Alarm method and device, storage medium and electronic equipment

Abstract

The embodiment of the invention provides an alarm method and device, a storage medium and electronic device.The method comprises the steps that operation parameters of a battery of a target valve are monitored according to a target sensor, and the operation parameters at least comprise one of current data of the battery, voltage data of the battery and temperature data of the battery; the operation state of the target valve is determined according to the operation parameters and an anomaly recognition algorithm, and the operation state comprises a normal state and an abnormal state; and under the condition that it is determined that the operation state of the target valve is an abnormal state, a multi-stage intelligent alarm mechanism is started to conduct alarm processing on the target valve, and the multi-stage intelligent alarm mechanism comprises local alarm and remote alarm. According to the method, the problem that the valve cannot operate normally due to the fact that the battery abnormity problem of the valve cannot be found in the related technology can be solved.

Description

Technical Field

[0001] The present application relates to the field of battery abnormality monitoring, and more specifically, to an alarm method and device, a storage medium, and an electronic device. Background Art

[0002] In a heating system, valves are a key component that controls the flow of hot water or steam to maintain the proper temperature. The proper operation of heating valves is critical to the efficiency and stability of the system. However, due to long-term use or external environmental factors, heating valves may malfunction, among which battery abnormality is a common problem. Battery abnormality may cause the valve to not operate normally, affecting the performance of the heating system.

[0003] Currently, no effective solution has been proposed to the problem that the battery abnormality of the valve cannot be detected in the relevant technology, resulting in the valve being unable to operate normally.

[0004] Therefore, it is necessary to improve the related technology to overcome the above-mentioned defects in the related technology. Summary of the invention

[0005] The embodiments of the present application provide an alarm method and device, a storage medium and an electronic device to at least solve the problem in the related art that the battery abnormality of the valve cannot be detected, resulting in the valve being unable to operate normally.

[0006] According to one embodiment of the present application, an alarm method is provided, comprising: monitoring the operating parameters of a battery of a target valve according to a target sensor, wherein the operating parameters include at least one of the following: current data of the battery, voltage data of the battery, and temperature data of the battery; determining the operating state of the target valve according to the operating parameters and an abnormality identification algorithm, wherein the operating state includes: a normal state and an abnormal state; when it is determined that the operating state of the target valve is an abnormal state, initiating a multi-level intelligent alarm mechanism to perform alarm processing on the target valve, wherein the multi-level intelligent alarm mechanism includes: a local alarm and a remote alarm.

[0007] In an exemplary embodiment, monitoring the operating parameters of a battery of a target valve according to a target sensor includes: when the target sensor is an integrated sensor, monitoring the battery according to the target sensor to obtain the operating parameters; when the target sensor is a single sensor, monitoring the current of the battery according to a current sensor to obtain the current data, and monitoring the voltage of the battery according to a voltage sensor to obtain the voltage data, and monitoring the temperature of the battery according to a temperature sensor to obtain the temperature data, wherein the target sensor includes: the current sensor, the voltage sensor, and the temperature sensor.

[0008] In an exemplary embodiment, determining the operating state of the target valve according to the operating parameters and the abnormality recognition algorithm includes: determining the operating state of the target valve in one of the following ways: determining whether the operating parameters are within a preset parameter range through the operating parameters and the abnormality recognition algorithm to determine the operating state of the target valve; determining the changing trend of the operating parameters through the operating parameters and the abnormality recognition algorithm to determine the operating state of the target valve; determining whether the difference between the health state of the battery and the preset health state is within a preset difference range through the operating parameters and the abnormality recognition algorithm to determine the operating state of the target valve.

[0009] In an exemplary embodiment, determining whether the operating parameter is within a preset parameter range through the operating parameter and the abnormality identification algorithm to determine the operating state of the target valve includes: determining whether the current data is within a preset current range, determining whether the voltage data is within a preset voltage range, and determining whether the temperature data is within a preset temperature range according to the abnormality identification algorithm, wherein the preset parameter range includes: the preset current range, the preset voltage range, and the preset temperature range; when the current data, the voltage data, and the temperature data are all outside the preset parameter range, determining that the operating state of the target valve is an abnormal state; When any one of the current data, the voltage data and the temperature data is within the preset parameter range and the other parameters are not within the preset parameter range, or when any one of the current data, the voltage data and the temperature data is not within the preset parameter range and the other parameters are within the preset parameter range, the operating parameters are secondary identified according to the abnormality identification algorithm to determine the operating state of the target valve, wherein the other parameters are the other parameters of the operating parameters except the any one parameter; when the current data, the voltage data and the temperature data are all within the preset parameter range, it is determined that the operating state of the target valve is normal.

[0010] In an exemplary embodiment, the change trend of the operating parameter is determined by the operating parameter and the abnormality identification algorithm to determine the operating state of the target valve, including: establishing a current sequence according to the current data, establishing a voltage sequence according to the voltage data, and establishing a temperature sequence according to the temperature data; determining a current change trend according to the current sequence, determining a voltage change trend according to the voltage data, and determining a temperature change trend according to the temperature sequence; determining whether the current change trend conforms to a preset current change trend according to the abnormality identification algorithm, determining whether the voltage change trend conforms to a preset voltage change trend, and determining whether the temperature change trend conforms to a preset temperature change trend; when the current change trend, the voltage change trend, and the temperature change trend do not conform to the preset parameter change trend, determining the operating state of the target valve; The state is an abnormal state, wherein the preset parameter change trend includes: the preset current change trend, the preset voltage change trend and the preset temperature change trend; any parameter change trend among the current change trend, the voltage change trend and the temperature change trend does not conform to the preset parameter change trend, and the other parameter change trends all conform to the preset parameter change trend, and when it is obtained that any parameter change trend conforms to the preset parameter change trend, and the other parameter change trends do not conform to the preset parameter change trend, the current change trend, the voltage change trend and the temperature change trend are secondary identified according to the abnormal identification algorithm to determine the operating state; when the current change trend, the voltage change trend and the temperature change trend all conform to the preset parameter change trend, the operating state is determined to be a normal state.

[0011] In an exemplary embodiment, the operating parameters and the abnormality recognition algorithm are used to determine whether the difference between the health state of the battery and the preset health state is within a preset difference range to determine the operating state of the target valve, including: training a battery model based on historical operating parameters of multiple preset batteries, and inputting the operating parameters into the battery model to obtain the health state of the battery, wherein the battery model is a model trained by a learning algorithm with the historical operating parameters as input values ​​and the target health state of the preset battery corresponding to the historical operating parameters as target values; determining the difference between the health state and the preset health state according to the abnormality recognition algorithm; determining that the operating state is an abnormal state if the difference is not within the preset difference range; and determining that the operating state is a normal state if the difference is within the preset difference range.

[0012] In an exemplary embodiment, a multi-level intelligent alarm mechanism is activated to perform alarm processing on the target valve, including: sending an alarm signal to a buzzer to make the buzzer emit an alarm sound, or converting the alarm signal into an optical signal, and sending the optical signal to a lighting device to make the lighting device emit an alarm light, wherein the local alarm includes: emitting an alarm sound or emitting an alarm light; and, determining the alarm information based on the operating status, and remotely sending the alarm information to the target device via wireless communication technology, wherein the target device is used to receive the alarm information, and the remote alarm includes: remotely sending the alarm information.

[0013] According to another embodiment of the present application, an alarm device is provided, including: a monitoring module, used to monitor the operating parameters of a battery of a target valve according to a target sensor, wherein the operating parameters include at least one of the following: current data of the battery, voltage data of the battery and temperature data of the battery; a determination module, used to determine the operating state of the target valve according to the operating parameters and an abnormality identification algorithm, wherein the operating state includes: a normal state and an abnormal state; an alarm module, used to start a multi-level intelligent alarm mechanism to perform alarm processing on the target valve when it is determined that the operating state of the target valve is an abnormal state, wherein the multi-level intelligent alarm mechanism includes: a local alarm and a remote alarm.

[0014] According to another embodiment of the present application, a computer-readable storage medium is provided, in which a computer program is stored, wherein the computer program is configured to execute the steps of any of the above method embodiments when run.

[0015] According to another embodiment of the present application, an electronic device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0016] According to another embodiment of the present application, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in any of the above method embodiments are implemented.

[0017] Through the embodiment of the present application, the target sensor monitors the operating parameters of the battery of the target valve, such as the current data, voltage data, and temperature data; the operating state of the target valve is determined according to the operating parameters and the abnormality recognition algorithm, and the operating state includes: normal state and abnormal state; when it is determined that the operating state of the target valve is an abnormal state, a multi-level intelligent alarm mechanism including local alarm and remote alarm is started to perform alarm processing on the target valve. That is to say, the embodiment of the present application monitors the operating parameters of the battery of the target valve through the target sensor, and then determines the operating state of the target valve through the abnormality recognition algorithm, so as to determine whether the target valve is in an abnormal state, and then when it is determined that the target valve is in an abnormal state, a multi-level intelligent alarm mechanism can be started to issue an alarm. Through the embodiment of the present application, the problem that the battery abnormality of the valve cannot be found in the related technology, resulting in the valve being unable to operate normally, can be solved. Therefore, the battery abnormality of the valve can be discovered in time. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0019] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0020] Figure 1 It is a hardware structure block diagram of a computer terminal device of an alarm method in an embodiment of the present application;

[0021] Figure 2 is a flowchart of an alarm method according to an embodiment of the present application;

[0022] Figure 3 is a device block diagram of a heating valve battery abnormality detection device according to an optional embodiment of the present application;

[0023] Figure 4 It is a structural block diagram of a device for determining a state of charge according to an embodiment of the present application. DETAILED DESCRIPTION

[0024] The embodiments of the present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0025] It should be noted that the terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0026] The method embodiments provided in the embodiments of the present application can be executed in a computer terminal device or a similar computing device. Taking running on a computer terminal device as an example, Figure 1 1 is a hardware structure block diagram of a computer terminal device of an alarm method in an embodiment of the present application. Figure 1 As shown, the computer terminal device may include one or more ( Figure 1 Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data, wherein the above-mentioned computer terminal device may also include a transmission device 106 and an input and output device 108 for communication functions. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above-mentioned computer terminal device. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0027] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the alarm method in the embodiment of the present application. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, to implement the above method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories can be connected to the computer terminal device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0028] The transmission device 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of a computer terminal device. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0029] In this embodiment, an alarm method is provided. Figure 2 is a flow chart of an alarm method according to an embodiment of the present application, such as Figure 2 As shown, the process includes the following steps:

[0030] Step S202, monitoring the operating parameters of the battery of the target valve according to the target sensor, wherein the operating parameters include at least one of the following: current data of the battery, voltage data of the battery, and temperature data of the battery;

[0031] Step S204, determining the operating state of the target valve according to the operating parameters and the abnormality identification algorithm, wherein the operating state includes: a normal state and an abnormal state;

[0032] Step S206, when it is determined that the operating state of the target valve is an abnormal state, a multi-level intelligent alarm mechanism is started to perform alarm processing on the target valve, wherein the multi-level intelligent alarm mechanism includes: local alarm and remote alarm.

[0033] Through the above steps, the target sensor monitors the operating parameters of the battery of the target valve, such as the current data, voltage data, and temperature data; the operating state of the target valve is determined according to the operating parameters and the abnormality recognition algorithm, and the operating state includes: normal state and abnormal state; when it is determined that the operating state of the target valve is an abnormal state, a multi-level intelligent alarm mechanism including local alarm and remote alarm is started to perform alarm processing on the target valve. That is to say, the embodiment of the present application monitors the operating parameters of the battery of the target valve through the target sensor, and then determines the operating state of the target valve through the abnormality recognition algorithm, so as to determine whether the target valve is in an abnormal state, and then when it is determined that the target valve is in an abnormal state, a multi-level intelligent alarm mechanism can be started to issue an alarm. Through the embodiment of the present application, the problem that the battery abnormality of the valve cannot be found in the related technology, resulting in the valve being unable to operate normally, can be solved. Therefore, the battery abnormality of the valve can be discovered in time.

[0034] Optionally, the above-mentioned step S202 of monitoring the operating parameters of the battery of the target valve according to the target sensor includes: when the target sensor is an integrated sensor, monitoring the battery according to the target sensor to obtain the operating parameters; when the target sensor is a single sensor, monitoring the current of the battery according to the current sensor to obtain the current data, and monitoring the voltage of the battery according to the voltage sensor to obtain the voltage data, and monitoring the temperature of the battery according to the temperature sensor to obtain the temperature data, wherein the target sensors include: the current sensor, the voltage sensor, and the temperature sensor.

[0035] It is understandable that in the heating valve battery abnormality detection method and device, the way of monitoring the battery operating parameters (current, voltage, temperature) will vary depending on the type of sensor. Specifically:

[0036] (1) Integrated sensor monitoring: Assume that the target sensor is an integrated sensor module that integrates the functions of current sensor, voltage sensor and temperature sensor. This sensor module is usually designed as a compact package that can be directly mounted on the battery casing, connecting the positive and negative electrodes of the battery and the casing through an internal circuit.

[0037] The monitoring process includes: data acquisition, the integrated sensor module collects the battery's current, voltage and temperature data in real time. For example, when the battery is charging or discharging, the current sensor measures the current intensity flowing through the battery; the voltage sensor measures the voltage difference between the two ends of the battery; the temperature sensor measures the surface or internal temperature of the battery.

[0038] Data processing and transmission: The microprocessor or signal processing chip inside the integrated sensor module performs preliminary processing on the collected data, such as A / D conversion, filtering, and data format conversion, and then sends the data to the controller via wired or wireless means.

[0039] Anomaly detection: The controller receives data from the integrated sensor module and uses anomaly recognition algorithms to perform real-time analysis on current, voltage, and temperature data. For example, if the temperature rises abnormally during charging, but the current and voltage data do not show signs of overcharging, the controller will mark the temperature data as abnormal and trigger further analysis or an alarm.

[0040] (2) Single sensor monitoring:

[0041] In another case, the target sensors are three separate sensors: a current sensor, a voltage sensor, and a temperature sensor. These sensors are connected to different parts of the battery and are used to monitor a specific parameter. The monitoring process is as follows: Current monitoring, for example, the current sensor obtains current data by measuring the current flowing through the circuit when the battery is charged or discharged. The current sensor is usually connected in the circuit between the battery and the load or charger to ensure that the battery's current usage can be accurately measured.

[0042] Voltage monitoring: The voltage sensor is connected between the positive and negative electrodes of the battery to monitor the voltage at both ends of the battery in real time to obtain voltage data. This helps to understand the battery's charge and discharge status and remaining power.

[0043] Temperature monitoring, the temperature sensor is installed on the surface or inside the battery to monitor the temperature of the battery to obtain temperature data. This can be achieved through contact sensors (such as thermocouples) or non-contact sensors (such as infrared sensors).

[0044] Data transmission and processing: The current sensor, voltage sensor and temperature sensor send their data to the controller through independent signal lines or wireless networks. After receiving the data, the controller stores it in the data buffer and then processes and analyzes it one by one according to the preset algorithm.

[0045] Abnormal detection and comprehensive evaluation, the controller first detects abnormalities in the current, voltage and temperature data to determine whether they exceed the preset normal operating range. Then, the controller performs a multi-parameter comprehensive analysis to further determine the overall operating status of the battery by detecting the correlation and dependency between the parameters. For example, if the voltage and current data are normal, but the temperature data is abnormally high, the controller may determine that the internal chemical reaction of the battery is abnormal, resulting in increased heat release.

[0046] Alarm and response: Once the controller identifies an abnormal battery condition, it will immediately trigger the alarm system and notify maintenance personnel through sound, light signals or remote communication. At the same time, the controller may adjust the battery charging and discharging strategy to mitigate the impact of the abnormal condition, or stop the battery from working in severe cases to prevent potential safety risks.

[0047] Through the above two monitoring methods, the embodiment of the present application can obtain the key operating parameters of the battery in real time, and use the abnormality recognition algorithm to analyze the data, timely discover and handle battery abnormalities, and ensure the stable operation of the heating system and user safety. This monitoring mechanism not only improves the efficiency of data collection, but also enhances the accuracy and real-time performance of abnormality detection.

[0048] Optionally, the above-mentioned step S204 of determining the operating state of the target valve according to the operating parameters and the abnormality recognition algorithm includes: determining the operating state of the target valve in one of the following ways: determining whether the operating parameters are within a preset parameter range through the operating parameters and the abnormality recognition algorithm to determine the operating state of the target valve; determining the changing trend of the operating parameters through the operating parameters and the abnormality recognition algorithm to determine the operating state of the target valve; determining whether the difference between the health state of the battery and the preset health state is within a preset difference range through the operating parameters and the abnormality recognition algorithm to determine the operating state of the target valve.

[0049] (1) determining whether the current data is within a preset current range, determining whether the voltage data is within a preset voltage range, and determining whether the temperature data is within a preset temperature range according to the abnormality identification algorithm, wherein the preset parameter range includes: the preset current range, the preset voltage range, and the preset temperature range; when the current data, the voltage data, and the temperature data are all not within the preset parameter range, determining that the operating state of the target valve is an abnormal state; when any one of the current data, the voltage data, and the temperature data is within the preset parameter range and the other parameters are not within the preset parameter range, or when any one of the current data, the voltage data, and the temperature data is not within the preset parameter range and the other parameters are within the preset parameter range, performing secondary identification on the operating parameters according to the abnormality identification algorithm to determine the operating state of the target valve, wherein the other parameters are other parameters of the operating parameters except the any one parameter; when the current data, the voltage data, and the temperature data are all within the preset parameter range, determining that the operating state of the target valve is a normal state.

[0050] It is understandable that in the heating valve battery anomaly detection method and device, the anomaly identification algorithm analyzes the current, voltage and temperature data according to the preset parameter range to determine the operating status of the battery and the target valve. Specifically:

[0051] 1) Determination of normal status.

[0052] First, the anomaly recognition algorithm checks whether the current current data, voltage data, and temperature data are within the preset parameter range. These preset parameter ranges are based on the normal operating conditions of the battery and historical data statistics, representing the typical value range of the battery in a healthy state.

[0053] For example, suppose the preset current range is 0-5A, the voltage range is 3.5V-4.2V, and the temperature range is 0℃-40℃. If the current data collected at a certain moment is 3A, the voltage data is 4V, and the temperature data is 25℃, then all three parameters are within the preset range, and the algorithm will determine that the operating state of the target valve is normal.

[0054] 2) Direct determination of abnormal conditions.

[0055] If the current data, voltage data, and temperature data all exceed the preset parameter range, the abnormality recognition algorithm will directly determine that the operating state of the target valve is abnormal, which usually indicates that there is a serious fault in the battery or the external environmental conditions are extremely abnormal.

[0056] For example: if the current data collected at a certain moment is 10A (outside the preset 0-5A range), the voltage data is 2.5V (outside the preset 3.5V-4.2V range), and the temperature data is 50°C (outside the preset 0℃-40℃ range), then all three parameters are in an abnormal state, and the operating state of the target valve is directly determined to be abnormal, and measures are taken quickly, such as triggering an alarm or stopping the battery operation.

[0057] 3) Secondary identification of abnormal status.

[0058] For complex situations where one or more parameters are outside the preset range while other parameters are within the range, the abnormality recognition algorithm will perform secondary recognition and comprehensively analyze the relationship between the data to determine whether the operating status of the target valve is truly abnormal.

[0059] For example: if at a certain moment the current data is 4.5A (within the preset range of 0-5A), and the voltage data is 2.0V (lower than the preset range of 3.5V-4.2V), and the temperature data is 20℃ (within the preset range of 0℃-40℃), then the voltage data is the only abnormal point, and the current and temperature data are within the preset range.

[0060] In this case, the algorithm performs a secondary identification, possibly using a more complex model (such as a machine learning model) to analyze the relationship between the abnormality of the voltage data and other parameters. For example, it checks whether the voltage data should be within the normal range under the current and temperature conditions. If the model predicts that the voltage should be higher than 3.5V under these conditions, and the actual data is 2.0V, then this may indicate that there is a fault inside the battery, and the operating status of the target valve will be marked as abnormal.

[0061] That is to say, the abnormality recognition algorithm can quickly determine the operating status of the target valve by comparing the current, voltage and temperature data with the preset parameter range. For complex abnormal situations, the algorithm will perform secondary recognition and comprehensively analyze the correlation between the data to improve the accuracy and robustness of abnormality detection. This dynamic monitoring and intelligent analysis method can detect and handle battery abnormalities in a timely manner, ensuring the stability and safety of the heating system.

[0062] Wherein, determining the change trend of the operating parameter through the operating parameter and the abnormal identification algorithm to determine the operating state of the target valve includes: establishing a current sequence according to the current data, establishing a voltage sequence according to the voltage data, and establishing a temperature sequence according to the temperature data; determining the current change trend according to the current sequence, determining the voltage change trend according to the voltage data, and determining the temperature change trend according to the temperature sequence; determining whether the current change trend conforms to the preset current change trend according to the abnormal identification algorithm, determining whether the voltage change trend conforms to the preset voltage change trend, and determining whether the temperature change trend conforms to the preset temperature change trend; when the current change trend, the voltage change trend and the temperature change trend do not conform to the preset parameter change trend, determining that the operating state is abnormal Normal state, wherein the preset parameter change trend includes: the preset current change trend, the preset voltage change trend and the preset temperature change trend; any parameter change trend among the current change trend, the voltage change trend and the temperature change trend does not conform to the preset parameter change trend, and the other parameter change trends all conform to the preset parameter change trend, and when it is obtained that any parameter change trend conforms to the preset parameter change trend, and the other parameter change trends do not conform to the preset parameter change trend, the current change trend, the voltage change trend and the temperature change trend are secondary identified according to the abnormal identification algorithm to determine the operating state; when the current change trend, the voltage change trend and the temperature change trend all conform to the preset parameter change trend, the operating state is determined to be a normal state.

[0063] It is understandable that in the heating valve battery anomaly detection method and device, in addition to real-time monitoring of the current current, voltage and temperature data, the anomaly recognition algorithm also analyzes the change trend of these data to more carefully and comprehensively evaluate the operating status of the battery. Specifically:

[0064] 1) Create a sequence:

[0065] When the target sensor detects the operating parameters of the battery, the abnormality recognition algorithm will establish a current sequence based on the current data, a voltage sequence based on the voltage data, and a temperature sequence based on the temperature data. These sequences reflect the changes in parameters over time.

[0066] For example: Assume that within 10 minutes, the current data are 2.5A, 2.4A, 2.3A, 2.2A, 2.1A, the voltage data are 3.8V, 3.7V, 3.6V, 3.5V, 3.4V, and the temperature data are 25℃, 26℃, 27℃, 28℃, 29℃. At this time, the current sequence shows that the current is gradually decreasing, the voltage sequence shows that the voltage is gradually decreasing, and the temperature sequence shows that the temperature is gradually increasing.

[0067] 2) Determine the trend of change:

[0068] The algorithm analyzes the current series, voltage series, and temperature series to determine the changing trends of current, voltage, and temperature. This may include identifying the direction of the trend (rising, falling, or stable), the rate of change, and periodic characteristics.

[0069] For example: Based on the above data, the algorithm can determine that the current trend is decreasing, the voltage trend is also decreasing, and the temperature trend is increasing.

[0070] 3) Compare with the preset change trend:

[0071] The abnormality identification algorithm compares the current change trend with the preset normal change trend. These preset change trends are based on the normal working behavior of the battery and the existing data statistics to determine whether the battery is operating as expected.

[0072] For example: Assume that the preset current change trend is stable, the voltage change trend is rising or falling with charging or discharging, and the temperature change trend is stable or slowly rising. If the actual change trend does not match the preset change trend, the algorithm will conduct further analysis.

[0073] 4) Direct determination of abnormal status:

[0074] If the current change trend, voltage change trend and temperature change trend do not conform to the preset parameter change trend, the abnormality recognition algorithm will directly determine that the battery's operating state is abnormal, and immediate measures may need to be taken.

[0075] For example: if the current sequence shows that the current is fluctuating irregularly and greatly, the voltage sequence shows that the voltage is rising abnormally, and the temperature sequence shows that the temperature is dropping sharply under abnormal circumstances, then the algorithm will determine that the change trends of these three parameters do not conform to the preset change trends and directly determine that the operating state is abnormal.

[0076] 5) Secondary identification:

[0077] When the change trend of one or some parameters does not conform to the preset change trend, while the change trends of other parameters are within the preset range, the abnormality recognition algorithm will perform secondary recognition and comprehensively analyze the data to determine whether the operating status is abnormal.

[0078] For example, if the current and voltage sequences show a normal trend, but the temperature sequence shows an abnormal rise in temperature during charging, the algorithm will perform a secondary identification of the temperature trend to check whether the temperature rise rate is within the preset normal range. If the temperature rises too quickly, even if the current and voltage appear normal, the battery may be at risk of overheating, and the operating status will be marked as abnormal.

[0079] 6) Determine the normal state:

[0080] If the current change trend, voltage change trend and temperature change trend all meet the preset parameter change trend, the abnormality recognition algorithm will determine that the operating state of the battery is normal.

[0081] For example: if the current sequence shows that the current gradually increases to a preset value during charging and then stabilizes, the voltage sequence shows that the voltage gradually increases until charging is completed, and the temperature sequence shows that the temperature slowly rises to a preset temperature range during charging and then stabilizes, then these change trends are consistent with the preset normal trends, and the algorithm will determine that the operating status is normal.

[0082] Through this detailed analysis of the changing trends of current, voltage and temperature data, the anomaly recognition algorithm can more accurately determine the operating status of the battery and promptly detect and handle potential anomalies. This approach not only considers the immediate parameter values, but also pays attention to the changes in parameters over time, which improves the sensitivity and reliability of anomaly detection.

[0083] Among them, determining whether the difference between the health state of the battery and the preset health state is within a preset difference range by using the operating parameters and the abnormality recognition algorithm to determine the operating state of the target valve, including: training a battery model according to historical operating parameters of multiple preset batteries, and inputting the operating parameters into the battery model to obtain the health state of the battery, wherein the battery model is a model trained by a learning algorithm with the historical operating parameters as input values ​​and the target health state of the preset battery corresponding to the historical operating parameters as target values; determining the difference between the health state and the preset health state according to the abnormality recognition algorithm; determining that the operating state is an abnormal state when the difference is not within the preset difference range; and determining that the operating state is a normal state when the difference is within the preset difference range.

[0084] It can be understood that in the heating valve battery anomaly detection method and device, using multiple preset battery historical operating parameters to train the battery model and then evaluate the current battery health status is an advanced monitoring and diagnosis technology. Specifically:

[0085] Historical data collection: It is necessary to collect historical operating parameters of multiple preset batteries under different operating conditions, including current, voltage and temperature data, as well as the corresponding battery health status. This data can come from laboratory tests, actual operation records or historical failure cases.

[0086] For example, the current, voltage and temperature data of 100 batteries of the same type under normal charging, discharging, sleep and other states were collected, as well as the health status assessment results of these batteries. For example, the health status level is 1-10, where 1 means that the battery status is extremely poor and 10 means that the status is extremely good.

[0087] Train the battery model: Use machine learning algorithms (such as support vector machines, neural networks, or decision trees) to train historical operating parameters and corresponding health states to build a battery model. This model can predict the health state of the battery based on the input current, voltage, and temperature data.

[0088] For example, a neural network model is trained using historical operating parameters as input features and health status level as output target. The model learns from a large amount of data and identifies the relationship between current, voltage and temperature data and battery health status.

[0089] Evaluate the current battery health status: Input the current battery operating parameters (current, voltage and temperature data) into the trained battery model, and the model will output a predicted health status. This step allows real-time evaluation of the battery's health level without direct physical testing.

[0090] For example, the current battery current data is 3.0A, the voltage data is 4.0V, and the temperature data is 30°C. These data are input into the trained neural network model, and the model predicts a health status level of 8.

[0091] Comparison with preset health status: The abnormal identification algorithm compares the current predicted health status of the battery with the preset health status and calculates the difference between them. The preset health status can be the average health status based on historical data statistics or the expected health status based on the current operating conditions.

[0092] For example: The preset health status level is 9, the current predicted health status of the battery is 8, and the difference is 1.

[0093] Determine the operating status: The abnormality recognition algorithm checks whether the difference is within the preset difference range. If the difference exceeds the preset range, it indicates that the battery health status is significantly different from the expected state, and the operating status is determined to be abnormal. Conversely, if the difference is within the preset range, the operating status is determined to be normal.

[0094] For example: Assume that the preset difference range is ±2, and the current difference is 1. Within the preset range, the operating status is determined to be normal. If the difference is 4, which exceeds the preset range, the operating status is determined to be abnormal, and further inspection may be required to determine whether the battery is aging, damaged, or other problems.

[0095] Through this monitoring method that combines historical data and machine learning, the present invention can evaluate the health status of the battery in real time and compare it with the preset health status, so as to promptly discover abnormal changes in the battery. This method not only improves the accuracy and efficiency of abnormal detection, but also can dynamically adjust the preset health status according to the actual operation of the battery to meet the needs under different environments and conditions.

[0096] Optionally, the multi-level intelligent alarm mechanism started in step S206 above is used to perform alarm processing on the target valve, including: sending an alarm signal to a buzzer to make the buzzer emit an alarm sound, or converting the alarm signal into an optical signal, and sending the optical signal to a lighting device to make the lighting device emit an alarm light, wherein the local alarm includes: emitting an alarm sound or emitting an alarm light; and, determining the alarm information according to the operating status, and remotely sending the alarm information to the target device via wireless communication technology, wherein the target device is used to receive the alarm information, and the remote alarm includes: remotely sending the alarm information.

[0097] In order to better understand the process of the above-mentioned alarm method, the implementation method flow of the above-mentioned alarm is described below in combination with an optional embodiment, but it is not used to limit the technical solution of the embodiment of the present application.

[0098] The following problems exist in the related technologies: Single parameter monitoring: Most existing technologies mainly rely on the monitoring of a single parameter (usually voltage) to determine the battery status. This leads to a lack of comprehensive understanding of the overall health of the battery. Insufficient real-time performance: Some traditional methods mainly rely on regular battery replacement or simple periodic detection. This method cannot provide real-time monitoring and alarms, and cannot cope with sudden battery abnormalities. Low sensitivity to multiple abnormalities: The existing technology lacks sufficient sensitivity and accuracy for various battery abnormalities, such as battery aging, internal battery damage, etc., and is prone to missed detection or false alarms.

[0099] In view of the above problems, the optional embodiment of the present application proposes a heating valve battery abnormality detection method and device, specifically:

[0100] Figure 3 is a device block diagram of a heating valve battery abnormality detection device according to an optional embodiment of the present application, such as Figure 3 As shown:

[0101] The battery abnormality detection device includes: a valve body (i.e., a target valve), a sensor (i.e., a target sensor), a controller, and an alarm system, wherein the alarm system includes: an LED light and a 4G remote card. Specifically:

[0102] Sensor assembly: installed on the heating valve battery, used to monitor the battery's voltage, current, temperature and other parameters (i.e., operating parameters) in real time. The sensor can be multiple individual sensors (i.e., a single sensor) or an integrated sensor module.

[0103] Controller: The controller is the core component of the entire device and contains an abnormality recognition algorithm. The controller receives the data collected by the sensor and processes and analyzes the data to identify whether there is any abnormality in the battery.

[0104] Alarm system: When the controller detects battery abnormality, the alarm system is triggered. The alarm system can warn through sound, light signal (LED, etc.) or remote notification so that timely countermeasures can be taken.

[0105] At the same time, the optional embodiment of the present application realizes real-time monitoring of the battery through precise abnormality recognition algorithm and real-time monitoring.

[0106] This device can monitor the status of the heating valve battery in real time and issue an alarm in time when an abnormality is detected to ensure the safe operation of the heating system. At the same time, due to the simple structure of the device, the cost is relatively low and it is easy to deploy and maintain.

[0107] Through the optional embodiment of the present application, the sensor component is responsible for real-time monitoring of the battery's voltage, current, temperature and other parameters. The controller receives the data collected by the sensor and processes and analyzes it through the built-in abnormality recognition algorithm to determine whether the battery has an abnormal condition. When an abnormality is detected, the alarm system will be triggered to emit a sound, light signal or alarm through remote notification so that appropriate measures can be taken in time. The optional embodiment of the present application can improve the comprehensive monitoring capability: it aims to monitor multiple parameters of the battery, such as voltage, current, temperature, etc., by introducing multi-sensor technology to achieve comprehensive monitoring of the battery status. This comprehensive monitoring method helps to understand the overall health status of the battery more accurately. Realize real-time monitoring and alarm: Taking into account the real-time requirements of the heating system, advanced data analysis technology is used to monitor the battery status in real time and issue an alarm immediately when an abnormality is detected. This helps to take quick measures to avoid heating system failures caused by battery problems. Improve the accuracy of abnormality detection: By introducing a deep learning algorithm for data analysis, the present invention can improve the sensitivity and accuracy of various battery abnormalities. This enables the system to more reliably identify problems such as battery aging and power exhaustion, reducing the possibility of false alarms and missed detections.

[0108] In general, the optional embodiments of the present application can improve the reliability and efficiency of abnormal detection of heating valve batteries, which will help improve the stability of the heating system, reduce maintenance costs, and provide more reliable heating services.

[0109] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0110] In this embodiment, an alarm device is also provided, which is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. As used below, the term "module" can implement a combination of software and / or hardware of a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0111] Figure 4 is a structural block diagram of an alarm device according to an embodiment of the present application, such as Figure 4As shown, the device comprises:

[0112] A monitoring module 42, configured to monitor operating parameters of a battery of a target valve according to a target sensor, wherein the operating parameters include at least one of the following: current data of the battery, voltage data of the battery, and temperature data of the battery;

[0113] A determination module 44, configured to determine the operating state of the target valve according to the operating parameters and an abnormality identification algorithm, wherein the operating state includes: a normal state and an abnormal state;

[0114] The alarm module 46 is used to start a multi-level intelligent alarm mechanism to perform alarm processing on the target valve when it is determined that the operating state of the target valve is an abnormal state, wherein the multi-level intelligent alarm mechanism includes: local alarm and remote alarm.

[0115] Through the above-mentioned device, the target sensor monitors the operating parameters of the battery of the target valve, such as the current data, voltage data, and temperature data; the operating state of the target valve is determined according to the operating parameters and the abnormality recognition algorithm, and the operating state includes: normal state and abnormal state; when it is determined that the operating state of the target valve is an abnormal state, a multi-level intelligent alarm mechanism including local alarm and remote alarm is started to perform alarm processing on the target valve. That is to say, the embodiment of the present application monitors the operating parameters of the battery of the target valve through the target sensor, and then determines the operating state of the target valve through the abnormality recognition algorithm, so as to determine whether the target valve is in an abnormal state, and then when it is determined that the target valve is in an abnormal state, a multi-level intelligent alarm mechanism can be started to issue an alarm. Through the embodiment of the present application, the problem that the battery abnormality of the valve cannot be found in the related technology, resulting in the valve being unable to operate normally, can be solved. Therefore, the battery abnormality of the valve can be discovered in time.

[0116] In an exemplary embodiment, the monitoring module 42 is also used to monitor the battery according to the target sensor to obtain the operating parameters when the target sensor is an integrated sensor; to monitor the current of the battery according to the current sensor to obtain the current data, and to monitor the voltage of the battery according to the voltage sensor to obtain the voltage data, and to monitor the temperature of the battery according to the temperature sensor to obtain the temperature data when the target sensor is a single sensor, wherein the target sensors include: the current sensor, the voltage sensor, and the temperature sensor.

[0117] In an exemplary embodiment, the determination module 44 is also used to determine the operating state of the target valve in one of the following ways: determining whether the operating parameter is within a preset parameter range through the operating parameter and the abnormality identification algorithm to determine the operating state of the target valve; determining the changing trend of the operating parameter through the operating parameter and the abnormality identification algorithm to determine the operating state of the target valve; determining whether the difference between the health state of the battery and the preset health state is within a preset difference range through the operating parameter and the abnormality identification algorithm to determine the operating state of the target valve.

[0118] In an exemplary embodiment, the determination module 44 is further used to determine whether the current data is within a preset current range, determine whether the voltage data is within a preset voltage range, and determine whether the temperature data is within a preset temperature range according to the abnormality identification algorithm, wherein the preset parameter range includes: the preset current range, the preset voltage range, and the preset temperature range; when the current data, the voltage data, and the temperature data are all not within the preset parameter range, determine that the operating state of the target valve is an abnormal state; when any parameter among the current data, the voltage data, and the temperature data is within the preset parameter range, and the other parameters are not within the preset parameter range, or when any parameter among the current data, the voltage data, and the temperature data is not within the preset parameter range, and the other parameters are within the preset parameter range, perform secondary identification on the operating parameters according to the abnormality identification algorithm to determine the operating state of the target valve, wherein the other parameters are other parameters among the operating parameters except the any parameter; when the current data, the voltage data, and the temperature data are all within the preset parameter range, determine that the operating state of the target valve is a normal state.

[0119] In an exemplary embodiment, the determination module 44 is further used to establish a current sequence according to the current data, a voltage sequence according to the voltage data, and a temperature sequence according to the temperature data; determine a current change trend according to the current sequence, determine a voltage change trend according to the voltage data, and determine a temperature change trend according to the temperature sequence; determine whether the current change trend conforms to a preset current change trend according to the abnormality identification algorithm, determine whether the voltage change trend conforms to a preset voltage change trend, and determine whether the temperature change trend conforms to a preset temperature change trend; when the current change trend, the voltage change trend, and the temperature change trend do not conform to a preset parameter change trend, determine that the operating state is an abnormal state, wherein the preset parameter change The trends include: the preset current change trend, the preset voltage change trend and the preset temperature change trend; when any parameter change trend among the current change trend, the voltage change trend and the temperature change trend does not conform to the preset parameter change trend, and the other parameter change trends all conform to the preset parameter change trend, and when it is obtained that any parameter change trend conforms to the preset parameter change trend, and the other parameter change trends do not conform to the preset parameter change trend, the current change trend, the voltage change trend and the temperature change trend are secondary identified according to the abnormal identification algorithm to determine the operating state; when the current change trend, the voltage change trend and the temperature change trend all conform to the preset parameter change trend, the operating state is determined to be a normal state.

[0120] In an exemplary embodiment, the determination module 44 is also used to train a battery model based on historical operating parameters of multiple preset batteries, and input the operating parameters into the battery model to obtain the health status of the battery, wherein the battery model is a model trained by a learning algorithm with the historical operating parameters as input values ​​and the target health status of the preset battery corresponding to the historical operating parameters as target values; the difference between the health status and the preset health status is determined according to the abnormality recognition algorithm; if the difference is not within the preset difference range, the operating status is determined to be an abnormal state; if the difference is within the preset difference range, the operating status is determined to be a normal state.

[0121] In an exemplary embodiment, the alarm module 46 is also used to send an alarm signal to a buzzer to make the buzzer emit an alarm sound, or convert the alarm signal into an optical signal and send the optical signal to a lighting device to make the lighting device emit an alarm light, wherein the local alarm includes: emitting an alarm sound or emitting an alarm light; and, determining the alarm information based on the operating status, and remotely sending the alarm information to a target device via wireless communication technology, wherein the target device is used to receive the alarm information, and the remote alarm includes: remotely sending the alarm information.

[0122] It should be noted that the above modules can be implemented by software or hardware. For the latter, it can be implemented in the following ways, but not limited to: the above modules are all located in the same processor; or the above modules are located in different processors in any combination.

[0123] An embodiment of the present application further provides a computer-readable storage medium, in which a computer program is stored, wherein the computer program is configured to execute the steps of any of the above method embodiments when running.

[0124] Optionally, in this embodiment, the storage medium may be configured to store program codes for executing the following steps:

[0125] S1, monitoring an operating parameter of a battery of a target valve according to a target sensor, wherein the operating parameter includes at least one of the following: current data of the battery, voltage data of the battery, and temperature data of the battery;

[0126] S2, determining the operating state of the target valve according to the operating parameters and the abnormality identification algorithm, wherein the operating state includes: a normal state and an abnormal state;

[0127] S3, when it is determined that the operating state of the target valve is an abnormal state, a multi-level intelligent alarm mechanism is started to perform alarm processing on the target valve, wherein the multi-level intelligent alarm mechanism includes: local alarm and remote alarm.

[0128] In an exemplary embodiment, the computer-readable storage medium may include, but is not limited to, various media that can store computer programs, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

[0129] An embodiment of the present application further provides an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0130] In an exemplary embodiment, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0131] Optionally, in this embodiment, the processor may be configured to perform the following steps through a computer program:

[0132] S1, monitoring an operating parameter of a battery of a target valve according to a target sensor, wherein the operating parameter includes at least one of the following: current data of the battery, voltage data of the battery, and temperature data of the battery;

[0133] S2, determining the operating state of the target valve according to the operating parameters and the abnormality identification algorithm, wherein the operating state includes: a normal state and an abnormal state;

[0134] S3, when it is determined that the operating state of the target valve is an abnormal state, a multi-level intelligent alarm mechanism is started to perform alarm processing on the target valve, wherein the multi-level intelligent alarm mechanism includes: local alarm and remote alarm.

[0135] An embodiment of the present application further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the steps in any one of the above method embodiments are implemented.

[0136] An embodiment of the present application further provides another computer program product, including a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in any of the above method embodiments are implemented.

[0137] An embodiment of the present application also provides a computer program, which includes computer instructions, which are stored in a computer-readable storage medium; a processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device performs the steps in any one of the above method embodiments.

[0138] Optionally, in this embodiment, the processor may be configured to perform the following steps through a computer program:

[0139] S1, monitoring an operating parameter of a battery of a target valve according to a target sensor, wherein the operating parameter includes at least one of the following: current data of the battery, voltage data of the battery, and temperature data of the battery;

[0140] S2, determining the operating state of the target valve according to the operating parameters and the abnormality identification algorithm, wherein the operating state includes: a normal state and an abnormal state;

[0141] S3, when it is determined that the operating state of the target valve is an abnormal state, a multi-level intelligent alarm mechanism is started to perform alarm processing on the target valve, wherein the multi-level intelligent alarm mechanism includes: local alarm and remote alarm.

[0142] For specific examples in this embodiment, reference may be made to the examples described in the above embodiments and exemplary implementation modes, and this embodiment will not be described in detail herein.

[0143] Obviously, those skilled in the art should understand that the above modules or steps of the present application can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order from that herein, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present application is not limited to any specific combination of hardware and software.

[0144] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the principles of the present application shall be included in the protection scope of the present application.

Claims

1. An alarm method, characterized in that: include: Monitoring an operating parameter of a battery of a target valve according to a target sensor, wherein the operating parameter includes at least one of the following: current data of the battery, voltage data of the battery, and temperature data of the battery; Determining the operating state of the target valve according to the operating parameters and the abnormality identification algorithm, wherein the operating state includes: a normal state and an abnormal state; When it is determined that the operating state of the target valve is abnormal, a multi-level intelligent alarm mechanism is started to perform alarm processing on the target valve, wherein the multi-level intelligent alarm mechanism includes: local alarm and remote alarm.

2. The method according to claim 1, characterized in that The target sensor monitors the operating parameters of the battery of the target valve, including: In the case where the target sensor is an integrated sensor, monitoring the battery according to the target sensor to obtain the operating parameter; In the case where the target sensor is a single sensor, the current of the battery is monitored according to the current sensor to obtain the current data, the voltage of the battery is monitored according to the voltage sensor to obtain the voltage data, and the temperature of the battery is monitored according to the temperature sensor to obtain the temperature data, wherein the target sensor includes: the current sensor, the voltage sensor, and the temperature sensor.

3. The method according to claim 1, characterized in that Determining the operating state of the target valve according to the operating parameters and the abnormality identification algorithm includes: The operating state of the target valve is determined by one of the following methods: Determining whether the operating parameter is within a preset parameter range by using the operating parameter and the abnormality identification algorithm to determine the operating state of the target valve; Determining a change trend of the operating parameter by using the operating parameter and the abnormality identification algorithm to determine the operating state of the target valve; The operating state of the target valve is determined by determining whether the difference between the health state of the battery and a preset health state is within a preset difference range through the operating parameters and the abnormality identification algorithm.

4. The method according to claim 3, characterized in that Determining whether the operating parameter is within a preset parameter range by using the operating parameter and the abnormality identification algorithm to determine the operating state of the target valve includes: Determine whether the current data is within a preset current range, determine whether the voltage data is within a preset voltage range, and determine whether the temperature data is within a preset temperature range according to the abnormality recognition algorithm, wherein the preset parameter range includes: the preset current range, the preset voltage range, and the preset temperature range; When the current data, the voltage data and the temperature data are all outside the preset parameter range, determining that the operating state of the target valve is an abnormal state; When any one of the current data, the voltage data and the temperature data is within the preset parameter range and the other parameters are not within the preset parameter range, or when any one of the current data, the voltage data and the temperature data is not within the preset parameter range and the other parameters are within the preset parameter range, the operating parameters are secondary identified according to the abnormality identification algorithm to determine the operating state of the target valve, wherein the other parameters are the other parameters of the operating parameters except the any one parameter; when the current data, the voltage data and the temperature data are all within the preset parameter range, it is determined that the operating state of the target valve is normal.

5. The method according to claim 3, characterized in that: Determining the change trend of the operating parameter by using the operating parameter and the abnormality identification algorithm to determine the operating state of the target valve includes: Establishing a current sequence according to the current data, establishing a voltage sequence according to the voltage data, and establishing a temperature sequence according to the temperature data; Determining a current variation trend according to the current sequence, determining a voltage variation trend according to the voltage data, and determining a temperature variation trend according to the temperature sequence; Determining whether the current change trend conforms to a preset current change trend according to the abnormality identification algorithm, determining whether the voltage change trend conforms to a preset voltage change trend, and determining whether the temperature change trend conforms to a preset temperature change trend; In the case that the current change trend, the voltage change trend and the temperature change trend all do not conform to the preset parameter change trend, determining that the operating state is an abnormal state, wherein the preset parameter change trend includes: the preset current change trend, the preset voltage change trend and the preset temperature change trend; When any parameter change trend among the current change trend, the voltage change trend and the temperature change trend does not conform to the preset parameter change trend, and the other parameter change trends all conform to the preset parameter change trend, obtaining that any parameter change trend conforms to the preset parameter change trend, and the other parameter change trends do not conform to the preset parameter change trend, performing secondary recognition on the current change trend, the voltage change trend and the temperature change trend according to the abnormality recognition algorithm to determine the operating state; When the current variation trend, the voltage variation trend, and the temperature variation trend all conform to the preset parameter variation trend, it is determined that the operating state is a normal state.

6. The method according to claim 3, characterized in that: Determining whether the difference between the health state of the battery and the preset health state is within a preset difference range by using the operating parameter and the abnormality identification algorithm, so as to determine the operating state of the target valve, includes: Training a battery model according to historical operating parameters of a plurality of preset batteries, and inputting the operating parameters into the battery model to obtain the health state of the battery, wherein the battery model is a model trained by a learning algorithm using the historical operating parameters as input values ​​and the target health state of the preset battery corresponding to the historical operating parameters as target values; Determining a difference between the health state and the preset health state according to the abnormality recognition algorithm; When the difference is not within the preset difference range, determining that the operating state is an abnormal state; When the difference is within the preset difference range, it is determined that the operating state is a normal state.

7. The method according to claim 1, characterized in that A multi-level intelligent alarm mechanism is initiated to perform alarm processing on the target valve, including: Sending an alarm signal to a buzzer so that the buzzer emits an alarm sound, or converting the alarm signal into an optical signal and sending the optical signal to a lighting device so that the lighting device emits an alarm light, wherein the local alarm includes: emitting an alarm sound or emitting an alarm light; and, Alarm information is determined according to the operating status, and the alarm information is remotely sent to a target device via wireless communication technology, wherein the target device is used to receive the alarm information, and the remote alarm includes: remotely sending the alarm information.

8. An alarm device, characterized in that: include: A monitoring module, configured to monitor operating parameters of a battery of a target valve according to a target sensor, wherein the operating parameters include at least one of the following: current data of the battery, voltage data of the battery, and temperature data of the battery; A determination module, configured to determine the operating state of the target valve according to the operating parameters and an abnormality identification algorithm, wherein the operating state includes: a normal state and an abnormal state; The alarm module is used to start a multi-level intelligent alarm mechanism to perform alarm processing on the target valve when it is determined that the operating state of the target valve is an abnormal state, wherein the multi-level intelligent alarm mechanism includes: local alarm and remote alarm.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein the program executes the method of any one of claims 1 to 7 when executed.

10. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to execute the method according to any one of claims 1 to 7 through the computer program.

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