs sensors at various key nodes of the secondary water supply to collect data such as water quality, water pressure, and water volume in real time, and transmits the data to the remote monitoring center, so that managers can check and control the status of the water supply system at any time.

[0003] Although existing technologies can collect basic water quality, water pressure and water quantity data, they are limited in processing and applying these data. They are not able to respond to rapidly changing water supply demands, making it difficult to achieve optimal resource allocation, which may lead to insufficient water supply during peak periods or waste of resources during off-peak periods. In addition, the lack of integration of network performance data may lead to delays or errors in data transmission, affecting overall efficiency and accuracy. Therefore, improvements are needed. 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 remote automatic monitoring system.

[0005] In order to achieve the above object, the present invention adopts the following technical solution: A secondary water supply remote automatic monitoring system comprises: The data collection module records network bandwidth, latency, packet loss rate, and daily water consumption of users through sensors and monitoring interfaces, and uploads them to the central database at regular intervals to obtain network and user data records; A data analysis module, which classifies and sorts the network and user data records, classifies the user data records according to water use frequency and time period, performs time series analysis on the network data, determines peaks and valleys, and generates user behavior characteristics; An adjustment strategy module calculates the required water volume and the data transmission speed matching the network conditions according to the user behavior characteristics, adjusts the water supply pump speed and the data transmission frequency, and obtains an optimized water resource allocation; An execution feedback module adjusts the water supply pump speed and the data transmission frequency according to the optimized water resource allocation, monitors the deviation between the adjusted water supply pump speed and the data transmission frequency and the established target, corrects the water supply pump speed and the data transmission frequency, and generates feedback on the adjustment effect; The monitoring feedback module utilizes the adjustment effect feedback to analyze the adjusted water supply efficiency and data transmission stability, optimize the control algorithm, and establish the regulation result.

[0006] Preferably, the steps of acquiring the network and user data records are: Through sensors and network interfaces installed in water supply pipelines, data on network bandwidth, latency, packet loss rate, and daily water consumption of users are regularly collected to obtain raw data records; Based on the original data records, the data is integrated and the consistency index of the data is calculated , the calculation formula is: in, is the data consistency index, represents the value of the i-th data point, is the total number of data points, is the average value of the data points, is the standard deviation, is the weight of each data point; Based on the data consistency index, data cleaning and formatting are performed to verify data consistency and obtain network and user data records.

[0007] Preferably, the steps of classifying the user data records according to water usage frequency and time period are: Based on the network and user data records, extract the daily water consumption of each user and the corresponding timestamp to obtain the user water consumption time series; Based on the user's water use time series, calculate the number of times each user uses water in each time period, and calculate the water use frequency weight of each user , the calculation formula is: in, is the water use frequency weight, is the total number of days during the measurement period, Represents the water usage time period, is the current time period, is the time attenuation coefficient, and the water use frequency weight in each time period is obtained; Based on the water usage frequency weight of each time period, user data is classified according to water usage frequency and time period.

[0008] Preferably, the steps of acquiring the user behavior characteristics are: Based on the network and user data records, extract the time series data of network bandwidth, delay and packet loss rate to obtain the network performance time series; Based on the network performance time series, calculate the rate of change of each data point , the calculation formula is: in, For the moment The rate of change of data points, For the moment The network data value, For the moment The network data value; Based on the change rate, the peak and valley points in the data points are identified, the correlation between the user behavior and the network status is analyzed, and the user behavior characteristics are generated.

[0009] Preferably, the steps of obtaining the optimized water resource allocation are: Based on the user behavior characteristics, determine the average water demand of each user in the target time period and the corresponding network data transmission demand, and obtain preliminary water volume and data transmission demand; Based on the preliminary water volume and data transmission requirements, calculate the adjusted water supply pump speed and data transmission frequency , the calculation formula is: in, is the water supply pump speed, is the data transmission frequency, and are the water supply pump speed adjustment coefficient and demand adjustment coefficient respectively. is the speed adjustment factor, For user water demand, is the data transmission frequency adjustment factor; Based on the adjusted water supply pump speed and data transmission frequency, configuration application and optimization are performed to obtain optimized water resource configuration.

[0010] Preferably, the steps of obtaining the adjustment effect feedback are: Based on the optimized water resource configuration, the water supply pump speed and the data transmission frequency are adjusted to match the demand, and the adjusted water supply pump speed and data transmission frequency are obtained; Monitor the adjusted water supply pump speed and data transmission frequency, compare with the performance target, and calculate the deviation correction value , the calculation formula is: in, is the deviation correction value, and is the adjustment factor, is the target water supply pump speed, is the current water supply pump speed, is the target data transmission frequency, is the current data transmission frequency; The water supply pump speed and data transmission frequency are readjusted according to the deviation correction value, and feedback on the adjustment effect is generated.

[0011] Preferably, the steps of obtaining the control result are: Based on the feedback of the adjustment effect, calculate the stability index , the calculation formula is: in, is the stability index, is the stability adjustment factor, For the The efficiency data of the measurement, is the average value of the efficiency data, and j is the total number of data points.

[0012] Preferably, based on the stability index, control algorithm parameters are optimized, and water supply and data transmission settings are readjusted to establish a regulation result.

[0013] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, by monitoring network bandwidth, latency, packet loss rate, and user daily water consumption data, a comprehensive insight into the operating status of the water supply system is achieved, so that the adjustment of water supply pump speed and data transmission frequency can more accurately respond to actual usage. This data-driven optimization not only improves the efficiency of water resource allocation, but also reduces operation and maintenance costs. In particular, through automated analysis of user behavior characteristics during peak water use periods, real-time adjustments can be made to ensure a balance between supply and demand. Furthermore, through time-series analysis of network and water use data, future water demand and potential network bottlenecks can be effectively predicted, and resource allocation and network optimization can be performed in advance, thereby avoiding overloads and water supply interruptions and ensuring service continuity and reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0015] 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.

[0016] See also Figure 1 The present invention provides a technical solution: a secondary water supply remote automatic monitoring system comprising: The data collection module records network bandwidth, latency, packet loss rate, and daily water consumption of users through sensors and monitoring interfaces, and uploads them to the central database at regular intervals to obtain network and user data records; The data analysis module classifies and sorts network and user data records, classifies user data records according to water use frequency and time period, performs time series analysis on network data, determines peaks and valleys, and generates user behavior characteristics; The adjustment strategy module calculates the required water volume and the data transmission speed that matches the network conditions based on the user behavior characteristics, adjusts the water supply pump speed and data transmission frequency, and obtains the optimized water resource allocation; The execution feedback module adjusts the water supply pump speed and data transmission frequency according to the optimized water resource allocation, monitors the deviation between the adjusted water supply pump speed and data transmission frequency and the established target, corrects the water supply pump speed and data transmission frequency, and generates feedback on the adjustment effect; The monitoring feedback module uses the adjustment effect feedback to analyze the adjusted water supply efficiency and data transmission stability, optimize the control algorithm, and establish the regulation results.

[0017] The steps to obtain network and user data records are: Through sensors and network interfaces installed in water supply pipelines, data on network bandwidth, latency, packet loss rate, and daily water consumption of users are regularly collected to obtain raw data records; Based on the original data records, the data is integrated and the consistency index of the data is calculated , the calculation formula is: in, is the data consistency index, represents the value of the i-th data point, is the total number of data points, is the average value of the data points, is the standard deviation, is the weight of each data point; Based on the data consistency index, data cleaning and formatting are performed to verify data consistency and obtain network and user data records.

[0018] Specifically, the process of collecting data on network bandwidth, latency, packet loss rate, and users' daily water consumption through sensors and network interfaces installed in water supply pipes involves precise configuration of sensors and calibration of network interfaces. First, the placement of sensors is determined to ensure the coverage and accuracy of data collection, and then the data is transmitted to the central processing system in real time through the network interface.

[0019] The usefulness of the formula is that by bringing in the real measured values ​​of different parameters and considering the weight of each data point, it is possible to quantify the distribution of the data around the mean; The steps for obtaining parameters are to record the specific values ​​at each time point, such as the network bandwidth is 250Mbps at a certain time, the delay is 35 milliseconds at a certain time, and regard it as a , where different types of data such as bandwidth or delay can be processed separately when calculating the consistency index, and then combined with the intermediate processing results to To summarize; The steps to obtain the parameters are as follows: The sum and quantity of Calculate the average value. For example, the network bandwidth values ​​obtained in the collection are 240, 260, 230, 270, and 255 Mbps. The sum of these five bandwidth values ​​is 1255 Mbps. The number of records is 5. Then 1255÷5=251Mbps; The steps to obtain the parameters are as follows: and Calculate the variance and take the square root of the deviation. Continuing with the above bandwidth data as an example, first calculate , then the variance is 1020÷5=204, = Mbps; The parameters are obtained by recording the importance of each data point and calculating the weight value based on the monitoring priority of bandwidth or delay in different time periods. For example, in the collection stage, the weight of the morning peak period can reach 1.2, while the weight of the night period is 0.8. These weight values ​​are derived from the evaluation of the importance of network load and pipeline flow. Calculation process: Each and The square of the difference, for example , etc., and multiply by the corresponding ; All calculation results are accumulated. For example, the accumulated amount of bandwidth deviation for morning peak data may be larger. Divide the accumulated result by ,like ,but , and finally get value; For example, if we enter the weight of a record =1.2, , then the result is , set the sum of the products of the weights corresponding to the five records to ,For example ,Then ,but The results show that when When the value increases, it means that the data points deviate greatly from the average value, and more attention should be paid to the sudden changes and abnormal fluctuations of the data. A smaller value means that the overall data deviation is not large.

[0020] The process of data cleaning and formatting based on the data consistency index first requires setting data cleaning rules, including removing outliers, filling missing values, and correcting erroneous data. This process requires a detailed quality assessment of the data to determine which data is non-compliant and which needs to be corrected. Anomalies in the data are detected through automated scripts, and the importance of data points is sorted according to the consistency index. Data points with high importance are processed first to improve the quality of the overall data set. Next, the formatting step involves converting the data into the format required for analysis, such as adjusting the timestamp format, unifying the representation method of numerical values, etc. This step ensures that the subsequent analysis module can proceed smoothly, thereby obtaining network and user data records.

[0021] The steps to classify user data records according to water use frequency and time period are as follows: Based on network and user data records, extract each user's daily water consumption and corresponding timestamp to obtain the user's water consumption time series; Based on the user's water use time series, calculate the number of times each user uses water in each time period, and calculate the water use frequency weight of each user , the calculation formula is: in, is the water use frequency weight, is the total number of days during the measurement period, Represents the water usage time period, is the current time period, is the time attenuation coefficient, and the water use frequency weight in each time period is obtained; Based on the water usage frequency weight of each time period, user data is classified according to water usage frequency and time period.

[0022] Specifically, based on the daily water consumption and corresponding timestamps of each user extracted from network and user data records, we obtain a user water use time series, which records in detail the user's water use behavior at each time point. By recording these data, we can directly observe the user's daily water use pattern and possible abnormal water use behavior, such as high water consumption at atypical times, which is extremely important for water resource management and budget allocation. The collection process of time series data involves a variety of data collection technologies, such as timestamp generation and data synchronization technology, to ensure the timeliness and accuracy of the data and obtain the user water use time series.

[0023] formula The benefit is that the time decay coefficient The introduction of can enhance the model's sensitivity to time, so that the data closer to the current time contributes more to the water use frequency weight. In practical applications, this can help more accurately predict users' water demand in near real time, thereby optimizing water supply strategies and resource allocation.

[0024] The step of obtaining the parameters is to count the total number of days during the measurement period based on the user records that have been integrated in the early stage. For example, the user is monitored continuously for 30 days in one month. That is 30, and the number of valid measurement days is confirmed by verifying the daily monitoring results; The steps to obtain the parameters are to mark each observation day in segments, for example, to divide 24 hours into multiple segments, and to accumulate effective water use behavior in each segment. It can be accumulated one by one according to 1, 2, 3... If the whole day is divided into 6 periods, =6; The steps to obtain the parameters are to lock the sequence number of the current time period. For example, if the current time period is the third of the six time periods mentioned above, then =3, obtained by comparing the timestamp of the actual water use behavior on that day; The steps to obtain the parameters are to determine the value of the time attenuation coefficient by comparing the recent and long-term water use frequencies. The attenuation distribution can be evaluated by referring to the difference analysis of the water use data of the last 7 days and the most distant 30 days. If the monitoring results show that the weight of the more recent period needs to be slightly higher, then It can be around 0.1; Calculation process: Sure =30 and the number of time periods on that day =6, and the current period =3; right Traverse from 1 to 6, in each Calculation , for example when = 0.1, and =1, then the internal expression is , the exponential function is ,when =3, , ; Sum the index values ​​obtained for all time periods and divide by , for example =1 to 6 are all calculated as corresponding exponent values ​​and added up to a certain sum ,like =6.897, then , at this time ; This result shows that when When the value is higher, the user's recent water use behavior is more concentrated in the current time period, and when the value is lower, it means that the current time period has a weaker impact on the user's overall water use behavior.

[0025] Based on the weight of water use frequency in each time period, user data can be effectively classified. This classification not only takes into account the amount of water used, but also combines the specific time of water use, making data analysis more detailed and targeted. In this way, water management departments can identify high-water-demand user groups and prioritize the allocation of resources to meet the needs of these users. The classified user data facilitates further data mining, such as pattern recognition and consumption forecasting. These data will directly affect future water resource planning and strategy adjustments.

[0026] The steps to obtain user behavior characteristics are: Based on network and user data records, extract the time series data of network bandwidth, delay and packet loss rate to obtain the network performance time series; Based on the network performance time series, calculate the rate of change of each data point , the calculation formula is: in, For the moment The rate of change of data points, For the moment The network data value, For the moment The network data value; Based on the rate of change, identify the peak and trough points in the data points, analyze the correlation between user behavior and network status, and generate user behavior characteristics.

[0027] Specifically, based on network and user data records, time series data such as network bandwidth, delay and packet loss rate are extracted to obtain network performance time series. This process involves real-time monitoring and recording of data. First, the data of network bandwidth, delay and packet loss rate are continuously collected by sensors installed at network nodes, reflecting the operating status of the network at any given point in time. Then, through the data collection system, these time series data are recorded and transmitted to the central database for subsequent analysis and processing. The time series processing of data includes timestamping the data at each time point to ensure that the order and integrity of the data are maintained.

[0028] formula The benefit is that by calculating the rate of change of network data between two consecutive time points, any significant changes in network performance can be quickly identified, thereby responding to fluctuations or failures in network status in a timely manner.

[0029] The benefit of the formula is that by performing differential comparisons on the network data values ​​at consecutive moments, the magnitude of changes between each monitoring moment can be accurately assessed; The parameter acquisition step is to read the network performance time series constructed earlier point by point. For example, if the bandwidth is measured at 480Mbps at 10:00, then If the delay is measured at 10:00 and is 30 milliseconds, then 30 milliseconds can be included in the calculation of the rate of change of the delay. ; The parameter acquisition step is to retrieve and read the record of the previous time immediately before the current time. For example, if the bandwidth at 09:30 is 450Mbps, then , the delay may be 25 milliseconds, and the value of the previous moment can also be corresponded in the same way; Calculation process: Set a time =10:00, record Mbps, last moment =09:30, record Mbps; Plug into the formula: but , so we can get ; The result shows that compared with the 09:30 period, the bandwidth data in the 10:00 period increased by about 6.67%. If the change rate is high, it means that the network bandwidth has fluctuated significantly in this half hour. If the value is negative, it means that the bandwidth has decreased.

[0030] Based on the change rate sequence, the peak and trough points are identified. These points represent the sudden changes in network performance. Through these mutation points, the correlation between user behavior and network status is analyzed to generate user behavior characteristics. In this process, it is first necessary to detect the peaks and troughs of the change rate sequence, and use mathematical extreme value identification methods to determine which data points represent significant performance increases or decreases. Then, by comparing these data points with user activity records, analyze how these changes in network performance are associated with user behavior patterns. For example, a sudden peak may correspond to the online activities of most users in a certain time period. Finally, this correlation analysis helps network administrators understand the dynamic relationship between user demand and network capacity in a specific time period.

[0031] The steps to optimize water resource allocation are: Based on user behavior characteristics, determine the average water demand of each user in the target time period and the corresponding network data transmission requirements, and obtain preliminary water volume and data transmission requirements; Calculate the adjusted water supply pump speed based on the preliminary water volume and data transmission requirements and data transmission frequency , the calculation formula is: in, is the water supply pump speed, is the data transmission frequency, and are the water supply pump speed adjustment coefficient and demand adjustment coefficient respectively. is the speed adjustment factor, For user water demand, is the data transmission frequency adjustment factor; Based on the adjusted water supply pump speed and data transmission frequency, configuration application and optimization are performed to obtain optimized water resource configuration.

[0032] Specifically, based on the data obtained from user behavior analysis, the water demand and network data transmission demand of each user in a specific time period are calculated. By monitoring and analyzing the historical data of network bandwidth, delay and packet loss rate, future network demand is predicted, and it is determined when and where the user's water demand and network data transmission demand are most urgent, so as to conduct targeted preliminary assessment of water volume and data transmission demand, and the assessment result is the preliminary water volume and data transmission demand.

[0033] formula The benefit is that by adjusting the coefficient , ,and The application can flexibly adapt to the needs of different user behaviors and changing network conditions, making the adjustment of water supply pump speed and data transmission frequency more precise and efficient.

[0034] The parameter acquisition step is to analyze the available data transmission frequency range (3 times / day to 30 times / day) on a daily basis based on the user water demand and network resource allocation results obtained previously, and record the test values ​​of network bandwidth occupancy by different transmission frequencies within this range; The parameter acquisition step is to collect the correlation between each speed and energy consumption and compare it with the actual water supply effect during the historical operation status of the pump, so as to set the corresponding adjustment coefficient for each speed. For example, if the comprehensive energy consumption and output water volume are better when the pump speed is 600rpm in multiple tests, this working condition can be used as the benchmark collection point to locate the Specific values ​​within a reasonable range (1.0 to 2.0); The parameter acquisition steps are as follows: normalize the actual measured speed characteristics and add a certain time period difference quantization value, obtain the possible correction value through the investigation of different water supply demands during the day and night, and then multiply the correction value with the speed normalization result to obtain the final speed adjustment factor; The parameter acquisition step is to conduct multiple rounds of tests based on the user water demand data sequence obtained previously, combined with the coupling relationship between pump speed and pipeline pressure, and compare multiple sets of data with the measurement results as needed. In this process, a mapping table is formed, and the demand adjustment coefficient can be locked between (0.1 and 0.8) in the end; The steps for obtaining parameters are to collect statistics on the water consumption distribution of each user in the target time period, remove abnormal values, accumulate their water consumption at the hourly or half-hourly level, calculate the average value, compare the peak and valley values ​​again, and confirm the range to obtain the user's water demand; The parameter acquisition step is to locate the data transmission frequency adjustment coefficient by recording the actual measured values ​​of the data transmission frequency and the network bandwidth occupancy in a certain period of time. In the correspondence between the collected frequency and bandwidth occupancy, the network occupancy impact value at excessively high frequency and the network occupancy impact value at normal frequency are scored and weighted averaged to determine Between (0.5 to 5.0); Calculation process: Given (tons / hour) as the user's water demand, As a speed adjustment factor (derived from the measurement and normalization of the hourly speed curve), , , ; Substitute into the formula respectively: The calculation results are , , if It is understood as the pump speed unit normalized according to the speed range (200rpm to 2000rpm), which corresponds to about 492rpm in actual value. This means that data transmission will be performed about 4 to 5 times a day; This result shows that when At this numerical level, the user's water demand at the moment can be met. This means that real-time data uploads must be performed 4 to 5 times a day.

[0035] Based on the adjusted water supply pump speed and data transmission frequency, the system configuration is optimized to ensure that the system is adjusted according to the actual water use and network data transmission needs. During the implementation process, the impact of the adjustment of the water supply pump speed and data transmission frequency on the system performance is analyzed. The optimization operation involves real-time monitoring and dynamic adjustment of system parameters to ensure that each adjustment is based on the latest user data and network status. The optimization result is to obtain optimized water resource configuration.

[0036] The steps to obtain adjustment effect feedback are as follows: Based on optimizing water resource allocation, the water supply pump speed and data transmission frequency are adjusted to match the demand, and the adjusted water supply pump speed and data transmission frequency are obtained; Monitor the adjusted water supply pump speed and data transmission frequency, compare with the performance target, and calculate the deviation correction value , the calculation formula is: in, is the deviation correction value, and is the adjustment factor, is the target water supply pump speed, is the current water supply pump speed, is the target data transmission frequency, is the current data transmission frequency; Based on the deviation correction value, the water supply pump speed and data transmission frequency are readjusted, and feedback on the adjustment effect is generated.

[0037] Specifically, based on the optimization of water resource allocation, the actual user needs are matched with the set water supply pump speed and data transmission frequency in real time to ensure that each adjustment can accurately respond to actual usage. First, the input data from the sensor is collected. These data reflect the current water consumption and network bandwidth. The system analyzes these real-time data, evaluates the difference between the current working status of the equipment and the preset parameters, and adjusts the water supply pump speed and data transmission frequency to make them more in line with the user's current water use and data transmission needs. In this way, it can respond to environmental changes and changes in user behavior in real time, ensuring the efficient operation of the water supply system and the stability of network data transmission.

[0038] formula The benefit is that it can combine the deviation of water supply pump speed and data transmission frequency to use the adjustment factor and To balance the impact of these two aspects and optimize the response of the entire system.

[0039] The parameter acquisition steps are as follows: by statistically analyzing historical operation and maintenance data, the influence of pump speed deviation on the outlet water pressure of the pipe network is quantified; after weighted averaging the measurement results of multiple groups of pump speeds and pressures, an adjustment coefficient between (0.5 and 2.0) is formed; The parameter acquisition step is to summarize the actual impact values ​​of different transmission frequencies on network bandwidth occupancy, select the relationship between data transmission and network load under several typical working conditions, and then map the result to the range of (0.1 to 1.5), so as to use it as the adjustment coefficient of network frequency deviation; The parameter acquisition steps are as follows: based on the target water supply pump speed obtained above, refer to the operation plan and the daily demand peak. If the plan requires a period of time to be maintained at about 700 rpm and the value is within a reasonable range, 700 rpm is converted into the target pump speed; The parameter acquisition step is to read the actual speed value from the real-time monitoring record of the current running pump speed (collected every 30 minutes). For example, when the measured value at 10:00 is 710rpm, ; The parameter acquisition step is to select a suitable value within the range of 3 to 30 times per day for the total number of daily transmissions according to the target data transmission frequency obtained above, for example, the target transmission frequency is set to 4 times per day during the daily peak period; The parameter acquisition step is to read the actual number of transmissions in the period from the current transmission configuration. For example, if monitoring finds that only two data reports were made in the same period on the same day, then ; Calculation process: set up , , given target pump speed (rpm), current pump speed (rpm), target transmission frequency (times / day), current transmission frequency (times / day); Substitute the above values ​​into the formula: get , a negative value indicates that the current combined deviation needs to be adjusted downward and focuses on reducing the pump speed; The result shows that the difference from the target value is quantified as -10.4, which can determine that the pump speed needs to be further reduced in the subsequent adjustment process, while the data transmission frequency needs to be increased.

[0040] According to the deviation correction value, the adjustment value is input to adjust the water supply pump speed and data transmission frequency, and the actual working status after adjustment is evaluated again for consistency with the established goals. Through this feedback mechanism, the efficiency of water supply and data transmission can be continuously optimized to ensure performance stability. The feedback mechanism includes historical data comparison, and the use of historical data to optimize future adjustment strategies, thereby achieving more precise control and higher system efficiency.

[0041] The steps to obtain the control results are: Calculate the stability index based on the feedback of the adjustment effect , the calculation formula is: in, is the stability index, is the stability adjustment factor, For the The efficiency data of the measurement, is the average value of efficiency data, j is the total number of data points; Based on the stability index, the control algorithm parameters are optimized, and the water supply and data transmission settings are readjusted to establish the regulation results.

[0042] Specifically, the formula The benefit is that by calculating the stability index , which can quantify the system’s performance fluctuations and thus more precisely adjust the control algorithm to optimize the stability of the entire water supply and data transmission system; The parameter acquisition steps are as follows: in the process of monitoring the water supply pump speed and data transmission frequency, the efficiency fluctuations that occur when each device is continuously running for more than 300 hours are recorded, and the impact of these fluctuations on the system under standard working conditions is counted. After weighting and sorting these difference data, the results are mapped to the range of (0.5 to 2.0), thereby obtaining the stability adjustment coefficient; The parameter acquisition step is to extract the first parameter from different time periods (such as peak and low peak) one by one using the specially recorded efficiency data sequence according to the adjustment effect feedback obtained previously. For example, if the water supply efficiency is 85% and the data transmission stability is 90% during a period of time, the two can be combined into one efficiency data through a pre-defined weighting scheme. ; The steps to obtain the parameters are to set all Add and divide by the total number of measurements Then get the average value. For example, when the five efficiency data are monitored to be 0.85, 0.90, 0.78, 0.92, and 0.88 respectively, the sum of these five data is 4.33. =4.33÷5≈0.866; The steps to obtain the parameters are to determine the total number of data points through the previous measurement results, and summarize the number of records in all valid measurement intervals. For example, in the continuous observation for 2 days, a total of 48 hours, with one measurement per hour, then =48, depending on the actual test stage, you can choose to test once every half an hour or more frequently; Calculation process: Given , which is the stability adjustment factor selected after multiple equipment tests; right The efficiency data are calculated separately , for example , , , calculate other and The square of the difference is then added up; Add the accumulated results to Divide and multiply , if the sum of squared differences is set to 0.022, the calculation process is as follows: Therefore, we can get ; The results show that the efficiency fluctuation of the current system is relatively low during the monitoring period. A further increase in the value means that the system fluctuations intensify, while a small value indicates that the system is relatively stable.

[0043] Based on stability index This index provides a quantitative method to measure the stability of system performance by monitoring real-time data and calculating , the system administrator can identify any inconsistencies or fluctuations in the water supply and data transmission performance. Through a detailed analysis of the stability index, possible efficiency losses or performance bottlenecks in the system can be discovered. Further, by adjusting the control algorithm parameters, such as feedback gain or adjustment time, the water supply pump speed and data transmission frequency can be precisely controlled to ensure that they always operate in the optimal state. Finally, after re-adjusting the water supply and data transmission settings, the entire system is reconfigured and a new regulation result is established to ensure that the system can continue to operate under optimal conditions and provide efficient and stable services.

Claims

1. A secondary water supply remote automatic monitoring system, characterized in that: The system comprises: The data collection module records network bandwidth, latency, packet loss rate, and daily water consumption of users through sensors and monitoring interfaces, and uploads them to the central database at regular intervals to obtain network and user data records; A data analysis module, which classifies and sorts the network and user data records, classifies the user data records according to water use frequency and time period, performs time series analysis on the network data, determines peaks and valleys, and generates user behavior characteristics; An adjustment strategy module calculates the required water volume and the data transmission speed matching the network conditions according to the user behavior characteristics, adjusts the water supply pump speed and the data transmission frequency, and obtains an optimized water resource allocation; An execution feedback module adjusts the water supply pump speed and the data transmission frequency according to the optimized water resource allocation, monitors the deviation between the adjusted water supply pump speed and the data transmission frequency and the established target, corrects the water supply pump speed and the data transmission frequency, and generates feedback on the adjustment effect; The monitoring feedback module utilizes the adjustment effect feedback to analyze the adjusted water supply efficiency and data transmission stability, optimize the control algorithm, and establish the regulation result.

2. The secondary water supply remote automatic monitoring system according to claim 1 is characterized in that: The steps of obtaining the network and user data records are as follows: Through sensors and network interfaces installed in water supply pipelines, data on network bandwidth, latency, packet loss rate, and daily water consumption of users are regularly collected to obtain raw data records; Based on the original data records, the data is integrated and the consistency index of the data is calculated , the calculation formula is: in, is the data consistency index, represents the value of the i-th data point, is the total number of data points, is the average value of the data points, is the standard deviation, is the weight of each data point; Based on the data consistency index, data cleaning and formatting are performed to verify data consistency and obtain network and user data records.

3. The secondary water supply remote automatic monitoring system according to claim 1 is characterized in that: The steps of classifying the user data records according to water use frequency and time period are as follows: Based on the network and user data records, extract the daily water consumption of each user and the corresponding timestamp to obtain the user water consumption time series; Based on the user's water use time series, calculate the number of times each user uses water in each time period, and calculate the water use frequency weight of each user , the calculation formula is: in, is the water use frequency weight, is the total number of days during the measurement period, Represents the water usage time period, is the current time period, is the time attenuation coefficient, and the water use frequency weight in each time period is obtained; Based on the water usage frequency weight of each time period, user data is classified according to water usage frequency and time period.

4. The secondary water supply remote automatic monitoring system according to claim 1 is characterized in that: The steps of obtaining the user behavior characteristics are as follows: Based on the network and user data records, extract the time series data of network bandwidth, delay and packet loss rate to obtain the network performance time series; Based on the network performance time series, calculate the rate of change of each data point , the calculation formula is: in, For the moment The rate of change of data points, For the moment The network data value, For the moment The network data value; Based on the change rate, the peak and valley points in the data points are identified, the correlation between the user behavior and the network status is analyzed, and the user behavior characteristics are generated.

5. The secondary water supply remote automatic monitoring system according to claim 1 is characterized in that: The steps for obtaining the optimized water resource allocation are: Based on the user behavior characteristics, determine the average water demand of each user in the target time period and the corresponding network data transmission demand, and obtain preliminary water volume and data transmission demand; Based on the preliminary water volume and data transmission requirements, calculate the adjusted water supply pump speed and data transmission frequency , the calculation formula is: in, is the water supply pump speed, is the data transmission frequency, and are the water supply pump speed adjustment coefficient and demand adjustment coefficient respectively. is the speed adjustment factor, For user water demand, is the data transmission frequency adjustment factor; Based on the adjusted water supply pump speed and data transmission frequency, configuration application and optimization are performed to obtain optimized water resource configuration.

6. The secondary water supply remote automatic monitoring system according to claim 1, characterized in that: The steps for obtaining the adjustment effect feedback are as follows: Based on the optimized water resource configuration, the water supply pump speed and the data transmission frequency are adjusted to match the demand, and the adjusted water supply pump speed and data transmission frequency are obtained; Monitor the adjusted water supply pump speed and data transmission frequency, compare with the performance target, and calculate the deviation correction value , the calculation formula is: in, is the deviation correction value, and is the adjustment factor, is the target water supply pump speed, is the current water supply pump speed, is the target data transmission frequency, is the current data transmission frequency; The water supply pump speed and data transmission frequency are readjusted according to the deviation correction value, and feedback on the adjustment effect is generated.

7. The secondary water supply remote automatic monitoring system according to claim 1 is characterized in that: The steps for obtaining the control result are: Based on the feedback of the adjustment effect, calculate the stability index , the calculation formula is: in, is the stability index, is the stability adjustment factor, For the The efficiency data of the measurement, is the average value of the efficiency data, and j is the total number of data points.

8. The secondary water supply remote automatic monitoring system according to claim 7, characterized in that: Also includes: Based on the stability index, control algorithm parameters are optimized, and water supply and data transmission settings are readjusted to establish a regulation result.

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