Fault analysis and alarm method and device based on internet of things valve control water meter universal for whole network

Abstract

The invention relates to a fault analysis and alarm method and device based on a whole-network universal internet of things valve control water meter. The method comprises the steps that fault monitoring data transmitted by all sensors and water meter transmission data of the Internet of Things valve control water meter are obtained, and equipment abnormity monitoring information of the Internet of Things valve control water meter is recognized through an equipment fault analysis strategy based on all the fault monitoring data; on the basis of the water meter transmission data, identifying operation abnormity monitoring information of the Internet of Things valve control water meter through an operation fault analysis strategy, and on the basis of the equipment abnormity monitoring information and the operation abnormity monitoring information, identifying target abnormity information of the Internet of Things valve control water meter through a fusion analysis strategy; on the basis of the target abnormal information of the Internet of Things valve control water meter, water meter fault information of the Internet of Things valve control water meter is recognized through the water meter fault map, and current fault early warning information of the Internet of Things valve control water meter is generated on the basis of the water meter fault information. By adopting the method, the fault identification efficiency of the intelligent valve control water meter can be improved.

Description

Technical Field

[0001] This application relates to the field of smart valve-controlled water meter technology, and in particular to a fault analysis and alarm method and device for a universal IoT valve-controlled water meter. Background Technology

[0002] Smart valve-controlled water meters are advanced water management devices that integrate water flow measurement and remote valve control. By combining IoT, wireless communication, and data processing technologies, they achieve automatic data collection, remote transmission, and intelligent management of water usage. However, malfunctions in smart valve-controlled water meters often stem from mechanical failures, equipment malfunctions, and aging issues in multiple components, including the valve's mechanical structure, intelligent sensors, and wireless communication devices. These problems lead to various faults affecting the normal operation of the smart valve-controlled water meter. Furthermore, the meter's structure is often not frequently inspected by personnel, resulting in poor timeliness of fault detection. Therefore, improving the accuracy and timeliness of fault detection for smart valve-controlled water meters is a current research focus.

[0003] Existing technologies often rely on manual periodic monitoring and the installation of sensors on the device to ensure the detection of smart valve-controlled water meters. However, the detection range of sensors is limited, and the timeliness of manual periodic monitoring is poor, which limits the accuracy and timeliness of monitoring smart valve-controlled water meters, resulting in low efficiency in identifying faults in smart valve-controlled water meters. Summary of the Invention

[0004] Therefore, it is necessary to provide a fault analysis and alarm method and device for IoT valve-controlled water meters that are universally applicable across the entire network, in order to address the above-mentioned technical problems.

[0005] Firstly, this application provides a fault analysis and alarm method for IoT valve-controlled water meters that are universally applicable across the entire network, including: The system acquires fault monitoring data transmitted by each sensor and water meter transmission data of the IoT valve-controlled water meter, and identifies abnormal monitoring information of the IoT valve-controlled water meter based on the fault monitoring data and through a device fault analysis strategy. Based on the water meter's transmitted data, an operational anomaly monitoring information of the IoT valve-controlled water meter is identified through a fault analysis strategy. Based on the device anomaly monitoring information and the operational anomaly monitoring information, a fusion analysis strategy is used to identify the target anomaly information of the IoT valve-controlled water meter. Based on the target anomaly information of the IoT valve-controlled water meter, the water meter fault information of the IoT valve-controlled water meter is identified through the water meter fault map, and based on the water meter fault information, the current fault warning information of the IoT valve-controlled water meter is generated.

[0006] Optionally, the step of identifying abnormal monitoring information of the IoT valve-controlled water meter based on the fault monitoring data and through a device fault analysis strategy includes: Based on the fault monitoring data, the current device monitoring data of each hardware device of the IoT valve-controlled water meter is identified, and the current device parameters of each hardware device are extracted based on the current device monitoring data of each hardware device. Based on the current device parameters of each hardware device, the current abnormal state of each hardware device is identified through the device fault analysis strategy of each hardware device. The current abnormal status of each of the aforementioned hardware devices is used as the device abnormality monitoring information of the IoT valve-controlled water meter.

[0007] Optionally, the step of identifying abnormal operation monitoring information of the IoT valve-controlled water meter based on the data transmitted by the water meter and by running a fault analysis strategy includes: The historical operating data of the IoT valve-controlled water meter is obtained, and based on the historical operating data of the IoT valve-controlled water meter, each historical fault operating data and each fault analysis data are identified; Based on the historical fault operation data and the fault analysis data, a fault anomaly map of the IoT valve-controlled water meter is constructed, and based on the water meter transmission data, the distribution information of the operation data of the IoT valve-controlled water meter is identified. Based on the operational data distribution information and according to the fault anomaly map, the abnormal data analysis network is used to identify the operational anomaly information of the IoT valve-controlled water meter corresponding to each of the operational fault types, and the operational anomaly information corresponding to all operational fault types is used as the operational anomaly monitoring information of the IoT valve-controlled water meter.

[0008] Optionally, the step of identifying target anomaly information of the IoT valve-controlled water meter based on the device anomaly monitoring information and the operational anomaly monitoring information through a fusion analysis strategy includes: Based on the current abnormal status of each hardware device, query the valve control abnormality database to identify the distribution information of each abnormal data corresponding to the IoT valve control water meter. Based on the operational anomaly information corresponding to each operational fault type and the distribution information of each anomaly data, the fault cause corresponding to the operational anomaly information for each operational fault type is determined through a data anomaly adaptation network; the fault cause includes equipment fault cause and non-equipment fault cause. When the cause of the fault is a device malfunction, the abnormal operation information and the current abnormal state of the hardware device corresponding to the abnormal operation information are taken as the device abnormal information of the IoT valve-controlled water meter. When the cause of the fault is not a device malfunction, the abnormal operation information is taken as the non-device abnormal information of the IoT valve-controlled water meter. The device anomaly information and the non-device anomaly information of the IoT valve-controlled water meter are used as the target anomaly information of the IoT valve-controlled water meter.

[0009] Optionally, the step of identifying water meter fault information of the IoT valve-controlled water meter based on the target anomaly information of the IoT valve-controlled water meter through a water meter fault map includes: For each device malfunction, based on the malfunction information and the current malfunction status of the hardware device, the device malfunction map in the water meter malfunction map is queried to determine the device malfunction information of the IoT valve-controlled water meter. For each non-equipment anomaly, based on the abnormal operation information and the non-equipment fault map in the water meter fault map, the operational fault information of the IoT valve-controlled water meter is determined.

[0010] Optionally, generating current fault warning information for the IoT valve-controlled water meter based on the water meter fault information includes: Based on the water meter fault information, a fault warning identification strategy is used to identify the fault warning level and the fault warning method corresponding to the water meter fault information. Based on the water meter fault information, the current fault warning information of the IoT valve-controlled water meter is generated through the fault warning level and the fault warning method.

[0011] Secondly, this application also provides a fault analysis and alarm device based on a universally applicable IoT valve-controlled water meter, including: The acquisition module is used to acquire fault monitoring data transmitted by each sensor and water meter transmission data of the IoT valve-controlled water meter, and based on the fault monitoring data, identify abnormal monitoring information of the IoT valve-controlled water meter through a device fault analysis strategy. The identification module is used to identify the abnormal operation monitoring information of the IoT valve-controlled water meter based on the data transmitted by the water meter and by running a fault analysis strategy, and to identify the target abnormal information of the IoT valve-controlled water meter based on the device abnormal monitoring information and the abnormal operation monitoring information by using a fusion analysis strategy. The generation module is used to identify water meter fault information of the IoT valve-controlled water meter based on the target abnormal information of the IoT valve-controlled water meter through the water meter fault map, and generate current fault warning information of the IoT valve-controlled water meter based on the water meter fault information.

[0012] Optionally, the acquisition module is specifically used for: Based on the fault monitoring data, the current device monitoring data of each hardware device of the IoT valve-controlled water meter is identified, and the current device parameters of each hardware device are extracted based on the current device monitoring data of each hardware device. Based on the current device parameters of each hardware device, the current abnormal state of each hardware device is identified through the device fault analysis strategy of each hardware device. The current abnormal status of each of the aforementioned hardware devices is used as the device abnormality monitoring information of the IoT valve-controlled water meter.

[0013] Optionally, the identification module is specifically used for: The historical operating data of the IoT valve-controlled water meter is obtained, and based on the historical operating data of the IoT valve-controlled water meter, each historical fault operating data and each fault analysis data are identified; Based on the historical fault operation data and the fault analysis data, a fault anomaly map of the IoT valve-controlled water meter is constructed, and based on the water meter transmission data, the distribution information of the operation data of the IoT valve-controlled water meter is identified. Based on the operational data distribution information and according to the fault anomaly map, the abnormal data analysis network is used to identify the operational anomaly information of the IoT valve-controlled water meter corresponding to each of the operational fault types, and the operational anomaly information corresponding to all operational fault types is used as the operational anomaly monitoring information of the IoT valve-controlled water meter.

[0014] Optionally, the identification module is specifically used for: Based on the current abnormal status of each hardware device, query the valve control abnormality database to identify the distribution information of each abnormal data corresponding to the IoT valve control water meter. Based on the operational anomaly information corresponding to each operational fault type and the distribution information of each anomaly data, the fault cause corresponding to the operational anomaly information for each operational fault type is determined through a data anomaly adaptation network; the fault cause includes equipment fault cause and non-equipment fault cause. When the cause of the fault is a device malfunction, the abnormal operation information and the current abnormal state of the hardware device corresponding to the abnormal operation information are taken as the device abnormal information of the IoT valve-controlled water meter. When the cause of the fault is not a device malfunction, the abnormal operation information is taken as the non-device abnormal information of the IoT valve-controlled water meter. The device anomaly information and the non-device anomaly information of the IoT valve-controlled water meter are used as the target anomaly information of the IoT valve-controlled water meter.

[0015] Optionally, the generation module is specifically used for: For each device malfunction, based on the malfunction information and the current malfunction status of the hardware device, the device malfunction map in the water meter malfunction map is queried to determine the device malfunction information of the IoT valve-controlled water meter. For each non-equipment anomaly, based on the abnormal operation information and the non-equipment fault map in the water meter fault map, the operational fault information of the IoT valve-controlled water meter is determined.

[0016] Optionally, the generation module is specifically used for: Based on the water meter fault information, a fault warning identification strategy is used to identify the fault warning level and the fault warning method corresponding to the water meter fault information. Based on the water meter fault information, the current fault warning information of the IoT valve-controlled water meter is generated through the fault warning level and the fault warning method.

[0017] Thirdly, this application provides a computer device. The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of the method described in any one of the first aspects.

[0018] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the steps of the method described in any one of the first aspects.

[0019] Fifthly, this application provides a computer program product. The computer program product includes a computer program that, when executed by a processor, implements the steps of the method described in any one of the first aspects.

[0020] The aforementioned fault analysis and alarm method and device based on a universally applicable IoT valve-controlled water meter acquires fault monitoring data transmitted from various sensors and water meter transmission data from the IoT valve-controlled water meter. Based on the fault monitoring data, a device fault analysis strategy is used to identify abnormal monitoring information of the IoT valve-controlled water meter. Based on the water meter transmission data, a fault analysis strategy is used to identify abnormal operational monitoring information of the IoT valve-controlled water meter. Based on the device fault monitoring information and the abnormal operational monitoring information, a fusion analysis strategy is used to identify target abnormal information of the IoT valve-controlled water meter. Based on the target abnormal information of the IoT valve-controlled water meter, a water meter fault map is used to identify water meter fault information of the IoT valve-controlled water meter. Based on the water meter fault information, a current fault warning information for the IoT valve-controlled water meter is generated. This solution breaks down and analyzes equipment failures from two perspectives: device malfunction and operational failure. Through a fusion analysis strategy designed in this solution, it combines device and operational failures to comprehensively determine the target anomaly information of IoT valve-controlled water meters. This enables real-time, efficient, and comprehensive fault monitoring of IoT valve-controlled water meters, while also improving the comprehensiveness of fault detection. It avoids the problems of low timeliness due to periodic monitoring and low accuracy due to single-sensor detection. Furthermore, the solution uses a pre-set water meter fault map to identify water meter fault information, generating current fault warning information. This not only efficiently and accurately identifies fault information in smart valve-controlled water meters but also provides immediate warnings to users, effectively improving the efficiency of fault identification for smart valve-controlled water meters. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart illustrating a fault analysis and alarm method for a universal IoT valve-controlled water meter in one embodiment. Figure 2 This is a flowchart illustrating a fault analysis and alarm example based on a universally applicable IoT valve-controlled water meter in one embodiment. Figure 3 This is a structural block diagram of a fault analysis and alarm device based on a universal IoT valve-controlled water meter in one embodiment; Figure 4 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0024] It should be noted that the terms "first," "second," etc., used in this application can be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "comprising" and "having," and any variations thereof, used in this application, are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more. The term "and / or" used in this application refers to one of the embodiments, or any combination of multiple embodiments.

[0025] The fault analysis and alarm method for IoT valve-controlled water meters based on a universally applicable IoT network provided in this application embodiment can be applied to a fault analysis and alarm system for IoT valve-controlled water meters based on a universally applicable IoT network. This system can be applied to terminals, including but not limited to various personal computers, laptops, and mid-range computers. The terminal analyzes equipment faults from two perspectives: device faults and operational faults. Through the fusion analysis strategy designed in this solution, it combines device faults and operational faults to comprehensively determine the target abnormal information of the IoT valve-controlled water meter. This enables real-time, efficient, and comprehensive fault monitoring of IoT valve-controlled water meters and improves the comprehensiveness of fault detection, avoiding the low timeliness of periodic monitoring and the low accuracy of single-sensor detection. Then, the water meter fault information is identified through a preset water meter fault map, generating current fault warning information. This not only efficiently and accurately identifies fault information of smart valve-controlled water meters but also provides immediate warnings to users, effectively improving the efficiency of fault identification for smart valve-controlled water meters.

[0026] In one exemplary embodiment, such as Figure 1 As shown, a fault analysis and alarm method for IoT valve-controlled water meters with universal network compatibility is provided. Taking the application of this method to a terminal as an example, the method includes the following steps S101 to S103. Wherein: Step S101: Obtain fault monitoring data transmitted by each sensor and water meter transmission data of the IoT valve-controlled water meter, and based on each fault monitoring data, identify abnormal monitoring information of the IoT valve-controlled water meter through equipment fault analysis strategy.

[0027] In this embodiment, the terminal receives sensor data collected in real time from various sensors installed on the IoT valve-controlled water meter, obtaining fault monitoring data transmitted by each sensor. Then, the terminal receives data transmitted from the IoT valve-controlled water meter in real time to obtain the real-time water usage data transmitted by the IoT valve-controlled water meter, thus obtaining the water meter's transmission data. This real-time water usage data includes, but is not limited to: Basic metering data: Real-time recording and uploading of core water usage indicators such as cumulative water consumption and instantaneous flow rate to ensure data accuracy and reliability; Status and control information: Transmitting the water meter's online status, valve open / close status, and execution results of remote control commands, supporting remote valve control operation; Environmental and water quality parameters: Some models can monitor water pressure, water temperature, and even water quality indicators (such as pH value and turbidity), providing data support for water usage safety; Abnormal alarm data: Automatically detecting abnormal events such as water leaks, pipe bursts, and abnormal water usage in vacant houses; Time series data: Collecting water usage data according to a preset period to form a time series record, facilitating the analysis of water usage patterns and trends. The sensor data collected by each sensor represents the device monitoring data for each hardware device. The Hebei detection data for each hardware device includes [details omitted]. Finally, based on the fault monitoring data, the terminal identifies abnormal monitoring information of the IoT valve-controlled water meter through a device fault analysis strategy. The device fault analysis strategy is a self-checking strategy for the abnormal status of each hardware device in the IoT valve-controlled water meter. This self-checking strategy identifies the device status of each hardware device by analyzing its parameters. The specific identification process will be explained in detail later.

[0028] Step S102: Based on the water meter transmission data, an operational anomaly monitoring information of the IoT valve-controlled water meter is identified through a fault analysis strategy. Based on the equipment anomaly monitoring information and the operational anomaly monitoring information, the target anomaly information of the IoT valve-controlled water meter is identified through a fusion analysis strategy.

[0029] In this embodiment, the terminal, based on water meter transmission data, identifies operational anomaly monitoring information of the IoT valve-controlled water meter through a fault analysis strategy. Based on both device anomaly monitoring information and operational anomaly monitoring information, a fusion analysis strategy identifies target anomaly information of the IoT valve-controlled water meter. Specifically, the operational fault analysis strategy combines water meter transmission data and historical water meter transmission data to analyze the operational anomaly information corresponding to various operational fault types in the IoT valve-controlled water meter. The fusion analysis strategy involves analyzing the distribution information of water meter anomaly data that may arise from abnormal states of various hardware devices, and then adapting this information to the operational anomaly detection information. This allows for the identification of which operational anomaly monitoring information is caused by device malfunction and which is caused by malfunctions inherent to the water meter itself. The specific identification process will be explained in detail later.

[0030] Step S103: Based on the target abnormal information of the IoT valve-controlled water meter, identify the water meter fault information of the IoT valve-controlled water meter through the water meter fault map, and generate the current fault warning information of the IoT valve-controlled water meter based on the water meter fault information.

[0031] In this embodiment, the terminal identifies water meter fault information based on the target anomaly information of the IoT valve-controlled water meter through a water meter fault map, and generates current fault warning information for the IoT valve-controlled water meter based on the water meter fault information. The water meter fault map includes a device fault map and a non-device fault map. The device fault map includes the correlation information between each device fault and its abnormal state and abnormal operation information, while the non-device fault map includes the correspondence between each operational fault and abnormal operation information. The water meter fault information includes both device fault information and operational fault information of the IoT valve-controlled water meter. The specific identification process will be explained in detail later.

[0032] Based on the above solution, equipment faults are analyzed from two perspectives: equipment failure and operational failure. Through a fusion analysis strategy designed in this solution, both equipment and operational faults are combined to comprehensively determine the target anomaly information of IoT valve-controlled water meters. This enables real-time, efficient, and comprehensive fault monitoring of IoT valve-controlled water meters, improving the comprehensiveness of fault detection and avoiding the problems of low timeliness due to periodic testing and low accuracy of single-sensor detection. Furthermore, the solution uses a preset water meter fault map to identify water meter fault information, generating current fault warning information. This not only efficiently and accurately identifies fault information in smart valve-controlled water meters but also provides immediate warnings to users, effectively improving the efficiency of fault identification for smart valve-controlled water meters.

[0033] Optionally, based on each fault monitoring data, an abnormal monitoring information of the IoT valve-controlled water meter is identified through an equipment fault analysis strategy. This includes: identifying the current equipment monitoring data of each hardware device of the IoT valve-controlled water meter based on each fault monitoring data, and extracting the current equipment parameters of each hardware device based on the current equipment monitoring data; identifying the current abnormal state of each hardware device based on the current equipment parameters of each hardware device through an equipment fault analysis strategy; and using the current abnormal state of each hardware device as the abnormal monitoring information of the IoT valve-controlled water meter.

[0034] In this embodiment, the terminal identifies the current device monitoring data of each hardware device in the IoT valve-controlled water meter based on various fault monitoring data, and extracts the current device parameters of each hardware device based on the current device monitoring data. The identification method for the current device monitoring data of each hardware device is as follows: the fault monitoring data includes the sensing data of each sensor. Each sensor is set on a hardware device, and each sensor is used to collect monitoring data for each hardware device. Then, the terminal uses the sensing data of each sensor set on each hardware device as the current device monitoring data for each hardware device. The extraction method is as follows: the terminal identifies the device parameters corresponding to each sensing data of the hardware device through the sensing data of each sensor set on each hardware device. Then, the terminal obtains the current device parameters of each hardware device by converting the sensor data into device parameters. The conversion formula for converting each sensing data into device parameters is a preset conversion formula in the terminal.

[0035] Next, based on the current device parameters of each hardware device, the terminal identifies the current abnormal state of each hardware device through the device fault analysis strategy. This device fault analysis strategy includes the range of device parameters corresponding to each abnormal state. The terminal then queries the device parameter range of a hardware device using its current device parameters and identifies the abnormal state corresponding to that range as the abnormal state of that hardware device. These devices include, but are not limited to, valves, water meter sensors, controllers, communication modules, power modules, and display modules.

[0036] Finally, the terminal uses the current abnormal status of each hardware device as the device abnormality monitoring information for the IoT valve-controlled water meter.

[0037] Based on the above scheme, by using the pre-set equipment fault analysis strategy for each hardware device and the equipment parameters of each hardware device, the abnormal status of each hardware device can be identified, which not only ensures the accuracy of identification but also improves the efficiency of identification.

[0038] Optionally, based on the water meter's transmitted data, an operational anomaly monitoring information of the IoT valve-controlled water meter is identified through a fault analysis strategy. This includes: acquiring historical operational data of the IoT valve-controlled water meter, and identifying historical fault operational data and fault analysis data based on this data; constructing a fault anomaly map of the IoT valve-controlled water meter based on the historical fault operational data and fault analysis data, and identifying the operational data distribution information of the IoT valve-controlled water meter based on the water meter's transmitted data; and identifying the operational anomaly information corresponding to each operational fault type of the IoT valve-controlled water meter through an anomaly data analysis network based on the operational data distribution information and the fault anomaly map, and using the operational anomaly information corresponding to all operational fault types as the operational anomaly monitoring information of the IoT valve-controlled water meter.

[0039] In this embodiment, the terminal acquires historical operating data from the IoT valve-controlled water meter and, based on this data, identifies various historical fault operation data and fault analysis data. Each historical fault operation data point corresponds one-to-one with each fault analysis data point. The historical fault operation data represents the operating data transmitted by the IoT valve-controlled water meter when it experienced an operational fault, while the fault analysis data represents the fault data obtained through manual inspection by staff or self-testing by the IoT valve-controlled water meter. This fault data includes the type of operational fault, the severity of the fault, and the cause of the fault.

[0040] Then, based on historical fault operation data and fault analysis data, the terminal constructs a fault anomaly map for the IoT valve-controlled water meter, and identifies the distribution information of the IoT valve-controlled water meter's operation data based on the water meter's transmitted data. This fault anomaly map includes each operational fault type, each fault severity, each fault cause, and the correlation information between each historical fault operation data point.

[0041] Then, based on the operational data distribution information and according to the fault anomaly map, the terminal identifies the operational anomaly information corresponding to each operational fault type of the IoT valve-controlled water meter through an anomaly data analysis network. Specifically, based on the operational data distribution information, the terminal calculates the similarity between the historical fault operational data in the fault anomaly map and selects the operational fault types corresponding to historical fault operational data with similarity values ​​greater than a threshold. These are then used as the operational fault types corresponding to the IoT valve-controlled water meter. The historical fault operational data, along with their corresponding fault severity and cause, are used as the operational anomaly information corresponding to each operational fault type. These operational fault types include, but are not limited to: communication faults: including weak or no signal (e.g., poor network coverage due to the water meter being installed in an underground well or basement), communication module faults (e.g., hardware damage or software crashes of the NB-IoT module), and SIM card anomalies (e.g., loose, unpaid, aging, or changes in operator policy); and power faults: commonly low or depleted battery voltage (due to expired battery life, poor quality, or abnormal power consumption leading to insufficient power supply to the water meter) and power management circuit faults (e.g., power consumption surges or inability to wake up normally). Hardware and mechanical failures: These include sensor malfunctions (such as flow sensors damaged by water impurities, aging, or external force), valve malfunctions (such as inability to open or close valves properly), and meter body leaks or damage (such as improper installation, freezing, or external force damage). Data-related failures: These include data jumps or abrupt changes (possibly caused by sensor malfunctions, strong magnetic interference, or water hammer), data remaining zero or unchanged (such as mechanical component jamming or sampling circuit malfunction), and large data deviations (such as metering chip calibration failure or impeller wear). Software and platform failures: These include platform parsing errors (data frame parsing rule issues leading to abnormal display), command execution failures (remote valve control or parameter setting commands failing due to communication interruption), and business logic conflicts (such as insufficient balance but accidental valve closure). Finally, the terminal uses the abnormal operation information corresponding to all types of operational failures as the abnormal operation monitoring information of the IoT valve-controlled water meter.

[0042] Based on the above scheme, by combining the historical fault operation data of the IoT valve-controlled water meter, an occasional fault anomaly spectrum is obtained, and then the operation anomaly information corresponding to each type of operation fault of the IoT valve-controlled water meter is identified, thereby improving the accuracy and efficiency of identifying the operation anomaly information of the IoT valve-controlled water meter.

[0043] Optionally, based on equipment anomaly monitoring information and operational anomaly monitoring information, a fusion analysis strategy is used to identify target anomaly information of the IoT valve-controlled water meter. This includes: querying the valve control anomaly database based on the current equipment anomaly status of each hardware device to identify the distribution information of each anomaly data corresponding to the IoT valve-controlled water meter; determining the fault cause corresponding to each operational fault type's operational anomaly information and the distribution information of each anomaly data based on the operational anomaly information corresponding to each operational fault type through a data anomaly adaptation network; the fault cause includes equipment fault causes and non-equipment fault causes; when the fault cause is equipment fault cause, the abnormal operational information and the current equipment anomaly status of the corresponding hardware device are used as the equipment anomaly information of the IoT valve-controlled water meter; when the fault cause is non-equipment fault cause, the abnormal operational information is used as the non-equipment anomaly information of the IoT valve-controlled water meter; and the equipment anomaly information and the non-equipment anomaly information of the IoT valve-controlled water meter are used as the target anomaly information of the IoT valve-controlled water meter.

[0044] In this embodiment, the terminal queries the valve control anomaly database based on the current abnormal status of each hardware device to identify the distribution information of abnormal data corresponding to the IoT valve-controlled water meter. This abnormal data distribution information refers to the operational data transmitted by the IoT valve-controlled water meter to the operator when one or more hardware devices are in a current abnormal state. This step is used to identify operational anomalies caused by hardware device interference among the various operational anomalies.

[0045] Then, based on the operational anomaly information corresponding to each operational fault type and the distribution information of each anomaly data, the terminal uses a data anomaly adaptation network to determine the cause of the fault corresponding to each operational fault type's operational anomaly information. This data anomaly adaptation network is an artificial neural network that calculates the similarity between each operational anomaly information and the anomaly data distribution information. When the similarity between anomaly data distribution information and operational anomaly information is greater than a preset similarity threshold on the terminal, the terminal determines the cause of the operational anomaly information as a device fault. When the similarity between anomaly data distribution information and operational anomaly information is not greater than the preset similarity threshold on the terminal, the terminal determines the cause of the operational anomaly information as a non-device fault. When the similarity between anomaly data distribution information and each operational anomaly information is less than the similarity threshold, the terminal deletes the current anomaly state of the hardware device corresponding to that anomaly data distribution information. This step indicates that the current anomaly state of the hardware device does not affect the actual operation of the IoT valve-controlled water meter; therefore, the current anomaly state of the hardware device is an acceptable anomaly state, and no feedback is needed for the hardware device's anomaly state.

[0046] The causes of this fault include both equipment malfunctions and non-equipment malfunctions. When the fault is due to equipment malfunction, the abnormal operation information and the current abnormal state of the corresponding hardware device are used as the equipment fault information of the IoT valve-controlled water meter. When the fault is due to non-equipment malfunction, the abnormal operation information is used as the non-equipment fault information of the IoT valve-controlled water meter. Then, the terminal uses both the equipment fault information and the non-equipment fault information of the IoT valve-controlled water meter as the target fault information of the IoT valve-controlled water meter.

[0047] Based on the above scheme, by querying the distribution information of abnormal data caused by equipment failure, we can query the abnormal operation information caused by equipment failure, the abnormal operation information not caused by equipment failure, and the current abnormal status of each hardware device that has not affected the operation, thereby improving the comprehensiveness and accuracy of the analysis of equipment abnormalities, operational abnormalities, and non-affected abnormalities.

[0048] Optionally, based on the target anomaly information of the IoT valve-controlled water meter, the water meter fault information of the IoT valve-controlled water meter is identified through the water meter fault map. This includes: for each device anomaly information, based on the abnormal operation information and the current abnormal state of the hardware device, querying the device fault map in the water meter fault map to determine the device fault information of the IoT valve-controlled water meter; for each non-device anomaly information, based on the abnormal operation information and the non-device fault map in the water meter fault map, determining the operational fault information of the IoT valve-controlled water meter.

[0049] In this embodiment, for each device anomaly, the terminal queries the device fault map in the water meter fault map based on the abnormal operation information and the current abnormal state of the hardware device to determine the device fault information of the IoT valve-controlled water meter. This device fault information is used to characterize the fault information of the hardware device.

[0050] For each non-equipment anomaly, based on the abnormal operation information and the non-equipment fault map in the water meter fault map, the operational fault information of the IoT valve-controlled water meter is determined. The query method for the aforementioned equipment fault map and non-equipment fault map is through an artificial neural network, querying each abnormal operation information or the current abnormal state of the hardware device in each fault map to find the corresponding equipment fault information and operational fault information. This operational fault information is used to characterize the fault information of the software program. This fault information includes, but is not limited to, the specific fault content, fault type, and fault severity of the hardware device or software program.

[0051] Based on the above solution, by identifying the operational fault information of IoT valve-controlled water meters from the perspectives of hardware devices and software programs, the comprehensiveness, efficiency, and accuracy of fault information identification are improved.

[0052] Optionally, based on the water meter fault information, current fault warning information for the IoT valve-controlled water meter is generated, including: based on the water meter fault information, identifying the fault warning level and the fault warning method corresponding to the water meter fault information through a fault warning identification strategy; and based on the water meter fault information, generating current fault warning information for the IoT valve-controlled water meter through the fault warning level and the fault warning method.

[0053] In this embodiment, the terminal issues separate warnings for each device fault and operational fault information within the water meter fault information. The fault warning identification strategy includes a correspondence between each device fault, operational fault, fault warning level, and fault warning method. Then, the terminal identifies the fault warning level and method corresponding to each device fault, and the same applies to each operational fault. The terminal then selects the highest fault warning level as the corresponding fault warning level for that water meter fault, and constructs the fault warning content for each device fault based on its fault warning level and the fault warning method. Finally, the terminal generates the fault warning content for each operational fault based on its fault warning level and the fault warning method. Each fault warning content is transmitted to the operator's client via the fault warning method. The fault warning methods include, but are not limited to, sound warning methods (sounds with different tones, rhythms, and frequencies), indicator light warning methods (warning light colors such as red, yellow, blue, and green), text warning methods (fault information and text content with fault warning levels), broadcast fault information methods (transmitting fault information and text content with fault warning levels via broadcast transmission), screen display of fault information methods (displaying fault information and text content with fault warning levels on the staff's client), and screen identification of fault information methods (identification information for different models).

[0054] Finally, the terminal fills the fault warning level corresponding to the water meter fault information, the fault warning content of each device fault information, and the fault warning content of each operational fault information into the fault warning template to obtain the current fault warning information of the IoT valve-controlled water meter.

[0055] Based on the above scheme, by processing fault warning information from different equipment fault information and operational fault information separately, the comprehensiveness and accuracy of fault warning information are improved.

[0056] This application also provides an example of fault analysis and alarm based on a universally applicable IoT valve-controlled water meter, such as... Figure 2 As shown, the specific processing procedure includes the following steps: Step S201: Obtain fault monitoring data transmitted by each sensor and water meter transmission data from the IoT valve-controlled water meter.

[0057] Step S202: Based on the fault monitoring data, identify the current device monitoring data of each hardware device of the IoT valve-controlled water meter, and extract the current device parameters of each hardware device based on the current device monitoring data of each hardware device.

[0058] Step S203: Based on the current device parameters of each hardware device, identify the current abnormal status of each hardware device through the device fault analysis strategy of each hardware device.

[0059] Step S204: The current abnormal status of each hardware device is used as the device abnormality monitoring information of the IoT valve-controlled water meter.

[0060] Step S205: Obtain historical operating data of IoT valve-controlled water meters, and based on the historical operating data of IoT valve-controlled water meters, identify each historical fault operating data and each fault analysis data.

[0061] Step S206: Based on historical fault operation data and fault analysis data, construct a fault anomaly map of IoT valve-controlled water meters, and identify the distribution information of operation data of IoT valve-controlled water meters based on water meter transmission data.

[0062] Step S207: Based on the operation data distribution information and in accordance with the fault anomaly map, the abnormal data analysis network is used to identify the operation anomaly information corresponding to each type of operation fault in the IoT valve-controlled water meter, and the operation anomaly information corresponding to all types of operation faults is used as the operation anomaly monitoring information of the IoT valve-controlled water meter.

[0063] Step S208: Based on the current abnormal status of each hardware device, query the valve control abnormality database to identify the distribution information of each abnormal data corresponding to the IoT valve control water meter.

[0064] Step S209: Based on the operational anomaly information corresponding to each operational fault type and the distribution information of each anomaly data, the fault cause corresponding to the operational anomaly information for each operational fault type is determined through the data anomaly adaptation network.

[0065] Step S210: When the cause of the fault is a device fault, the abnormal operation information and the current abnormal status of the hardware device corresponding to the abnormal operation information are used as the device abnormal information of the IoT valve-controlled water meter. When the cause of the fault is not a device fault, the abnormal operation information is used as the non-device abnormal information of the IoT valve-controlled water meter.

[0066] Step S211: The device abnormality information and the non-device abnormality information of the IoT valve-controlled water meter are taken as the target abnormality information of the IoT valve-controlled water meter.

[0067] Step S212: For each device abnormality information, based on the abnormal operation information and the current abnormal status of the hardware device, query the device fault map in the water meter fault map to determine the device fault information of the IoT valve-controlled water meter.

[0068] Step S213: For each non-equipment abnormality information, based on the abnormal operation information and the non-equipment fault map in the water meter fault map, determine the operation fault information of the IoT valve-controlled water meter.

[0069] Step S214: Based on the water meter fault information, identify the fault warning level and the fault warning method corresponding to the water meter fault information through the fault warning identification strategy.

[0070] Step S215: Based on the water meter fault information, generate the current fault warning information of the IoT valve-controlled water meter through the fault warning level and fault warning method.

[0071] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0072] Based on the same inventive concept, this application also provides a fault analysis and alarm device for a universally applicable IoT valve-controlled water meter, which implements the aforementioned fault analysis and alarm method for a universally applicable IoT valve-controlled water meter. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more embodiments of the fault analysis and alarm device for a universally applicable IoT valve-controlled water meter provided below can be found in the limitations of the fault analysis and alarm method for a universally applicable IoT valve-controlled water meter described above, and will not be repeated here.

[0073] In one exemplary embodiment, such as Figure 3 As shown, a fault analysis and alarm device for IoT valve-controlled water meters with universal network compatibility is provided, including: an acquisition module 310, an identification module 320, and a generation module 330, wherein: The acquisition module 310 is used to acquire fault monitoring data transmitted by each sensor and water meter transmission data of the IoT valve-controlled water meter, and based on the fault monitoring data, identify the abnormal monitoring information of the IoT valve-controlled water meter through a device fault analysis strategy. The identification module 320 is used to identify the abnormal operation monitoring information of the IoT valve-controlled water meter based on the data transmitted by the water meter and by running a fault analysis strategy, and to identify the target abnormal information of the IoT valve-controlled water meter based on the device abnormal monitoring information and the abnormal operation monitoring information by using a fusion analysis strategy. The generation module 330 is used to identify the water meter fault information of the IoT valve-controlled water meter based on the target abnormal information of the IoT valve-controlled water meter through the water meter fault map, and generate the current fault warning information of the IoT valve-controlled water meter based on the water meter fault information.

[0074] Optionally, the acquisition module 310 is specifically used for: Based on the fault monitoring data, the current device monitoring data of each hardware device of the IoT valve-controlled water meter is identified, and the current device parameters of each hardware device are extracted based on the current device monitoring data of each hardware device. Based on the current device parameters of each hardware device, the current abnormal state of each hardware device is identified through the device fault analysis strategy of each hardware device. The current abnormal status of each of the aforementioned hardware devices is used as the device abnormality monitoring information of the IoT valve-controlled water meter.

[0075] Optionally, the identification module 320 is specifically used for: The historical operating data of the IoT valve-controlled water meter is obtained, and based on the historical operating data of the IoT valve-controlled water meter, each historical fault operating data and each fault analysis data are identified; Based on the historical fault operation data and the fault analysis data, a fault anomaly map of the IoT valve-controlled water meter is constructed, and based on the water meter transmission data, the distribution information of the operation data of the IoT valve-controlled water meter is identified. Based on the operational data distribution information and according to the fault anomaly map, the abnormal data analysis network is used to identify the operational anomaly information of the IoT valve-controlled water meter corresponding to each of the operational fault types, and the operational anomaly information corresponding to all operational fault types is used as the operational anomaly monitoring information of the IoT valve-controlled water meter.

[0076] Optionally, the identification module 320 is specifically used for: Based on the current abnormal status of each hardware device, query the valve control abnormality database to identify the distribution information of each abnormal data corresponding to the IoT valve control water meter. Based on the operational anomaly information corresponding to each operational fault type and the distribution information of each anomaly data, the fault cause corresponding to the operational anomaly information for each operational fault type is determined through a data anomaly adaptation network; the fault cause includes equipment fault cause and non-equipment fault cause. When the cause of the fault is a device malfunction, the abnormal operation information and the current abnormal state of the hardware device corresponding to the abnormal operation information are taken as the device abnormal information of the IoT valve-controlled water meter. When the cause of the fault is not a device malfunction, the abnormal operation information is taken as the non-device abnormal information of the IoT valve-controlled water meter. The device anomaly information and the non-device anomaly information of the IoT valve-controlled water meter are used as the target anomaly information of the IoT valve-controlled water meter.

[0077] Optionally, the generation module 330 is specifically used for: For each device malfunction, based on the malfunction information and the current malfunction status of the hardware device, the device malfunction map in the water meter malfunction map is queried to determine the device malfunction information of the IoT valve-controlled water meter. For each non-equipment anomaly, based on the abnormal operation information and the non-equipment fault map in the water meter fault map, the operational fault information of the IoT valve-controlled water meter is determined.

[0078] Optionally, the generation module 330 is specifically used for: Based on the water meter fault information, a fault warning identification strategy is used to identify the fault warning level and the fault warning method corresponding to the water meter fault information. Based on the water meter fault information, the current fault warning information of the IoT valve-controlled water meter is generated through the fault warning level and the fault warning method.

[0079] The various modules in the aforementioned fault analysis and alarm device based on the universally applicable IoT valve-controlled water meter can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a computer device in hardware form or independent of it, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0080] In one exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 4 As shown, the computer device includes a processor, memory, input / output interfaces, a communication interface, a display unit, and an input device. The processor, memory, and input / output interfaces are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interfaces. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The input / output interfaces are used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When the computer program is executed by the processor, it implements a fault analysis and alarm method for a universally applicable IoT valve-controlled water meter. The display unit is used to form a visually visible image and can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0081] Those skilled in the art will understand that Figure 4The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0082] In one exemplary embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the steps of a beer warehouse inventory optimization method.

[0083] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of a beer warehouse inventory optimization method.

[0084] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of a beer warehouse inventory optimization method.

[0085] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0086] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0087] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0088] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A fault analysis and alarm method for IoT valve-controlled water meters with universal network compatibility, characterized in that, The method includes: The system acquires fault monitoring data transmitted by each sensor and water meter transmission data of the IoT valve-controlled water meter, and identifies abnormal monitoring information of the IoT valve-controlled water meter based on the fault monitoring data and through a device fault analysis strategy. Based on the water meter's transmitted data, an operational anomaly monitoring information of the IoT valve-controlled water meter is identified through a fault analysis strategy. Based on the device anomaly monitoring information and the operational anomaly monitoring information, a fusion analysis strategy is used to identify the target anomaly information of the IoT valve-controlled water meter. Based on the target anomaly information of the IoT valve-controlled water meter, the water meter fault information of the IoT valve-controlled water meter is identified through the water meter fault map, and based on the water meter fault information, the current fault warning information of the IoT valve-controlled water meter is generated.

2. The method according to claim 1, characterized in that, Based on the fault monitoring data, the identification of abnormal monitoring information of the IoT valve-controlled water meter through equipment fault analysis strategies includes: Based on the fault monitoring data, the current device monitoring data of each hardware device of the IoT valve-controlled water meter is identified, and the current device parameters of each hardware device are extracted based on the current device monitoring data of each hardware device. Based on the current device parameters of each hardware device, the current abnormal state of each hardware device is identified through the device fault analysis strategy of each hardware device. The current abnormal status of each of the aforementioned hardware devices is used as the device abnormality monitoring information of the IoT valve-controlled water meter.

3. The method according to claim 2, characterized in that, The step of identifying abnormal operation monitoring information of the IoT valve-controlled water meter based on the data transmitted by the water meter and by running a fault analysis strategy includes: The historical operating data of the IoT valve-controlled water meter is obtained, and based on the historical operating data of the IoT valve-controlled water meter, each historical fault operating data and each fault analysis data are identified. Based on the historical fault operation data and the fault analysis data, a fault anomaly map of the IoT valve-controlled water meter is constructed, and based on the water meter transmission data, the distribution information of the operation data of the IoT valve-controlled water meter is identified. Based on the operational data distribution information and according to the fault anomaly map, the abnormal data analysis network is used to identify the operational anomaly information of the IoT valve-controlled water meter corresponding to each of the operational fault types, and the operational anomaly information corresponding to all operational fault types is used as the operational anomaly monitoring information of the IoT valve-controlled water meter.

4. The method according to claim 3, characterized in that, The process of identifying target anomaly information of the IoT valve-controlled water meter based on the device anomaly monitoring information and the operational anomaly monitoring information, through a fusion analysis strategy, includes: Based on the current abnormal status of each hardware device, query the valve control abnormality database to identify the distribution information of each abnormal data corresponding to the IoT valve control water meter. Based on the operational anomaly information corresponding to each operational fault type and the distribution information of each anomaly data, the fault cause corresponding to the operational anomaly information for each operational fault type is determined through a data anomaly adaptation network; the fault cause includes equipment fault cause and non-equipment fault cause. When the cause of the fault is a device malfunction, the abnormal operation information and the current abnormal state of the hardware device corresponding to the abnormal operation information are taken as the device abnormal information of the IoT valve-controlled water meter. When the cause of the fault is not a device malfunction, the abnormal operation information is taken as the non-device abnormal information of the IoT valve-controlled water meter. The device anomaly information and the non-device anomaly information of the IoT valve-controlled water meter are used as the target anomaly information of the IoT valve-controlled water meter.

5. The method according to claim 4, characterized in that, The step of identifying water meter fault information based on the target anomaly information of the IoT valve-controlled water meter, through a water meter fault map, includes: For each device malfunction, based on the malfunction information and the current malfunction status of the hardware device, the device malfunction map in the water meter malfunction map is queried to determine the device malfunction information of the IoT valve-controlled water meter. For each non-equipment anomaly, based on the abnormal operation information and the non-equipment fault map in the water meter fault map, the operational fault information of the IoT valve-controlled water meter is determined.

6. The method according to claim 5, characterized in that, The step of generating current fault warning information for the IoT valve-controlled water meter based on the water meter fault information includes: Based on the water meter fault information, a fault warning identification strategy is used to identify the fault warning level and the fault warning method corresponding to the water meter fault information. Based on the water meter fault information, the current fault warning information of the IoT valve-controlled water meter is generated through the fault warning level and the fault warning method.

7. A fault analysis and alarm device for a universally applicable IoT valve-controlled water meter, characterized in that, The device includes: The acquisition module is used to acquire fault monitoring data transmitted by each sensor and water meter transmission data of the IoT valve-controlled water meter, and based on the fault monitoring data, identify abnormal monitoring information of the IoT valve-controlled water meter through a device fault analysis strategy. The identification module is used to identify the abnormal operation monitoring information of the IoT valve-controlled water meter based on the data transmitted by the water meter and by running a fault analysis strategy, and to identify the target abnormal information of the IoT valve-controlled water meter based on the device abnormal monitoring information and the abnormal operation monitoring information by using a fusion analysis strategy. The generation module is used to identify water meter fault information of the IoT valve-controlled water meter based on the target abnormal information of the IoT valve-controlled water meter through the water meter fault map, and generate current fault warning information of the IoT valve-controlled water meter based on the water meter fault information.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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