Water supply equipment operation state real-time monitoring method and system based on Internet of Things

Abstract

The invention relates to the technical field of data processing, in particular to a water supply equipment running state real-time monitoring method and system based on the Internet of Things. The method comprises the steps of obtaining a power value and a flow value at the current moment in the running process of the water supply pump in real time; determining noise suspiciousness at the current moment; determining the noise degree at the current moment; whether noise exists at the current moment is identified according to the noise degree at the current moment; determining a corrected power value at the current moment; the cumulative sum is obtained in real time through a cumulative sum control chart algorithm, so that the running state of the water supply equipment is monitored in real time. According to the invention, through identification and correction of the noise suspiciousness and the noise degree, interference of noise on operation state monitoring of the water supply equipment is reduced, and data accuracy is ensured; the power value can be acquired and corrected in real time, anomaly detection is performed by using an accumulation and control chart algorithm, and real-time monitoring of the running state of the equipment is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a method and system for real-time monitoring of the operating status of water supply equipment based on the Internet of Things. Background Art

[0002] As the living standards of residents continue to improve, the demand for water supply systems has also increased. Urban water supply systems have become more and more complex, and the water supply network has a wide coverage. Traditional manual monitoring and management methods can no longer meet the needs of modern water supply. Water supply pumps play a vital role in urban water supply. They are responsible for transporting water from water sources or water plants to various users. The normal operation of water supply pumps ensures stable water pressure and sufficient water volume. They are the core components of urban water supply systems. If the power of the water supply pump is abnormal, such as too high or too low power, it may cause the efficiency of the pump to decrease or even fail, affecting the stability of the water supply system. An existing method for power data anomaly detection of water supply pumps is the Cumulative Sum Control Chart (CUSUM) algorithm. This algorithm has the advantages of simplicity, effectiveness, strong interpretability and wide adaptability in power data anomaly detection of water supply pumps, making it a common method for processing time series data and performing anomaly detection analysis.

[0003] The patent document with announcement number CN112559969B discloses a small leak detection method based on a cumulative sum algorithm, including: collecting flow data during a period of stable water consumption, then estimating a benchmark water consumption range during the period of stable water consumption through frequency statistics, then verifying the rationality of the estimated benchmark water consumption range by setting a confidence level, and then eliminating the interference of large low-value fluctuations in flow data based on the benchmark water consumption range, and finally using the CUSUM algorithm to detect small leaks in the DMA pipeline network.

[0004] However, the above patent document does not take into account that when collecting and transmitting the power data of the water supply pump, the electromagnetic interference sources around the water supply pump (such as other electrical equipment) may affect the signal transmission of the sensor, resulting in random noise in the data; and the CUSUM algorithm performs anomaly detection on data points based on the data at the current moment and several previous moments. If there is noise in the data, the authenticity of the data point will be reduced, resulting in errors in the final anomaly detection results at the current moment and several previous moments. Summary of the invention

[0005] In order to solve the problem that noise in the data leads to errors in the abnormal detection results at the current moment and several previous moments, thereby affecting the accuracy of real-time monitoring of the operating status of water supply equipment, the present invention provides a real-time monitoring method and system for the operating status of water supply equipment based on the Internet of Things.

[0006] In the first aspect, the present invention provides a method for real-time monitoring of the operating status of a water supply device based on the Internet of Things, which adopts the following technical solutions:

[0007] The power value and flow value at the current moment in the operation of the water supply pump are obtained in real time; the noise suspicion at the current moment is determined, and the noise suspicion is positively correlated with the difference between the power value at the current moment and the power values ​​at each of the previous moments, the average of the changes in the power values ​​of all two adjacent moments at the current moment and the previous moments, and the information entropy value of all power values ​​at the current moment and the previous moments; the noise degree at the current moment is determined, and the noise degree is positively correlated with the noise suspicion at the current moment, the difference in the relative size of the power value and the flow value at the current moment, and the difference between the average intervals of all adjacent extreme value points in the power value and the flow value at the current moment and the previous moments; according to the size of the noise degree at the current moment, whether there is noise at the current moment is identified, and the current moment is corrected to obtain the corrected power value at the current moment; the cumulative sum control chart algorithm is used to perform abnormal detection on the corrected power value at the current moment and the previous moments, and the cumulative sum at the current moment and the previous moments is obtained to realize real-time monitoring of the operating status of the water supply equipment.

[0008] By identifying and correcting the suspiciousness and noise level of noise, the interference of noise on the operating status monitoring of water supply equipment is reduced, ensuring the accuracy of the data; the power value can be acquired and corrected in real time, and the cumulative and control chart algorithm is used for anomaly detection to achieve real-time monitoring of the operating status of the equipment; by analyzing the changes in power and flow values, the noise can be accurately identified and corrected to avoid the impact of noise on the monitoring results; combined with the Internet of Things technology, remote monitoring and data management can be achieved, improving the efficiency and intelligence level of equipment management.

[0009] Furthermore, the method for obtaining the power value and flow value at the current moment is: digitally converting the collected water supply pump power data and water supply pump flow data at the current moment during the operation of the water supply pump to obtain the power value and flow value at the current moment.

[0010] Furthermore, the noise suspicion degree satisfies the following relationship:

[0011] Where P is the noise suspicion at the current moment, N is the total number of moments before the current moment, W is the power value at the current moment, and W n is the power value at the nth moment in the previous moments, is the mean of the power value changes between the current moment and the previous moments, S is the information entropy value of all power values ​​between the current moment and the previous moments, α is a hyperparameter, ∏ is the continuous product function, and || is the absolute value symbol.

[0012] By comprehensively considering multiple factors such as the difference between the current power value and the historical power value, the mean of the power change and the information entropy, it is helpful to accurately identify and quantify the impact of noise and reduce the interference of noise on the data; combining multiple dimensions such as power difference, change amount and information entropy, the impact of noise on the monitoring results is comprehensively evaluated to improve accuracy.

[0013] Furthermore, the noise level satisfies the following relationship:

[0014] Where T is the noise level at the current moment, P is the noise suspicion level at the current moment, and W is min and D min are the minimum values ​​of power and flow at the current moment and several previous moments, W and D are the power and flow at the current moment, respectively. W The sum is the difference between the maximum and minimum power values ​​at the current moment and several previous moments, J D is the difference between the maximum and minimum flow values ​​at the current moment and several previous moments. and are the average intervals of all adjacent extreme points in power and flow values ​​at the current moment and several previous moments, α is a hyperparameter, and || is the absolute value symbol.

[0015] By simultaneously considering the changes in power and flow, and combining multi-dimensional factors such as the difference between minimum and maximum values, and the average interval between extreme points, it is possible to more comprehensively evaluate the impact of noise and improve the accuracy of noise detection; effectively identify and quantify noise, reduce its impact on the monitoring system, and improve the stability and reliability of the system.

[0016] Furthermore, the identifying whether there is noise at the current moment based on the noise level at the current moment includes: in response to the noise level at the current moment being greater than a preset noise threshold, determining that there is noise at the current moment; in response to the noise level at the current moment being not greater than the preset noise threshold, determining that there is no noise at the current moment.

[0017] By setting a noise threshold, the noise identification process can be simplified, making the determination of whether noise exists intuitive and efficient; the system can automatically detect the presence of noise based on real-time data without human intervention, thereby improving monitoring efficiency; the noise threshold can be adjusted according to different environments and needs, thereby enhancing the adaptability and robustness of the system in changing environments.

[0018] Furthermore, the correction of the current moment includes: in response to the presence of noise at the current moment, the corrected power value at the current moment is equal to the average of the power values ​​of several moments before the current moment, so as to obtain the corrected power value at the current moment; in response to the absence of noise at the current moment, the corrected power value at the current moment is equal to the power value at the current moment, so as to obtain the corrected power value at the current moment.

[0019] By using the power average of several previous moments to correct the power value at the current moment when noise exists, the impact of instantaneous fluctuations of noise on the data can be effectively reduced, making the power value more stable; when noise exists, the corrected power value is closer to the actual power change trend, reducing the interference of noise on system judgment, thereby improving the accuracy and reliability of the data; according to whether there is noise at the current moment, the correction strategy is flexibly adjusted to automatically respond to environmental changes, thereby improving the system's adaptability; when there is no noise, the current power value is directly used to avoid unnecessary calculations and improve the system's real-time response capability and efficiency.

[0020] Furthermore, the real-time monitoring of the operating status of the water supply equipment is achieved, including: in response to the cumulative sum being greater than a judgment threshold, determining that the water supply pump has an abnormality at the current moment and several previous moments, and issuing an early warning prompt, thereby completing real-time monitoring of the operating status of the water supply equipment based on the Internet of Things.

[0021] In the second aspect, the present invention provides a real-time monitoring system for the operation status of water supply equipment based on the Internet of Things, which adopts the following technical solutions:

[0022] The real-time monitoring system for the operation status of water supply equipment based on the Internet of Things includes: a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the real-time monitoring method for the operation status of water supply equipment based on the Internet of Things is implemented.

[0023] By adopting the above technical solution, the above-mentioned real-time monitoring method of the operating status of water supply equipment based on the Internet of Things is generated into a computer program and stored in a memory to be loaded and executed by a processor, so that a terminal device is made according to the memory and the processor for easy use.

[0024] The present invention has the following technical effects:

[0025] Since noise data will interfere with the detection accuracy of the CUSUM algorithm, by identifying and correcting the noise data, the impact of outliers on the CUSUM algorithm can be reduced, thereby improving the accuracy of anomaly detection; since the power value is collected and corrected in real time, an accurate and stable data sequence can be obtained, which can better adapt to the requirements of the CUSUM algorithm and improve the robustness of the algorithm; by analyzing the correlation between the power value and the flow value of the water supply pump, more targeted and accurate screening can be performed when dealing with noise; the corrected data sequence usually reduces the noise component, so that the CUSUM algorithm can better capture the real signal changes and improve the reliability and accuracy of the anomaly detection results of the CUSUM algorithm; in summary, by combining the feature analysis and correlation information of the noise data, the power value of the water supply pump can be judged and corrected in real time, which can significantly improve the anomaly detection effect of the CUSUM algorithm, so that the CUSUM algorithm can better reflect the real data trends and changes, thereby enhancing its reliability and practicality in practical applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] By reading the detailed description below with reference to the accompanying drawings, the above and other purposes, features and advantages of the exemplary embodiments of the present invention will become readily understood. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-restrictive manner, and the same or corresponding reference numerals represent the same or corresponding parts.

[0027] Figure 1 It is a method flow chart of a method for real-time monitoring of the operating status of water supply equipment based on the Internet of Things in an embodiment of the present invention. DETAILED DESCRIPTION

[0028] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0029] It should be understood that when the terms "first", "second", etc. are used in the claims, descriptions, and drawings of the present invention, they are only used to distinguish different objects, rather than to describe a specific order. The terms "include" and "comprise" used in the description and claims of the present invention indicate the presence of the described features, wholes, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their collections.

[0030] The core purpose of this solution is to use the CUSUM algorithm to perform real-time anomaly detection on power data. During the process, the real-time power value will be judged and corrected for noise. It is mainly based on the analysis of the numerical change characteristics of the power data and flow data of the water supply pump.

[0031] The embodiment of the present invention discloses a real-time monitoring method for the operation status of a water supply device based on the Internet of Things. Figure 1 , comprising steps S1 to S6:

[0032] S1: Obtain the power value and flow value of the water supply pump at the current moment in operation in real time.

[0033] When collecting the power data and flow data of the water supply pump during its operation, use a power analyzer and a flow meter to collect and record the power data and flow data respectively, ensuring that the collection duration and frequency are the same; for example, the collection duration is one hour and the collection frequency is once per second. Then use an analog-to-digital conversion device to digitize the power data and flow data to obtain the power value and flow value.

[0034] Specifically, the method for obtaining the power value and flow value at the current moment is:

[0035] The collected water supply pump power data and water supply pump flow data at the current moment during the operation of the water supply pump are digitally converted to obtain the power value and flow value at the current moment.

[0036] S2: Determine the noise suspiciousness at the current moment.

[0037] It should be noted that the suspicious degree of noise at the current moment is obtained by analyzing the numerical performance and numerical variation characteristics of the real-time power value of the water supply pump. When analyzing this indicator, the more special the performance of the power value at the current moment and the more drastic the numerical variation characteristics of the surrounding data, the greater the suspicious degree of noise of the power value at the current moment.

[0038] The noise suspicion is positively correlated with the difference between the power value at the current moment and the power values ​​at each of the previous moments, the mean of the change in the power values ​​of all two adjacent moments at the current moment and the previous moments, and the information entropy value of all power values ​​at the current moment and the previous moments.

[0039] Implementers can set the number of previous moments according to the specific implementation situation, for example, 50. If the number of previous moments is less than 50, pre-collection can be performed when the equipment is turned on to ensure that there is sufficient historical data for reference for each data point at the beginning of monitoring.

[0040] Specifically, the noise suspicion degree satisfies the following relationship:

[0041]

[0042] Where P is the noise suspicion at the current moment, N is the total number of moments before the current moment, W is the power value at the current moment, and W n is the power value at the nth moment in the previous moments, is the mean of the power value changes between the current moment and the previous moments, S is the information entropy value of all power values ​​between the current moment and the previous moments, α is a hyperparameter, ∏ is the continuous product function, and || is the absolute value symbol.

[0043] Implementers can set hyperparameters according to specific implementation conditions, for example, 0.001. The existence of hyperparameters is to prevent |WW n When |=0, the formula becomes meaningless.

[0044] in, It represents the continuous product of the differences between the power value at the current moment and each power value in its reference data segment (i.e., several previous moments). The larger the value, the more special the value of the power value at the current moment is compared with all the power values ​​in its reference data segment, and the greater the possibility that it belongs to a noise data point, and the greater the corresponding noise suspicion; The larger S is, the more drastic the change characteristics of the power values ​​in the reference data segment of the power value at the current moment are, the more reasonable the sudden change in the power value at the current moment will be, the greater the possibility that it belongs to a noise data point will be, and the greater the corresponding noise suspicion will be; the larger S is, the more drastic the change characteristics of the power values ​​in the reference data segment of the power value at the current moment are, and the greater the credibility will be. Similarly, it can be said that the possibility of abnormal change in the power value at the current moment is greater, and the corresponding noise suspicion will be greater.

[0045] S3: Determine the noise level at the current moment.

[0046] It should be noted that the noise data that may be encountered is similar to the actual abnormal data values ​​generated by the water supply pump; according to scenario research, the relationship between the flow and power of the water supply pump is closely related. Usually when the water supply pump is operating normally, the power is usually in a certain functional relationship with its flow. As the flow increases, the power will also increase; therefore, the analysis of the correlation between the flow data and power data change characteristics of the water supply pump can be combined to optimize the noise suspicion level at the current moment and obtain the noise degree at the current moment; the weaker the correlation between the power value and the flow value, the stronger the possibility of noise data existing at that moment, and the greater the noise degree will be.

[0047] The noise level is positively correlated with the noise suspicion level at the current moment, the relative size difference between the power value and the flow value at the current moment, and the difference between the average intervals of all adjacent extreme value points in the power value and the flow value at the current moment and several previous moments.

[0048] Specifically, the noise level satisfies the following relationship:

[0049]

[0050] Where T is the noise level at the current moment, P is the noise suspicion level at the current moment, and W is min and D min are the minimum values ​​of power and flow at the current moment and several previous moments, W and D are the power and flow at the current moment, respectively. W The sum is the difference between the maximum and minimum power values ​​at the current moment and several previous moments, J D is the difference between the maximum and minimum flow values ​​at the current moment and several previous moments. and are the average intervals of all adjacent extreme points in power and flow values ​​at the current moment and several previous moments, α is a hyperparameter, and || is the absolute value symbol.

[0051] Implementers can set hyperparameters according to specific implementation conditions, for example, 0.001. The existence of hyperparameters is to prevent and , making the formula meaningless.

[0052] Among them, the formula Indicates the relative size of the power value at the current moment in its reference data segment. Indicates the relative size of the current flow value in its reference data segment; Indicates the difference between the two. The larger the value, the lower the correlation between the power value and the flow value at the current moment. Then, the possibility that the power value at the current moment belongs to noise data is greater, and its noise degree is greater. It represents the difference between the average intervals of all adjacent extreme points in the power value and flow value at the current moment and its reference data segment respectively. The larger the value is, the greater the difference in the changing rules of the two data during this period of time, and the lower the correlation between the changes of the two data during this period of time. Therefore, the lower the correlation between the power value and the flow value at the current moment, the greater the credibility. Therefore, the possibility that the power value at the current moment belongs to noise data will be greater, and the noise degree of the power value at the current moment will be greater.

[0053] S4: Identify whether there is noise at the current moment according to the noise level at the current moment.

[0054] Specifically, the step of identifying whether there is noise at the current moment according to the noise level at the current moment includes:

[0055] In response to the noise level at the current moment being greater than a preset noise threshold, determining that noise exists at the current moment;

[0056] In response to the noise level at the current moment not being greater than the preset noise threshold, it is determined that there is no noise at the current moment.

[0057] Implementers can set the noise threshold according to actual implementation conditions, for example, 0.9.

[0058] S5: Determine the corrected power value at the current moment.

[0059] And correct the current moment to obtain the corrected power value at the current moment.

[0060] Specifically, the correction of the current time includes:

[0061] In response to the presence of noise at the current moment, the corrected power value at the current moment is equal to the average of the power values ​​at several moments before the current moment, thereby obtaining the corrected power value at the current moment;

[0062] In response to the absence of noise at the current moment, the corrected power value at the current moment is equal to the power value at the current moment, thereby obtaining the corrected power value at the current moment.

[0063] Implementers can set the number of previous moments according to actual implementation conditions, for example, 20.

[0064] S6: The cumulative sum is obtained in real time using the cumulative sum control chart algorithm to achieve real-time monitoring of the operating status of the water supply equipment.

[0065] The cumulative sum control chart algorithm is used to detect abnormalities in the corrected power values ​​at the current moment and several previous moments, and the cumulative sum at the current moment and several previous moments is obtained to achieve real-time monitoring of the operating status of the water supply equipment.

[0066] Implementers can set various parameters of the CUSUM algorithm according to the specific implementation situation. For example, the target value is 10KW; the sliding threshold is 0.05KW; the cumulative and initial values ​​are 0; when performing anomaly detection on data points, a total of 50 data points immediately before the sampling time are selected.

[0067] Specifically, the real-time monitoring of the operating status of the water supply equipment includes:

[0068] In response to the cumulative sum being greater than a determination threshold, it is determined that an abnormality has occurred in the water supply pump at the current moment and at several moments before, and an early warning is issued, thereby completing real-time monitoring of the operating status of the water supply equipment based on the Internet of Things.

[0069] Implementers can set the determination threshold according to actual implementation conditions, for example, 0.6KW.

[0070] An embodiment of the present invention also discloses a real-time monitoring system for the operating status of water supply equipment based on the Internet of Things, including a processor and a memory, wherein the memory stores computer program instructions. When the computer program instructions are executed by the processor, a real-time monitoring method for the operating status of water supply equipment based on the Internet of Things according to the present invention is implemented.

[0071] The real-time monitoring system for water supply equipment based on the Internet of Things can monitor the power data of water supply pumps in real time and perform intelligent analysis to promptly identify potential problems with water supply pumps and issue early warnings, thereby improving the automation and intelligence level of the water supply system, reducing manual intervention, and improving management efficiency.

[0072] The above system also includes other components well known to those skilled in the art, such as a communication bus and a communication interface, and their configuration and functions are known in the art, so they will not be described in detail here.

[0073] In the present invention, the aforementioned memory may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, a computer-readable storage medium may be any suitable magnetic storage medium or magneto-optical storage medium, or any other medium that can be used to store the required information and can be accessed by an application, module, or both. Any such computer storage medium may be part of the device or accessible or connectable to the device.

[0074] Although this specification has shown and described a number of embodiments of the present invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Those skilled in the art will conceive of many modifications, changes and alternatives without departing from the ideas and spirit of the present invention. It should be understood that in the practice of the present invention, various alternatives to the embodiments of the present invention described herein may be employed.

[0075] The above are all preferred embodiments of the present invention, and are not intended to limit the protection scope of the present invention. Therefore, any equivalent changes made based on the structure, shape, and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A real-time monitoring method for the operation status of water supply equipment based on the Internet of Things, characterized in that: include: Real-time acquisition of the power and flow values ​​of the water supply pump at the current moment during operation; Determine the noise suspicion at the current moment, which is positively correlated with the difference between the power value at the current moment and the power values ​​at each of the previous moments, the average of the changes in the power values ​​of all two adjacent moments at the current moment and the previous moments, and the information entropy value of all power values ​​at the current moment and the previous moments; determine the noise degree at the current moment, which is positively correlated with the noise suspicion at the current moment, the difference in the relative sizes of the power value and the flow value at the current moment, and the difference between the average intervals of all adjacent extreme value points in the power value and the flow value at the current moment and the previous moments; According to the noise level at the current moment, identify whether there is noise at the current moment, and correct the current moment to obtain the corrected power value at the current moment; The cumulative sum control chart algorithm is used to detect abnormalities in the corrected power values ​​at the current moment and several previous moments, and the cumulative sum at the current moment and several previous moments is obtained to achieve real-time monitoring of the operating status of the water supply equipment.

2. The method for real-time monitoring of the operation status of water supply equipment based on the Internet of Things according to claim 1 is characterized in that: The method for obtaining the power value and flow value at the current moment is: The collected water supply pump power data and water supply pump flow data at the current moment during the operation of the water supply pump are digitally converted to obtain the power value and flow value at the current moment.

3. The method for real-time monitoring of the operation status of water supply equipment based on the Internet of Things according to claim 1 is characterized in that: The noise suspicion degree satisfies the following relationship: Where P is the noise suspicion at the current moment, N is the total number of moments before the current moment, W is the power value at the current moment, and W n is the power value at the nth moment in the previous moments, is the mean of the power value changes between the current moment and the previous moments, S is the information entropy value of all power values ​​between the current moment and the previous moments, α is a hyperparameter, ∏ is the continuous product function, and || is the absolute value symbol.

4. The method for real-time monitoring of the operation status of water supply equipment based on the Internet of Things according to claim 1 is characterized in that: The noise level satisfies the following relationship: Where T is the noise level at the current moment, P is the noise suspicion level at the current moment, and W is min and D min are the minimum values ​​of power and flow at the current moment and several previous moments, W and D are the power and flow at the current moment, respectively. W The sum is the difference between the maximum and minimum power values ​​at the current moment and several previous moments, J D is the difference between the maximum and minimum flow values ​​at the current moment and several previous moments. and are the average intervals of all adjacent extreme points in power and flow values ​​at the current moment and several previous moments, α is a hyperparameter, and || is the absolute value symbol.

5. The method for real-time monitoring of the operation status of water supply equipment based on the Internet of Things according to claim 1 is characterized in that: The step of identifying whether there is noise at the current moment according to the noise level at the current moment includes: In response to the noise level at the current moment being greater than a preset noise threshold, determining that noise exists at the current moment; In response to the noise level at the current moment not being greater than the preset noise threshold, it is determined that there is no noise at the current moment.

6. The method for real-time monitoring of the operation status of water supply equipment based on the Internet of Things according to claim 1 is characterized in that: The correcting of the current time includes: In response to the presence of noise at the current moment, the corrected power value at the current moment is equal to the average of the power values ​​at several moments before the current moment, thereby obtaining the corrected power value at the current moment; In response to the absence of noise at the current moment, the corrected power value at the current moment is equal to the power value at the current moment, thereby obtaining the corrected power value at the current moment.

7. The method for real-time monitoring of the operation status of water supply equipment based on the Internet of Things according to claim 1 is characterized in that: The method for realizing real-time monitoring of the operating status of the water supply equipment includes: In response to the cumulative sum being greater than a determination threshold, it is determined that an abnormality has occurred in the water supply pump at the current moment and at several moments before, and an early warning is issued, thereby completing real-time monitoring of the operating status of the water supply equipment based on the Internet of Things.

8. The real-time monitoring system of water supply equipment operation status based on the Internet of Things is characterized by: include: A processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a real-time monitoring method for the operating status of a water supply device based on the Internet of Things according to any one of claims 1 to 7 is implemented.

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