Sprinkler detection system based on artificial intelligence

Abstract

The invention belongs to the field of shower head detection, relates to a data analysis technology, is used for solving the problem that a shower head detection system in the prior art cannot automatically check reasons causing abnormality when the operation of a shower head is abnormal, and particularly relates to an artificial intelligence-based shower head detection system, 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 head spraying coverage range of the irrigation area and marking the corresponding shower head as a spraying abnormal object when the spraying coverage state does not meet the requirement; according to the invention, the spraying coverage range of the shower heads in the irrigation area can be detected and analyzed, the spraying coverage state of the spraying coverage area of each shower head is analyzed in a periodic detection mode, and the shower heads with abnormal spraying coverage states are differentially marked.

Description

Technical Field

[0001] The present invention belongs to the field of shower detection and relates to data analysis technology, in particular to a shower detection system based on artificial intelligence. Background Art

[0002] Shower heads, also known as shower heads, were originally devices for watering flowers, potted plants, and other plants. Now, a rotating structure is generally installed on shower heads and used in the field of garden irrigation. The rotating shower heads spray irrigation water evenly to improve the efficiency of garden irrigation.

[0003] The shower detection system in the prior art is unable to analyze the spray water pressure and operation faults according to the coverage status of the shower, resulting in the inability to automatically troubleshoot the cause of the abnormality when the shower operates abnormally, thereby affecting the efficiency of handling the abnormality.

[0004] In response to the above technical problems, this application proposes a solution. Summary of the Invention

[0005] The purpose of the present invention is to provide an artificial intelligence-based shower detection system to solve the problem that the shower detection system in the prior art cannot automatically detect the cause of the abnormality when the shower operates abnormally.

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

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] An artificial intelligence-based shower detection system includes a coverage detection module, the coverage detection module 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, and 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 showers in the irrigation area, and when the spray coverage status does not meet the requirements, mark the corresponding shower as a spray abnormality object, generate a water pressure analysis signal and send the water pressure analysis signal to the water pressure analysis module;

[0010] The water pressure analysis module is used to perform water pressure analysis on the sprinklers in the irrigation area: the water pressure deviation value, deviation duration value and deviation statistical value of the spray abnormality object within the detection period are obtained and numerically calculated to obtain the water pressure coefficient, and the water pressure coefficient is used to determine whether the spray water pressure of the spray abnormality object is abnormal. If an abnormality exists, a pipeline detection signal is generated and sent to the pipeline detection module; if no abnormality exists, 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 operation fault 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 range of sprinklers in an irrigation area includes: marking sprinklers in the irrigation area as detection objects, randomly selecting a number of detection points within the spray coverage range of the detection object and setting rain sensors at the detection points, generating a detection cycle and obtaining the rainfall value of the rain sensor at the detection point 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 spray standard range of the detection object and marking the average of the maximum and minimum values of the spray standard range as the spray standard value, marking the absolute value of the difference between the spray value and the spray standard value as spray data, performing variance calculation on 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 performing numerical calculation on 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 of determining whether the spray coverage status of the detection object meets the requirements includes: comparing the coverage coefficient of the detection object with a preset coverage threshold: if the coverage coefficient is less than the coverage threshold, it is determined that the spray coverage status of the detection object meets the requirements; if the coverage coefficient is greater than or equal to the coverage threshold, it is determined that the spray coverage status of the detection object does not meet the requirements.

[0015] As a preferred embodiment of the present invention, the process of obtaining the water pressure deviation value includes: obtaining the spraying water pressure value of the spraying abnormality object in real time during the detection period, calling the water pressure standard range, marking the average value of the maximum value and the minimum value of the water pressure standard range as the water pressure standard value, and marking the absolute value of the difference between the spraying 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 spraying water pressure value of the spraying abnormality object is outside the water pressure standard range during the detection period, and the process of obtaining the deviation statistical value includes: marking the spraying behavior in which the spraying water pressure value is continuously outside the water pressure standard range as a deviation behavior, and marking the number of deviation behaviors within the detection period as a deviation statistical value.

[0016] As a preferred embodiment of the present invention, the specific process of determining whether there is an abnormality in the spray water pressure of the spray abnormality object includes: comparing the water pressure coefficient of the spray abnormality object during 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 there is no abnormality in the spray water pressure of the spray abnormality object during the detection period; if the water pressure coefficient is greater than or equal to the water pressure threshold, it is determined that there is an abnormality in the spray water pressure of the spray abnormality object during the detection period.

[0017] As a preferred embodiment of the present invention, the specific process of the pipeline detection module detecting and analyzing the operating status of the sprinkler water supply pipeline in the irrigation area includes: marking all sprinklers corresponding to the connecting pipelines of the spray abnormality object as objects in the same group, obtaining the water pressure coefficient of the objects in the same group during the detection period, marking the absolute value of the difference between the water pressure coefficient of the spray abnormality object and the water pressure coefficient of the objects in the same group as the same group pressure difference value, summing and averaging the same group pressure difference values of all objects in the same group to obtain the pipeline abnormality value, and 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 sprinkler water supply pipeline, a water supply maintenance signal is generated and the water supply maintenance signal is sent to the mobile phone terminal of the administrator; 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 sprinkler water supply pipeline, a fault monitoring signal is generated and the fault monitoring signal is sent to the fault monitoring module.

[0018] As a preferred embodiment of the present invention, the specific process of the fault monitoring module monitoring and analyzing the operation failure of the shower includes: generating a monitoring cycle and dividing the monitoring cycle into several monitoring time periods, obtaining the rainfall value of the rain sensor within the spray coverage range of the spray abnormality object during the monitoring period and marking it as the monitoring value of the detection point, sorting the detection points in order of the monitoring value from small to large to obtain a water volume sequence of the monitoring period, and then at the end of the monitoring cycle, performing variance calculation on the sequence number of the detection point in the water volume sequence of all monitoring periods to obtain a regular value of the detection point, summing and averaging the regular values of all detection points to obtain a regular coefficient, and comparing the regular coefficient with a preset regular threshold: if the regular coefficient is less than the regular threshold, it is determined that the spray abnormality object has a spray hole blockage fault, generating a blockage fault signal and sending the blockage fault signal to the mobile phone terminal of the administrator; if the regular coefficient is greater than or equal to the regular threshold, it is determined that the spray abnormality object has a rotation fault, generating a rotation fault signal and sending the rotation fault signal to the mobile phone terminal of the administrator.

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

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

[0021] 2. The water pressure analysis module can be used to analyze the water pressure of the sprinklers in the irrigation area, obtain various water pressure parameters of the sprinkler abnormality objects during 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 sprinkler water during the detection period is fed back, thereby performing differentiated fault factor troubleshooting for sprinklers with different degrees of water pressure abnormality;

[0022] 3. The management and monitoring module can detect and analyze the operating status of the sprinkler water supply pipeline in the irrigation area. Differential analysis of the water pressure coefficients of all sprinklers connected to the same water supply pipeline can be performed to obtain pipeline abnormality values. The pipeline abnormality values can then be used to provide feedback on the degree of correlation between water pressure abnormalities and the sprinkler abnormality objects themselves, and pipeline maintenance can be carried out in a timely manner when abnormalities occur in the water supply pipeline.

[0023] 4. The fault monitoring module can monitor and analyze the operation faults of the shower head. 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, a comprehensive analysis is performed on the water volume sequence of all monitoring periods to obtain the regularity coefficient. This is used to determine whether the shower head fault is caused by blockage or abnormal rotation, further improving the efficiency of shower head fault handling. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[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 DESCRIPTION

[0027] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0028] Example 1

[0029] like Figure 1 As shown, an artificial intelligence-based shower detection system includes a coverage detection module, the coverage detection module 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, and 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 in the irrigation area are marked as detection objects, a number of detection points are randomly selected within the spray coverage of the detection object and rain sensors are set at the detection points, a detection cycle is generated and the rainfall value of the rain sensor at the detection point is obtained at the end of the detection cycle, the rainfall values of all detection points are summed and averaged to obtain the spray value of the detection object, the spray standard range of the detection object is obtained and the average of the maximum and minimum values of the spray standard range is marked as the spray standard value, the absolute value of the difference between the spray value and the spray standard value is marked as the spray data PE, and the variance of the rainfall values corresponding to all detection points of the detection object is calculated to obtain the uniform data JY; the formula FG=k1×PE+k2×JY is used to obtain to the coverage coefficient FG of the detection object, where k1 and k2 are both proportional coefficients, and k1>k2>1; compare the coverage coefficient FG of the detection object with the preset coverage threshold FGmax: if the coverage coefficient FG is less than the coverage threshold FGmax, it is determined that the spray coverage status of the detection object meets the requirements; if the coverage coefficient FG is greater than or equal to the coverage threshold FGmax, it is determined that the spray coverage status of the detection object does not meet the requirements, and the corresponding detection object is marked as a spray abnormality object, and a water pressure analysis signal is generated and sent to the water pressure analysis module; the spray coverage range of the sprinklers in the irrigation area is detected and analyzed, and the spray coverage status of the spray coverage area of each sprinkler is analyzed in a periodic detection manner, and the sprinklers with abnormal spray coverage status are differentiated and marked.

[0031] The water pressure analysis module is used to perform water pressure analysis on the sprinklers in the irrigation area: obtain the water pressure deviation value SP, deviation duration value PS and deviation statistical value PT of the spray abnormality object within the detection period, and the process of obtaining the water pressure deviation value SP includes: obtaining the spray water pressure value of the spray abnormality object in real time within the detection period, calling the water pressure standard range, marking the average value 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 SP; the deviation duration value PS is the spray water pressure of the spray abnormality object within the detection period. The process of obtaining the deviation statistical value PT includes: marking the spraying behavior in which the spraying water pressure value is continuously outside the water pressure standard range as a deviation behavior, and marking the number of deviation behaviors within the detection period as the deviation statistical value PT; the water pressure coefficient SY of the spraying abnormal object within the detection period is obtained by the formula SY = (SP*z1+PS*z2+PT*z3) / 3, where z1, z2, and z3 are weight proportional coefficients and are assigned values based on the influence of SP, PS, and PT on the result SY. Here, z1>z2>z3>1;

[0032] The water pressure coefficient SY of the abnormal spray 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 there is no abnormality in the spray water pressure of the abnormal spray object during the detection period, and a fault monitoring signal is generated and 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 there is an abnormality in the spray water pressure of the abnormal spray object during the detection period, and a pipeline detection signal is generated and sent to the pipeline detection module; the sprinklers in the irrigation area are subjected to water pressure analysis, and various water pressure parameters of the abnormal spray object during the detection period are obtained and comprehensively analyzed and calculated to obtain the water pressure coefficient. According to the water pressure coefficient, the degree of water pressure abnormality of the abnormal spray water of the abnormal spray object during the detection period is fed back, so as to perform differentiated fault factor troubleshooting on sprinklers with different degrees of water pressure abnormality.

[0033] The pipeline detection module is used to detect and analyze the operating status of the sprinkler water supply pipeline in the irrigation area: mark all the sprinklers corresponding to the connection pipeline of the spray abnormal object as the same group objects, obtain the water pressure coefficient SY of the same group objects during the detection period, mark the absolute value of the difference between the water pressure coefficient SY of the spray abnormal object and the water pressure coefficient SY of the same group objects as the same group pressure difference value, sum and average the same group pressure difference values of all the same group objects to obtain the pipeline abnormality value, and compare the pipeline abnormality value with the preset pipeline abnormality threshold: if the pipeline abnormality value is less than the pipeline abnormality threshold, it is determined that there is water pressure abnormality in the sprinkler water supply pipeline , generate a water supply maintenance signal and send the water supply maintenance signal to the mobile phone terminal of the manager; 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, and a fault monitoring signal is generated and sent to the fault monitoring module; the operation status of the shower water supply pipeline in the irrigation area is detected and analyzed, and the water pressure coefficient differentiation analysis of all showers connected to the same water supply pipeline is performed to obtain the pipeline abnormality value, so as to feedback the degree of correlation between the water pressure abnormality and the spray abnormality object itself through the pipeline abnormality value, and timely perform 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 operation fault of the shower after receiving the fault monitoring signal: generates a monitoring cycle and divides the monitoring cycle into several monitoring time periods, obtains the rainfall value of the rain sensor within the spray coverage range of the abnormal spray object during the monitoring period and marks it as the monitoring value of the detection point, sorts the detection points in the order of the monitoring value from small to large to obtain the water volume sequence of the monitoring period, and then at the end of the monitoring cycle, calculates the variance of the sequence number of the detection point in the water volume sequence of all monitoring periods to obtain the regularity value of the detection point, sums and averages the regularity values of all detection points to obtain the regularity coefficient, and compares the regularity coefficient with the preset regularity threshold: if the regularity coefficient is less than the regularity threshold, If the regularity coefficient exceeds the regularity threshold, it is determined that the abnormal spray object has a blockage fault, a blockage fault signal is generated and sent to the administrator's mobile phone terminal; 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, a rotation fault signal is generated and sent to the administrator's mobile phone terminal; the operation fault of the shower is monitored and analyzed, and the detection points are sorted according to the rainfall value collected by the rain sensor in each monitoring period of the monitoring cycle. At the end of the monitoring cycle, a comprehensive analysis is performed on the water volume sequence of all monitoring periods to obtain the regularity coefficient, so as to determine whether the shower fault is caused by blockage or rotation abnormality, further improving the efficiency of shower fault handling.

[0036] A sprinkler detection system based on artificial intelligence, when in operation, marks sprinklers in an irrigation area as detection objects, randomly selects several detection points within the spray coverage range of the detection objects and sets rain sensors at the detection points, generates a detection cycle, and obtains the spray data PE and uniform data JY of the detection objects at the end of the detection cycle. The spray data PE and uniform data JY are numerically calculated to obtain the coverage coefficient FG. 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, a water pressure analysis is performed. When the water pressure is normal, the operation faults of the sprinklers are monitored and analyzed. When the water pressure is abnormal, the operation status of the sprinkler water supply pipeline in the irrigation area is detected and analyzed.

[0037] Thresholds, preset values, and preset ranges are set for comparative analysis of results to determine whether they are good or bad. The values are set based on a combination of large-scale model analysis of sample data and manual experience, and can also be adjusted appropriately based on seasonal or common-sense factors.

[0038] The settings of weight ratio coefficients, influencing factors, etc. are assigned specific values according to the influence of each parameter on the result, which ultimately reflects the impact on the result. They are also set and entered into storage through a combination of large-scale model analysis of sample data and manual experience. Appropriate adjustments can also be made based on seasonal or common-sense influencing conditions.

[0039] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of these 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 any 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, the coverage detection module is communicatively connected to the water pressure analysis module, the water pressure analysis module is communicatively connected to the pipeline detection module and the fault monitoring module, and the management monitoring module is communicatively connected to the fault monitoring module; The coverage detection module is used to detect and analyze the spray coverage of the showers in the irrigation area, and when the spray coverage status does not meet the requirements, mark the corresponding shower as a spray abnormality object, generate a water pressure analysis signal and send the water pressure analysis signal to the water pressure analysis module; The water pressure analysis module is used to perform water pressure analysis on the sprinklers in the irrigation area: the water pressure deviation value, deviation duration value and deviation statistical value of the spray abnormality object within the detection period are obtained and numerically calculated to obtain the water pressure coefficient, and the water pressure coefficient is used to determine whether the spray water pressure of the spray abnormality object is abnormal. If an abnormality exists, a pipeline detection signal is generated and sent to the pipeline detection module; if no abnormality exists, 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 operation fault of the shower head after receiving the fault monitoring signal.

2. The artificial intelligence-based shower detection system according to claim 1, characterized in that: The specific process of detecting and analyzing the spray coverage of sprinklers in an irrigation area includes: marking sprinklers in the irrigation area as 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 obtaining the rainfall value of the rain sensor at the detection point 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 spray standard range of the detection object and marking the average of the maximum and minimum values of the spray standard range as the spray standard value, marking the absolute value of the difference between the spray value and the spray standard value as the spray data, performing variance calculation on 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 performing numerical calculation on the spray data and the uniform data; and judging whether the spray coverage status of the detection object meets the requirements by using the coverage coefficient.

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

4. The artificial intelligence-based shower detection system according to claim 3, characterized in that: The process of obtaining the water pressure deviation value includes: obtaining the spraying water pressure value of the spraying abnormal object in real time during the detection period, calling the water pressure standard range, marking the average of the maximum value and the minimum value of the water pressure standard range as the water pressure standard value, and marking the absolute value of the difference between the spraying 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 spraying water pressure value of the spraying abnormal object is outside the water pressure standard range during the detection period, and the process of obtaining the deviation statistical value includes: marking the spraying behavior in which the spray water pressure value is continuously outside the water pressure standard range as deviation behavior, and marking the number of deviation behaviors within the detection period as deviation statistical value.

5. The artificial intelligence-based shower detection system according to claim 4, characterized in that: The specific process of determining whether there is an abnormality in the spray water pressure of the spray abnormality object includes: comparing the water pressure coefficient of the spray abnormality object during 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 there is no abnormality in the spray water pressure of the spray abnormality object during the detection period; if the water pressure coefficient is greater than or equal to the water pressure threshold, it is determined that there is an abnormality in the spray water pressure of the spray abnormality object during the detection period.

6. The artificial intelligence-based shower detection system according to claim 5, characterized in that: The specific process of the pipeline detection module detecting and analyzing the operating status of the sprinkler water supply pipeline in the irrigation area includes: marking all sprinklers corresponding to the connecting pipelines of the spray abnormality object as objects in the same group, obtaining the water pressure coefficient of the objects in the same group during the detection period, marking the absolute value of the difference between the water pressure coefficient of the spray abnormality object and the water pressure coefficient of the objects in the same group as the same group pressure difference value, summing and averaging the same group pressure difference values of all objects in the same group to obtain the pipeline abnormality value, and 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 sprinkler water supply pipeline, a water supply maintenance signal is generated, and the water supply maintenance signal is sent to the mobile phone terminal of the administrator; 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 sprinkler water supply pipeline, a fault monitoring signal is generated, and the fault monitoring signal is sent to the fault monitoring module.

7. The artificial intelligence-based shower detection system according to claim 6, characterized in that: The specific process of the fault monitoring module monitoring and analyzing the operation failure of the shower includes: generating a monitoring cycle and dividing the monitoring cycle into several monitoring time periods, obtaining the rainfall value of the rain sensor within the spray coverage range of the abnormal spray object during the monitoring period and marking it as the monitoring value of the detection point, sorting the detection points in order of the monitoring value from small to large to obtain the water volume sequence of the monitoring period, and then at the end of the monitoring cycle, calculating the variance of the sequence number of the detection point in the water volume sequence of all monitoring periods to obtain the regular value of the detection point, summing and averaging the regular values of all detection points to obtain the regular coefficient, and comparing the regular coefficient with the preset regular threshold: if the regular coefficient is less than the regular 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 phone terminal of the manager; if the regular coefficient is greater than or equal to the regular 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 phone terminal of the manager.

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