Solenoid valve fault on-line monitoring method, device, equipment and medium

Abstract

The invention relates to the field of electromagnetic valve monitoring, in particular to an electromagnetic valve fault online monitoring method and device, equipment and a medium. The method comprises the following steps: acquiring gas output data and gas supply data of a to-be-monitored solenoid valve, and determining whether the to-be-monitored solenoid valve has a suspicious fault or not based on the gas output data and the gas supply data; if the to-be-monitored solenoid valve has a suspicious fault, acquiring detection data corresponding to the to-be-monitored solenoid valve, determining a fault type corresponding to the suspicious fault based on the detection data, generating a detection task based on the fault type corresponding to the suspicious fault, receiving a detection result corresponding to the detection task, and generating a fault monitoring report based on the detection result, the fault of the electromagnetic valve can be found and positioned in time, the reliability and safety of equipment are improved, a scientific basis is provided for repair and maintenance of the equipment, and the labor cost for maintaining the equipment is effectively reduced.

Description

Technical Field

[0001] The present application relates to the field of solenoid valve monitoring, and in particular to a method, device, equipment and medium for online monitoring of solenoid valve faults. Background Art

[0002] Solenoid valves are industrial equipment controlled by electromagnetics. They are basic components for controlling fluids. They are actuators and are not limited to hydraulic or pneumatic. They are used in industrial control systems to adjust the direction, flow, speed and other parameters of the medium.

[0003] As a key component in industrial automation systems, the operating status of solenoid valves is directly related to the stability and efficiency of the entire system. Therefore, there are usually special personnel to regularly inspect the solenoid valves to promptly discover and deal with potential problems of the solenoid valves, such as wear, aging, looseness, etc.; or when there is a problem with the solenoid valve, special personnel will inspect and repair it to ensure the stable operation of the system. However, the method of conducting inspections at special times and points or repairing after a failure requires a lot of labor costs. Summary of the invention

[0004] In order to reduce the labor cost in the process of solenoid valve fault monitoring, the present application provides a solenoid valve fault online monitoring method, device, equipment and medium.

[0005] In a first aspect, the present application provides a method for online monitoring of solenoid valve faults, which adopts the following technical solution: A solenoid valve fault online monitoring method, comprising: Obtain the gas output volume data and gas delivery volume data of the solenoid valve to be monitored; Based on the gas output data and the gas delivery data, determining whether the solenoid valve to be monitored has a suspected fault; If the solenoid valve to be monitored has a suspected fault, then obtaining detection data corresponding to the solenoid valve to be monitored, the detection data including valve voltage, valve caliber, valve current and valve accuracy; Based on the detection data, determine the fault type corresponding to the suspected fault, and based on the fault type corresponding to the suspected fault, generate a detection task, the detection task including a task air delivery volume and task detection data; Receive the detection result corresponding to the detection task, and generate a fault monitoring report based on the detection result.

[0006] By adopting the above technical scheme, by obtaining the gas output data and gas delivery data of the solenoid valve to be monitored, the working status of the solenoid valve can be understood in real time, ensuring the accuracy and completeness of the data, and providing a reliable basis for subsequent analysis. Then, based on the comparison and analysis of the gas output data and the gas delivery data, it is possible to preliminarily judge whether the solenoid valve has a suspected fault. If the solenoid valve has a suspected fault, further obtain the detection data related to the solenoid valve, including valve voltage, valve caliber, valve current and valve accuracy, and then determine the fault type based on the detection data, and generate the corresponding detection task. The detection task clarifies the specific content and requirements that need to be detected, including the task gas output and task detection data, etc., to ensure the pertinence and effectiveness of fault detection. Then, receive the detection results corresponding to the detection task, and generate a fault monitoring report based on the detection results. In summary, by obtaining and analyzing the gas output data, gas delivery data and detection data of the solenoid valve, the fault of the solenoid valve can be discovered and located in time, and the corresponding detection task and fault monitoring report can be generated. This not only improves the reliability and safety of the equipment, but also provides a scientific basis for the repair and maintenance of the equipment, and effectively reduces the labor cost in the process of maintaining the equipment.

[0007] In a possible implementation, determining whether the solenoid valve to be monitored has a suspected fault based on the gas output volume data and the gas delivery volume data includes: Determining whether the gas output volume data includes a plurality of gas output quanta data; If the gas output volume data includes a gas output sub-data, determining whether the gas output volume data is consistent with the gas delivery volume data; if the gas output volume data is inconsistent with the gas delivery volume data, determining that the solenoid valve to be monitored has the suspected fault; If the gas output volume data contains at least two gas output quantum data, determine whether the gas output volume data is consistent with the gas supply volume data; if the gas output volume data is inconsistent with the gas supply volume data, determine that the solenoid valve to be monitored has the suspected fault; if the gas output volume data is consistent with the gas supply volume data, obtain the gas volume distribution principle corresponding to the solenoid valve to be monitored, and based on the gas volume distribution principle, determine the standard gas output quantum data corresponding to the gas output volume data, and compare each standard gas output quantum data with the corresponding gas output quantum data to determine whether each standard gas output quantum data is consistent with the corresponding gas output quantum data; if there is inconsistency between the standard gas output quantum data and the corresponding gas output quantum data, determine that the solenoid valve to be monitored has the suspected fault.

[0008] By adopting the above technical solution, by determining whether the air supply volume data contains multiple air supply quantum data, a basis is provided for subsequent analysis. When the air supply volume data contains only one air supply quantum data, the consistency of the air output volume data and the air supply volume data is directly compared. If the two are inconsistent, it is located that the solenoid valve to be monitored may have a suspected fault; when the air supply volume data contains at least two air supply quantum data, in addition to comparing the overall consistency of the air output volume data and the air supply volume data, the air volume distribution principle corresponding to the solenoid valve to be monitored is further introduced, thereby reflecting an in-depth understanding of the working characteristics of the solenoid valve, because different solenoid valves may have different air volume distribution requirements. By comparing the standard air supply quantum data determined based on the air volume distribution principle with the actual air supply quantum data, the technology can more accurately identify whether there is an abnormality in the air volume distribution of the solenoid valve. If there is a situation where the standard air supply quantum data is inconsistent with the corresponding air supply quantum data, it is accurately judged that the solenoid valve to be monitored has a suspected fault. The technology achieves accurate judgment on whether the monitored solenoid valve has suspected faults through detailed analysis of air supply volume data, comparison of air outlet volume data with air supply volume data, and comparison of standard air supply quantum data with air supply quantum data based on the air volume distribution principle, thereby improving the efficiency and accuracy of solenoid valve fault detection.

[0009] In a possible implementation manner, the fault type is a component fault or an electromagnetic fault, and determining the fault type corresponding to the suspected fault based on the detection data includes: Calculating gas consumption data based on the valve voltage, the valve caliber, and the valve accuracy; Determining whether the gas output data is consistent with the gas consumption data; If the gas output data is inconsistent with the gas consumption data, determining that the fault type corresponding to the suspected fault is a component fault; If the gas output data is consistent with the gas consumption data, it is determined that the fault type corresponding to the suspected fault is an electromagnetic fault.

[0010] By adopting the above technical solution, the gas consumption data is calculated by comprehensively considering multiple parameters such as valve voltage, valve diameter, valve current and valve accuracy, which can more accurately simulate or predict the gas consumption of the solenoid valve under normal working conditions. Compare the calculated gas consumption data with the actual gas output data. If the gas output data is inconsistent with the gas consumption data, it means that there is a problem with the gas control of the solenoid valve, which may be caused by component damage, aging or blockage. Therefore, the technology determines the fault type corresponding to the suspected fault as a component fault; if the gas output data is consistent with the gas consumption data, it means that the solenoid valve performs normally in gas control, but there is still a suspicious fault signal. The technology determines the fault type corresponding to the suspected fault as an electromagnetic fault. In summary, by comprehensively considering multiple parameters to calculate the gas consumption data and comparing it with the actual gas output data, combined with the working principle and characteristics of the solenoid valve, the fault type corresponding to the suspected fault can be accurately determined, which provides strong support for subsequent fault handling.

[0011] In a possible implementation, when the fault type corresponding to the suspected fault is a component fault, generating a detection task based on the fault type corresponding to the suspected fault includes: Acquire historical detection data of the solenoid valve to be monitored, wherein the historical detection data includes historical detection results and historical parameters of each component corresponding to each periodic detection, and the historical detection results are detection pass or detection fail; Based on the historical detection data, determining the correlation between every two components in the solenoid valve to be monitored, wherein the correlation is the degree of mutual influence between the components; Based on the correlation between every two components and the fault type corresponding to the suspected fault, the components to be detected are determined, and the detection tasks corresponding to the components to be detected are generated.

[0012] By adopting the above technical solution, the historical detection data of the solenoid valve to be monitored is obtained, and the correlation between every two components inside the solenoid valve is calculated using the historical detection data. Based on the correlation between every two components and the fault type (component fault) corresponding to the suspected fault, the component to be detected is determined, and the corresponding detection task is generated. By giving priority to the detection of components with a high correlation with the fault type, the source of the fault can be located more quickly, and the detection efficiency and accuracy can be improved. In summary, by obtaining historical detection data, calculating the component correlation, and determining the component to be detected based on the correlation and fault type, an efficient and reliable method is provided for the precise positioning and detection of solenoid valve component faults. It not only improves the detection efficiency, but also helps to reduce misjudgments and missed detections, providing strong technical support for the maintenance and maintenance of solenoid valves.

[0013] In a possible implementation, determining the correlation between every two components of the solenoid valve to be monitored based on the historical detection data includes: Filtering target detection sub-data from the historical detection data, the target detection sub-data being the target historical detection time and component corresponding to the detection failure; Based on formula 1: , get the target historical time period between every two elements, where T is the target historical time period and t is the time. is the historical time period corresponding to the first element of any two elements, is a historical time period corresponding to the second element of the arbitrary two elements, and the common target historical detection moment includes the target historical detection moments corresponding to the arbitrary two elements respectively; Based on the historical detection data and formula 2, the parameter change degree corresponding to each component is determined, where formula 2 is , C is the degree of parameter change, is the historical parameter corresponding to the historical detection moment of the common target, for The historical parameters corresponding to the last detection time of for The historical parameters corresponding to the end detection time in the corresponding target historical time period, for The historical parameters corresponding to the start detection time in the corresponding target historical time period; Based on the parameter change degree corresponding to each component and the target historical time period between every two components, and based on Formula 3, the correlation between every two components is determined, where Formula 3 is: , S is the correlation degree, and are the parameter change degrees corresponding to the two components respectively.

[0014] By adopting the above technical solution, an effective method is provided for the correlation analysis between the internal components of the solenoid valve by screening the target detection sub-data, calculating the target historical time period, calculating the parameter change degree and correlation, etc. It not only reveals the mutual influence relationship between the components, but also provides strong data support and technical means for subsequent fault location and detection.

[0015] In a possible implementation, determining the components to be detected based on the correlation between every two components and the fault type corresponding to the suspected fault includes: Based on the historical inspection data, a first component sequence is generated in descending order of the number of times that the inspection result of each component fails the inspection; Determine the priority corresponding to each element based on the first element sequence and the association degree between every two elements; A second component sequence is generated based on the priority corresponding to each component, and the first component in the second component sequence is determined as the component to be detected.

[0016] By adopting the above technical solution, an effective method is provided for fault detection of internal components of solenoid valves by collating historical detection data, adjusting component priority by using correlation, and generating a second component sequence. Not only the fault frequency of the component itself is considered, but also the mutual influence between components is considered, so that the components to be detected can be determined more accurately, and the detection efficiency and accuracy can be improved.

[0017] In a possible implementation manner, when the fault type corresponding to the suspected fault is an electromagnetic fault, generating a detection task based on the fault type corresponding to the suspected fault includes: Acquire standard data corresponding to the solenoid valve to be monitored, wherein the standard data includes standard magnetic force and standard voltage; Based on the standard data, determining the detection magnetic force and the detection voltage of the electromagnetic valve to be monitored to obtain task detection data, and determining the task gas output corresponding to the standard data; A detection task is generated based on the task gas output and the task detection data.

[0018] By adopting the above technical solution, an effective method is provided for the detection of electromagnetic faults of solenoid valves through the steps of obtaining standard data, measuring actual electromagnetic performance parameters, determining task gas output, and generating detection tasks. This method not only improves the detection efficiency and accuracy, but also provides strong technical support for the maintenance and care of solenoid valves.

[0019] In a second aspect, the present application provides an online monitoring device for electromagnetic valve faults, which adopts the following technical solution: An online monitoring device for electromagnetic valve faults, comprising: The first acquisition module is used to acquire the gas output volume data and gas delivery volume data of the solenoid valve to be monitored; A determination module, used for determining whether the solenoid valve to be monitored has a suspected fault based on the gas output volume data and the gas delivery volume data; A second acquisition module is used to acquire detection data corresponding to the solenoid valve to be monitored if there is a suspected fault in the solenoid valve to be monitored, wherein the detection data includes valve voltage, valve caliber, valve current and valve accuracy; A generating module, configured to determine the fault type corresponding to the suspected fault based on the detection data, and generate a detection task based on the fault type corresponding to the suspected fault, wherein the detection task includes a task air delivery volume and task detection data; The receiving module is used to receive the detection result corresponding to the detection task and generate a fault monitoring report based on the detection result.

[0020] In a third aspect, the present application provides an electronic device, which adopts the following technical solution: An electronic device, comprising: at least one processor; Memory; At least one application, wherein the at least one application is stored in a memory and configured to be executed by at least one processor, and the at least one application is configured to: execute the solenoid valve fault online monitoring method described in the first aspect above.

[0021] In a fourth aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution: A computer-readable storage medium includes: a computer program that can be loaded by a processor and execute the solenoid valve fault online monitoring method described in the first aspect.

[0022] In summary, this application includes the following beneficial technical effects: By obtaining the gas output data and gas delivery data of the solenoid valve to be monitored, the working status of the solenoid valve can be understood in real time, ensuring the accuracy and completeness of the data, and providing a reliable basis for subsequent analysis. Then, based on the comparison and analysis of the gas output data and the gas delivery data, it is possible to preliminarily determine whether the solenoid valve has a suspected fault. If the solenoid valve has a suspected fault, further obtain the detection data related to the solenoid valve, including valve voltage, valve caliber, valve current, and valve accuracy, and then determine the fault type based on the detection data and generate the corresponding detection task. The detection task clarifies the specific content and requirements that need to be detected, including the task gas output and task detection data, etc., to ensure the pertinence and effectiveness of fault detection. Then, receive the detection results corresponding to the detection task, and generate a fault monitoring report based on the detection results. In summary, by obtaining and analyzing the gas output data, gas delivery data, and detection data of the solenoid valve, the fault of the solenoid valve can be discovered and located in time, and the corresponding detection task and fault monitoring report can be generated. This not only improves the reliability and safety of the equipment, but also provides a scientific basis for the repair and maintenance of the equipment, effectively reducing the labor cost in the process of maintaining the equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1It is a flow chart of a solenoid valve fault online monitoring method provided in an embodiment of the present application; Figure 2 It is a block diagram of an online monitoring device for electromagnetic valve faults provided in an embodiment of the present application; Figure 3 It is a schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0024] The following is combined with Figure 1 -Attached Figure 3 This application is described in further detail.

[0025] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0026] There is a closed cavity in the solenoid valve with through holes at different positions. Each hole is connected to a different oil pipe. There is a piston in the middle of the cavity and two electromagnets on both sides. The valve body will be attracted to the side where the magnet coil is energized. The different oil drain holes are opened or closed by controlling the movement of the valve body. The oil inlet hole is normally open, and the hydraulic oil will enter different oil drain pipes. The oil pressure then pushes the piston of the oil cylinder, which in turn drives the piston rod, and the piston rod drives the mechanical device.

[0027] In industrial automation systems, solenoid valves play a vital role, and their operating status is directly related to the stability and production efficiency of the entire system. In order to ensure that the solenoid valves can work continuously and reliably, companies often arrange special technicians to conduct regular inspections on them. These inspections are aimed at timely discovering and dealing with potential problems that may exist in the solenoid valves, such as wear and aging of components, or loose connections, so as to effectively prevent the occurrence of faults and ensure the smooth operation of the system.

[0028] However, traditional inspection methods often rely on manual regular and fixed-point inspections, or repairs after the solenoid valve fails. Although this method can ensure the reliability of the solenoid valve to a certain extent, it hides high labor costs. Each inspection requires technicians to go to the site in person to conduct meticulous investigations and tests, which is not only time-consuming and labor-intensive, but also increases the company's operating costs.

[0029] The present application embodiment provides a method for online monitoring of electromagnetic valve faults, such as Figure 1As shown, the method provided in the embodiment of the present application is performed by an electronic device, which can be a server or a terminal device, wherein the server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. The terminal device can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc., but is not limited thereto. The terminal device and the server can be directly or indirectly connected via wired or wireless communication, which is not limited in the embodiment of the present application. The method includes steps S101 to S105, wherein: Step S101, obtaining the gas output volume data and gas delivery volume data of the solenoid valve to be monitored.

[0030] Among them, a flow meter is installed upstream (i.e., the air inlet end) and downstream (i.e., the air outlet end) of the solenoid valve to be monitored, which is used to measure the air outlet data and air supply data of the solenoid valve. Specifically, when the solenoid valve is opened, the gas enters the solenoid valve through the upstream flow meter. At this time, the data detected by the upstream flow meter is the air supply data of the solenoid valve; after passing through the solenoid valve, the gas will flow through the downstream flow meter, and the data detected by the downstream flow meter is the air outlet data.

[0031] More specifically, the electronic device is connected to the flow meter upstream and downstream of the electromagnetic valve to be monitored, respectively, and the electronic device can directly obtain the data detected by the flow meter of the electromagnetic valve to be monitored per unit time, so as to obtain the gas output volume data and gas delivery volume data of the electromagnetic valve to be monitored at the current moment. Furthermore, the electronic device stores the obtained gas output volume data and gas delivery volume data of the electromagnetic valve to be monitored in the database corresponding to the electromagnetic valve to be monitored.

[0032] Step S102: Determine whether the solenoid valve to be monitored has a suspected fault based on the air outlet volume data and the air supply volume data.

[0033] When the solenoid valve is working normally, the air output data and air supply data of the solenoid valve are generally consistent. Therefore, by comparing the air output data and air supply data of the solenoid valve to be monitored, it can be determined whether the solenoid valve to be monitored has a fault. Specifically, after obtaining the air output data and air supply data of the solenoid valve to be monitored, it can be determined whether the air output data and the air supply data are consistent to determine whether the solenoid valve to be monitored has a suspected fault. More specifically, the air output data is compared with the air supply data to determine whether the air output data is consistent with the air supply data. If the air output data is consistent with the air supply data, it is determined that the solenoid valve to be monitored does not have a suspected fault; if the air output data is inconsistent with the air supply data, it is determined that the solenoid valve to be monitored has a suspected fault.

[0034] More specifically, there are two situations in which the air outlet volume data is inconsistent with the air supply volume data, namely, the air outlet volume data is greater than the air supply volume data and the air outlet volume data is less than the air supply volume data. When the air outlet volume data is greater than the air supply volume data, it means that the flow meter on the solenoid valve to be monitored may be faulty. At this time, an alarm message is directly generated to notify the staff to inspect the solenoid valve to be monitored and the corresponding flow meter; when the air outlet volume data is less than the air supply volume data, it is determined that the solenoid valve to be monitored has a suspected fault.

[0035] Step S103: if the solenoid valve to be monitored has a suspected fault, then obtain the detection data corresponding to the solenoid valve to be monitored.

[0036] Among them, the detection data includes valve voltage, valve caliber, valve current and valve accuracy. Specifically, the valve voltage and valve current of the solenoid valve during normal operation are generally only related to the coil, that is, the valve voltage and valve current of the solenoid valve to be monitored are related to the model of the solenoid valve. The normal operating voltage corresponding to the solenoid valve to be monitored can be obtained from the database corresponding to the solenoid valve to be monitored to obtain the valve voltage, and the accuracy corresponding to the solenoid valve to be monitored can be obtained from the database corresponding to the solenoid valve to be monitored to obtain the valve accuracy; the proportional relationship between the flow rate and the caliber corresponding to the solenoid valve to be monitored can be obtained from the database corresponding to the solenoid valve to be monitored, and the air supply volume data can be brought into the proportional relationship to obtain the valve caliber; the current data of the current sensor installed on the solenoid valve to be monitored at the current moment can be obtained to obtain the valve current of the solenoid valve to be monitored.

[0037] Step S104: Determine the fault type corresponding to the suspected fault based on the detection data, and generate a detection task based on the fault type corresponding to the suspected fault.

[0038] The detection task includes the task gas delivery volume and the task detection data. The fault type can be a type I fault or a type II fault, wherein a type I fault is a fault that requires maintenance by a staff member, and a type II fault is a fault that does not require maintenance by a staff member.

[0039] After obtaining the detection data, the gas consumption data corresponding to the electromagnetic valve to be monitored at the current moment can be calculated based on the detection data, and the fault type corresponding to the suspected fault can be determined based on the gas consumption data, where the gas consumption refers to the volume of gas consumed from the gas source when the electromagnetic valve is opened and swung within a certain period of time. Specifically, based on gas consumption = (valve pressure × valve diameter) ÷ (unit time × valve accuracy), the gas consumption data corresponding to the electromagnetic valve to be monitored is calculated.

[0040] Furthermore, determine whether the gas consumption data corresponding to the solenoid valve to be monitored is consistent with the gas delivery data. If the gas consumption data is inconsistent with the gas delivery data, it can be determined that the fault of the solenoid valve to be monitored is a fault that requires maintenance by staff, that is, a Class I fault; if the gas consumption data is consistent with the gas delivery data, it is determined that the fault of the solenoid valve to be monitored is a fault that does not require maintenance by staff, that is, a Class II fault.

[0041] Furthermore, when the fault of the solenoid valve to be monitored is a type of fault, the process of generating a detection task can be: using the valve voltage and valve current in the detection data as task detection data, and obtaining the task air supply volume preset in the database corresponding to the solenoid valve to be monitored, so as to obtain the task air supply volume corresponding to the detection task, and obtain the detection task based on the task detection data and task air supply volume corresponding to the solenoid valve to be monitored.

[0042] When the fault of the solenoid valve to be monitored is a Class II fault, the process of generating a detection task can be: obtaining the preset task air supply volume, preset task valve voltage, preset task valve current and preset task valve caliber in the database corresponding to the solenoid valve to be monitored, and using the preset task air supply volume, preset task valve voltage and valve current as the first detection subtask. Similarly, using the preset task air supply volume, preset task valve current and valve voltage as the second detection subtask, and using the first detection subtask and the second detection subtask as detection tasks.

[0043] Furthermore, after obtaining the detection task, the parameters of the solenoid valve to be monitored are controlled according to the task air supply volume corresponding to the detection task and the task detection data, so that the solenoid valve to be monitored performs the detection task.

[0044] Step S105: receiving the detection result corresponding to the detection task, and generating a fault monitoring report based on the detection result.

[0045] The detection result may include the detected gas output. Specifically, after the electromagnetic valve to be monitored performs the detection task, the gas output data of the electromagnetic valve to be monitored may be obtained to obtain the detected gas output, thereby obtaining the detection result.

[0046] Furthermore, when the fault of the solenoid valve to be monitored is a Class I fault, it is determined whether the detected air output volume is consistent with the task air supply volume. If the detected air output volume is consistent with the task air supply volume, it is determined that the solenoid valve to be monitored has not failed, that is, there is no suspected fault in the solenoid valve to be monitored, and a fault monitoring report is generated that the air output volume data of the solenoid valve to be monitored is consistent with the air supply volume data; if the detected air output volume is inconsistent with the task air supply volume, it is determined that the solenoid valve to be monitored has failed, and a fault monitoring report that the air output volume data is inconsistent with the air supply volume data is generated.

[0047] When the fault of the solenoid valve to be monitored is a Class II fault, the detected gas output corresponding to the first detection subtask is compared with the task gas supply volume, and the detected gas output corresponding to the second detection subtask is compared with the task gas supply volume, so as to determine whether the detected gas output corresponding to the first detection subtask is consistent with the task gas supply volume, and whether the detected gas output corresponding to the second detection subtask is consistent with the task gas supply volume. If the detected gas output corresponding to the first detection subtask is consistent with the task gas supply volume, and the detected gas output corresponding to the second detection subtask is consistent with the task gas supply volume, it is determined that the solenoid valve to be monitored has no fault, that is, there is no suspected fault in the solenoid valve to be monitored, and a fault monitoring report is generated that the gas output data of the solenoid valve to be monitored is consistent with the gas supply data. If the detected gas output corresponding to the first detection subtask is inconsistent with the task gas supply volume, and the detected gas output corresponding to the second detection subtask is consistent with the task gas supply volume, it is determined that the valve voltage parameter of the solenoid valve to be monitored has a fault, and a fault monitoring report that the valve voltage parameter has a fault is generated. If the detected gas output corresponding to the first detection subtask is consistent with the task gas supply judgment, and the detected gas output corresponding to the second detection subtask is inconsistent with the task gas supply judgment, it is determined that the valve current parameter of the solenoid valve to be monitored is faulty, and a fault monitoring report indicating that the valve current parameter is faulty is generated.

[0048] The embodiment of the present application provides an online monitoring method for electromagnetic valve faults. By acquiring the gas output data and gas delivery data of the electromagnetic valve to be monitored, the working state of the electromagnetic valve can be understood in real time, ensuring the accuracy and integrity of the data, and providing a reliable basis for subsequent analysis. Then, based on the comparison and analysis of the gas output data and the gas delivery data, it is possible to preliminarily determine whether the electromagnetic valve has a suspected fault. If the electromagnetic valve has a suspected fault, further obtain the detection data related to the electromagnetic valve, including valve voltage, valve caliber, valve current and valve accuracy, and then determine the fault type based on the detection data, and generate a corresponding detection task. The detection task clarifies the specific content and requirements that need to be detected, including the task gas output and task detection data, etc., to ensure the pertinence and effectiveness of fault detection. Then, receive the detection result corresponding to the detection task, and generate a fault monitoring report based on the detection result. In summary, by acquiring and analyzing the gas output data, gas delivery data and detection data of the electromagnetic valve, the fault of the electromagnetic valve can be discovered and located in time, and the corresponding detection task and fault monitoring report can be generated. This not only improves the reliability and safety of the equipment, but also provides a scientific basis for the repair and maintenance of the equipment, and effectively reduces the labor cost in the process of maintaining the equipment.

[0049] In a possible implementation of the embodiment of the present application, in the above step S102, determining whether the solenoid valve to be monitored has a suspected fault based on the gas output data and the gas delivery data may specifically include: Determine whether the gas output data includes multiple gas output quanta data; If the gas output volume data includes a gas output sub-data, it is determined whether the gas output volume data is consistent with the gas supply volume data. If the gas output volume data is inconsistent with the gas supply volume data, it is determined that there is a suspected fault in the solenoid valve to be monitored; If the gas output volume data contains at least two gas output quantum data, determine whether the gas output volume data is consistent with the gas supply volume data; if the gas output volume data is inconsistent with the gas supply volume data, determine that there is a suspected fault in the solenoid valve to be monitored; if the gas output volume data is consistent with the gas supply volume data, obtain the gas volume distribution principle corresponding to the solenoid valve to be monitored, and based on the gas volume distribution principle, determine the standard gas output quantum data corresponding to the gas output volume data, and compare each standard gas output quantum data with the corresponding gas output quantum data to determine whether each standard gas output quantum data is consistent with the corresponding gas output quantum data; if there is an inconsistency between the standard gas output quantum data and the corresponding gas output quantum data, determine that there is a suspected fault in the solenoid valve to be monitored.

[0050] Among them, when the gas output volume data contains multiple gas output sub-data, the gas output volume will be distributed to multiple gas output ends according to a certain principle (ie, the gas output volume distribution principle), and each gas output end corresponds to a flow meter for measuring the gas output sub-data.

[0051] Specifically, obtain from the database of the solenoid valve to be monitored whether there is a gas distribution principle for the solenoid valve to be monitored at the current moment. If the solenoid valve to be monitored has a gas distribution principle at the current moment, determine that the gas output data includes multiple gas output quantum data; if the solenoid valve to be monitored does not have a gas distribution principle at the current moment, determine that the gas output data includes multiple gas output quantum data.

[0052] Furthermore, when the gas output volume data only contains one gas output sub-data, it is directly determined whether the gas output volume data is consistent with the gas supply volume data. If the gas output volume data is inconsistent with the gas supply volume data, it is determined that there is a suspected fault in the solenoid valve to be monitored; if the gas output volume data is consistent with the gas supply volume data, it is determined that there is no suspected fault in the solenoid valve to be monitored.

[0053] When the gas output volume data includes multiple gas output quantum data, it can be determined whether the gas output volume data is consistent with the gas supply volume data. If the gas output volume data is inconsistent with the gas supply volume data, it is determined that the solenoid valve to be monitored has a suspected fault; if the gas output volume data is consistent with the gas supply volume data, the gas volume distribution principle corresponding to the solenoid valve to be monitored is obtained from the database of the solenoid valve to be monitored, and based on the gas volume distribution principle, each standard gas output quantum data is obtained, and each standard gas output quantum data is compared with the corresponding gas output quantum data to determine whether there is an inconsistency between the standard gas output quantum data and the corresponding gas output quantum data. If there is an inconsistency between the standard gas output quantum data and the corresponding gas output quantum data, it is determined that the solenoid valve to be monitored has a suspected fault; if there is no inconsistency between the standard gas output quantum data and the corresponding gas output quantum data, it is determined that the solenoid valve to be monitored has no suspected fault.

[0054] A possible implementation manner of the embodiment of the present application, in the above embodiment, the fault type may be a component fault or an electromagnetic fault, and determining the fault type corresponding to the suspected fault based on the detection data may specifically include: Based on valve voltage, valve diameter and valve accuracy, the gas consumption data is calculated; Determine whether the gas output data is consistent with the gas consumption data; If the gas output data is inconsistent with the gas consumption data, it is determined that the fault type corresponding to the suspected fault is a component fault; If the gas output data is consistent with the gas consumption data, it is determined that the fault type corresponding to the suspected fault is an electromagnetic fault.

[0055] Among them, the component failure is the failure of the components inside the solenoid valve, and the electromagnetic failure is the failure that the set parameters of the solenoid valve are inconsistent with the actual parameters.

[0056] After obtaining the detection data, the gas consumption data corresponding to the electromagnetic valve to be monitored at the current moment can be calculated based on the detection data, and the fault type corresponding to the suspected fault can be determined based on the gas consumption data, where the gas consumption refers to the volume of gas consumed from the gas source when the electromagnetic valve is opened and swung within a certain period of time. Specifically, based on gas consumption = (valve pressure × valve diameter) ÷ (unit time × valve accuracy), the gas consumption data corresponding to the electromagnetic valve to be monitored is calculated.

[0057] Furthermore, it is determined whether the gas consumption data corresponding to the solenoid valve to be monitored is consistent with the gas supply data. If the gas consumption data is inconsistent with the gas supply data, it is determined that the fault of the solenoid valve to be monitored is a component fault; if the gas consumption data is consistent with the gas supply data, it is determined that the fault of the solenoid valve to be monitored is an electromagnetic fault.

[0058] A possible implementation manner of the embodiment of the present application, in the above embodiment, when the fault type corresponding to the suspected fault is a component fault, generating a detection task based on the fault type corresponding to the suspected fault includes: Obtain historical detection data of the solenoid valve to be monitored, the historical detection data including historical detection results and historical parameters of each component corresponding to each periodic detection, the historical detection results being detection pass or detection fail; Based on historical detection data, determine the correlation between every two components of the solenoid valve to be monitored, where the correlation is the degree of mutual influence between the components; Based on the correlation between every two components and the fault type corresponding to the suspected fault, the components to be detected are determined, and the detection tasks corresponding to the components to be detected are generated.

[0059] Each solenoid valve database stores a component detection task corresponding to each internal component of the corresponding solenoid valve, that is, the database of the solenoid valve to be monitored stores a component detection task corresponding to each internal component of the solenoid valve to be monitored. The component detection task includes component detection data and task air delivery volume.

[0060] In the solenoid valve system, there are often complex interactions between components, and the failure of one component may affect the normal operation of other components. Therefore, by calculating the correlation degree, it is possible to identify which components may have a strong mutual influence, so as to locate the fault.

[0061] During the operation of the solenoid valve, there is a certain correlation between the components inside some solenoid valves. One of the two components with strong correlation may affect the wear of the other component. Therefore, the detection task can be generated according to the influence of the associated component on the component. Specifically, after obtaining the historical detection data of each component, the similarity of the changes of the two components is obtained according to the changes of the historical parameters corresponding to any two components in multiple identical historical time periods, and the similarity of the changes is used as the correlation between the two components. More specifically, the historical parameters corresponding to any two components can be displayed in the coordinate system, and the slope similarity of any two components can be calculated in each identical historical time period to obtain the slope similarity of multiple identical historical segments, and the average value of multiple slope similarities is calculated as the similarity of the changes of the two components.

[0062] Furthermore, after obtaining the correlation between every two components and the fault type corresponding to the fault, the two components with the highest correlation can be used as components to be detected, and the component detection tasks corresponding to the components to be detected can be obtained from the database corresponding to the monitoring solenoid valve to be built, and based on the component detection tasks, the detection tasks corresponding to the components to be detected can be obtained.

[0063] A possible implementation of the embodiment of the present application, in the above embodiment, based on historical detection data, determining the correlation between every two components in the solenoid valve to be monitored includes: Filter out target detection sub-data from the historical detection data, where the target detection sub-data is the target historical detection time and component corresponding to the detection failure; Based on formula 1: , get the target historical time period between every two elements, where T is the target historical time period and t is the time. is the historical time period corresponding to the first element of any two elements, is a historical time period corresponding to the second element of any two elements, and the common target historical detection moment includes the target historical detection moments corresponding to each of the any two elements; Based on the historical test data and formula 2, the parameter change degree corresponding to each component is determined, where formula 2 is , C is the degree of parameter change, is the historical parameter corresponding to the historical detection moment of the common target, for The historical parameters corresponding to the last detection time of for The historical parameters corresponding to the end detection time in the corresponding target historical time period, for The historical parameters corresponding to the start detection time in the corresponding target historical time period; Based on the parameter change degree corresponding to each component and the target historical time period between every two components, and based on Formula 3, the correlation between every two components is determined, where Formula 3 is: , S is the correlation degree, and are the parameter change degrees corresponding to the two components respectively.

[0064] Among them, each periodic detection corresponds to a detection time, and the time period from the detection result of each component from passing the detection to failing the detection is used as the historical time period corresponding to the component. Specifically, based on the historical detection data of the solenoid valve to be monitored, the time when each detection result of each component is failing the detection can be obtained to obtain the target historical detection time corresponding to each component. The same time between the target historical detection times corresponding to two components is the common target historical detection time. More specifically, based on the historical time periods corresponding to each of the two components, based on Formula 1: , we can get the target historical time period between every two components. Where T is the target historical time period, t is the time, is the historical time period corresponding to the first element of any two elements, The historical time period corresponding to the second element of any two elements, the common target historical detection moment includes the target historical detection moments corresponding to each of the any two elements.

[0065] Furthermore, for each component, the parameter change amount between every two adjacent historical detection moments is the parameter change amount, that is, each parameter change amount corresponds to a historical detection moment. Therefore, based on the historical parameters corresponding to each component, the parameter change degree corresponding to each component can be calculated. Specifically, based on the historical parameters corresponding to the historical detection moment corresponding to each component, and based on Formula 2 , calculate the parameter change degree of each component. Where C is the parameter change degree, is the historical parameter corresponding to the historical detection moment of the common target, for The historical parameters corresponding to the last detection time of for The historical parameters corresponding to the end detection time in the corresponding target historical time period, for The historical parameters corresponding to the start detection time in the corresponding target historical time period.

[0066] Furthermore, after obtaining the parameter change degree corresponding to each component and the target historical time period between each two components, the correlation between each two components can be calculated based on the parameter change degree corresponding to each component and the target historical time period between each two components. Specifically, based on Formula 3: The correlation between each two components is calculated by taking into account the parameter change degree of each component and the target historical time period between each two components. Among them, S is the correlation, and are the parameter change degrees corresponding to the two components respectively.

[0067] A possible implementation of the embodiment of the present application, in the above embodiment, determining the component to be detected based on the correlation between every two components and the fault type corresponding to the suspected fault includes: Based on the historical inspection data, a first component sequence is generated in descending order of the number of times the inspection result of each component fails the inspection; Determine the priority of each component based on the first component sequence and the correlation between every two components; A second component sequence is generated based on the priority corresponding to each component, and the first component in the second component sequence is determined as the component to be detected.

[0068] Specifically, the historical inspection data is traversed to count the number of times each component fails the inspection. Based on the statistical results, the components are sorted from large to small according to the number of times they fail the inspection, generating a first component sequence. The order of the components in this sequence reflects the frequency of their failure in the past inspections, and the higher the frequency, the higher the ranking.

[0069] Further, a priority list is initialized to store the priority of each component. Specifically, each component in the first component sequence is traversed, and for each component: other components associated with the component are found (based on the correlation data between each two components), and the priority of the component is calculated. Specifically, based on the number of times the component itself fails to pass the detection, the number of times other components associated with the component (the degree of association with the component) fail to pass the detection, and the weights corresponding to each component, the component score of each component is calculated, and according to the component score corresponding to each component, in order from large to small, each component is given a priority, and the obtained priority is stored in the priority list, corresponding to the current component. More specifically, the calculation method of the component score corresponding to each component can be: based on the correlation corresponding to the component and each other component, the correlation greater than the correlation threshold is screened out as the target correlation, and the other components corresponding to the target correlation are used as the associated components corresponding to the component, and the component score corresponding to the component is calculated based on the number of times the component itself fails to pass the detection and the first weight and the number of times the associated components fail to pass the detection and the second weight. Among them, the first weight and the second weight are preset in advance, and the first weight is greater than the second weight.

[0070] Furthermore, according to the priority list, the components are sorted from high to low in order of priority to generate a second component sequence. Specifically, components with higher priorities are placed in front of the second component sequence, indicating that these components are more likely to be the source of the fault or need to be detected first. The first component is taken from the second component sequence as the component to be detected.

[0071] A possible implementation manner of the embodiment of the present application, in the above embodiment, when the fault type corresponding to the suspected fault is an electromagnetic fault, generating a detection task based on the fault type corresponding to the suspected fault includes: Obtain standard data corresponding to the solenoid valve to be monitored, the standard data including standard magnetic force and standard voltage; Based on the standard data, determine the detection magnetic force and detection voltage of the electromagnetic valve to be monitored to obtain task detection data, and determine the task gas output corresponding to the standard data; Generate a detection task based on the task gas output and task detection data.

[0072] When the fault type corresponding to the suspected fault is an electromagnetic fault, the task detection data may include detecting magnetic force and detecting voltage.

[0073] Since the working principle of the solenoid valve involves the attraction and release of the electromagnet, an electromagnetic fault may cause the solenoid valve to fail to work properly. Therefore, the fault type corresponding to the suspected fault can be determined as an electromagnetic fault. Specifically, the standard data of the solenoid valve to be monitored is obtained from the database corresponding to the solenoid valve to be monitored, and the standard data includes a standard magnetic force and a standard voltage.

[0074] Furthermore, after obtaining the standard data, the standard magnetic force and standard voltage are used as the detection magnetic force and detection voltage of the solenoid valve to be monitored, so as to obtain the task detection data, and then, the standard data table corresponding to the solenoid valve to be monitored is obtained from the database corresponding to the solenoid valve to be monitored, and the standard data table includes the gas output data of the solenoid valves under different magnetic forces and voltages. And according to the measured detection magnetic force and detection voltage, the corresponding task gas output is obtained by searching or interpolating in the standard data table. Among them, if the corresponding gas output is not directly given in the standard data table, the interpolation method or approximation method can be used for calculation. This method is a conventional technical means of the personnel in this field, and the embodiments of this application will not be repeated here.

[0075] Furthermore, after the task gas output and the task detection data are obtained, a detection task for the solenoid valve to be monitored is generated.

[0076] The above embodiment introduces a solenoid valve fault online monitoring method from the perspective of method flow, and the following embodiment introduces a solenoid valve fault online monitoring device from the perspective of a virtual module or a virtual unit. For details, please refer to the following embodiment.

[0077] See also Figure 2 The solenoid valve fault online monitoring device 20 may specifically include: a first acquisition module 201, a determination module 202, a second acquisition module 203, a generation module 204 and a receiving module 205, wherein: An on-line monitoring device 20 for electromagnetic valve faults, comprising: The first acquisition module 201 is used to acquire the gas output volume data and gas delivery volume data of the solenoid valve to be monitored; A determination module 202 is used to determine whether the solenoid valve to be monitored has a suspected fault based on the gas output data and the gas delivery data; The second acquisition module 203 is used to acquire the detection data corresponding to the solenoid valve to be monitored if there is a suspected fault in the solenoid valve to be monitored, and the detection data includes valve voltage, valve caliber, valve current and valve accuracy; A generating module 204 is used to determine the fault type corresponding to the suspected fault based on the detection data, and generate a detection task based on the fault type corresponding to the suspected fault, wherein the detection task includes a task gas delivery volume and task detection data; The receiving module 205 is used to receive the detection result corresponding to the detection task and generate a fault monitoring report based on the detection result.

[0078] In a possible implementation of the embodiment of the present application, when the determination module 202 determines whether the solenoid valve to be monitored has a suspected fault based on the gas output volume data and the gas delivery volume data, it can be specifically used to: Determine whether the gas output data includes multiple gas output quanta data; If the gas output volume data includes a gas output sub-data, it is determined whether the gas output volume data is consistent with the gas supply volume data. If the gas output volume data is inconsistent with the gas supply volume data, it is determined that there is a suspected fault in the solenoid valve to be monitored; If the gas output volume data contains at least two gas output quantum data, determine whether the gas output volume data is consistent with the gas supply volume data; if the gas output volume data is inconsistent with the gas supply volume data, determine that there is a suspected fault in the solenoid valve to be monitored; if the gas output volume data is consistent with the gas supply volume data, obtain the gas volume distribution principle corresponding to the solenoid valve to be monitored, and based on the gas volume distribution principle, determine the standard gas output quantum data corresponding to the gas output volume data, and compare each standard gas output quantum data with the corresponding gas output quantum data to determine whether each standard gas output quantum data is consistent with the corresponding gas output quantum data; if there is an inconsistency between the standard gas output quantum data and the corresponding gas output quantum data, determine that there is a suspected fault in the solenoid valve to be monitored.

[0079] In a possible implementation of the embodiment of the present application, the fault type is a component fault or an electromagnetic fault. When the determination module 202 determines the fault type corresponding to the suspected fault based on the detection data, it can be specifically used to: Based on valve voltage, valve diameter and valve accuracy, the gas consumption data is calculated; Determine whether the gas output data is consistent with the gas consumption data; If the gas output data is inconsistent with the gas consumption data, it is determined that the fault type corresponding to the suspected fault is a component fault; If the gas output data is consistent with the gas consumption data, it is determined that the fault type corresponding to the suspected fault is an electromagnetic fault.

[0080] In a possible implementation manner of the embodiment of the present application, when the fault type corresponding to the suspected fault is a component fault, the generation module 204, when generating a detection task based on the fault type corresponding to the suspected fault, can be specifically used to: Obtain historical detection data of the solenoid valve to be monitored, the historical detection data including historical detection results and historical parameters of each component corresponding to each periodic detection, the historical detection results being detection pass or detection fail; Based on historical detection data, determine the correlation between every two components of the solenoid valve to be monitored, where the correlation is the degree of mutual influence between the components; Based on the correlation between every two components and the fault type corresponding to the suspected fault, the components to be detected are determined, and the detection tasks corresponding to the components to be detected are generated.

[0081] In a possible implementation of the embodiment of the present application, when the generation module 204 determines the correlation between every two components in the solenoid valve to be monitored based on the historical detection data, it can be specifically used to: Filter out target detection sub-data from the historical detection data, where the target detection sub-data is the target historical detection time and component corresponding to the detection failure; Based on formula 1: , get the target historical time period between every two elements, where T is the target historical time period and t is the time. is the historical time period corresponding to the first element of any two elements, is a historical time period corresponding to the second element of any two elements, and the common target historical detection moment includes the target historical detection moments corresponding to each of the any two elements; Based on the historical test data and formula 2, the parameter change degree corresponding to each component is determined, where formula 2 is , C is the degree of parameter change, is the historical parameter corresponding to the historical detection moment of the common target, for The historical parameters corresponding to the last detection time of for The historical parameters corresponding to the end detection time in the corresponding target historical time period, for The historical parameters corresponding to the start detection time in the corresponding target historical time period; Based on the parameter change degree corresponding to each component and the target historical time period between every two components, and based on Formula 3, the correlation between every two components is determined, where Formula 3 is: , S is the correlation degree, and are the parameter change degrees corresponding to the two components respectively.

[0082] In a possible implementation of the embodiment of the present application, when the generation module 204 determines the component to be detected based on the correlation between every two components and the fault type corresponding to the suspected fault, it can be specifically used to: Based on the historical inspection data, a first component sequence is generated in descending order of the number of times the inspection result of each component fails the inspection; Determine the priority of each component based on the first component sequence and the correlation between every two components; A second component sequence is generated based on the priority corresponding to each component, and the first component in the second component sequence is determined as the component to be detected.

[0083] In a possible implementation of the embodiment of the present application, when the fault type corresponding to the suspected fault is an electromagnetic fault, the generation module 204 may be used to generate a detection task based on the fault type corresponding to the suspected fault: Obtain standard data corresponding to the solenoid valve to be monitored, the standard data including standard magnetic force and standard voltage; Based on the standard data, determine the detection magnetic force and detection voltage of the electromagnetic valve to be monitored to obtain task detection data, and determine the task gas output corresponding to the standard data; Generate a detection task based on the task gas output and task detection data.

[0084] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0085] See also Figure 3 , the embodiment of the present application also introduces an electronic device from the perspective of a physical device, such as Figure 3 As shown, Figure 3 The electronic device 30 shown includes: a processor 301 and a memory 303. The processor 301 and the memory 303 are connected, such as through a bus 302. Optionally, the electronic device 30 may also include a transceiver 304. It should be noted that in actual applications, the transceiver 304 is not limited to one, and the structure of the electronic device 30 does not constitute a limitation on the embodiments of the present application.

[0086] The processor 301 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It may implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of this application. The processor 301 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0087] The bus 302 may include a path to transmit information between the above components. The bus 302 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus 302 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0088] The memory 303 may be a ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compressed optical disk, laser disk, optical disk, digital versatile disk, Blu-ray disk, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.

[0089] The memory 303 is used to store the application code for executing the solution of the present application, and the execution is controlled by the processor 301. The processor 301 is used to execute the application code stored in the memory 303 to implement the contents shown in the above method embodiment.

[0090] The electronic devices include but are not limited to: mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc., and can also be servers, etc. Figure 3 The electronic device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0091] An embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer-readable storage medium is run on a computer, the computer can execute the corresponding content in the aforementioned method embodiment.

[0092] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0093] The above are only some implementation methods of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A solenoid valve fault online monitoring method, characterized in that: include: Obtain the gas output volume data and gas delivery volume data of the solenoid valve to be monitored; Based on the gas output data and the gas delivery data, determining whether the solenoid valve to be monitored has a suspected fault; If the solenoid valve to be monitored has a suspected fault, then obtaining detection data corresponding to the solenoid valve to be monitored, the detection data including valve voltage, valve caliber, valve current and valve accuracy; Based on the detection data, determine the fault type corresponding to the suspected fault, and based on the fault type corresponding to the suspected fault, generate a detection task, the detection task including a task air delivery volume and task detection data; Receive the detection result corresponding to the detection task, and generate a fault monitoring report based on the detection result.

2. The solenoid valve fault online monitoring method according to claim 1 is characterized in that: The determining whether the solenoid valve to be monitored has a suspected fault based on the gas output volume data and the gas delivery volume data includes: Determining whether the gas output volume data includes a plurality of gas output quanta data; If the gas output volume data includes a gas output sub-data, determining whether the gas output volume data is consistent with the gas delivery volume data; if the gas output volume data is inconsistent with the gas delivery volume data, determining that the solenoid valve to be monitored has the suspected fault; If the gas output volume data contains at least two gas output quantum data, determine whether the gas output volume data is consistent with the gas supply volume data; if the gas output volume data is inconsistent with the gas supply volume data, determine that the solenoid valve to be monitored has the suspected fault; if the gas output volume data is consistent with the gas supply volume data, obtain the gas volume distribution principle corresponding to the solenoid valve to be monitored, and based on the gas volume distribution principle, determine the standard gas output quantum data corresponding to the gas output volume data, and compare each standard gas output quantum data with the corresponding gas output quantum data to determine whether each standard gas output quantum data is consistent with the corresponding gas output quantum data; if there is an inconsistency between the standard gas output quantum data and the corresponding gas output quantum data, determine that the solenoid valve to be monitored has the suspected fault.

3. The method for online monitoring of electromagnetic valve faults according to claim 2, characterized in that: The fault type is a component fault or an electromagnetic fault, and determining the fault type corresponding to the suspected fault based on the detection data includes: Calculating gas consumption data based on the valve voltage, the valve caliber, and the valve accuracy; Determining whether the gas output data is consistent with the gas consumption data; If the gas output data is inconsistent with the gas consumption data, determining that the fault type corresponding to the suspected fault is a component fault; If the gas output data is consistent with the gas consumption data, it is determined that the fault type corresponding to the suspected fault is an electromagnetic fault.

4. The method for online monitoring of electromagnetic valve faults according to claim 3 is characterized in that: When the fault type corresponding to the suspected fault is a component fault, generating a detection task based on the fault type corresponding to the suspected fault includes: Acquire historical detection data of the solenoid valve to be monitored, wherein the historical detection data includes historical detection results and historical parameters of each component corresponding to each periodic detection, and the historical detection results are detection pass or detection fail; Based on the historical detection data, determining the correlation between every two components in the solenoid valve to be monitored, wherein the correlation is the degree of mutual influence between the components; Based on the correlation between every two components and the fault type corresponding to the suspected fault, the components to be detected are determined, and the detection tasks corresponding to the components to be detected are generated.

5. The method for online monitoring of electromagnetic valve faults according to claim 4, characterized in that: The step of determining the correlation between every two components of the solenoid valve to be monitored based on the historical detection data includes: Filtering target detection sub-data from the historical detection data, the target detection sub-data being the target historical detection time and component corresponding to the detection failure; Based on formula 1: , get the target historical time period between every two elements, where T is the target historical time period and t is the time. is the historical time period corresponding to the first element of any two elements, is a historical time period corresponding to the second element of the arbitrary two elements, and the common target historical detection moment includes the target historical detection moments corresponding to the arbitrary two elements respectively; Based on the historical detection data and Formula 2, the parameter change degree corresponding to each component is determined, where Formula 2 is , C is the degree of parameter change, is the historical parameter corresponding to the historical detection moment of the common target, for The historical parameters corresponding to the last detection time of for The historical parameters corresponding to the end detection time in the corresponding target historical time period, for The historical parameters corresponding to the start detection time in the corresponding target historical time period; Based on the parameter change degree corresponding to each component and the target historical time period between every two components, and based on Formula 3, the correlation between every two components is determined, where Formula 3 is: , S is the correlation degree, and are the parameter change degrees corresponding to the two components respectively.

6. The method for online monitoring of electromagnetic valve faults according to claim 4 or 5, characterized in that: The step of determining the component to be detected based on the correlation between every two components and the fault type corresponding to the suspected fault includes: Based on the historical inspection data, a first component sequence is generated in descending order of the number of times that the inspection result of each component fails the inspection; Determine the priority corresponding to each element based on the first element sequence and the association degree between every two elements; A second component sequence is generated based on the priority corresponding to each component, and the first component in the second component sequence is determined as the component to be detected.

7. The method for online monitoring of electromagnetic valve faults according to claim 3, characterized in that: When the fault type corresponding to the suspected fault is an electromagnetic fault, generating a detection task based on the fault type corresponding to the suspected fault includes: Acquire standard data corresponding to the solenoid valve to be monitored, wherein the standard data includes standard magnetic force and standard voltage; Based on the standard data, determining the detection magnetic force and the detection voltage of the electromagnetic valve to be monitored to obtain task detection data, and determining the task gas output corresponding to the standard data; A detection task is generated based on the task gas output and the task detection data.

8. An online monitoring device for electromagnetic valve faults, characterized in that: include: The first acquisition module is used to acquire the gas output volume data and gas delivery volume data of the solenoid valve to be monitored; A determination module, used for determining whether the solenoid valve to be monitored has a suspected fault based on the gas output volume data and the gas delivery volume data; A second acquisition module is used to acquire detection data corresponding to the solenoid valve to be monitored if there is a suspected fault in the solenoid valve to be monitored, wherein the detection data includes valve voltage, valve caliber, valve current and valve accuracy; A generating module, configured to determine the fault type corresponding to the suspected fault based on the detection data, and generate a detection task based on the fault type corresponding to the suspected fault, wherein the detection task includes a task air delivery volume and task detection data; The receiving module is used to receive the detection result corresponding to the detection task and generate a fault monitoring report based on the detection result.

9. An electronic device, characterized in that: The electronic device includes: at least one processor; Memory; At least one application, wherein the at least one application is stored in a memory and configured to be executed by at least one processor, and the at least one application is configured to: execute the solenoid valve fault online monitoring method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed in a computer, the computer is caused to execute the solenoid valve fault online monitoring method according to any one of claims 1 to 7.

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