Method and device for completing community water use data based on time period compensation

Abstract

The present disclosure provides a method and device for completing community water use data based on time period compensation, the method comprising: obtaining total community water use data; the total community water use data includes total water use data of multiple missing days and total water use data of multiple complete days; dividing each complete day into multiple time periods according to a preset interval time, extracting water use data of each time period in each complete day respectively, and obtaining reference data of multiple complete days; calculating the water use coefficient of each time period in the target complete day according to the reference data of the target complete day; calculating the water use data of the missing time period of the missing day according to the water use coefficient of each time period of the target complete day and the average value of the reference data of all time periods of the target complete day; completing the total water use data of the missing day according to the water use data of the missing time period of the missing day. The method and device for completing community water use data based on time period compensation provided by the present disclosure can improve the completion effect of total community water use data.

Description

Technical Field

[0001] The present disclosure belongs to the field of data processing technology, and more specifically, relates to a method and device for completing community water use data based on time period compensation. Background Art

[0002] Smart water data governance leverages big data, cloud computing, and artificial intelligence technologies to collect, process, analyze, and apply comprehensive, efficient, and multi-dimensional water resource data for scientific utilization and management. Applied to residential water management, smart water data governance provides a comprehensive and accurate understanding of community water use, predicts and prevents overuse and pollution, and promotes the rational allocation and utilization of water resources.

[0003] The foundation of smart water data management is accurate water usage data. In practice, data uploaded by water meters can be interrupted or lost due to power outages, leading to missing data. Existing data completion methods are far from effective. Summary of the Invention

[0004] The purpose of the present disclosure is to provide a method and device for completing water use data of a residential area based on time period compensation, so as to improve the accuracy of water use data completion.

[0005] A first aspect of the embodiments of the present disclosure provides a method for completing community water usage data based on time period compensation, comprising:

[0006] Obtaining total water usage data for the community; the total water usage data for the community includes total water usage data for multiple missing days and total water usage data for multiple complete days, wherein the missing days are water usage dates with missing data, and the complete days are water usage dates other than the missing days;

[0007] Divide each full day into multiple time periods according to the preset interval time, extract the water consumption data of each time period within each full day, and obtain reference data for multiple full days;

[0008] Calculate the water use coefficient for each time period within the target complete day based on the reference data for the target complete day; wherein the water use coefficient for any time period is equal to the ratio of the average value of the reference data for that time period within the target complete day to the average value of the reference data for all time periods within the target complete day; the target complete day includes a plurality of complete days;

[0009] Calculate the water consumption data for the missing periods of the missing days based on the water consumption coefficient for each period of the target complete day and the average value of the reference data for all periods of the target complete day;

[0010] The total water consumption data for the missing day is supplemented based on the water consumption data for the missing period of the missing day.

[0011] A second aspect of the embodiments of the present disclosure provides a device for completing community water usage data based on time period compensation, comprising:

[0012] A data acquisition unit is used to acquire total water usage data of a community; the total water usage data of the community includes total water usage data of multiple missing days and total water usage data of multiple complete days, the missing days are water usage dates with missing data, and the complete days are water usage dates other than the missing days;

[0013] A data extraction unit is used to divide each complete day into multiple time periods according to a preset interval time, and extract the water consumption data of each time period within each complete day to obtain reference data for multiple complete days;

[0014] A first calculation unit is configured to calculate a water usage coefficient for each time period within the target complete day based on the reference data of the target complete day; wherein the water usage coefficient for any time period is equal to the ratio of the average value of the reference data for that time period within the target complete day to the average value of the reference data for all time periods within the target complete day; the target complete day includes a plurality of complete days;

[0015] The second calculation unit is used to calculate the water use data of the missing period of the missing day based on the water use coefficient of each period of the target complete day and the average value of the reference data of all periods of the target complete day;

[0016] The data completion unit is used to complete the total water consumption data of the missing day according to the water consumption data of the missing period of the missing day.

[0017] According to a third aspect of an embodiment of the present disclosure, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein when the processor executes the computer program, the steps of the above-mentioned method for completing community water usage data based on time period compensation are implemented.

[0018] In a fourth aspect of an embodiment of the present disclosure, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned method for completing community water usage data based on time period compensation are implemented.

[0019] The beneficial effects of the method and device for completing community water use data based on time period compensation provided by the embodiments of the present disclosure are:

[0020] In the disclosed embodiments, the total water consumption data for a community on a missing day may be the total water consumption data for the entire day, or it may be the total water consumption data for a portion of a time period. Existing methods for completing total water consumption data for a community typically first estimate the daily water consumption data for the missing day, and then complete the data for the missing time period based on the daily water consumption data. This can easily result in the existing water consumption data for the missing day being greater than the estimated daily water consumption data for the missing day. Using this method to complete the data can result in significant errors in the total water consumption data for the community.

[0021] The embodiment of the present disclosure divides each complete day into multiple time periods and extracts the water consumption data of each time period in each complete day to obtain reference data for multiple complete days; then, based on the reference data for multiple complete days, the water consumption coefficient of each time period in all complete days or certain complete days is calculated; finally, based on the water consumption coefficient of each time period in all complete days or certain complete days, the water consumption data of the corresponding time periods in the missing days are calculated, and the water consumption data of the missing days are supplemented based on the water consumption data of the corresponding time periods in the missing days.

[0022] Compared with the existing data completion method, the embodiment of the present disclosure adopts a method of directly completing the data of the missing period, which can achieve accurate data completion. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0024] Figure 1 A flowchart of a method for completing community water use data based on time period compensation provided in one embodiment of the present disclosure;

[0025] Figure 2 A structural block diagram of a cell water use data completion device based on time period compensation provided by an embodiment of the present disclosure;

[0026] Figure 3 A schematic block diagram of an electronic device provided in one embodiment of the present disclosure. DETAILED DESCRIPTION

[0027] In the following description, specific details such as specific system structures and techniques are provided for purposes of illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present disclosure. However, it will be apparent to those skilled in the art that the present disclosure may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obscuring the description of the present disclosure with unnecessary detail.

[0028] In order to make the purpose, technical solutions and advantages of the present disclosure more clear, specific embodiments will be described below with reference to the accompanying drawings.

[0029] Please refer to Figure 1 , Figure 1 A flow chart of a method for completing community water use data based on time period compensation according to an embodiment of the present disclosure is provided, the method comprising:

[0030] S101: Obtain total water usage data for a community; the total water usage data for a community includes total water usage data for multiple missing days and total water usage data for multiple complete days, where a missing day is a water usage date with missing data, and a complete day is a water usage date other than a missing day.

[0031] In this embodiment, total community water usage data can be obtained by acquiring data from a community water usage summary table over a recent period (e.g., one year). Specifically, the total community water usage data includes total water usage data for multiple water usage dates. Some water usage dates may have complete or partial missing data. These dates are referred to as missing days, while the remaining dates are referred to as complete days.

[0032] S102: Divide each full day into multiple time periods according to a preset interval time, extract water consumption data for each time period within each full day, and obtain reference data for multiple full days.

[0033] In this embodiment, each full day is a complete date, that is, 24 hours. Each full day can be divided into multiple time periods according to preset intervals. Each time period has two endpoints, namely a first time point and a second time point. The water consumption data for each time period can be obtained by subtracting the total water consumption data corresponding to the two time points.

[0034] Considering that water usage data for each time period is related to residents' daily water habits, for example, 6-9 a.m. is the first small peak in water usage after waking up, and this period is mainly used for washing, including brushing teeth, washing face, and washing hands; 5-10 p.m. is another peak in water usage, when residents return home from get off work and take showers, cook, and other activities; after 10 p.m., water usage gradually decreases. Therefore, water usage data for multiple time periods can be used as reference data to supplement water usage data for corresponding time periods on missing days.

[0035] Those skilled in the art can flexibly design the preset time interval, such as 5 minutes, 10 minutes, etc. Taking a preset time interval of 5 minutes as an example, the preset time interval divides the 24 hours of a complete day into 24 × 60 ÷ 5 = 288 time periods, namely the first time period, the second time period, ... the 288th time period. If data is missing for the second time period of a missing day, the water consumption data for the second time period of the missing day will be supplemented based on the water consumption data for the second time period of the complete day.

[0036] S103: Calculate the water consumption coefficient of each time period within the target complete day based on the reference data of the target complete day; wherein the water consumption coefficient of any time period is equal to the ratio of the average value of the reference data of the time period within the target complete day to the average value of the reference data of all time periods within the target complete day; the target complete day includes several complete days.

[0037] In this embodiment, some or all complete days can be selected as target complete days based on actual needs. For example, only the data from all Mondays within all complete days can be selected to fill in the missing data for Mondays. For each time period within the target complete day, the average of the reference data for that time period is first calculated. For example, for the time period from 0:00 to 0:05, the water usage data for that time period is added together from all selected target complete days and then divided by the number of target complete days to obtain the average reference data for that time period.

[0038] Then calculate the average value of the reference data for all time periods within the target complete day, that is, add up all the reference data for each time period within the target complete day, and then divide it by the total number of time periods to obtain the average value of the reference data.

[0039] Finally, the water use coefficient for any given time period is the ratio of the average reference data for that time period within the target full day to the average reference data for all time periods within the target full day. The water use coefficient represents the relative proportion of water use in any given time period relative to the total water use for the entire day, reflecting the differences in water use between time periods; the average reference data ensures overall water use.

[0040] S104: Calculate the water consumption data of the missing time period of the missing day based on the water consumption coefficient of each time period of the target complete day and the average value of the reference data of all time periods of the target complete day.

[0041] In this embodiment, the average value of the reference data of all time periods of the target complete day is multiplied by the water consumption coefficient of each time period to obtain the water consumption data of the corresponding time period of the missing day, thereby achieving targeted compensation for the missing data of each time period.

[0042] For example, if there is missing data in the first period of a missing day, the water consumption coefficient of the first period is multiplied by the average value of the reference data of all periods of the target complete day to obtain the water consumption data of the first period of the missing day.

[0043] S105: Completing the total water consumption data of the missing day according to the water consumption data of the missing period of the missing day.

[0044] In this embodiment, after the water consumption data of each (missing) time period of a missing day is supplemented according to steps S101 to S105, the total water consumption data of the missing day is supplemented.

[0045] From the above, it can be concluded that this embodiment divides each complete day into multiple time periods and extracts the water consumption data of each time period in each complete day to obtain reference data for multiple complete days; then, based on the reference data of multiple complete days, the water consumption coefficient of each time period in all complete days or certain complete days is calculated; finally, based on the water consumption coefficient of each time period in all complete days or certain complete days, the water consumption data of the corresponding time period in the missing days is calculated, and the water consumption data of the missing days is supplemented based on the water consumption data of the corresponding time period in the missing days.

[0046] Compared with the existing data completion method, the embodiment of the present disclosure adopts a method of directly completing the data of the missing period, which can achieve accurate data completion.

[0047] In one embodiment of the present disclosure, the water usage data for the missing period of the missing day is calculated based on the water usage coefficient of each period of the target complete day and the average value of the reference data of all periods of the target complete day, including:

[0048] If the first correlation coefficient between the existing data of the missing day and the water consumption coefficient of the corresponding time period is greater than or equal to the first threshold, the water consumption data of the missing time period of the missing day is calculated based on the water consumption coefficient of each time period of the target complete day and the average value of the reference data of all time periods of the target complete day;

[0049] If the first correlation coefficient between the existing data of the missing day and the water consumption coefficient of the corresponding time period is less than the first threshold, the water consumption curve of the missing day is fitted according to the existing data of the missing day, and the water consumption data of the missing time period of the missing day is calculated based on the water consumption curve.

[0050] In this embodiment, when a missing day involves a special event or abnormal situation, the water usage patterns for each time period may differ significantly from those of a normal, complete day. For example, if a community holds a large-scale event on a missing day, such as an outdoor wedding or a community sports meet, water consumption may increase significantly during the event period. Alternatively, a water pipe rupture in the community may cause an increase in water consumption, which may be continuous or periodic, and difficult to match the water consumption coefficients for each time period calculated based on a complete day. In this case, if the water consumption data for the missing period of the missing day is still calculated based on the water consumption coefficients for each time period of the target complete day, the water consumption data for the missing period will be subject to significant errors.

[0051] To address the above issues, considering that water usage behavior among residents and communities has a certain degree of continuity, the water usage during the missing period can be estimated based on the changing trends of water usage during adjacent periods. For example, if water usage in the previous period shows an upward trend and water usage in the subsequent period continues to rise, it can be inferred that water usage during the missing period is also on the rise. Therefore, this embodiment combines the water usage data for the target complete day with the existing data for the missing day to complete the water usage data for the missing period of the missing day. The existing data for the missing day refers to the water usage data for the non-missing periods of the missing day.

[0052] Specifically, we can first calculate the first correlation coefficient between the existing data of the missing day and the water use coefficient of the corresponding time period. The water use coefficient is used to characterize the degree of correlation between the existing data of the missing day and the water use coefficient of the corresponding time period. The higher the first correlation coefficient, the stronger the correlation. The existing Pearson correlation coefficient can be used to calculate the first correlation coefficient between the existing data and the water use coefficient of the corresponding time period.

[0053] When the first correlation coefficient is greater than or equal to the first threshold, it indicates that there is a strong linear relationship between the existing data for the missing day and the water usage coefficient. In this case, the method of the above embodiment can be used to calculate the water usage data for the missing period of the missing day based on the water usage coefficient for each period of the target complete day and the average of the reference data for all periods of the target complete day. The first threshold is a preset constant that can be determined by those skilled in the art based on historical water usage data.

[0054] When the first correlation coefficient is less than the first threshold, it indicates that the linear relationship between the existing data for the missing day and the water usage coefficient is weak. Calculating the water usage data for the missing period of the missing day based on the water usage coefficient of the target complete day and the average value of the reference data may result in a significant deviation from the actual situation. Therefore, a water usage curve for the missing day can be fitted based on the existing data for the missing day. By fitting the curve, the water usage patterns of the missing day itself can be discovered from the existing data, intuitively understanding the water usage trends for the missing day, and then calculating the water usage data for the missing period of the missing day based on this trend.

[0055] For example, the water usage curve can be fitted by polynomial fitting, spline curve fitting, etc. The horizontal axis of the curve can be the time period, and the vertical axis can be the water usage data. Based on the water usage curve, the water usage data corresponding to the missing time period can be obtained.

[0056] From the above, it can be concluded that in this embodiment, when the first correlation coefficient between the existing data of the missing day and the water consumption coefficient of the corresponding time period is greater than the first threshold, the water consumption data of the missing day is calculated using the water consumption pattern of the complete day; when the first correlation coefficient between the existing data of the missing day and the water consumption coefficient of the corresponding time period is less than the first threshold, the water consumption data is calculated by fitting the water consumption curve of the missing day itself, which can better adapt to emergencies and make the completed water consumption data more consistent with the actual water consumption pattern of the community, thereby improving the accuracy of data completion.

[0057] In one embodiment of the present disclosure, fitting a water usage curve for a missing day based on existing data for the missing day includes:

[0058] If the amount of existing data for the missing day is less than or equal to a second threshold, fitting the water consumption curve for the missing day based on a polynomial fitting method;

[0059] If the amount of existing data on the missing day is greater than a second threshold, calculating a second correlation coefficient between the existing data in the first time interval and the existing data in the second time interval on the missing day;

[0060] If the amount of existing data for the missing day is greater than the second threshold, and the second correlation coefficient is greater than or equal to the third threshold, fitting the water consumption curve for the missing day based on a polynomial fitting method;

[0061] If the amount of existing data for the missing day is greater than the second threshold and the second correlation coefficient is less than the third threshold, the water usage curve for the missing day is fitted based on a spline curve fitting method.

[0062] In this embodiment, the first time interval of the missing day is the morning period of the missing day, that is, 6:00-12:00 of the missing day; the second time interval of the missing day is the afternoon period of the missing day, that is, 12:00-18:00 of the missing day.

[0063] Considering that the spline curve fitting method is implemented by constructing a piecewise polynomial function between data points, compared with the polynomial fitting method, the spline curve fitting method requires a larger amount of data and a larger amount of calculation. Correspondingly, the spline curve fitting method has higher accuracy.

[0064] Therefore, if the amount of existing data for the missing day is less than or equal to the second threshold, it indicates that the amount of existing data for the missing day is small. In this case, it is easier to accurately fit the water usage curve for the missing day using the polynomial fitting method.

[0065] If the amount of existing data on the missing day is greater than the second threshold, it indicates that the amount of existing data on the missing day is large. At this time, the specific fitting method can be determined based on the second correlation coefficient between the existing data in the first time interval and the second time interval of the missing day. Specifically, if the second correlation coefficient is greater than or equal to the third threshold, it indicates that the trend of change in water use data is relatively simple. At this time, the polynomial fitting method can still be used to fit the water use curve of the missing day to reduce the amount of calculation for curve fitting; if the second correlation coefficient is less than the third threshold, it indicates that there are complex fluctuations in water use data, such as multiple local peaks and troughs. At this time, the spline curve fitting method can be used to fit the water use curve of the missing day to better capture the change characteristics of each local area. The second threshold and the third threshold are both preset constants, which can be set by those skilled in the art based on empirical values. The second correlation coefficient can use the existing Pearson correlation coefficient.

[0066] From the above, it can be concluded that this embodiment selects a corresponding curve fitting method to fit the water use curve according to different situations of data volume and data correlation, which can reduce the amount of data calculation as much as possible while ensuring the fitting accuracy.

[0067] In one embodiment of the present disclosure, the method for completing community water use data further includes:

[0068] If the sampling period of the total water consumption data of the community for multiple complete days is greater than or equal to the preset time length, the sampling period is determined as the interval time;

[0069] If the sampling period of the total water consumption data of the community for multiple complete days is less than the preset time length, the preset time length is determined as the interval time.

[0070] In this embodiment, the sampling period of the total water consumption data of a community is also the collection period of the total water consumption meter of the community. When dividing the time periods of a complete day based on the preset interval time, it is considered that too small an interval time will increase the data processing burden, while too large an interval time will affect the accuracy of data completion. At the same time, considering that the sampling period of the total water consumption data of each community is different, in order to unify the setting standard of the interval time and take into account the requirements of data processing burden and data completion accuracy, this embodiment first sets a preset time length based on experience, and then compares the preset time length with the sampling period of the total water consumption data of the community. When the sampling period is greater than or equal to the preset time length, it indicates that the sampling period itself is relatively long. In this case, using the sampling period as the interval time for time period division can match the data collection rhythm. When the sampling period is less than the preset time length, it indicates that the sampling period is relatively short. In this case, if the sampling period is still used as the interval time for time period division, it will result in too many and too detailed time periods, increasing the data processing burden. Therefore, determining the preset time length as the interval time can make the number of divided time periods more reasonable.

[0071] From the above, it can be concluded that this embodiment determines the interval time by comparing the sampling period of the total water consumption data of the community with the preset time length, which can make the time period division in the process of completing the community water consumption data more reasonable, taking into account the data processing burden and data completion accuracy requirements.

[0072] In one embodiment of the present disclosure, the method for completing cell water use data based on time period compensation further includes:

[0073] In a specified time period of each complete day, the preset duration is determined as the first duration;

[0074] In other time periods of each complete day, the preset duration is determined as the second duration; the other time periods are time periods other than the specified time period in each complete day, and the first duration is greater than the second duration.

[0075] In this embodiment, the designated time period can be selected based on the actual characteristics and patterns of water use in the community. For example, between 1:00 AM and 5:00 AM, most residents are asleep, and household water activities such as laundry, bathing, and cooking are largely suspended. During this time, aside from some small-volume water use (such as small toilet flushes), the community's total water consumption will be relatively stable, remaining at a relatively low level without significant fluctuations. In this case, overly detailed time period division is unnecessary; the preset duration can be set to a larger first duration, such as 30 minutes or 1 hour. A larger first duration can reduce the amount of data computation required.

[0076] During other time periods, such as between 6:00 AM and 9:00 AM, water usage peaks when residents wake up, wash, prepare breakfast, and use the restroom. This simultaneous large number of residents can lead to a sharp increase in community water usage. Furthermore, different households have different water habits and water equipment usage, making the total water usage during this period more complex and drastic. In this case, the preset duration needs to be set to a smaller second duration, such as 5 minutes or 10 minutes, to more precisely capture changes in water usage.

[0077] It can be concluded from the above that this embodiment uses different preset durations of equipment based on water usage characteristics in different time periods, which is beneficial to further reduce the amount of data calculation on the basis of ensuring data completion accuracy.

[0078] In one embodiment of the present disclosure, each complete day is divided into multiple time periods according to a preset interval time, and before extracting the water consumption data of each time period within each complete day, the method further includes:

[0079] Extract time series data of water consumption, time series data of water use duration, and time series data of water use frequency based on the total water use data for a complete day;

[0080] Processing the time series data of water consumption based on the first neural network to extract the first feature in the time dimension;

[0081] The time series data of water use time and water use frequency are processed based on the second neural network to extract the long-term dependency relationship in the time series and obtain the second feature;

[0082] Fusing the first feature and the second feature to obtain a fused feature;

[0083] Perform cluster analysis on the fusion features and obtain abnormal data in the total water consumption data based on the cluster analysis results;

[0084] Remove outliers from total water consumption data.

[0085] In this embodiment, during the collection and transmission of the total water consumption data for a complete day, data transmission errors or garbled data may be generated due to interference signals. Therefore, before completing the water consumption data of the missing day based on the water consumption data of the complete day, it is necessary to eliminate abnormal data to avoid erroneous reference to these abnormal data when completing the data, which may lead to deviations in the completed results.

[0086] Specifically, the following method can be used to extract the time series data of water consumption, time series data of water use, and time series data of water use frequency:

[0087] (1) Time series data of water consumption: The total water consumption data for each complete day can be divided into multiple first time periods according to a first time interval, and the difference between the total water consumption data at the two end points of each first time period is calculated to obtain the water consumption of each first time period. The first time interval is greater than the interval time in the aforementioned embodiment. For example, the first time interval can be set to 15 minutes.

[0088] Arrange the water consumption for each first period in chronological order by timestamp to form a one-dimensional array or list. This will yield time series data for water consumption. For example, if the first time interval is 15 minutes, the time series data for water consumption for a full day might be: [water consumption at 0:00, water consumption at 0:15, water consumption at 0:30, …, water consumption at 23:45].

[0089] (2) Time series data of water usage: Traverse the time series data of water consumption for a full day, and determine the specific time point of each water use based on the water consumption not being zero. For example, if a record has a timestamp of 9:15 am and the water consumption is greater than zero, then 9:15 am is a water use time point.

[0090] Arrange all water usage time points in chronological order to form time series data for water usage. For example, the time series data for water usage time on a full day might be: [8:00 AM, 9:15 AM, 11:30 AM, ...].

[0091] (3) Divide the total water consumption data for each complete day into multiple second time periods according to a second time interval, and count the number of water consumption behaviors (i.e., water consumption greater than zero) occurring in each second time period. The second time interval is greater than the first time interval in the aforementioned embodiment. For example, the second time interval can be set to 1 hour.

[0092] Arrange the water usage counts for each second period in chronological order to form a time series of water usage frequency. Taking a single hour as an example, the time series of water usage frequency for a full day might look like this: [number of water usage counts between 8:00 AM and 9:00 AM, number of water usage counts between 9:00 AM and 10:00 AM, …, number of water usage counts between 11:00 AM and 12:00 AM]. This time series data is also a one-dimensional array or list, reflecting the water usage frequency at different time periods throughout the day.

[0093] Based on the time series data of water consumption, time series data of water use, and time series data of water use frequency, the first neural network can be used to process the time series data of water consumption and extract features in the time dimension; at the same time, the second neural network can be used to process the series data of water use time and frequency to extract long-term dependencies in the time series, and then the features of the two modalities can be fused to obtain fused features.

[0094] Among them, the first neural network can specifically adopt convolutional neural network (CNN), temporal convolutional network (TCN), etc., and the second neural network can specifically adopt long short-term memory network (LSTM), gated recurrent unit (GRU), self-attention mechanism, etc.

[0095] Cluster analysis based on the above fusion features can identify abnormal data points. For example, the density-based clustering method DBSCAN can be used. DBSCAN determines abnormalities based on the density of data points. In the time-frequency-water consumption space of normal water use, normal data points will be surrounded by a certain density of other data points. If the density around a data point is very low, below the set density threshold (for example, there are not enough data points within a specific radius), then this data point is determined to be an abnormal data point. The total water consumption data for the entire day corresponding to the abnormal data point is the abnormal data in the total water consumption data. By removing the above abnormal data from the total water consumption data, accurate total water consumption data can be obtained.

[0096] In one embodiment of the present disclosure, the first neural network is a convolutional neural network, and the method for completing community water consumption data based on time period compensation further includes:

[0097] The water use time series data is divided into a first data set, a second data set, and a third data set; the first data set is a data set containing the water use time series data on weekdays, the second data set is a data set containing the water use time series data on weekends, and the third data set is a data set containing the water use time series data on designated holidays; the water use time series includes the time series data of water use amount, the time series data of water use time, and the time series data of water use frequency;

[0098] When processing the first type of data in the first data set based on the first neural network, the size of the convolution kernel of the first neural network is set to a first value; the first type of data is time series data of water consumption;

[0099] When processing the first type of data in the second data set based on the first neural network, setting the size of the convolution kernel of the first neural network to a second value;

[0100] When processing the first type of data in the third data set based on the first neural network, setting the size of the convolution kernel of the first neural network to a third value;

[0101] The first value, the second value, and the third value increase sequentially.

[0102] In this embodiment, designated holidays include traditional holidays such as the Spring Festival, Qingming Festival, Labor Day, Dragon Boat Festival, Mid-Autumn Festival, National Day, and New Year's Day. Considering the different water use patterns during weekdays, weekends, and designated holidays, for example, on weekdays, community residents' water use patterns are relatively regular, primarily concentrated in the morning and evening hours. On weekends, residents spend more time at home and engage in more diverse water activities, resulting in significant variations in water use timing and volume compared to weekdays. On designated holidays, residents spend longer at home and may engage in more special water use activities, such as the opening of water features and frequent family gatherings, leading to further differences in water use patterns compared to weekdays and weekends. Therefore, in this embodiment, water use time series data (including time series data on water volume, time series data on water use timing, and time series data on water use frequency) are divided into a first dataset corresponding to weekdays, a second dataset corresponding to weekends, and a third dataset corresponding to designated holidays. Different convolution kernel sizes are used when processing the first, second, and third datasets to more accurately capture the characteristics of water use data during different time periods.

[0103] Specifically, when processing the first dataset (weekdays), since weekday water usage patterns are relatively simple, with relatively fixed peak and trough periods, a smaller convolution kernel size (the first value) can more precisely capture the rapidly changing characteristics of periods such as morning and evening peaks. For example, a smaller convolution kernel can better focus on water usage changes within a few hours, accurately extracting detailed features such as rising and falling water consumption trends.

[0104] When processing the data of the second data set (weekends), since residents' water use activities on weekends are more diverse, the water use time distribution is more dispersed, and the water consumption changes are more complex than on weekdays, a larger convolution kernel size (the second value) can cover a wider time range, which helps to capture the water use change trend during various leisure activities from morning to evening, and extract overall water use characteristics such as the small peak that may occur at noon and the changes in water use during leisure activities in the afternoon.

[0105] When processing the third dataset (designated holidays), residents spend extended periods at home during designated holidays, leading to frequent and diverse water use activities. This can lead to fluctuations in water use throughout the day. For example, the opening of a water feature can lead to continuous water use during the day, while family gatherings can significantly increase water use at night. A larger convolution kernel size (the third value) can capture overall water use trends over a full day or longer, ignoring small local fluctuations and better focusing on macro-level water use characteristics such as changes in total water use throughout the holiday period and the correlation between water use activities at different times of the day.

[0106] Exemplarily, a baseline value of the convolution kernel size, a first proportional parameter corresponding to weekdays, a second proportional parameter corresponding to weekends, and a third proportional parameter corresponding to designated holidays can be pre-set, and then the baseline value of the convolution kernel size is multiplied by the first proportional parameter to obtain a first value, the baseline value of the convolution kernel size is multiplied by the second proportional parameter to obtain a second value, and the baseline value of the convolution kernel size is multiplied by the third proportional parameter to obtain a third value.

[0107] From the above, it can be concluded that this embodiment divides the time series data of water consumption into a first data set corresponding to weekdays, a second data set corresponding to weekends, and a third data set corresponding to designated holidays. When processing the data of the first data set, the second data set, and the third data set, different convolution kernel sizes are used to more accurately capture the characteristics of water consumption data in different time periods.

[0108] In one embodiment of the present disclosure, the second neural network is a long short-term memory network, and the method for completing community water use data based on time period compensation further includes:

[0109] When processing the second type of data of the first data set based on the second neural network, the number of hidden layers of the second neural network is set to the first number; the first type of data includes time series data of water use time and time series data of water use frequency;

[0110] When processing the second type of data of the second data set based on the second neural network, setting the number of hidden layers of the second neural network to a second number;

[0111] When processing the second type of data of the third data set based on the second neural network, setting the number of hidden layers of the second neural network to a third number;

[0112] The first number, the second number, and the third number increase sequentially.

[0113] In this embodiment, the same method as the above embodiment is adopted. Taking into account the different complexities and time dependencies of water consumption data on weekdays, weekends, and designated holidays, different numbers of hidden layers are set in the long short-term memory network when processing the first data set, the second data set, and the third data set based on the second neural network.

[0114] Specifically, when processing the first dataset (weekdays), since weekday water usage is relatively regular in duration and frequency, primarily concentrated in the morning and evening, and the temporal dependencies are relatively simple, a smaller number of hidden layers (the first number) can effectively capture these relatively simple time series features. For example, the first number can be set to 1-2 layers to learn the basic time series features of the morning and evening peaks, such as the time of peak occurrence and the approximate changes in water usage frequency.

[0115] When processing the second dataset (weekends), since residents spend more time at home on weekends, the variations in water usage timing and frequency are more complex, potentially resulting in multiple small peaks or a more dispersed distribution of water use activities throughout the day. A larger number of hidden layers (the second number of layers) can better handle these complex temporal dependencies. For example, the second number of layers can be set to 2-3. The first layer can capture key features such as the late morning and evening peaks. The second layer can learn the correlation between the midday peak or the changes in water use frequency caused by afternoon leisure activities and the morning and evening peaks. The third layer can integrate this information to extract more complex long-term dependencies between water use timing and frequency throughout the weekend, such as the order of water use and the mutual influence between different activities.

[0116] When processing the third dataset (designated holidays), since water use on designated holidays covers almost the entire day, water use is also frequent, and special water-using activities (such as family gatherings and holiday celebrations) may occur, the time series data is most complex and has the highest long-term dependencies. Therefore, more hidden layers (the third dimension) can help to deeply explore these complex features. For example, the third dimension can be set to 3-4 layers. The early layers can gradually learn features such as the temporal sequence and frequency changes of various water use activities from washing up in the morning to before going to bed at night, as well as the impact of special holiday activities on water use. The later layers can integrate this information to construct a comprehensive and deep feature representation of the time and frequency of water use during the entire holiday period, thereby better processing and understanding this highly complex data.

[0117] From the above, it can be concluded that this embodiment sets different numbers of hidden layers for the first data set corresponding to weekdays, the second data set corresponding to weekends, and the third data set corresponding to designated holidays, so as to effectively extract time series features of different complexities.

[0118] Corresponding to the method for completing the water use data of a residential area based on time period compensation in the above embodiment, Figure 2 This is a structural block diagram of a cell water usage data completion device based on time period compensation provided by an embodiment of the present disclosure. For ease of explanation, only the parts related to the embodiment of the present disclosure are shown. Figure 2 The community water consumption data completion device 20 based on time period compensation includes: a data acquisition unit 21, a data extraction unit 22, a first calculation unit 23, a second calculation unit 24 and a data completion unit 25.

[0119] The data acquisition unit 21 is used to acquire the total water usage data of the community; the total water usage data of the community includes the total water usage data of multiple missing days and the total water usage data of multiple complete days, where the missing days are water usage dates with missing data, and the complete days are water usage dates other than the missing days;

[0120] The data extraction unit 22 is used to divide each complete day into multiple time periods according to a preset interval time, and extract the water consumption data of each time period in each complete day to obtain reference data for multiple complete days;

[0121] The first calculation unit 23 is configured to calculate the water use coefficient for each time period within the target complete day based on the reference data of the target complete day; wherein the water use coefficient for any time period is equal to the ratio of the average value of the reference data for that time period within the target complete day to the average value of the reference data for all time periods within the target complete day; the target complete day includes a plurality of complete days;

[0122] The second calculation unit 24 is configured to calculate the water consumption data for the missing period of the missing day based on the water consumption coefficient for each period of the target complete day and the average value of the reference data for all periods of the target complete day;

[0123] The data completion unit 25 is used to complete the total water consumption data of the missing day according to the water consumption data of the missing period of the missing day.

[0124] In one embodiment of the present disclosure, the second calculation unit 24 is specifically configured to:

[0125] If the first correlation coefficient between the existing data of the missing day and the water consumption coefficient of the corresponding time period is greater than or equal to the first threshold, the water consumption data of the missing time period of the missing day is calculated based on the water consumption coefficient of each time period of the target complete day and the average value of the reference data of all time periods of the target complete day;

[0126] If the first correlation coefficient between the existing data of the missing day and the water consumption coefficient of the corresponding time period is less than the first threshold, the water consumption curve of the missing day is fitted according to the existing data of the missing day, and the water consumption data of the missing time period of the missing day is calculated based on the water consumption curve.

[0127] In one embodiment of the present disclosure, the data extraction unit 22 is specifically configured to:

[0128] If the sampling period of the total water consumption data of the community for multiple complete days is greater than or equal to the preset time length, the sampling period is determined as the interval time;

[0129] If the sampling period of the total water consumption data of the community for multiple complete days is less than the preset time length, the preset time length is determined as the interval time.

[0130] In one embodiment of the present disclosure, the data extraction unit 22 is further configured to:

[0131] In a specified time period of each complete day, the preset duration is determined as the first duration;

[0132] In other time periods of each complete day, the preset duration is determined as the second duration; the other time periods are time periods other than the specified time period in each complete day, and the first duration is greater than the second duration.

[0133] In one embodiment of the present disclosure, the data extraction unit 22 is specifically configured to:

[0134] Extract time series data of water consumption, time series data of water use duration, and time series data of water use frequency based on the total water use data for a complete day;

[0135] Processing the time series data of water consumption based on the first neural network to extract the first feature in the time dimension;

[0136] The time series data of water use time and water use frequency are processed based on the second neural network to extract the long-term dependency relationship in the time series and obtain the second feature;

[0137] Fusing the first feature and the second feature to obtain a fused feature;

[0138] Perform cluster analysis on the fusion features and obtain abnormal data in the total water consumption data based on the cluster analysis results;

[0139] Remove outliers from total water consumption data.

[0140] In one embodiment of the present disclosure, the data extraction unit 22 is further configured to:

[0141] The water use time series data is divided into a first data set, a second data set, and a third data set; the first data set is a data set containing the water use time series data on weekdays, the second data set is a data set containing the water use time series data on weekends, and the third data set is a data set containing the water use time series data on designated holidays; the water use time series includes the time series data of water use amount, the time series data of water use time, and the time series data of water use frequency;

[0142] When processing the first type of data in the first data set based on the first neural network, the size of the convolution kernel of the first neural network is set to a first value; the first type of data is time series data of water consumption;

[0143] When processing the first type of data in the second data set based on the first neural network, setting the size of the convolution kernel of the first neural network to a second value;

[0144] When processing the first type of data in the third data set based on the first neural network, setting the size of the convolution kernel of the first neural network to a third value;

[0145] The first value, the second value, and the third value increase sequentially.

[0146] In one embodiment of the present disclosure, the data extraction unit 22 is further configured to:

[0147] When processing the second type of data of the first data set based on the second neural network, the number of hidden layers of the second neural network is set to the first number; the first type of data includes time series data of water use time and time series data of water use frequency;

[0148] When processing the second type of data of the second data set based on the second neural network, setting the number of hidden layers of the second neural network to a second number;

[0149] When processing the second type of data of the third data set based on the second neural network, setting the number of hidden layers of the second neural network to a third number;

[0150] The first number, the second number, and the third number increase sequentially.

[0151] See also Figure 3 , Figure 3 This is a schematic block diagram of an electronic device provided by an embodiment of the present disclosure. Figure 3 The electronic device 300 in the embodiment shown may include: one or more processors 301, one or more input devices 302, one or more output devices 303, and one or more memories 304. The processors 301, input devices 302, output devices 303, and memories 304 communicate with each other via a communication bus 305. The memory 304 is used to store computer programs, which include program instructions. The processor 301 is used to execute the program instructions stored in the memory 304. The processor 301 is configured to call the program instructions to execute the functions of the modules / units in the above-mentioned device embodiments, such as Figure 2 The functions of modules 21 to 25 are shown.

[0152] It should be understood that in the embodiments of the present disclosure, the processor 301 may be a central processing unit (CPU), or may be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0153] The input device 302 may include a touchpad, a fingerprint collection sensor (for collecting user fingerprint information and fingerprint direction information), a microphone, etc. The output device 303 may include a display (LCD, etc.), a speaker, etc.

[0154] The memory 304 may include a read-only memory and a random access memory, and provides instructions and data to the processor 301. A portion of the memory 304 may also include a non-volatile random access memory. For example, the memory 304 may also store device type information.

[0155] In a specific implementation, the processor 301, input device 302, and output device 303 described in the embodiments of the present disclosure can execute the implementation methods described in the first and second embodiments of the community water use data completion method based on time period compensation provided by the embodiments of the present disclosure, and can also execute the implementation methods of the electronic device described in the embodiments of the present disclosure, which will not be repeated here.

[0156] In another embodiment of the present disclosure, a computer-readable storage medium is provided. The computer-readable storage medium stores a computer program. The computer program includes program instructions. When the program instructions are executed by a processor, all or part of the process of the method in the above embodiment is implemented. The computer program can also be used to instruct related hardware to complete the process. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of each of the above method embodiments are implemented. The computer program includes computer program code, which can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium.

[0157] The computer-readable storage medium can be an internal storage unit of the electronic device in any of the aforementioned embodiments, such as a hard disk or memory of the electronic device. The computer-readable storage medium can also be an external storage device of the electronic device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash memory card, etc. Furthermore, the computer-readable storage medium can include both an internal storage unit of the electronic device and an external storage device. The computer-readable storage medium is used to store computer programs and other programs and data required by the electronic device. The computer-readable storage medium can also be used to temporarily store data that has been output or is about to be output.

[0158] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the above description has generally described the composition and steps of each example according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this disclosure.

[0159] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the electronic devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0160] In the several embodiments provided in this application, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces or units, or can be an electrical, mechanical or other form of connection.

[0161] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, i.e., they may be located in one place or distributed across multiple network units. Some or all of these units may be selected based on actual needs to achieve the objectives of the embodiments of the present disclosure.

[0162] In addition, the functional units in the various embodiments of the present disclosure may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0163] The above are only specific embodiments of the present disclosure, but the scope of protection of the present disclosure is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or replacements within the technical scope disclosed in this disclosure, and such modifications or replacements should be included in the scope of protection of the present disclosure. Therefore, the scope of protection of the present disclosure should be based on the scope of protection of the claims.

Claims

1. A method for completing community water consumption data based on time period compensation, characterized in that: include: Obtaining total water usage data of the community; the total water usage data of the community includes total water usage data of multiple missing days and total water usage data of multiple complete days, wherein the missing days are water usage dates with missing data, and the complete days are water usage dates other than the missing days; Divide each full day into multiple time periods according to preset intervals, extract water consumption data for each time period in each full day, and obtain reference data for multiple full days; Calculate the water use coefficient for each time period in the target complete day based on the reference data of the target complete day; wherein the water use coefficient for any time period is equal to the ratio of the average value of the reference data for that time period in the target complete day to the average value of the reference data for all time periods in the target complete day; the target complete day includes several complete days; Calculate the water consumption data for the missing time period of the missing day based on the water consumption coefficient for each time period of the target complete day and the average value of the reference data for all time periods of the target complete day; Completing the total water consumption data of the missing day according to the water consumption data of the missing period of the missing day; Before dividing each complete day into multiple time periods according to a preset interval time and extracting the water consumption data of each time period in each complete day, the method further includes: Based on the total water use data for a complete day, extract the time series data of water use quantity, time series data of water use time, and time series data of water use frequency; Processing the time series data of water consumption based on the first neural network to extract the first feature in the time dimension; The time series data of water use time and the time series data of water use frequency are processed based on the second neural network, and the long-term dependency relationship in the time series is extracted to obtain the second feature; Fusing the first feature and the second feature to obtain a fused feature; Performing cluster analysis on the fusion features, and obtaining abnormal data in the total water usage data based on the cluster analysis results; The abnormal data is deleted from the total water consumption data.

2. The method for completing community water consumption data based on time period compensation according to claim 1, characterized in that: The water use data of the missing time period of the missing day is calculated based on the water use coefficient of each time period of the target complete day and the average value of the reference data of all time periods of the target complete day, including: If the first correlation coefficient between the existing data of the missing day and the water use coefficient of the corresponding time period is greater than or equal to the first threshold, the water use data of the missing time period of the missing day is calculated according to the water use coefficient of each time period of the target complete day and the average value of the reference data of all time periods of the target complete day; If the first correlation coefficient between the existing data of the missing day and the water usage coefficient of the corresponding time period is less than a first threshold, the water usage curve of the missing day is fitted according to the existing data of the missing day, and the water usage data of the missing time period of the missing day is calculated based on the water usage curve.

3. The method for completing community water consumption data based on time period compensation according to claim 1, characterized in that: Also includes: If the sampling period of the total water consumption data of the community for multiple complete days is greater than or equal to the preset time length, the sampling period is determined as the interval time; If the sampling period of the total water consumption data of the community for a plurality of complete days is less than a preset time length, the preset time length is determined as the interval time.

4. The method for completing community water consumption data based on time period compensation as claimed in claim 3, characterized in that: Also includes: In a specified time period of each complete day, the preset duration is determined as a first duration; In other time periods of each complete day, the preset duration is determined as the second duration; the other time periods are time periods other than the designated time period in each complete day, and the first duration is greater than the second duration.

5. The method for completing community water consumption data based on time period compensation according to claim 1, characterized in that: The first neural network is a convolutional neural network, and the method for completing community water consumption data based on time period compensation further includes: The water use time series data is divided into a first data set, a second data set and a third data set; the first data set is a data set containing the water use time series data on weekdays, the second data set is a data set containing the water use time series data on weekends, and the third data set is a data set containing the water use time series data on designated holidays; the water use time series includes the time series data of water use amount, the time series data of water use time and the time series data of water use frequency; When processing the first type of data in the first data set based on the first neural network, the size of the convolution kernel of the first neural network is set to a first value; the first type of data is time series data of water consumption; When processing the first type of data in the second data set based on the first neural network, setting the size of the convolution kernel of the first neural network to a second value; When processing the first type of data in the third data set based on the first neural network, setting the size of the convolution kernel of the first neural network to a third value; The first value, the second value and the third value increase sequentially.

6. The method for completing community water consumption data based on time period compensation as claimed in claim 5, characterized in that: The second neural network is a long short-term memory network, and the method for completing the water consumption data of a residential area based on time period compensation further includes: When processing the second type of data of the first data set based on the second neural network, the number of hidden layers of the second neural network is set to a first number; the first type of data includes time series data of water use time and time series data of water use frequency; When processing the second type of data of the second data set based on the second neural network, setting the number of hidden layers of the second neural network to a second number; When processing the second type of data of the third data set based on the second neural network, setting the number of hidden layers of the second neural network to a third number; The first number, the second number, and the third number increase sequentially.

7. A device for completing water consumption data of a residential area based on time period compensation, characterized in that: include: A data acquisition unit, used to acquire total water use data of a community; the total water use data of the community includes total water use data of multiple missing days and total water use data of multiple complete days, wherein the missing days are water use dates with missing data, and the complete days are water use dates other than the missing days; A data extraction unit, used to divide each complete day into multiple time periods according to a preset interval time, and extract the water consumption data of each time period in each complete day to obtain reference data of multiple complete days; A first calculation unit is used to calculate the water use coefficient of each time period in the target complete day according to the reference data of the target complete day; wherein the water use coefficient of any time period is equal to the ratio of the average value of the reference data of the time period in the target complete day to the average value of the reference data of all time periods in the target complete day; the target complete day includes a plurality of complete days; The second calculation unit is used to calculate the water use data of the missing time period of the missing day according to the water use coefficient of each time period of the target complete day and the average value of the reference data of all time periods of the target complete day; A data completion unit, used to complete the total water consumption data of the missing day according to the water consumption data of the missing period of the missing day; The data extraction unit is specifically used for: Based on the total water use data for a complete day, extract the time series data of water use quantity, time series data of water use time, and time series data of water use frequency; Processing the time series data of water consumption based on the first neural network to extract the first feature in the time dimension; The time series data of water use time and the time series data of water use frequency are processed based on the second neural network, and the long-term dependency relationship in the time series is extracted to obtain the second feature; Fusing the first feature and the second feature to obtain a fused feature; Perform cluster analysis on the fusion features, and obtain abnormal data in the total water consumption data based on the cluster analysis results; Remove outliers from total water use data.

8. The device for completing water consumption data of a residential area based on time period compensation as claimed in claim 7, characterized in that: The second computing unit is specifically used for: If the first correlation coefficient between the existing data of the missing day and the water use coefficient of the corresponding time period is greater than or equal to the first threshold, the water use data of the missing time period of the missing day is calculated according to the water use coefficient of each time period of the target complete day and the average value of the reference data of all time periods of the target complete day; If the first correlation coefficient between the existing data of the missing day and the water usage coefficient of the corresponding time period is less than a first threshold, the water usage curve of the missing day is fitted according to the existing data of the missing day, and the water usage data of the missing time period of the missing day is calculated based on the water usage curve.

9. The device for completing water consumption data of a residential area based on time period compensation according to claim 7, characterized in that: The data extraction unit is specifically used for: If the sampling period of the total water consumption data of the community for multiple complete days is greater than or equal to the preset time length, the sampling period is determined as the interval time; If the sampling period of the total water consumption data of the community for a plurality of complete days is less than a preset time length, the preset time length is determined as the interval time.

Images