A shower head detection system based on artificial intelligence

Abstract

The present application belongs to the field of shower detection, and relates to a data analysis technique, which is used to solve the problem that the shower detection system in the prior art cannot automatically investigate the cause of abnormal operation of a shower, and specifically relates to a shower detection system based on artificial intelligence, which comprises a coverage detection module, the coverage detection module is in communication connection with a water pressure analysis module, the water pressure analysis module is in communication connection with a pipeline detection module and a fault monitoring module, and the management monitoring module is in communication connection with the fault monitoring module; the coverage detection module is used for detecting and analyzing the shower spraying coverage range of an irrigation area, and marking the corresponding shower as a spraying abnormal object when the spraying coverage state does not meet the requirements; the present application can detect and analyze the shower spraying coverage range of an irrigation area, analyze the spraying coverage state of the spraying coverage area of each shower in a periodic detection manner, and differentially mark the shower with an abnormal spraying coverage state.

Description

Technical Field

[0001] This invention belongs to the field of shower head detection and involves data analysis technology, specifically an artificial intelligence-based shower head detection system. Background Technology

[0002] A shower head, also known as a watering can, was originally a device for watering flowers, potted plants, and other plants. Now, it is generally used in garden irrigation by installing a rotating structure on the shower head. The rotating shower head sprays irrigation water evenly, improving the efficiency of garden irrigation.

[0003] Existing shower head detection systems cannot analyze spray water pressure and operational faults based on the shower head's coverage status. This results in the inability to automatically troubleshoot the causes of shower head malfunctions, thus affecting the efficiency of handling such malfunctions.

[0004] To address the aforementioned technical problems, this application proposes a solution. Summary of the Invention

[0005] The purpose of this invention is to provide an artificial intelligence-based shower head detection system to solve the problem that existing shower head detection systems cannot automatically investigate the cause of the abnormality when the shower head malfunctions.

[0006] The technical problem to be solved by this invention is: how to provide an artificial intelligence-based shower head detection system that can automatically troubleshoot the cause of the malfunction when the shower head malfunctions.

[0007] The objective of this invention can be achieved through the following technical solutions:

[0008] An artificial intelligence-based shower head detection system includes a coverage detection module, which is communicatively connected to a water pressure analysis module. The water pressure analysis module is communicatively connected to a pipeline detection module and a fault monitoring module. The management monitoring module is communicatively connected to the fault monitoring module.

[0009] The coverage detection module is used to detect and analyze the spray coverage of the sprinkler in the irrigation area, and when the spray coverage does not meet the requirements, the corresponding sprinkler is marked as an abnormal spray object, a water pressure analysis signal is generated and sent to the water pressure analysis module.

[0010] The water pressure analysis module is used to perform water pressure analysis on the shower heads in the irrigation area: it obtains the water pressure deviation value, deviation duration value, and deviation statistics value of the abnormal spray object within the detection period and performs numerical calculation to obtain the water pressure coefficient. The water pressure coefficient is used to determine whether there is an abnormality in the spray water pressure of the abnormal spray object. If there is an abnormality, a pipeline detection signal is generated and sent to the pipeline detection module. If there is no abnormality, a fault monitoring signal is generated and sent to the fault monitoring module.

[0011] The pipeline detection module is used to detect and analyze the operating status of the shower water supply pipeline in the irrigation area;

[0012] The fault monitoring module monitors and analyzes the operating faults of the shower head after receiving the fault monitoring signal.

[0013] As a preferred embodiment of the present invention, the specific process of detecting and analyzing the spray coverage of the irrigation area includes: marking the showers in the irrigation area as the detection objects; randomly selecting several detection points within the spray coverage of the detection objects and setting rain sensors at the detection points; generating a detection cycle and acquiring the rainfall values ​​of the rain sensors at the detection points at the end of the detection cycle; summing and averaging the rainfall values ​​of all detection points to obtain the spray value of the detection object; obtaining the standard spray range of the detection object and marking the average of the maximum and minimum values ​​of the standard spray range as the standard spray value; marking the absolute value of the difference between the spray value and the standard spray value as the spray data; calculating the variance of the rainfall values ​​corresponding to all detection points of the detection object to obtain uniform data; obtaining the coverage coefficient of the detection object by numerically calculating the spray data and the uniform data; and determining whether the spray coverage status of the detection object meets the requirements based on the coverage coefficient.

[0014] As a preferred embodiment of the present invention, the specific process for determining whether the spray coverage status of the test object meets the requirements includes: comparing the coverage coefficient of the test object with a preset coverage threshold; if the coverage coefficient is less than the coverage threshold, the spray coverage status of the test object is determined to meet the requirements; if the coverage coefficient is greater than or equal to the coverage threshold, the spray coverage status of the test object is determined to not meet the requirements.

[0015] In a preferred embodiment of the present invention, the process of obtaining the water pressure deviation value includes: acquiring the spray water pressure value of the abnormal spray object in real time during the detection period, retrieving the water pressure standard range, marking the average of the maximum and minimum values ​​of the water pressure standard range as the water pressure standard value, and marking the absolute value of the difference between the spray water pressure value and the water pressure standard value as the water pressure deviation value; the deviation duration value is the duration during which the spray water pressure value of the abnormal spray object is outside the water pressure standard range during the detection period, and the process of obtaining the deviation statistics value includes: marking spraying behavior where the spray water pressure value is continuously outside the water pressure standard range as a deviation behavior, and marking the number of deviation behaviors during the detection period as the deviation statistics value.

[0016] As a preferred embodiment of the present invention, the specific process for determining whether the spray water pressure of the abnormal spray object is abnormal includes: comparing the water pressure coefficient of the abnormal spray object within the detection period with a preset water pressure threshold; if the water pressure coefficient is less than the water pressure threshold, it is determined that the spray water pressure of the abnormal spray object within the detection period is not abnormal; if the water pressure coefficient is greater than or equal to the water pressure threshold, it is determined that the spray water pressure of the abnormal spray object within the detection period is abnormal.

[0017] In a preferred embodiment of the present invention, the specific process of the pipeline detection module detecting and analyzing the operating status of the shower water supply pipeline in the irrigation area includes: marking all shower heads corresponding to the connecting pipelines of the abnormal spraying object as objects in the same group; obtaining the water pressure coefficient of the objects in the same group within the detection period; marking the absolute value of the difference between the water pressure coefficient of the abnormal spraying object and the water pressure coefficient of the objects in the same group as the pressure difference value of the same group; summing and averaging the pressure difference values ​​of all objects in the same group to obtain the pipeline abnormal value; comparing the pipeline abnormal value with a preset pipeline abnormal threshold: if the pipeline abnormal value is less than the pipeline abnormal threshold, it is determined that there is a water pressure abnormality in the shower water supply pipeline, a water supply maintenance signal is generated and sent to the mobile terminal of the management personnel; if the pipeline abnormal value is greater than or equal to the pipeline abnormal threshold, it is determined that there is no water pressure abnormality in the shower water supply pipeline, a fault monitoring signal is generated and sent to the fault monitoring module.

[0018] In a preferred embodiment of the present invention, the specific process of the fault monitoring module for monitoring and analyzing the operational faults of the shower head includes: generating a monitoring period and dividing the monitoring period into several monitoring time periods; acquiring the rainfall values ​​of the rain sensors within the spray coverage area of ​​the abnormal spray object during the monitoring time period and marking them as monitoring values ​​of the detection points; sorting the detection points in ascending order of monitoring value values ​​to obtain the water volume sequence of the monitoring time period; then, at the end of the monitoring period, calculating the variance of the sequence number of the detection points in the water volume sequence of all monitoring time periods to obtain the regularity value of the detection points; summing the regularity values ​​of all detection points and taking the average value to obtain the regularity coefficient; comparing the regularity coefficient with a preset regularity threshold: if the regularity coefficient is less than the regularity threshold, it is determined that the abnormal spray object has a spray hole blockage fault, generating a blockage fault signal and sending the blockage fault signal to the mobile terminal of the management personnel; if the regularity coefficient is greater than or equal to the regularity threshold, it is determined that the abnormal spray object has a rotation fault, generating a rotation fault signal and sending the rotation fault signal to the mobile terminal of the management personnel.

[0019] The present invention has the following beneficial effects:

[0020] 1. The coverage detection module can detect and analyze the spray coverage of the sprinkler in the irrigation area. It can analyze the spray coverage status of each sprinkler in a periodic detection manner and mark the sprinklers with abnormal spray coverage status differently.

[0021] 2. The water pressure analysis module can analyze the water pressure of the shower heads in the irrigation area, obtain various water pressure parameters of the abnormal spray objects within the detection period, and perform comprehensive analysis and calculation to obtain the water pressure coefficient. Based on the water pressure coefficient, the degree of water pressure abnormality of the spray water of the abnormal spray objects within the detection period is fed back, so as to carry out differentiated fault factor investigation for shower heads with different degrees of water pressure abnormality.

[0022] 3. The management and monitoring module can detect and analyze the operating status of the shower water supply pipeline in the irrigation area, perform differential analysis of water pressure coefficients on all showers connected to the same water supply pipeline to obtain pipeline abnormal values, and then use the pipeline abnormal values ​​to provide feedback on the correlation between water pressure abnormalities and the abnormal spray objects themselves, and promptly carry out pipeline repair when there are abnormalities in the water supply pipeline.

[0023] 4. The fault monitoring module can monitor and analyze the operation faults of the shower head. During each monitoring period of the monitoring cycle, the detection points are sorted according to the rainfall value collected by the rain sensor. At the end of the monitoring cycle, the water volume sequence of all monitoring periods is combined for comprehensive analysis to obtain the regularity coefficient, thereby determining whether the shower head fault is caused by blockage or abnormal rotation, and further improving the efficiency of shower head fault handling. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 This is a system block diagram of Embodiment 1 of the present invention;

[0026] Figure 2 This is a system block diagram of Embodiment 2 of the present invention. Detailed Implementation

[0027] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] Example 1

[0029] like Figure 1 As shown, an artificial intelligence-based shower head detection system includes a coverage detection module, which is communicatively connected to a water pressure analysis module. The water pressure analysis module is communicatively connected to a pipeline detection module and a fault monitoring module. The management monitoring module is communicatively connected to the fault monitoring module.

[0030] The coverage detection module is used to detect and analyze the spray coverage of the sprinklers in the irrigation area: The sprinklers within the irrigation area are marked as the detection objects. Several detection points are randomly selected within the spray coverage area of ​​the detection objects, and rain sensors are installed at these points. A detection cycle is generated, and the rainfall values ​​from the rain sensors at the detection points are acquired at the end of the detection cycle. The average of the rainfall values ​​from all detection points is summed to obtain the spray value of the detection object. The standard spray range of the detection object is obtained, and the average of the maximum and minimum values ​​within the standard spray range is marked as the standard spray value. The absolute value of the difference between the spray value and the standard spray value is marked as the spray data PE. The variance of the rainfall values ​​at all detection points corresponding to the detection object is calculated to obtain the uniform data JY. The result is obtained using the formula FG = k1 × PE + k2 × JY. The coverage coefficient FG of the detected object is calculated, where k1 and k2 are proportionality coefficients, and k1 > k2 > 1. The coverage coefficient FG of the detected object is compared with the preset coverage threshold FGmax: if the coverage coefficient FG is less than the coverage threshold FGmax, the spray coverage status of the detected object is determined to meet the requirements; if the coverage coefficient FG is greater than or equal to the coverage threshold FGmax, the spray coverage status of the detected object is determined to not meet the requirements, the corresponding detected object is marked as an abnormal spraying object, a water pressure analysis signal is generated and sent to the water pressure analysis module; the spray coverage range of the shower head in the irrigation area is detected and analyzed, and the spray coverage status of each shower head is analyzed in a periodic detection manner, and the shower heads with abnormal spray coverage status are differentially marked.

[0031] The water pressure analysis module is used to perform water pressure analysis on the showerheads in the irrigation area: It acquires the water pressure deviation value SP, deviation duration value PS, and deviation statistics value PT of the abnormal spraying object within the detection period. The process of acquiring the water pressure deviation value SP includes: acquiring the spray water pressure value of the abnormal spraying object in real time within the detection period; retrieving the water pressure standard range; marking the average of the maximum and minimum values ​​within the water pressure standard range as the water pressure standard value; and marking the absolute value of the difference between the spray water pressure value and the water pressure standard value as the water pressure deviation value SP. The deviation duration value PS represents the spray water pressure of the abnormal spraying object within the detection period. The process of obtaining the deviation statistical value PT, which measures the duration of the water pressure value outside the standard range, includes: marking spraying behavior where the spray water pressure value is continuously outside the standard range as deviation behavior, and marking the number of deviation behaviors within the detection period as the deviation statistical value PT; obtaining the water pressure coefficient SY of the abnormal spraying object within the detection period using the formula SY=(SP*z1+PS*z2+PT*z3) / 3, where z1, z2, and z3 are all weighting coefficients, assigned according to the influence of SP, PS, and PT on the result SY, with z1>z2>z3>1 here;

[0032] The water pressure coefficient SY of the abnormal sprinkler object during the detection period is compared with the preset water pressure threshold SYmax. If the water pressure coefficient SY is less than the water pressure threshold SYmax, it is determined that the sprinkler water pressure of the abnormal sprinkler object is not abnormal during the detection period, a fault monitoring signal is generated, and the fault monitoring signal is sent to the fault monitoring module. If the water pressure coefficient SY is greater than or equal to the water pressure threshold SYmax, it is determined that the sprinkler water pressure of the abnormal sprinkler object is abnormal during the detection period, a pipeline detection signal is generated, and the pipeline detection signal is sent to the pipeline detection module. Water pressure analysis is performed on the shower heads in the irrigation area to obtain various water pressure parameters of the abnormal sprinkler object during the detection period. The water pressure coefficient is obtained through comprehensive analysis and calculation. The degree of water pressure abnormality of the sprinkler water during the detection period is fed back based on the water pressure coefficient, so as to carry out differentiated fault factor investigation for shower heads with different degrees of water pressure abnormality.

[0033] The pipeline detection module is used to detect and analyze the operating status of the shower water supply pipeline in the irrigation area: All showerheads connected to the abnormal spraying object are marked as belonging to the same group; the water pressure coefficient SY of the group is obtained within the detection period; the absolute value of the difference between the water pressure coefficient SY of the abnormal spraying object and the water pressure coefficient SY of the group is marked as the group pressure difference value; the group pressure difference values ​​of all objects in the same group are summed and averaged to obtain the pipeline abnormality value; the pipeline abnormality value is compared with a preset pipeline abnormality threshold; if the pipeline abnormality value is less than the pipeline abnormality threshold, it is determined that there is a water pressure abnormality in the shower water supply pipeline. The system generates a water supply maintenance signal and sends it to the mobile terminal of the management personnel. If the abnormal value of the pipeline is greater than or equal to the abnormal value of the pipeline, it is determined that there is no water pressure abnormality in the shower water supply pipeline, and a fault monitoring signal is generated and sent to the fault monitoring module. The system detects and analyzes the operating status of the shower water supply pipeline in the irrigation area, performs water pressure coefficient difference analysis on all showers connected to the same water supply pipeline to obtain the pipeline abnormal value, and then uses the pipeline abnormal value to provide feedback on the correlation between water pressure abnormality and the abnormal spraying object itself, and promptly carries out pipeline maintenance when there is an abnormality in the water supply pipeline.

[0034] Example 2

[0035] like Figure 2As shown, the fault monitoring module monitors and analyzes the showerhead's operational faults after receiving a fault monitoring signal: It generates a monitoring period and divides it into several monitoring time periods. Within each monitoring time period, it acquires the rainfall values ​​from the rain sensors within the spray coverage area of ​​the abnormal spraying object and marks them as monitoring values ​​for detection points. The detection points are sorted in ascending order of monitoring value values ​​to obtain a water volume sequence for each monitoring time period. Then, at the end of the monitoring period, the variance of the detection point's position in the water volume sequence of all monitoring time periods is calculated to obtain the pattern value of the detection point. The pattern values ​​of all detection points are summed and averaged to obtain the pattern coefficient. The pattern coefficient is compared with a preset pattern threshold: if the pattern coefficient is less than the threshold value... If the regularity coefficient is greater than or equal to the regularity threshold, it is determined that the shower head has a spray hole blockage fault, a blockage fault signal is generated and sent to the manager's mobile terminal; if the regularity coefficient is greater than or equal to the regularity threshold, it is determined that the shower head has a rotation fault, a rotation fault signal is generated and sent to the manager's mobile terminal; the operation faults of the shower head are monitored and analyzed. In each monitoring period of the monitoring cycle, the detection points are sorted according to the rainfall value collected by the rain sensor. At the end of the monitoring cycle, the water volume sequence of all monitoring periods is combined for comprehensive analysis to obtain the regularity coefficient, so as to determine whether the shower head fault is caused by blockage or rotation abnormality, thereby further improving the fault handling efficiency of the shower head.

[0036] An artificial intelligence-based shower head detection system, during operation, marks the shower heads within the irrigation area as detection objects. Several detection points are randomly selected within the spray coverage area of ​​the detection objects, and rainfall sensors are installed at these points. A detection cycle is generated, and at the end of the detection cycle, the spray data PE and uniformity data JY of the detection objects are acquired. A coverage coefficient FG is obtained by numerically calculating the spray data PE and uniformity data JY. The coverage coefficient FG is used to determine whether the spray coverage status of the detection objects meets the requirements. If the requirements are not met, water pressure analysis is performed. When the water pressure is normal, the system monitors and analyzes the operational faults of the shower heads. When the water pressure is abnormal, the system detects and analyzes the operational status of the shower head water supply pipeline in the irrigation area.

[0037] Thresholds, preset values, preset ranges, etc. are set for result comparison and analysis to determine whether they are good or bad. The value of these thresholds is determined by a combination of large-scale model analysis of sample data and human experience. They can also be adjusted appropriately based on seasonal or common-sense influences.

[0038] Furthermore, the settings for weighting ratios, influence factors, etc., are based on the magnitude of each parameter's influence on the results. The specific values ​​are allocated to ultimately reflect the impact on the results. The settings for input and storage are also determined by a combination of large-scale model analysis of sample data and human experience. Appropriate adjustments can also be made based on seasonal or rational influence conditions.

[0039] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

Claims

1. A shower head detection system based on artificial intelligence, characterized in that, It includes a coverage detection module, which is communicatively connected to a water pressure analysis module. The water pressure analysis module is communicatively connected to a pipeline detection module and a fault monitoring module. The pipeline detection module and the fault monitoring module are communicatively connected. The coverage detection module is used to detect and analyze the spray coverage of the sprinkler in the irrigation area, and when the spray coverage does not meet the requirements, the corresponding sprinkler is marked as an abnormal spray object, a water pressure analysis signal is generated and sent to the water pressure analysis module. The water pressure analysis module is used to perform water pressure analysis on the shower heads in the irrigation area: it obtains the water pressure deviation value, deviation duration value, and deviation statistics value of the abnormal spray object within the detection period and performs numerical calculation to obtain the water pressure coefficient. The water pressure coefficient is used to determine whether there is an abnormality in the spray water pressure of the abnormal spray object. If there is an abnormality, a pipeline detection signal is generated and sent to the pipeline detection module. If there is no abnormality, a fault monitoring signal is generated and sent to the fault monitoring module. The pipeline detection module is used to detect and analyze the operating status of the shower water supply pipeline in the irrigation area; The fault monitoring module monitors and analyzes the operating faults of the shower head after receiving the fault monitoring signal. The specific process of detecting and analyzing the coverage of the sprinkler spray in the irrigation area includes: marking the sprinklers in the irrigation area as the detection objects, randomly selecting several detection points within the spray coverage of the detection objects and setting up rain sensors at the detection points, generating a detection cycle, and obtaining the rainfall value of the rain sensors at the detection points at the end of the detection cycle. The specific process of the fault monitoring module for monitoring and analyzing the operational faults of the shower head includes: generating a monitoring period and dividing the monitoring period into several monitoring time periods; acquiring the rainfall values ​​of the rain sensors within the spray coverage area of ​​the abnormal shower object within the monitoring time period and marking them as the monitoring values ​​of the detection points; sorting the detection points in ascending order of the monitoring values ​​to obtain the water volume sequence of the monitoring time period; then, at the end of the monitoring period, calculating the variance of the sequence number of the detection points in the water volume sequence of all monitoring time periods to obtain the regularity value of the detection points; summing the regularity values ​​of all detection points and taking the average to obtain the regularity coefficient; comparing the regularity coefficient with a preset regularity threshold: if the regularity coefficient is less than the regularity threshold, it is determined that the abnormal shower object has a shower hole blockage fault, generating a blockage fault signal and sending the blockage fault signal to the mobile terminal of the management personnel; if the regularity coefficient is greater than or equal to the regularity threshold, it is determined that the abnormal shower object has a rotation fault, generating a rotation fault signal and sending the rotation fault signal to the mobile terminal of the management personnel.

2. The shower head detection system based on artificial intelligence according to claim 1, characterized in that, The specific process for detecting and analyzing the sprinkler coverage of the irrigation area also includes: summing and averaging the rainfall values ​​at all detection points to obtain the spray value of the test object; obtaining the standard spray range of the test object and marking the average of the maximum and minimum values ​​within the standard spray range as the standard spray value; marking the absolute value of the difference between the spray value and the standard spray value as the spray data; calculating the variance of the rainfall values ​​at all detection points corresponding to the test object to obtain uniform data; obtaining the coverage coefficient of the test object by numerically calculating the spray data and the uniform data; and determining whether the spray coverage status of the test object meets the requirements based on the coverage coefficient.

3. The shower head detection system based on artificial intelligence according to claim 2, characterized in that, The specific process for determining whether the spray coverage status of the test object meets the requirements includes: comparing the coverage coefficient of the test object with a preset coverage threshold; if the coverage coefficient is less than the coverage threshold, the spray coverage status of the test object is determined to meet the requirements; if the coverage coefficient is greater than or equal to the coverage threshold, the spray coverage status of the test object is determined to not meet the requirements.

4. The shower head detection system based on artificial intelligence according to claim 3, characterized in that, The process of obtaining the water pressure deviation value includes: acquiring the spray water pressure value of the abnormal spray object in real time during the detection period, retrieving the water pressure standard range, marking the average of the maximum and minimum values ​​of the water pressure standard range as the water pressure standard value, and marking the absolute value of the difference between the spray water pressure value and the water pressure standard value as the water pressure deviation value; the deviation duration value is the duration during which the spray water pressure value of the abnormal spray object is outside the water pressure standard range during the detection period. The process of obtaining the deviation statistics value includes: marking spraying behavior where the spray water pressure value is continuously outside the water pressure standard range as deviation behavior, and marking the number of deviation behaviors during the detection period as the deviation statistics value.

5. The shower head detection system based on artificial intelligence according to claim 4, characterized in that, The specific process for determining whether the spray water pressure of an abnormal spraying object is abnormal includes: comparing the water pressure coefficient of the abnormal spraying object within the detection period with a preset water pressure threshold; if the water pressure coefficient is less than the water pressure threshold, it is determined that the spray water pressure of the abnormal spraying object within the detection period is not abnormal; if the water pressure coefficient is greater than or equal to the water pressure threshold, it is determined that the spray water pressure of the abnormal spraying object within the detection period is abnormal.

6. The shower head detection system based on artificial intelligence according to claim 5, characterized in that, The specific process by which the pipeline detection module detects and analyzes the operating status of the shower water supply pipeline in the irrigation area includes: marking all shower heads corresponding to the connecting pipelines of the abnormal spraying object as objects in the same group; obtaining the water pressure coefficient of the objects in the same group within the detection period; marking the absolute value of the difference between the water pressure coefficient of the abnormal spraying object and the water pressure coefficient of the objects in the same group as the pressure difference value of the same group; summing and averaging the pressure difference values ​​of all objects in the same group to obtain the pipeline abnormality value; comparing the pipeline abnormality value with a preset pipeline abnormality threshold: if the pipeline abnormality value is less than the pipeline abnormality threshold, it is determined that there is a water pressure abnormality in the shower water supply pipeline, a water supply maintenance signal is generated and sent to the mobile terminal of the management personnel; if the pipeline abnormality value is greater than or equal to the pipeline abnormality threshold, it is determined that there is no water pressure abnormality in the shower water supply pipeline, a fault monitoring signal is generated and sent to the fault monitoring module.

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