Intelligent water meter data acquisition system

Abstract

The invention relates to the technical field of intelligent water meters, in particular to an intelligent water meter data acquisition system which comprises a memory, a processor and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the following steps are realized: acquiring a water consumption data sequence acquired by an intelligent water meter of a target user in a current preset period, and obtaining a water consumption data change degree; performing noise data interference analysis on the water consumption data sequence to obtain a noise interference degree; obtaining the complexity of the water data by combining the noise interference degree and the change degree of the water data, obtaining the battery capacity of the intelligent water meter at the last sampling moment of the current preset period, and obtaining the adaptive sampling frequency of the next preset period according to the complexity of the water data and the battery capacity. The intelligent water meter is used for collecting the water consumption data of the target user in the next preset period, complete recording of water consumption characteristics is ensured, redundant data are reduced, and the cruising ability of the intelligent water meter is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of smart water meters, and in particular to a smart water meter data acquisition system. Background Art

[0002] Smart water meters integrate high-precision measurement, multi-parameter perception, real-time communication and edge intelligence technologies, breaking through the limitations of low precision, single function and data lag of traditional water meters, and realizing minute-level collection of water consumption data, instant alarm of water leakage / burst, and dynamic optimization of pipe network operation. Among them, the water consumption data collection of smart water meters is of great significance. It collects water consumption data through a specific sampling frequency and obtains the user's water consumption data changes in a timely manner, which helps to analyze the user's water consumption habits and greatly reduces the cost of manual meter reading.

[0003] When collecting user water usage data, the traditional method generally uses a fixed sampling frequency to collect user water usage data. However, the fixed sampling frequency may lose important data during periods of drastic fluctuations in water usage, and it is easy to miss key changes in water usage. In addition, the fixed sampling frequency will generate a large amount of redundant (invalid) data during zero flow or continuous and stable water usage stages, resulting in a significant shortening of the device's battery life. While reducing the service life of the equipment, it also increases the cost of using the smart water meter.

[0004] Therefore, how to dynamically adjust the sampling frequency of smart water meters to reduce a large amount of redundant water usage data has become an urgent problem to be solved. Summary of the invention

[0005] In view of this, an embodiment of the present invention provides a smart water meter data collection system to solve the problem of how to dynamically adjust the sampling frequency of the smart water meter to reduce a large amount of redundant water usage data.

[0006] An embodiment of the present invention provides a smart water meter data acquisition system, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the following steps when executing the computer program: Obtain the water consumption data sequence collected by the smart water meter of the target user within the current preset period; According to the flow intensity and volatility of the water use data sequence, the degree of change of the water use data in the current preset period is obtained; a water use data change curve of the water use data sequence is constructed to obtain the peak points and valley points of the water use data change curve, the horizontal axis of the water use data change curve is time, and the vertical axis is water use data; according to the amplitude fluctuation difference of the water use data corresponding to all the peak points and valley points, the noise data interference analysis is performed on the water use data sequence to obtain the degree of noise interference in the current preset period; Combining the noise interference degree and the change degree of water consumption data in the current preset period, the complexity of the water consumption data of the target user in the current preset period is obtained. At the last sampling moment of the current preset period, the battery power of the intelligent water meter is obtained. According to the complexity of the water consumption data of the target user in the current preset period and the battery power, the adaptive sampling frequency of the next preset period after the current preset period is obtained, which is used to collect the water consumption data of the target user in the next preset period.

[0007] Preferably, the obtaining of the change degree of the water consumption data in the current preset period according to the flow intensity and the volatility of the water consumption data sequence includes: Performing a first-order difference processing on the water consumption data sequence to obtain a first-order difference sequence, and obtaining the fluctuation degree of the water consumption data sequence according to the mean value of the difference values of the first-order difference sequence; obtaining the flow intensity of the water consumption data sequence according to the mean value of the water consumption data of the water consumption data sequence; and obtaining the change degree of the water consumption data in the current preset period according to the product of the fluctuation degree and the flow intensity of the water consumption data sequence.

[0008] Preferably, the noise interference analysis of the water consumption data sequence according to the amplitude fluctuation difference of the water consumption data corresponding to all the peak points and valley points to obtain the noise interference degree in the current preset period includes: Taking each valley point as a segmentation point, dividing the water consumption change curve into at least two sub-curves, and obtaining the water hammer interference degree in the current preset period according to the water consumption data in each sub-curve and the water consumption data of each peak point in the water consumption change curve; Obtaining the water bubble interference degree in the current preset period according to the quantity ratio of the peak points and valley points in the water consumption change curve and the fluctuation kurtosis of each sub-curve; and taking the sum of the water hammer interference degree and the water bubble interference degree as the noise interference degree in the current preset period.

[0009] Preferably, the obtaining of the water hammer interference degree in the current preset period according to the water consumption data in each sub-curve and the water consumption data of each peak point in the water consumption change curve includes: According to the water consumption data of each peak point in the water consumption change curve, calculating the mean value of the water consumption data, denoted as the peak data mean value, calculating the mean value of the water consumption data of all data points in the water consumption change curve, denoted as the overall data mean value, and calculating the ratio of the peak data mean value to the overall data mean value; Calculate the skewness of the water consumption data of all data points in each of the sub-curves respectively to obtain the mean skewness. Take the mean skewness as the independent variable of the exponential function with the natural constant as the base to obtain the degree of deviation of the data distribution of the water consumption data change curve from symmetry; take the product of the ratio and the degree of deviation of the data distribution from symmetry as the degree of water hammer interference in the current preset period.

[0010] Preferably, the obtaining the degree of water bubble interference in the current preset period according to the proportion of the number of peak points and valley points in the water consumption data change curve and the fluctuation kurtosis of each sub-curve includes: Obtain the proportion of the number of all peak points and valley points in the total number of data points of the water consumption data change curve. Calculate the kurtosis of the water consumption data of all data points in each sub-curve respectively to obtain the mean kurtosis. Take the mean kurtosis as the independent variable of the exponential function with the natural constant as the base to obtain the steepness of the data distribution form of the water consumption data change curve; take the product of the proportion and the steepness of the data distribution form as the degree of water bubble interference in the current preset period.

[0011] Preferably, the obtaining the complexity of the water consumption data of the target user in the current preset period by combining the degree of noise interference and the degree of change of the water consumption data in the current preset period includes: Perform positive proportional normalization on the degree of noise interference using the exponential function with the natural constant as the base to obtain the credibility of the water consumption data in the current preset period; take the product of the credibility of the water consumption data and the degree of change of the water consumption data as the complexity of the water consumption data of the target user in the current preset period.

[0012] Preferably, the obtaining the adaptive sampling frequency of the next preset period after the current preset period according to the complexity of the water consumption data of the target user in the current preset period and the battery power includes: Obtain the maximum sampling frequency and the minimum sampling frequency preset for the intelligent water meter to obtain the sampling frequency range between the maximum sampling frequency and the minimum sampling frequency. Take the opposite number of the product of the complexity of the water consumption data in the current preset period and the battery power as the independent variable of the exponential function with the natural constant as the base to obtain the corresponding function value. Take the difference between the constant 1 and the function value as the adjustment ratio; obtain the product of the adjustment ratio and the sampling frequency range, denoted as the sampling frequency adjustment amount. According to the difference between the minimum sampling frequency and the sampling frequency adjustment amount, obtain the adaptive sampling frequency of the next preset period after the current preset period.

[0013] The beneficial effects of the embodiments of the present invention compared with the prior art are: By analyzing the characteristics of the water consumption data of the target user within the current preset period, the degree of change in the water consumption data is obtained. According to the data change fluctuation of the water consumption data sequence, the degree of noise interference within the current preset period is obtained to eliminate the noise interference caused by water hammer effect, bubbles, etc., and the actual user situation within the current preset period, that is, the complexity of the water consumption data within the current preset period, is obtained. Further, in combination with the battery status of the intelligent water meter, the sampling frequency of the next preset period is automatically adjusted, which not only ensures the complete recording of water consumption characteristics, reduces redundant data, but also improves the battery life of the intelligent water meter. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the accompanying drawings required for use in the embodiments or the description of the prior art. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0015] Figure 1 is a flowchart of a method for collecting intelligent water meter data provided in Embodiment 1 of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0016] The following will describe in detail the embodiments of the present disclosure. The examples of the embodiments are shown in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to explain the present disclosure, and should not be construed as a limitation to the present disclosure.

[0017] It should be noted that the terms "first", "second", etc. in the specification of the present disclosure and the above accompanying drawings are used to distinguish similar objects, and do not necessarily need to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. On the contrary, they are only examples of devices and methods consistent with some aspects of the present disclosure.

[0018] In order to illustrate the technical solutions of the present invention, the following will be described through specific embodiments.

[0019] An embodiment of the present invention provides an intelligent water meter data acquisition system, including a processor and a memory. The processor executes the computer program stored in the memory to implement an intelligent water meter data acquisition method, as Figure 1 shown, an intelligent water meter data acquisition method includes the following steps: Step S101, obtaining a water consumption data sequence collected by the intelligent water meter of the target user within the current preset period.

[0020] After installing the intelligent water meter in the household, it is first necessary to set the data collection period, the initial sampling frequency, and the sampling frequency range. Considering the situation where users use a large amount of water, it is not in a short period of time, and the water use duration is relatively long. For example, the duration of washing clothes with a washing machine is generally 50 minutes, while the duration of washing hands is only 30 seconds to 1 minute. Therefore, in order to ensure a relatively large correlation of effective information between adjacent periods, in the embodiments of the present invention, the data collection period is preferably set to 10 minutes, the initial sampling frequency is 10 seconds per time, and the sampling frequency range is [2s, 60s]. There is no limitation here. The larger the value of the sampling frequency, the smaller the number of data points collected. That is, the number of data points collected at a sampling frequency of 60 is less than the number of data points collected at a sampling frequency of 20. If you want to intelligently adjust the sampling frequency of the intelligent water meter, it is necessary to collect the water use data of one period for analysis. Therefore, after completing the installation of the intelligent water meter and setting the corresponding initial parameters, run for one period according to the set initial parameters to obtain the basic water use data for analysis, and then automatically adjust the sampling frequency of the water use data in the next period according to the analysis result.

[0021] Taking any user as an example in the embodiments of the present invention, and denoting it as the target user. Since the automatic adjustment of the sampling frequency belongs to dynamic adjustment, before collecting data in the current preset period, the adaptive sampling frequency of the current preset period has been obtained from the water use data analysis of the previous period. Therefore, the embodiments of the present invention can obtain the water use data sequence collected by the intelligent water meter of the target user in the current preset period for obtaining the sampling frequency of the water use data in the next preset period.

[0022] Step S102, according to the flow intensity and water use data volatility of the water use data sequence, obtain the degree of change of the water use data in the current preset period; construct a water use data change curve of the water use data sequence to obtain the peak points and valley points of the water use data change curve. The horizontal axis of the water use data change curve is time, and the vertical axis is water use data. According to the amplitude fluctuation difference of the water use data corresponding to all peak points and valley points, perform noise data interference analysis on the water use data sequence to obtain the degree of noise interference in the current preset period.

[0023] When the user's water use data has a high frequency and large fluctuations in water consumption in a certain stage, it is necessary to collect water use data at a higher frequency to ensure the integrity of the water use data and avoid information loss; while when the user has no water use for a long time or the water use frequency is low, it can be adjusted to a lower frequency of collection to monitor the change of water use data while reducing the battery energy consumption of the intelligent electric meter.

[0024] Therefore, after obtaining the water consumption data sequence within the current preset period, according to the flow intensity and volatility of the water consumption data within this period, the degree of change of the water consumption data within the current preset period is obtained. When the water flow intensity is greater and the volatility of the water consumption data is stronger, it indicates that the target user is in a state of intensive water consumption within the current preset period, and the water consumption state in the next preset period is also likely to be in a state of intensive water consumption. Therefore, the water consumption data needs to be collected at a higher frequency in the next preset period to retain key water consumption data.

[0025] Among them, the method for obtaining the degree of change of the water consumption data within the current preset period is as follows: perform a first-order difference processing on the water consumption data sequence to obtain a first-order difference sequence, and obtain the degree of volatility of the water consumption data sequence according to the mean value of the difference values of the first-order difference sequence; obtain the flow intensity of the water consumption data sequence according to the mean value of the water consumption data of the water consumption data sequence; and obtain the degree of change of the water consumption data within the current preset period according to the product of the degree of volatility and the flow intensity of the water consumption data sequence.

[0026] In one embodiment, the calculation formula for the degree of change of the water consumption data within the current preset period is:

[0027] Among them, represents the degree of change of the water consumption data within the current preset period, represents the mean value of the water consumption data of the water consumption data sequence, represents the mean value of the difference values of the first-order difference sequence of the water consumption data sequence.

[0028] It should be noted that the larger the mean value of the water consumption data , the greater the water consumption of the target user within the current preset period, and the more critical its water consumption data; the first-order difference sequence is used to characterize the change rate between two adjacent water consumption data in the water consumption data sequence. The greater the change rate, the stronger the corresponding water consumption fluctuation. Therefore, the larger the value of

[0029] , the stronger the volatility of the water consumption data within the current preset period, the greater the degree of change of the water consumption data within the current preset period, and the higher the sampling frequency in the next preset period should be.

[0030] When water hammer effect occurs within a cycle, it is manifested as instantaneous high-pressure pulses, with an amplitude several times that of normal flow, that is, the gradient of peak data within the cycle is relatively large, and the skewness of the water hammer effect shows a right skew; while when bubble interference occurs within the cycle, the cycle shows relatively frequent small fluctuations, that is, the peak density is relatively high, and its kurtosis shows a sharp peak and thick tail; therefore, according to the characteristics of water hammer effect and bubble interference, noise data interference analysis can be carried out on the water consumption data sequence to obtain the degree of noise interference within the current preset cycle.

[0031] Specifically, construct the water consumption data change curve of the water consumption data sequence, and use the peak-valley algorithm to obtain the peak points and valley points of the water consumption data change curve. The horizontal axis of the water consumption data change curve is time, and the vertical axis is water consumption data. The peak-valley algorithm belongs to the prior art and will not be elaborated here. Taking each of the valley points as a segmentation point, divide the water consumption data change curve into at least two sub-curves, and one sub-curve represents a fluctuation interval. When the data fluctuation within the cycle is caused by the water hammer effect, its fluctuating part is manifested as instantaneous high-pressure pulses, and its skewness is to the right. Therefore, if the water consumption data fluctuation within the cycle is caused by the water hammer effect, the data gradient of its fluctuating part is relatively large, manifested as a relatively large ratio of the mean value of water consumption data at the fluctuation to the overall data mean value, and the skewness to the right means that a small number of extremely high values within the cycle make the overall data mean value greater than the median. Then, the embodiments of the present invention can obtain the water hammer interference degree within the current preset cycle according to the water consumption data within each of the sub-curves and the water consumption data at each peak point in the water consumption data change curve, specifically as follows: According to the water consumption data at each peak point in the water consumption data change curve, calculate the mean value of water consumption data, denoted as the peak data mean value, calculate the mean value of the water consumption data of all data points in the water consumption data change curve, denoted as the overall data mean value, and calculate the ratio of the peak data mean value to the overall data mean value; Calculate the skewness of the water consumption data of all data points in each of the sub-curves respectively to obtain the skewness mean value, take the skewness mean value as the independent variable of the exponential function with the natural constant as the base, and obtain the degree of deviation of the data distribution of the water consumption data change curve from symmetry; take the product of the ratio and the degree of deviation of the data distribution from symmetry as the water hammer interference degree within the current preset cycle.

[0032] Among them, the calculation formula for the water hammer interference degree within the current preset cycle is:

[0033] Among them, represents the water hammer interference degree within the current preset cycle, represents the peak data mean value, represents the overall data mean value, represents the skewness of the water consumption data of all data points in the i-th sub-curve, represents the number of sub - curves, represents the exponential function with the natural constant as the base.

[0034] It should be noted that skewness belongs to the prior art and will not be elaborated in detail here. The larger the value of, the larger the ratio of the peak value mean of the water consumption data in the current preset period to the overall data mean, and the greater the difference between the amplitude of the water consumption data at the fluctuation point and the overall data amplitude. Then it is more likely to be a high - pressure pulse caused by the water hammer effect, and the higher the degree of water hammer noise in the current preset period, corresponding the larger the value; The larger the value of, the greater the possibility of right - skewness at each fluctuation point (peak point), and the higher the degree of water hammer noise in the current preset period, corresponding the larger the value.

[0035] When the data fluctuation within the period is caused by bubble interference, the fluctuating part shows relatively frequent small - amplitude fluctuations, and its kurtosis shows a sharp - tailed and thick - peaked shape. Therefore, if the water consumption data fluctuation within the period is caused by bubble interference, its fluctuation is relatively frequent, that is, the data fluctuation within the period is relatively frequent, and the proportion of its fluctuation turning points is relatively large, that is, the proportion of peak points and valley points within the period is large; and the kurtosis at the fluctuation points within the period shows a sharp - tailed and thick - peaked shape, that is, its corresponding kurtosis is greater than three. Then in the embodiments of the present invention, according to the proportion of the number of peak points and valley points in the water consumption data change curve, and the fluctuation kurtosis of each sub - curve, the degree of bubble interference in the current preset period is obtained. Specifically: Obtain the proportion of the number of all peak points and valley points in the total number of data points of the water consumption data change curve, calculate the kurtosis of the water consumption data of all data points in each sub - curve respectively to obtain the kurtosis mean value, take the kurtosis mean value as the independent variable of the exponential function with the natural constant as the base to obtain the steepness of the data distribution form of the water consumption data change curve; take the product of the proportion and the steepness of the data distribution form as the degree of bubble interference in the current preset period.

[0036] Among them, the calculation formula for the degree of bubble interference in the current preset period is:

[0037] Among them, represents the degree of bubble interference in the current preset period, represents the number of peak points, represents the number of valley points, N represents the total number of data points in the water consumption data curve, represents the kurtosis of the water consumption data of all data points in the i - th sub - curve, represents the number of sub - curves, Denotes the exponential function with the natural constant as the base.

[0038] It should be noted that kurtosis belongs to the prior art and will not be elaborated in detail here. The larger the value of, the greater the fluctuation frequency of the water consumption data in the current preset period, and the greater the degree of bubble interference in the current preset period. The larger the value of, the greater the possibility that the curve shape of each sub-curve shows a sharp tail and thick peak, corresponding to the greater degree of bubble interference in the current preset period.

[0039] Based on the above, the degree of water hammer interference and the degree of water bubble interference in the current preset period are respectively obtained. Then, the sum of the degree of water hammer interference and the degree of water bubble interference is used as the degree of noise interference in the current preset period to characterize the overall noise level in the current preset period.

[0040] Step S103: Combine the degree of noise interference and the degree of change in water consumption data in the current preset period to obtain the complexity of the water consumption data of the target user in the current preset period. At the last sampling moment of the current preset period, obtain the battery power of the intelligent water meter. According to the complexity of the water consumption data of the target user in the current preset period and the battery power, obtain the adaptive sampling frequency for the next preset period after the current preset period, which is used to collect the water consumption data of the target user in the next preset period.

[0041] The greater the degree of noise interference in the current preset period, the lower the credibility of the water consumption data information in the current preset period. Furthermore, by combining the degree of noise interference and the degree of change in water consumption data in the current preset period, the complexity of the water consumption data of the user in the current preset period is obtained to exclude noise interference caused by water hammer effect, bubbles, etc., and to reflect the actual user situation in the current preset period. Among them, the method for obtaining the complexity of water consumption data is as follows: Use the exponential function with the natural constant as the base to perform proportional normalization on the degree of noise interference to obtain the credibility of the water consumption data in the current preset period; Multiply the credibility of the water consumption data and the degree of change in the water consumption data as the complexity of the water consumption data of the target user in the current preset period.

[0042] In one embodiment, the calculation formula for the complexity of water consumption data is:

[0043] Wherein, Denotes the complexity of the water consumption data of the target user in the current preset period, Denotes the degree of change in the water consumption data in the current preset period, Denotes the degree of water hammer interference in the current preset period, Denotes the exponential function with the natural constant as the base, Indicates the degree of bubble interference within the current preset period, where 1 represents a constant.

[0044] It should be noted that is used to characterize the credibility of water usage data within the current preset period. The smaller the value, the greater the credibility of the water usage data, corresponding to a greater complexity of the water usage data of the target user within the current preset period, and a higher frequency of data collection is required in the next preset period.

[0045] Furthermore, smart water meters are usually powered by batteries, and high-frequency sampling consumes more power and shortens the battery life. Therefore, when the power is limited, not only the complexity of the user's water usage data needs to be considered, but also the battery power of the smart water meter needs to be combined to dynamically adjust the sampling frequency and extend the device usage time. In the embodiments of the present invention, first, the battery power of the smart water meter is obtained at the last sampling moment of the current preset period. Among them, the battery power can be detected through software or the device itself. For example, electronic devices (such as mobile phones and laptops) can view the remaining power through the system battery management software. Then, according to the complexity of the water usage data of the target user within the current preset period and the battery power, the adaptive sampling frequency of the next preset period after the current preset period is obtained. Specifically: Obtain the maximum sampling frequency and the minimum sampling frequency preset for the smart water meter, and get the sampling frequency range between the maximum sampling frequency and the minimum sampling frequency. Take the opposite of the product of the complexity of the water usage data within the current preset period and the battery power as the independent variable of the exponential function with the natural constant as the base, and obtain the corresponding function value. Take the difference between the constant 1 and the function value as the adjustment ratio; obtain the product of the adjustment ratio and the sampling frequency range, denoted as the sampling frequency adjustment amount. According to the difference between the minimum sampling frequency and the sampling frequency adjustment amount, obtain the adaptive sampling frequency of the next preset period after the current preset period.

[0046] Among them, the calculation formula for the adaptive sampling frequency of the next preset period is:

[0047] Among them, represents the adaptive sampling frequency of the next preset period. represents the minimum sampling frequency preset for the smart water meter (60s in the embodiments of the present invention). represents the maximum sampling frequency preset for the smart water meter (2s in the embodiments of the present invention). represents the complexity of the water usage data of the target user within the current preset period. represents the battery power of the smart water meter. represents the exponential function with the natural constant as the base, and 1 represents a constant.

[0048] It should be noted that the greater the complexity of the water consumption data in the current preset period, the more effective water consumption information is represented for this period. Correspondingly, the sampling frequency should be increased in the next preset period to increase the quantity of effective water consumption data. The more remaining battery power in the current preset period, the more the sampling frequency should be increased to ensure the integrity of key data. On the contrary, when the battery power is low and the complexity of the water consumption data is low, the sampling frequency in the next period is decreased to save resources.

[0049] After obtaining the adaptive sampling frequency for the next preset period, the water consumption data of the target user in the next preset period is collected according to the adaptive sampling frequency, and the collected water consumption data is uploaded to the cloud for storage, which provides strong assistance for the staff to analyze the user's water consumption status and is an important link in the construction of intelligent water services.

[0050] Through the intelligent water meter deployed at the water consumption terminal, data such as water consumption volume and water consumption time can be collected in real time and accurately. After being encrypted, these data are stably transmitted to the cloud by means of the Internet of Things technology. With the help of the cloud data analysis platform, the staff can intuitively view the change trend of the water consumption data. For example, the daily and monthly water consumption volumes of users are presented through line charts to analyze their peak and trough water consumption periods and determine whether their water consumption habits are regular. If the water consumption of a certain user surges abnormally at night, it may imply a potential water leakage problem; if the water consumption is long-term lower than that of users of the same type, it is necessary to further check whether there is a measurement deviation caused by facility aging. In addition, the cloud storage supports multi-dimensional data comparison, which can compare the user's current water consumption data with the data of the same period in history and users in the same region to accurately locate abnormal water consumption. Based on these analysis results, the staff can actively provide water-saving suggestions for users, repair faulty facilities in a timely manner, and improve the quality of water supply services and the utilization efficiency of water resources.

[0051] It should be noted that the focus of the present invention lies in the dynamic adjustment of the sampling frequency.

[0052] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. An intelligent water meter data acquisition system, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, the following steps are implemented: Obtain the water consumption data sequence collected by the intelligent water meter of the target user within the current preset period; According to the flow intensity and water consumption data volatility of the water consumption data sequence, obtain the degree of change of water consumption data within the current preset period; construct the water consumption data change curve of the water consumption data sequence, and obtain the peak points and valley points of the water consumption data change curve. The horizontal axis of the water consumption data change curve is time, and the vertical axis is water consumption data. According to the amplitude fluctuation difference of the water consumption data corresponding to all peak points and valley points, perform noise data interference analysis on the water consumption data sequence to obtain the degree of noise interference within the current preset period; Combine the degree of noise interference and the degree of change of water consumption data within the current preset period to obtain the complexity of the water consumption data of the target user within the current preset period. Obtain the battery power of the intelligent water meter at the last sampling moment of the current preset period. According to the complexity of the water consumption data of the target user within the current preset period and the battery power, obtain the adaptive sampling frequency of the next preset period after the current preset period, which is used to collect the water consumption data of the target user in the next preset period.

2. The intelligent water meter data acquisition system according to claim 1, characterized in that, The obtaining the degree of change of water consumption data within the current preset period according to the flow intensity and water consumption data volatility of the water consumption data sequence includes: Perform first-order difference processing on the water consumption data sequence to obtain a first-order difference sequence, and obtain the degree of fluctuation of the water consumption data sequence according to the mean value of the difference values of the first-order difference sequence; obtain the flow intensity of the water consumption data sequence according to the mean value of the water consumption data of the water consumption data sequence; and obtain the degree of change of water consumption data within the current preset period according to the product of the degree of fluctuation and the flow intensity of the water consumption data sequence.

3. The intelligent water meter data acquisition system according to claim 1, characterized in that, The performing noise data interference analysis on the water consumption data sequence according to the amplitude fluctuation difference of the water consumption data corresponding to all peak points and valley points to obtain the degree of noise interference within the current preset period includes: Taking each valley point as a segmentation point, divide the water consumption data change curve into at least two sub-curves, and obtain the degree of water hammer interference within the current preset period according to the water consumption data within each sub-curve and the water consumption data of each peak point in the water consumption data change curve; Obtain the degree of bubble interference within the current preset period according to the quantity ratio of peak points and valley points in the water consumption data change curve and the fluctuation kurtosis of each sub-curve; and take the sum of the degree of water hammer interference and the degree of bubble interference as the degree of noise interference within the current preset period.

4. The intelligent water meter data acquisition system according to claim 3, characterized in that, The obtaining the degree of water hammer interference within the current preset period according to the water consumption data within each sub-curve and the water consumption data of each peak point in the water consumption data change curve includes: According to the water consumption data of each peak point in the water consumption data change curve, calculate the mean value of the water consumption data, denoted as the peak data mean value, calculate the mean value of the water consumption data of all data points in the water consumption data change curve, denoted as the overall data mean value, and calculate the ratio of the peak data mean value to the overall data mean value; Calculate the skewness of the water consumption data of all data points in each of the sub-curves respectively to obtain the mean skewness. Use the mean skewness as the independent variable of an exponential function with the natural constant as the base to obtain the degree of deviation of the data distribution of the water consumption data change curve from symmetry. Take the product of the ratio and the degree of deviation of the data distribution from symmetry as the water hammer interference degree in the current preset period.

5. The intelligent water meter data acquisition system according to claim 3, characterized in that, The obtaining of the water bubble interference degree in the current preset period according to the proportion of the number of peak points and valley points in the water consumption data change curve and the fluctuation kurtosis of each sub-curve includes: Obtain the proportion of the number of all peak points and valley points in the total number of data points of the water consumption data change curve. Calculate the kurtosis of the water consumption data of all data points in each sub-curve respectively to obtain the mean kurtosis. Use the mean kurtosis as the independent variable of an exponential function with the natural constant as the base to obtain the steepness degree of the data distribution form of the water consumption data change curve. Take the product of the proportion and the steepness degree of the data distribution form as the water bubble interference degree in the current preset period.

6. The intelligent water meter data acquisition system according to claim 1, characterized in that, The obtaining of the complexity of the water consumption data of the target user in the current preset period by combining the noise interference degree and the water consumption data change degree in the current preset period includes: Perform positive proportional normalization on the noise interference degree using an exponential function with the natural constant as the base to obtain the credibility of the water consumption data in the current preset period. Take the product of the credibility of the water consumption data and the water consumption data change degree as the complexity of the water consumption data of the target user in the current preset period.

7. The intelligent water meter data acquisition system according to claim 1, characterized in that The obtaining of the adaptive sampling frequency of the next preset period after the current preset period according to the complexity of the water consumption data of the target user in the current preset period and the battery power includes: Obtain the maximum sampling frequency and the minimum sampling frequency preset for the intelligent water meter to get the sampling frequency range between the maximum sampling frequency and the minimum sampling frequency. Use the negative of the product of the complexity of the water consumption data in the current preset period and the battery power as the independent variable of an exponential function with the natural constant as the base to obtain the corresponding function value. Take the difference between the constant 1 and the function value as the adjustment ratio. Obtain the product of the adjustment ratio and the sampling frequency range, denoted as the sampling frequency adjustment amount. Obtain the adaptive sampling frequency of the next preset period after the current preset period according to the difference between the minimum sampling frequency and the sampling frequency adjustment amount.

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