Ball valve leakage detection method and equipment

Abstract

The present application is applicable to the field of ball valve detection technology, and in particular to a ball valve leakage detection method and device, the method comprising: obtaining a detection signal; wherein the detection signal comprises a detection point identifier of the ball valve and detection point data of the ball valve; generating detection information according to the detection signal; determining the risk point location information of the ball valve according to the detection information, and generating an information collection instruction corresponding to the risk point location information; controlling the collection device to collect target information according to the information collection instruction corresponding to the risk point location information; determining the leakage point of the ball valve from the risk point of the ball valve according to the target information. This method not only improves the accuracy of ball valve leakage detection in complex scenarios, but also can discover potential leakage hazards of ball valves.

Description

Technical Field

[0001] The present application belongs to the technical field of ball valve detection, and in particular, to a ball valve leakage detection method and equipment. Background Art

[0002] Ball valve is a common type of valve, widely used in industry, construction, agriculture and other fields. Ball valve uses a ball (valve core) to control the flow of fluid, and has the advantages of simple structure, good sealing performance and easy operation. There is a hole in the middle of the ball. When the valve handle is turned 90 degrees, the hole of the ball is aligned with the pipeline, allowing the fluid to pass; when it is turned 90 degrees again, the ball will block the pipeline and the fluid cannot pass.

[0003] In the prior art, if a ball valve has multiple leaks (such as leaks at multiple locations on the valve seat, valve stem, and flange), it is difficult to accurately locate each leak. Some detection equipment can only detect the overall leakage situation and cannot be refined to the specific leaking location. Some leakage problems may accumulate slowly and are not easy to detect in a short period of time. For example, minor leaks often do not immediately show obvious abnormalities, but only appear in long-term monitoring. Traditional detection methods may find it difficult to capture these potential hidden dangers.

[0004] In summary, when detecting ball valve leakage, there are problems such as multi-point leakage that is difficult to locate and potential hidden dangers that are difficult to capture. Summary of the invention

[0005] The embodiments of the present application provide a ball valve leakage detection method and device, which can solve the problem in the related art that when detecting ball valve leakage, there are multiple leakage points that are difficult to locate and potential hidden dangers that are difficult to capture.

[0006] In a first aspect, an embodiment of the present application provides a ball valve leakage detection method, comprising:

[0007] Acquire a detection signal; wherein the detection signal includes a detection point identifier of the ball valve and detection point data of the ball valve;

[0008] Generate detection information according to the detection signal; wherein the detection information includes the detection point position of the ball valve, the detection time of the detection point of the ball valve, and the detection point data of the ball valve;

[0009] Determine the risk point position information of the ball valve according to the detection information, and generate an information collection instruction corresponding to the risk point position information; wherein the risk point of the ball valve is used to characterize the detection point of the ball valve with leakage risk and / or leakage anomaly, and the information collection instruction is used to instruct the collection device to collect the risk point detection data corresponding to the risk point of the ball valve;

[0010] According to the information collection instruction corresponding to the risk point location information, control the collection device to collect target information; wherein the target information includes risk point detection data corresponding to the risk point of the ball valve;

[0011] According to the target information, the leakage point of the ball valve is determined from the risk points of the ball valve.

[0012] The above technical solutions in the embodiments of the present application have at least the following technical effects:

[0013] The ball valve leakage detection method provided in the present application first obtains the detection signal, which is conducive to knowing the detection point identification and detection point data of the ball valve, and then generates detection information (detection point position of the ball valve, detection point detection time of the ball valve, detection point data of the ball valve) according to the detection signal, and then determines the risk point position information of the ball valve according to the detection information, which is conducive to knowing the detection point position information of the ball valve with leakage risk and / or leakage abnormality, and generates information collection instructions corresponding to the risk point position information, and then controls the collection device to collect target information according to the information collection instructions corresponding to the risk point position information, which is conducive to knowing the risk point detection data corresponding to the risk point of the ball valve, and finally determines the leakage point of the ball valve from the risk point of the ball valve according to the target information. This method can process the data of multiple detection points of the ball valve at the same time. For the case of multi-point leakage, the specific leakage point of the ball valve can be determined by comprehensive analysis, which solves the problem that it is difficult to accurately locate multi-point leakage by traditional means, and improves the accuracy of ball valve leakage detection in complex scenarios. This method can not only detect existing leaks, but also discover potential leakage hazards through the detection of risk points, thereby helping to prevent ball valve failures in advance.

[0014] In a possible implementation manner of the first aspect, generating detection information according to the detection signal includes:

[0015] Calling a position mapping table, searching the position corresponding to the detection point identifier of the ball valve from the position mapping table, and obtaining the detection point position of the ball valve;

[0016] Determine the time of acquiring the detection signal as the detection time of the detection point of the ball valve;

[0017] The detection point position of the ball valve, the detection time of the detection point of the ball valve and the detection point data of the ball valve are integrated to obtain the detection information.

[0018] In a possible implementation manner of the first aspect, determining the risk point position information of the ball valve according to the detection information includes:

[0019] Obtaining historical information of each detection point and a safety threshold of each detection point; wherein the historical information of each detection point is obtained through a ball valve database;

[0020] Compare the detection data of each detection point in the detection sub-information of each detection point with the safety threshold of each detection point, determine whether the detection data of each detection point is abnormal, and obtain a determination result of each detection point; wherein the detection information includes the detection sub-information of each detection point, the detection sub-information of each detection point includes the position of each detection point, the detection time of each detection point, and the detection data of each detection point, and the determination result includes an abnormal result or a normal result;

[0021] The risk point position information of the ball valve is determined according to the judgment result of each detection point, the detection sub-information of each detection point and the historical information of each detection point.

[0022] In a possible implementation manner of the first aspect, determining the risk point position information of the ball valve according to the judgment result of each detection point, the detection sub-information of each detection point, and the historical information of each detection point includes:

[0023] Performing data analysis on the detection sub-information of each detection point whose result is judged to be a normal result and the historical information of each detection point whose result is judged to be a normal result, to obtain the first risk point location information;

[0024] Based on the detection sub-information of each detection point whose judgment result is an abnormal result, statistical operations are performed on the historical judgment results in the historical information of each detection point whose judgment result is an abnormal result to obtain the second risk point position information;

[0025] The first risk point location information and the second risk point location information are integrated to obtain the risk point location information of the ball valve.

[0026] In a possible implementation manner of the first aspect, the performing data analysis on the detection sub-information of each detection point whose judgment result is a normal result and the historical information of each detection point whose judgment result is a normal result to obtain the first risk point location information includes:

[0027] Based on the historical detection data in the historical information of the detection point of each normal result, a baseline model corresponding to the detection point of each normal result is constructed;

[0028] Analyze the data trend corresponding to each detection point of the normal result according to the baseline model corresponding to each detection point of the normal result and the detection data of each detection point of the normal result to obtain the trend result corresponding to each detection point of the normal result;

[0029] Each detection point whose trend result is an increasing trend is determined as a first risk point, and the position of each detection point whose trend result is an increasing trend is determined as the position of the first risk point to obtain first risk point position information.

[0030] In a possible implementation manner of the first aspect, based on the detection sub-information of each detection point whose judgment result is an abnormal result, performing a statistical operation on the historical judgment results in the historical information of each detection point whose judgment result is an abnormal result to obtain the second risk point location information includes:

[0031] Calculate the number of times the historical judgment results in the historical information of each abnormal result detection point are abnormal results, and obtain the number of abnormalities corresponding to each abnormal result detection point;

[0032] Determine whether the number of abnormalities corresponding to the detection point of each abnormal result exceeds the number threshold corresponding to the detection point of each abnormal result, and determine each detection point whose number of abnormalities exceeds the corresponding number threshold as a second risk point;

[0033] The position of each detection point where the number of abnormalities exceeds the corresponding number threshold is determined as the position of the second risk point, and the second risk point position information is obtained.

[0034] In a possible implementation manner of the first aspect, the method further includes:

[0035] Compare each sub-data in the detection data of each detection point with the safety threshold corresponding to each sub-data, determine whether each sub-data in the detection data of each detection point exceeds the safety threshold corresponding to each sub-data, and obtain the determination result of each sub-data of each detection point;

[0036] Determine the detection point where the judgment result of each sub-data indicates that each sub-data exceeds the safety threshold corresponding to each sub-data as the leakage point of the ball valve;

[0037] The position of the detection point where the judgment result of each sub-data indicates that each sub-data exceeds the safety threshold corresponding to each sub-data is determined as the position of the leakage point of the ball valve, and the leakage point position information of the ball valve is obtained.

[0038] In a possible implementation manner of the first aspect, the generating an information collection instruction corresponding to the risk point location information includes:

[0039] Get specific test information;

[0040] Generate a control instruction corresponding to the specific test according to the specific test information; wherein the control instruction corresponding to the specific test is used to control the specific test equipment to perform the test operation;

[0041] According to the control instruction corresponding to the specific test and the risk point location information, an information collection instruction corresponding to the risk point location information is generated.

[0042] In a possible implementation manner of the first aspect, generating, according to the control instruction corresponding to the specific test and the risk point location information, an information collection instruction corresponding to the risk point location information includes:

[0043] Determine the data collection duration according to the test duration information in the control instruction corresponding to the specific test, and obtain the data collection duration information;

[0044] Determine the data collection frequency according to the specific test information in the control instruction corresponding to the specific test, and obtain data collection frequency information; wherein the data collection frequency information includes the data collection frequency corresponding to the sub-data of each detection point in the risk point location information;

[0045] According to the data collection duration information, the data collection frequency information and the risk point location information, an information collection instruction corresponding to the risk point location information is generated.

[0046] In a possible implementation manner of the first aspect, determining the leakage point of the ball valve from the risk points of the ball valve according to the target information includes:

[0047] Performing data trend analysis on each sub-data of each detection point in the risk point detection data in the target information, determining whether each sub-data of each detection point in the risk point detection data is an increasing trend, and obtaining an analysis result of each sub-data; wherein the risk point detection data is collected within the collection time in the data collection time information;

[0048] Comparing each sub-data of each detection point in the risk point detection data with the safety threshold corresponding to each sub-data, determining whether each sub-data of each detection point in the risk point detection data exceeds the safety threshold corresponding to each sub-data, and obtaining a comparison result of each sub-data;

[0049] The detection point corresponding to the sub-data whose analysis result is an increasing trend and whose comparison result is exceeding the corresponding safety threshold is determined as the leakage point of the ball valve.

[0050] In a second aspect, an embodiment of the present application provides a ball valve leakage detection device, comprising:

[0051] An acquisition unit, used for acquiring a detection signal; wherein the detection signal includes a detection point identification of the ball valve and detection point data of the ball valve;

[0052] A detection information generating unit, configured to generate detection information according to the detection signal; wherein the detection information includes the detection point position of the ball valve, the detection time of the detection point of the ball valve, and the detection point data of the ball valve;

[0053] A risk point determination unit, used to determine the risk point position information of the ball valve according to the detection information, and generate an information collection instruction corresponding to the risk point position information; wherein the risk point of the ball valve is used to characterize the detection point of the ball valve with leakage risk and / or leakage anomaly, and the information collection instruction is used to instruct the collection device to collect the risk point detection data corresponding to the risk point of the ball valve;

[0054] A data collection unit, configured to control the collection device to collect target information according to the information collection instruction corresponding to the risk point location information; wherein the target information includes risk point detection data corresponding to the risk point of the ball valve;

[0055] A leakage point determination unit is used to determine the leakage point of the ball valve from the risk points of the ball valve according to the target information.

[0056] In a third aspect, an embodiment of the present application provides a ball valve leakage detection device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method described in any one of the embodiments of the first aspect when executing the computer program.

[0057] It can be understood that the beneficial effects of the second to third aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0059] Figure 1 It is a flow chart of a ball valve leakage detection method provided in one embodiment of the present application;

[0060] Figure 2 It is a schematic diagram of a partial implementation flow of step S300 in the ball valve leakage detection method provided in an embodiment of the present application;

[0061] Figure 3 This is a partial flow chart of a ball valve leakage detection method provided by another embodiment of the present application;

[0062] Figure 4 It is another partial implementation flow diagram of step S300 in the ball valve leakage detection method provided in an embodiment of the present application;

[0063] Figure 5 It is a schematic diagram of the implementation process of step S500 in the ball valve leakage detection method provided in one embodiment of the present application;

[0064] Figure 6 It is a structural schematic diagram of a ball valve leakage detection device provided in an embodiment of the present application;

[0065] Figure 7 It is a structural schematic diagram of a ball valve leakage detection device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0066] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0067] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0068] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0069] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is detected" or "in response to detecting [described condition or event]", depending on the context.

[0070] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0071] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0072] In the related technology, if the ball valve has multiple leaks (such as leaks at multiple locations of the valve seat, valve stem and flange at the same time), it is difficult to accurately locate each leak point. Some detection equipment can only detect the overall leakage situation and cannot be refined to the specific leakage location. Some leakage problems may accumulate slowly and are not easy to detect in a short period of time. For example, minor leaks often do not immediately show obvious abnormalities, but only appear in long-term monitoring. Traditional detection methods may find it difficult to capture these potential hidden dangers.

[0073] In order to solve the above problems, the embodiment of the present application provides a ball valve leakage detection method and device. In this method, firstly, by acquiring the detection signal, it is convenient to know the detection point identification and detection point data of the ball valve, and then generate the detection information (the detection point position of the ball valve, the detection point detection time of the ball valve, and the detection point data of the ball valve) according to the detection signal, and then determine the risk point position information of the ball valve according to the detection information, which is convenient to know the detection point position information of the ball valve with leakage risk and / or leakage abnormality, and generate the information collection instruction corresponding to the risk point position information, and then control the collection device to collect the target information according to the information collection instruction corresponding to the risk point position information, which is convenient to know the risk point detection data corresponding to the risk point of the ball valve, and finally determine the leakage point of the ball valve from the risk point of the ball valve according to the target information. This method can process the data of multiple detection points of the ball valve at the same time. In the case of multi-point leakage, the specific leakage point of the ball valve can be determined through comprehensive analysis, which solves the problem that it is difficult to accurately locate multi-point leakage by traditional means, and improves the accuracy of ball valve leakage detection in complex scenarios. This method can not only detect existing leaks, but also discover potential leaks by detecting risk points, thus helping to prevent ball valve failures in advance.

[0074] The ball valve leakage detection method provided in the embodiment of the present application can be applied to a ball valve leakage detection device. In this case, the ball valve leakage detection device is the executor of the ball valve leakage detection method provided in the embodiment of the present application. The embodiment of the present application does not impose any restrictions on the specific type of the ball valve leakage detection device.

[0075] Exemplarily, the ball valve leakage detection device may include a collection device, a specific test device, and a control device that is communicatively connected to the collection device and the specific test device. The collection device is a device that can collect data from the detection point of the ball valve, for example, it may include a flow sensor, a pressure sensor, a temperature sensor, etc.; the specific test device is a device that can perform specific tests on the ball valve, for example, it may include a water pressure test device, a gas pressure test device, a high temperature furnace, etc.; the control device is a device that can process data and control the collection device and the specific test device, for example, it may be a mobile phone, a tablet computer, a laptop computer, an ultra-mobile personal computer (ultra-mobile personal computer, UMPC), a netbook, a personal digital assistant (personal digital assistant, PDA), a desktop computer, a smart screen, a computing device or other processing devices connected to a wireless modem, a computer, a laptop computer, a handheld computing device, a satellite wireless device, a customer premise equipment (customer premise equipment, CPE) and / or other devices for communicating on a wireless system and a next-generation communication system, for example, a mobile terminal in a 5G network or a mobile terminal in a future evolved public land mobile network (Public Land Mobile Network, PLMN), etc.

[0076] In order to better understand the ball valve leakage detection method provided in the embodiment of the present application, the specific implementation process of the ball valve leakage detection method provided in the embodiment of the present application is exemplarily introduced below.

[0077] Figure 1 A schematic flow chart of a ball valve leakage detection method provided in an embodiment of the present application is shown, and the ball valve leakage detection method includes:

[0078] S100, obtaining a detection signal, wherein the detection signal includes a detection point identifier of the ball valve and detection point data of the ball valve.

[0079] It can be understood that the detection signal can be an ultrasonic signal. An ultrasonic detection device is used to detect ball valve leakage by emitting and receiving ultrasonic signals. The use of ultrasonic waves to detect ball valve leakage does not require direct contact with the object being detected, and is suitable for high pressure or dangerous environments. The detection signal can be a sensor signal, such as a pressure sensor signal, a flow sensor signal, a temperature sensor signal, etc. In the embodiment of the present application, the sensor signal is used as the detection signal below.

[0080] The detection point can be a key position of the ball valve, such as the valve seat, valve stem, valve body connection, pipeline, etc. The detection point data of the ball valve is obtained through sensor monitoring, such as the pressure value of the detection point, the flow value of the detection point, the temperature value of the detection point, etc. The detection point identification of the ball valve is the unique identification of the location of the sensor, and the identification can be in a variety of ways, such as digital number, physical location description or network protocol-based device address (such as IP address).

[0081] For example, sensors can be installed at key locations (detection points) of ball valves, and the sensors can monitor physical or environmental parameters in real time, such as pressure, temperature, flow, vibration, etc. The sensors can work continuously and read the environmental parameters (detection point data) of the detection points through preset time intervals or event trigger mechanisms. The sensors capture the detection point data, and the sensors can transmit the detection point data to the central control system or cloud platform for processing through wired or wireless networks, which may involve Industrial Internet of Things (IIoT) technology, through protocols such as Modbus, CAN bus or other proprietary communication protocols. Through the detection signal, the system can identify the specific sensor and the data information collected by the sensor. The data corresponding to each sensor can be recorded in the database in the form of identification and specific values, laying the foundation for subsequent detection information generation and risk analysis of ball valves.

[0082] S200, generating detection information according to the detection signal, wherein the detection information includes the detection point position of the ball valve, the detection time of the detection point of the ball valve, and the detection point data of the ball valve.

[0083] For example, the system can search for the specific location of the detection point from a database or a preset configuration file through the detection point identification. For example, the detection point identification can be mapped to the physical location of the detection point of the ball valve, which can be determined through location information (such as GPS coordinates, detection point location number).

[0084] The system can add a detection timestamp to each detection signal, indicating the specific time of data collection. It can be the time when the sensor collects data, or the time when the detection point data is transmitted to the central system. The detection time of the detection point can be in a standardized format, such as ISO 8601 time format.

[0085] The received inspection point data, the corresponding inspection point location and the inspection time are integrated into a complete inspection information. The generated inspection information can be stored in the database as a basis for subsequent risk analysis, trend prediction or maintenance decision-making. Each inspection information record can be classified by time sequence or location to facilitate further data analysis and visualization.

[0086] Through this step, the original detection signal collected by the sensor can be converted into organized and structured detection information, which not only provides a basis for subsequent ball valve risk point detection, but also helps monitoring personnel understand the operating status of the equipment in real time.

[0087] In a possible implementation, S200, generating detection information according to the detection signal, includes:

[0088] S210, calling a position mapping table, searching the position corresponding to the detection point identifier of the ball valve from the position mapping table, and obtaining the detection point position of the ball valve.

[0089] It can be understood that the position mapping table can be a table or a file, which contains the mapping relationship between the identification of each detection point of the ball valve and the actual physical position of the detection point. For example, as shown in the following figure:

[0090]

[0091] For example, upon receiving the detection signal, the system can call the position mapping table from the ball valve database and query the position corresponding to the detection point identifier in the detection signal in the position mapping table. For example, the detection point identifier is ID001, and the system searches the position corresponding to ID001 in the position mapping table to determine that the detection point actually monitors the valve stem of the ball valve.

[0092] S220, determining the time of acquiring the detection signal as the detection time of the detection point of the ball valve.

[0093] For example, when receiving the detection signal, the system can automatically record the time of receiving the detection signal and determine it as the detection time of the detection point of the ball valve. The detection time can be recorded in a standard timestamp format (such as ISO 8601), which is conducive to accurately marking the collection time of each detection information.

[0094] S230, integrating the detection point position of the ball valve, the detection time of the detection point of the ball valve and the detection point data of the ball valve to obtain detection information.

[0095] For example, the detection point location of the ball valve, the detection time of the ball valve, and the detection point data of the ball valve can be integrated into a document (detection information), and the integrated detection information will be stored in the database for subsequent risk analysis, trend monitoring or equipment maintenance. The detection information can be archived in chronological order or location index to facilitate future query and analysis of data in a specific area or time period.

[0096] S300, determining the risk point location information of the ball valve according to the detection information, and generating an information collection instruction corresponding to the risk point location information. The risk point of the ball valve is used to characterize the detection point of the ball valve with leakage risk and / or leakage anomaly, and the information collection instruction is used to instruct the collection device to collect the risk point detection data corresponding to the risk point of the ball valve.

[0097] For example, the detection information can be analyzed to identify potential abnormal conditions of the ball valve. For example, data outside the normal range, such as pressure, temperature, flow and other parameters exceeding the predetermined safety threshold, may mean there is a risk of leakage; abnormal data fluctuations, where the parameters detected by the sensor change dramatically in a short period of time, may indicate a fault in the pipeline or ball valve.

[0098] The status of the ball valve can be evaluated based on manually set risk judgment criteria. Risk judgment criteria can include threshold judgment. For example, if the pressure exceeds the safety range, the detection point is marked as a risk point; trend analysis, if a parameter of the ball valve continues to be abnormal over a period of time (such as a slow drop in pressure or an abnormal increase in flow), it may also indicate a leakage risk. When an abnormal or out-of-safety detection point is identified, the system marks the detection point as a risk point. Each risk point corresponds to a specific location, and the location information of the detection point can be associated through the detection point identification.

[0099] For each determined risk point, a corresponding information collection instruction may be generated, the instruction explicitly instructing the collection device to further acquire relevant data of the risk point, and the collection device may be the sensor in step S100. After the information collection instruction is generated, it is transmitted to the corresponding sensor, and the sensor starts to collect new data to confirm and further analyze the risk situation of the ball valve.

[0100] Through this step, after potential risks are detected, timely measures can be taken to further collect data to confirm and solve the problems.

[0101] In one possible implementation, see Figure 2 In step S300, the risk point position information of the ball valve is determined according to the detection information, including:

[0102] S310, obtaining historical information of each detection point and a safety threshold of each detection point, wherein the historical information of each detection point is obtained through a ball valve database.

[0103] It can be understood that the ball valve database can be a system that centrally stores information related to ball valves, which may include equipment information, basic information such as the model, manufacturer, and installation date of the ball valve; detection point information, the identification, location, and corresponding sensor information of each detection point; historical data, which is the historical detection data (such as pressure, temperature, flow, etc.) of each detection point collected through long-term monitoring to form time series data; maintenance records, which record the maintenance and inspection history of each ball valve, including the maintenance date, maintenance content, and results.

[0104] For example, the unique identifier of the detection point can be used to query the ball valve database, and the historical information of the detection point in the past period of time can be extracted from the ball valve database, which can include detection data, such as pressure, temperature, flow and other data; trend analysis results, statistical analysis of historical data, and analysis of data change trends (such as gradual increase or decrease). The extracted historical information is organized into a format that is easy to analyze, which is conducive to data integrity and consistency.

[0105] It can be understood that the safety threshold refers to the fluctuation range allowed for the detection point data of each detection point under normal operation of the ball valve. The safety threshold of each detection point can include an upper limit, which is the upper limit value of the parameter of the normal operation of the detection point; and a lower limit, which is the lower limit value of the parameter of the normal operation of the detection point.

[0106] For example, the safety threshold value input by the user can be obtained through the human-computer interaction window. The average value and fluctuation range under normal operation can be identified by analyzing historical data as the safety threshold value.

[0107] S320, comparing the detection data of each detection point in the detection sub-information of each detection point with the safety threshold of each detection point, judging whether the detection data of each detection point is abnormal, and obtaining the judgment result of each detection point. The detection information includes the detection sub-information of each detection point, the detection sub-information of each detection point includes the position of each detection point, the detection time of each detection point, and the detection data of each detection point, and the judgment result includes an abnormal result or a normal result.

[0108] Exemplarily, if the detection data of the detection point is higher than the upper limit of the safety threshold, the data is judged to be abnormal; if the detection data of the detection point is lower than the lower limit of the safety threshold, the data is judged to be abnormal; if the detection data of the detection point is between the upper limit and the lower limit of the safety threshold, it is judged to be normal. The judgment result of each detection point can be generated. For example, if the detection data is within the safety range, the judgment result of the detection point can be marked as normal; if the detection data exceeds the safety threshold range, the judgment result of the detection point can be marked as abnormal. The judgment result of each detection point can be integrated with the corresponding detection sub-information to generate complete judgment information.

[0109] S330, determining the risk point position information of the ball valve according to the judgment result of each detection point, the detection sub-information of each detection point and the historical information of each detection point.

[0110] For example, the detection data of the detection point (abnormal point) judged as an abnormal result can be analyzed, and the greater the magnitude of the detection data of the abnormal point exceeding the safety threshold, the higher the risk. For example, a pressure value far above the upper limit may indicate a valve seal failure.

[0111] The detection data of the abnormal point can be combined with the historical information of the abnormal point to determine the risk point of the ball valve. If the abnormal point has shown abnormalities many times in the past historical data and the trend has not improved, the risk of the abnormal point is high; if the detection data before the abnormal point is normal, but the abnormality suddenly appears, it may also be a potential risk point caused by system failure or external influence.

[0112] Based on the above comprehensive analysis, the abnormal point is marked as a risk point, and the location of the abnormal point is the location of the risk point.

[0113] The severity of ball valve leakage risk can also be assessed based on risk points. For example, if the test data of multiple abnormal points are consistent with historical data, indicating that the ball valve may have a serious fault, then the ball valve leakage is high risk; if the test data of a single abnormal point is outstanding, but there is no significant deterioration trend in historical data, then the ball valve leakage is medium risk; if a certain test point is occasionally abnormal, but the overall data is normal, then the ball valve leakage is low risk.

[0114] Through the above steps, the risk points of ball valves can be effectively identified and evaluated, providing reliable data support for subsequent maintenance decisions.

[0115] Optionally, S330, determining the risk point location information of the ball valve according to the judgment result of each detection point, the detection sub-information of each detection point and the historical information of each detection point, includes:

[0116] S331, performing data analysis on the detection sub-information of each detection point whose judgment result is a normal result and the historical information of each detection point whose judgment result is a normal result, to obtain first risk point position information.

[0117] For example, the detection sub-information of the detection point (normal point) whose result is judged as a normal result can be compared with the historical data to analyze the trend and change of the detection data of each normal point. For example, the long-term change trend of the detection data of the normal point can be checked to analyze whether there is a continuous upward or downward trend, even if the data is still within the normal range.

[0118] Statistical analysis or machine learning models can be used to analyze historical data to identify abnormal trends or small changes in abnormalities, which may indicate potential risks even though the current status is normal. For example, if the detection data of a normal point is gradually approaching the upper or lower limit of the safety threshold corresponding to the normal point, the normal point can be regarded as a risk point.

[0119] Based on the results of the above data analysis, detect points (normal points) with potential risks are identified, i.e., the first risk points. Although the current detection data of the normal points is normal, the historical trends and data characteristics of the normal points may indicate potential risks, such as parameters gradually approaching the threshold, irregular fluctuations, or abnormal change trends.

[0120] The identified detection points of potential risks are summarized to generate the first risk point location information including the risk point location, detection time, detection data and other information, which can be used for further risk assessment and preventive maintenance.

[0121] Exemplarily, in S331, data analysis is performed on the detection sub-information of each detection point whose judgment result is a normal result and the historical information of each detection point whose judgment result is a normal result to obtain the first risk point location information, including:

[0122] S3311, constructing a baseline model corresponding to each detection point of the normal result based on the historical detection data in the historical information of the detection point of each normal result.

[0123] It can be understood that the baseline model can capture the normal data fluctuation range and characteristics of the detection point (normal point) of the normal result, predict the normal behavior of the detection point, and identify abnormal trends that deviate from the baseline. According to the characteristics of the detection data of the detection point and the scale of the historical detection data, the appropriate baseline model type can be selected. The baseline model can include: the moving average model, which is suitable for stationary time series, and can capture the baseline level of the detection point by calculating the average value within the sliding window; the exponential smoothing model (ETS), which is suitable for situations where the data contains trends and seasonality, and predicts the trend of subsequent data by exponentially weighting the historical detection data; the autoregressive model (ARIMA), which is suitable for situations where the data contains long-term trends, periodicity and random fluctuations, and can capture the autocorrelation characteristics in the series; the machine learning model, for complex and nonlinear data, can use time series prediction models such as LSTM (Long Short-Term Memory Network) and Prophet to build a more flexible baseline model.

[0124] Exemplarily, historical detection data can be used to train a selected baseline model and construct a prediction model based on the historical detection data of normal points. The training goal is to enable the baseline model to accurately describe the behavior and fluctuation range of normal points under normal conditions.

[0125] Training process of baseline model: The parameters of the model can be adjusted to make the model better fit the historical test data (such as the p, d, and q parameters in the ARIMA model). The cross-validation method can be used to evaluate the generalization performance of the model, which is conducive to the model not only fitting the historical test data, but also effectively predicting future data. A portion of the historical test data is reserved as a test set, and the baseline model is used to predict the historical test data and compare it with the true value. The error (such as mean square error, absolute percentage error, etc.) is calculated to verify the accuracy of the model. If the model performs poorly, you can adjust the parameters, select a different model, or add data features to improve the accuracy of the prediction.

[0126] Saving the trained baseline model and associating it with the corresponding normal point will help the model run in real time and can be used at any time to compare real-time detection data with historical baselines and capture abnormal trends.

[0127] As time goes by, the characteristics of the detection points may change, so the baseline model can be retrained and updated regularly to ensure that the baseline model's predictions on the current data remain accurate.

[0128] S3312, analyzing the data trend corresponding to each normal result detection point according to the baseline model corresponding to each normal result detection point and the detection data of each normal result detection point, to obtain the trend result corresponding to each normal result detection point.

[0129] For example, the constructed baseline model can be used to make predictions for the current time period of each normal point to obtain the normal data range (i.e., baseline data) predicted by the model during this period. The test data is compared with the prediction results of the baseline model to analyze the deviation and difference between the two. The deviation and difference between the two may include the deviation direction, the test data is continuously higher or lower than the baseline prediction value; the deviation amplitude, the difference between the test data and the baseline prediction value; and the duration, the length of time the test data continuously deviates from the baseline.

[0130] The short-term trend of the test data can be analyzed to determine whether the test data is rising, falling or stable. By comparing the test data with the baseline model, it can be detected whether the data fluctuation in the short term is within the normal range. The changes in the test data on a longer time scale can be monitored to determine whether the overall trend of the test data is rising, falling or stable. For the growth trend, it can be monitored whether the growth rate continues to exceed the normal range of the baseline model.

[0131] If the test data shows a continuous growth and the deviation value gradually increases (that is, the test data is continuously higher than the baseline), it is determined that the normal point has an increasing trend. If the test data shows a continuous decline and the deviation value gradually increases (that is, the test data is continuously lower than the baseline), it is determined that the normal point has a downward trend. If the test data is close to the baseline model prediction value and the fluctuation range is small, it is determined that the normal point maintains a stable trend.

[0132] For each detection point of normal results, record the trend results, including trend type (increasing, decreasing, stable), trend strength (deviation amplitude and duration), and data deviation characteristics (amplitude above / below the baseline).

[0133] S3313, determining each detection point whose trend result is an increasing trend as a first risk point, and determining the position of each detection point whose trend result is an increasing trend as the position of the first risk point, to obtain the first risk point position information.

[0134] Exemplarily, all detection points with a growing trend can be determined as the first risk point, which may indicate that the detection data is increasing, and there is a potential risk or the possibility of exceeding the safety threshold. The location of the detection point with a growing trend is determined as the location of the first risk point, and all detection points with a growing trend and their location information are summarized to generate the first risk point location information. The first risk point location information may include the location of the normal point, the trend result of the normal point, the detection data of the normal point, and the historical data characteristics of the normal point.

[0135] Through the above steps, the baseline model is compared and analyzed with the detection data, which can timely discover the detection points that are still within the normal range but have shown an abnormal growth trend, providing early warning for preventing potential failures.

[0136] S332, based on the detection sub-information of each detection point whose judgment result is an abnormal result, statistical operations are performed on the historical judgment results in the historical information of each detection point whose judgment result is an abnormal result to obtain second risk point position information.

[0137] For example, the historical judgment results of the detection points (abnormal points) whose judgment results are abnormal results can be counted, and the abnormal frequency, time interval, abnormal duration, etc. of the abnormal points can be analyzed. For example, the number of abnormal occurrences can be counted to analyze whether the abnormality is sporadic or continuous; the interval time between abnormal occurrences can be calculated to determine whether the abnormality is periodic or random; the severity of the abnormal state can be analyzed, such as the degree to which the abnormal value deviates from the normal threshold.

[0138] Based on the above statistical results, the risk level of the detection point can be evaluated. For example, if the abnormal frequency of a certain abnormal point is high, the duration is long, or the abnormal value deviates seriously from the normal range, then the abnormal point may be a high-risk point. Based on the results of the statistical analysis, the high-risk detection point is identified to form the second risk point.

[0139] Integrate the statistical analysis results of all abnormal points with potential risks (second risk points) to generate the second risk point location information. The second risk point location information may include the location of the risk point, the statistical characteristics of historical abnormalities (such as abnormal frequency, duration, severity, etc.), and the current abnormal detection sub-information.

[0140] Exemplarily, S332, based on the detection sub-information of each detection point whose judgment result is an abnormal result, statistical operations are performed on the historical judgment results in the historical information of each detection point whose judgment result is an abnormal result to obtain the second risk point location information, including:

[0141] S3321, calculating the number of times the historical judgment results in the historical information of each abnormal result detection point are abnormal results, and obtaining the number of abnormalities corresponding to each abnormal result detection point.

[0142] Exemplarily, the number of abnormal results is extracted from the historical information of each detection point, and the number of abnormal results of each detection point represents the total number of times the detection point is judged to be abnormal in historical detections. Since the current detection is also judged to be abnormal, the current abnormal result can be added to the statistics. Therefore, the number of abnormal results of each detection point = the number of historical abnormal results + 1 (current abnormality).

[0143] S3322, determine whether the number of abnormalities corresponding to the detection point of each abnormal result exceeds the number threshold corresponding to the detection point of each abnormal result, and determine each detection point whose number of abnormalities exceeds the corresponding number threshold as a second risk point.

[0144] It can be understood that the number threshold represents the maximum number of abnormalities allowed for a detection point within a specified time period. A number threshold can be set for each detection point. If there is no individually set threshold, a default threshold can be set based on the characteristics of different detection points (such as criticality, failure probability).

[0145] Exemplarily, the number threshold of each detection point set by the user can be obtained through the human-computer interaction window, and the number threshold corresponding to the detection point of the abnormal result can be extracted therefrom.

[0146] The number of abnormalities of the detection point of the abnormal result is compared with the number threshold corresponding to the detection point. If the number of abnormalities is less than or equal to the number threshold, it means that although the detection point has an abnormal history, it has not reached the risk level; if the number of abnormalities is greater than the number threshold, it means that the number of abnormal occurrences of the detection point has exceeded the set safety range and there is a high risk.

[0147] Based on the above comparison results, all detection points whose abnormal times exceed the corresponding times threshold may be marked as second risk points.

[0148] S3323, determine the position of each detection point where the number of abnormalities exceeds the corresponding number threshold as the position of the second risk point, and obtain the second risk point position information.

[0149] For example, the relevant information of all detection points marked as second risk points can be summarized to generate second risk point location information, which can include the location of the detection point, the number of abnormalities of the detection point, and the number threshold of the detection point. The generated second risk point location information will be used for subsequent early warning, maintenance and risk management, and priority actions will be taken on these high-risk points that exceed the threshold to prevent potential faults from further deteriorating.

[0150] S333, integrating the first risk point location information and the second risk point location information to obtain the risk point location information of the ball valve.

[0151] For example, the location information of the first risk point and the second risk point can be merged to form a unified data structure. If a detection point appears in both the first and second risk point information (it has both potential risk and abnormal history), the detection data of the detection point is integrated to avoid duplication. Such detection points can be marked as high-priority risk points, and the comprehensive features of the detection point are summarized.

[0152] For the integrated risk points, risk level assessment can be performed based on the characteristics of the risk points. For example, a risk level (such as low, medium, or high) can be assigned to each risk point based on factors such as abnormal frequency, potential trend, and degree of deviation of abnormal values. The results of the assessment are sorted so that high-risk locations can be processed first in the future.

[0153] The integrated risk point location information is summarized into a unified report to obtain the risk point location information of the ball valve, which can include the location of the risk point, the detection data and time of the risk point, the risk characteristics of the risk point, and the risk level of the risk point. The generated risk point location information can be used for maintenance planning, risk warning and management decision-making, and priority should be given to maintenance or further detection of points with high risk levels to prevent potential failures or leaks.

[0154] Through the above steps, the different analysis results of normal and abnormal detection points are combined to more comprehensively evaluate the status of the ball valve, which is conducive to the effective monitoring and management of both potential and explicit risks.

[0155] In one possible implementation, see Figure 3 , the ball valve leakage detection method also includes:

[0156] S10, comparing each sub-data in the detection data of each detection point with the safety threshold corresponding to each sub-data, judging whether each sub-data in the detection data of each detection point exceeds the safety threshold corresponding to each sub-data, and obtaining the judgment result of each sub-data of each detection point.

[0157] It can be understood that each sub-data in the detection data of each detection point can be data obtained by installing multiple sensors at the detection point for detection. For example, pressure sensors, flow sensors, temperature sensors and other sensors respectively provide corresponding detection data (sub-data).

[0158] Exemplarily, the security threshold corresponding to each sub-data input by the user may be obtained through the human-computer interaction window.

[0159] Each sub-data of each detection point can be compared with the corresponding safety threshold of each sub-data one by one to determine whether each sub-data exceeds the corresponding safety threshold. If the sub-data is less than or equal to the corresponding safety threshold, the value of the sub-data is within the allowable range and the result is normal; if the sub-data is greater than the corresponding safety threshold, the value of the sub-data exceeds the allowable range and the result is abnormal.

[0160] A list containing sub-data judgment results can be generated for each detection point. Each sub-data has a judgment result, indicating whether the sub-data exceeds the corresponding safety threshold (abnormal or normal). For example, a detection point contains three sub-data (pressure, flow, and temperature). The list may include pressure, the judgment result is normal (the value is within the threshold); flow, the judgment result is abnormal (the value exceeds the threshold); temperature, the judgment result is normal (the value is within the threshold).

[0161] S20, determining a detection point where the judgment result of each sub-data indicates that each sub-data exceeds a safety threshold corresponding to each sub-data as a leakage point of the ball valve.

[0162] Exemplarily, for each detection point, if the judgment results of all sub-data of the detection point are abnormal, the detection point is determined to be a leakage point of the ball valve, which means that all key parameters of the detection point do not meet the safety range and there is a risk of leakage.

[0163] S30, determining the position of the detection point where the judgment result of each sub-data indicates that each sub-data exceeds the safety threshold corresponding to each sub-data as the position of the leakage point of the ball valve, and obtaining the leakage point position information of the ball valve.

[0164] For example, all the detection points determined to be leakage points can be summarized, and the position of each detection point determined to be a leakage point can be recorded as the position of the leakage point, and the leakage point position information of the ball valve can be generated. The leakage point position information of the ball valve can include the leakage point position and the abnormal situation of the sub-data. The generated leakage point position information is taken as the key inspection and processing object, so as to carry out further maintenance or emergency processing to prevent the actual leakage from causing greater harm.

[0165] Through the above steps, the detection points of potential leakage in the ball valve can be accurately identified, which is conducive to the timely detection and treatment of leakage problems.

[0166] In one possible implementation, see Figure 4 In step S300, an information collection instruction corresponding to the risk point location information is generated, including:

[0167] S301, obtaining specific test information.

[0168] It is understood that the specific test item may be a pressure test, which is used to verify the sealing and structural integrity of the ball valve under certain pressure conditions, so as to prevent leakage within the working pressure range. The specific test equipment for the pressure test may include: a water pressure test equipment, which is used to perform a pressure test on the ball valve with pressurized water; and a gas pressure test equipment, which is used to perform an air tightness test using compressed air or nitrogen.

[0169] Specific test items may be temperature tests, which are used to verify the performance and sealing of ball valves under high temperature, low temperature or temperature cycle conditions, which is beneficial to the stability of valves under extreme environments. Specific test equipment for temperature tests may include: high temperature furnace / heating device, which is used to simulate high temperature conditions; low temperature cooling equipment, which is used to simulate low temperature conditions.

[0170] For example, specific test information of the ball valve can be obtained from the ball valve database. The specific test information is related to specific detection purposes and conditions, and the specific test information may include test items, test parameters, test equipment, test environment requirements, etc.

[0171] S302: Generate a control instruction corresponding to the specific test according to the specific test information, wherein the control instruction corresponding to the specific test is used to control the specific test device to perform the test operation.

[0172] For example, the specific test information may be parsed to extract test parameters, which are used to configure the operation of the specific test equipment, such as test time, test pressure value, test temperature, number of cycles, etc.

[0173] According to the test items, the model, operating characteristics, connection method and supported instruction set of the specific test equipment can be determined. According to the test parameters, control instructions are generated to set the start and stop of the test equipment and the working parameters of the test equipment. For example, the start / stop of the test equipment, the instruction is used to start or stop the operation of the test equipment, which is conducive to the test equipment to execute and end the operation as expected. The instructions can be START_TEST, STOP_TEST; pressure test, the instruction is used to set the target pressure value, pressurization speed and holding time of the pressure test equipment, the instruction can be SET_PRESSURE=100psi; RAMP_RATE=10psi / s; HOLD_TIME=60s; temperature test, the instruction is used to set the temperature range and heating / cooling rate of the heating or cooling equipment, the instruction can be SET_TEMPERATURE_RANGE=-20C~150C; HEAT_RATE=5C / min.

[0174] The generated control instructions can be formatted into a command format recognizable by the test equipment according to the control protocol of the test equipment (such as MODBUS, RS232, Ethernet / IP, etc.). The control instructions are transmitted to the test equipment through a suitable communication method to perform the test operation. During the test, the real-time feedback of the test equipment can also be monitored to facilitate the normal execution of the test equipment and meet the test requirements. According to the real-time feedback data, the control instructions can be adjusted to change the test parameters or control operations.

[0175] S303: Generate an information collection instruction corresponding to the risk point location information according to the control instruction corresponding to the specific test and the risk point location information.

[0176] Exemplarily, the collection target can be set based on the characteristics of the risk point. For example, for the risk point of abnormal pressure, the collection frequency and data accuracy of the pressure sensor can be set; for the risk point of abnormal flow, the collection range of the flow sensor can be set. Collection parameters such as data collection frequency, time interval, collection range, etc. can be set according to specific tests and risk point characteristics, so that the collected data can accurately reflect the status of the risk point.

[0177] Based on the control requirements of specific test equipment and the location information of risk points, information collection instructions for performing data collection operations are generated. The information collection instructions will be passed to the collection equipment to collect data for the risk points.

[0178] Optionally, S303, generating an information collection instruction corresponding to the risk point location information according to the control instruction corresponding to the specific test and the risk point location information, includes:

[0179] S3031, determining the data collection duration according to the test duration information in the control instruction corresponding to the specific test, and obtaining the data collection duration information.

[0180] Exemplarily, a test duration parameter can be extracted from the control instruction corresponding to a specific test, and the test duration parameter is used to indicate the total time of the test execution. Using the test duration as the data collection duration is conducive to data collection covering the entire test process. The data collection duration can be consistent with the test duration, or it can be appropriately extended to capture the state changes before and after the test. The determined data collection duration is recorded in a standardized form, for example, DATA_COLLECTION_DURATION=300s (300 seconds data collection duration).

[0181] S3032, according to the specific test information in the control instruction corresponding to the specific test, determine the data collection frequency to obtain data collection frequency information. The data collection frequency information includes the data collection frequency corresponding to the sub-data of each detection point in the risk point location information.

[0182] For example, the test parameters can be extracted from the control instructions, and the appropriate data acquisition frequency can be determined according to the parameter change rate, data accuracy, and sub-data characteristics of each detection point required by the test. For example, a higher acquisition frequency is set for fast-changing sub-data, and a relatively lower acquisition frequency is set for slowly changing sub-data.

[0183] Generate corresponding acquisition frequency information for each detection point's sub-data, which helps the acquisition frequency to be high enough to capture key data. For example, PRESSURE_SENSOR_FREQ=1Hz (collect pressure data once per second); FLOW_SENSOR_FREQ=0.5Hz (collect flow data once every 2 seconds).

[0184] S3033, generating information collection instructions corresponding to the risk point location information based on the data collection duration information, the data collection frequency information and the risk point location information.

[0185] For example, the previously obtained risk point location and related detection sub-data can be extracted, and each risk point can include the location of the detection point and the type of monitored sub-data. According to the acquisition frequency information, an appropriate acquisition frequency is assigned to the sub-data of each risk point, which is conducive to matching the acquisition process with the characteristics of the risk point.

[0186] Based on the data collection duration information, data collection frequency information and risk point location information, a complete information collection instruction is generated for each risk point. The information collection instruction will define when to start collecting data for the risk point, how long to collect data, and how often to collect data.

[0187] Through the above steps, the collection instructions can comprehensively and accurately cover the risk point data that need to be monitored during the test process, which is conducive to the completeness and usability of the obtained test data.

[0188] S400, according to the information collection instruction corresponding to the risk point location information, control the collection device to collect target information, wherein the target information includes risk point detection data corresponding to the risk point of the ball valve.

[0189] For example, the information collection instruction can be transmitted to the collection device (sensor) deployed near the risk point of the ball valve. After receiving the collection instruction, the sensor collects data at the risk point. For example, the pressure of the liquid or gas entering and leaving the ball valve can be monitored by the pressure sensor installed in the valve body and the pipeline. If the pressure continues to drop or exceeds the normal range, it may indicate that the valve has a leakage risk; the temperature sensor can detect the temperature change around the ball valve, and abnormal temperature fluctuations may indicate that the thermal expansion and contraction of the ball valve has caused a sealing problem; the flow sensor can be used to monitor the medium flow through the ball valve, and abnormal flow changes may mean that the ball valve is leaking internally.

[0190] After collecting target information, the sensor transmits the data back to the central control system through a wired or wireless network. The system receives the target information collected by the sensor and can store and preliminarily process the data. The preliminary processing may include data filtering, denoising, etc. to ensure the accuracy and completeness of the collected data.

[0191] Through this step, the sensor can be accurately controlled to collect real-time data of the risk points of the ball valve according to the collection instructions, and then the operating status of the ball valve and whether there is a leakage risk can be analyzed.

[0192] S500: Determine the leakage point of the ball valve from the risk points of the ball valve according to the target information.

[0193] For example, the leakage point of the ball valve can be determined by combining the data of multiple sensors. For example, if the pressure around the valve stem drops and the temperature rises, it may indicate that the valve stem seal has failed, causing fluid leakage along the valve stem; if the flow sensor in the valve seat area still shows fluid flow when the valve is closed, accompanied by abnormal changes in temperature or pressure, it indicates that the valve seat may be the main location of the leakage.

[0194] Data pattern recognition technology can be used to analyze abnormal data patterns of specific leakage phenomena. For example, if the pressure at a certain detection point fluctuates regularly in a short period of time, it may be caused by leakage bubbles inside the ball valve or uneven fluid flow; if an abnormal temperature peak is detected near the valve seat, it may indicate that there is friction at the local leakage point or unstable fluid flow.

[0195] After comprehensive analysis of risk point detection data, a detailed leak point report can be generated, which lists possible leak point locations and their corresponding detection data. The report may include: leak point location, specific leak area, such as valve stem, valve seat or valve body connection, etc.; detection data, key data leading to leak points, including pressure, temperature, flow, vibration, etc.

[0196] Based on the analysis results of the leak point, the corresponding repair or maintenance work can be initiated. For example, if it is determined that there is a large risk of leakage at the valve seat or valve stem, the system can issue an alarm and arrange for inspection or replacement; if the leak is small or develops slowly, the data in this area can be continuously monitored to further determine whether immediate treatment is required.

[0197] Through this step, the leakage point of the ball valve can be effectively determined to prevent the potential leakage risk from escalating.

[0198] In one possible implementation, see Figure 5 , S500, according to the target information, determines the leakage point of the ball valve from the risk points of the ball valve, including:

[0199] S510, performing data trend analysis on each sub-data of each detection point in the risk point detection data in the target information, determining whether each sub-data of each detection point in the risk point detection data is an increasing trend, and obtaining an analysis result of each sub-data. The risk point detection data is collected within the collection time in the data collection time information.

[0200] Exemplarily, the detection data of all risk points within the collection time can be obtained from the collection device, and the detection data can include all sub-data (such as pressure, flow, temperature, etc.) and time series of each detection point.

[0201] You can select a suitable analysis method to perform data trend analysis on each sub-data of each detection point to determine the change trend of each sub-data within the collection time. The analysis methods may include: linear fitting, linear fitting of time series data (sub-data), judging the data trend according to the slope of data change. If the slope is positive and obvious, the sub-data is increasing; moving average, calculating the moving average of the sub-data to determine whether the change in the average value is an upward trend; statistical analysis, calculating the growth rate or percentage change of the sub-data to determine the significance of the sub-data change.

[0202] The trend of each sub-data is judged to obtain the analysis results. If the sub-data continues to rise during the collection period, it can be marked as a growth trend; if the sub-data remains stable or decreases during the collection period, it can be marked as a non-growth trend.

[0203] S520, comparing each sub-data of each detection point in the risk point detection data with the safety threshold corresponding to each sub-data, determining whether each sub-data of each detection point in the risk point detection data exceeds the safety threshold corresponding to each sub-data, and obtaining the comparison result of each sub-data.

[0204] Exemplarily, the detection value of each sub-data within the collection time can be compared with the safety threshold corresponding to each sub-data. If the detection value of the sub-data continues to exceed the corresponding safety threshold, the comparison result can be marked as exceeding the safety threshold; if the detection value of the sub-data does not exceed the corresponding safety threshold, the comparison result can be marked as not exceeding the safety threshold.

[0205] S530: Determine the detection point corresponding to the sub-data whose analysis result is an increasing trend and whose comparison result is exceeding the corresponding safety threshold as the leakage point of the ball valve.

[0206] Exemplarily, the data trend analysis results can be combined with the safety threshold comparison results to filter out the detection points whose analysis results of the sub-data show an increasing trend and whose comparison results of the sub-data show an over-safety threshold, and mark the detection points as leakage points of the ball valve.

[0207] The identified leakage points can be recorded to generate leakage point location information, which can include the location of the leakage point (specific location identification of the leakage point), the sub-data of the leakage point (the sub-data showing an increasing trend and exceeding the safety threshold), and the sub-data characteristics of the leakage point (the growth trend characteristics of the sub-data, the amplitude and duration exceeding the safety threshold). The generated leakage point location information is output and summarized for subsequent maintenance, detection and risk treatment.

[0208] Through the above steps, the detection points showing abnormal growth and exceeding the safety threshold can be accurately identified, which is conducive to timely detection and prevention of leakage risks of ball valves.

[0209] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0210] Corresponding to the ball valve leakage detection method described in the above embodiment, the embodiment of the present application further provides a ball valve leakage detection device, and each unit of the device can implement each step of the ball valve leakage detection method. Figure 6 A structural block diagram of a ball valve leakage detection device provided in an embodiment of the present application is shown. For ease of explanation, only the parts related to the embodiment of the present application are shown.

[0211] Reference Figure 6 , the device comprises:

[0212] The acquisition unit is used to acquire the detection signal, wherein the detection signal includes the detection point identification of the ball valve and the detection point data of the ball valve.

[0213] The detection information generating unit is used to generate detection information according to the detection signal, wherein the detection information includes the detection point position of the ball valve, the detection time of the detection point of the ball valve, and the detection point data of the ball valve.

[0214] The risk point determination unit is used to determine the risk point location information of the ball valve according to the detection information, and generate an information collection instruction corresponding to the risk point location information. The risk point of the ball valve is used to characterize the detection point of the ball valve with leakage risk and / or leakage anomaly, and the information collection instruction is used to instruct the collection device to collect the risk point detection data corresponding to the risk point of the ball valve.

[0215] The data collection unit is used to control the collection device to collect target information according to the information collection instruction corresponding to the risk point location information, wherein the target information includes the risk point detection data corresponding to the risk point of the ball valve.

[0216] The leakage point determination unit is used to determine the leakage point of the ball valve from the risk points of the ball valve according to the target information.

[0217] It should be noted that the information interaction, execution process and other contents between the above-mentioned units are based on the same concept as the method embodiment of the present application. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0218] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned device can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0219] The present application also provides a ball valve leakage detection device, Figure 7 The schematic diagram of the structure of the ball valve leakage detection device provided in one embodiment of the present application. The ball valve leakage detection device includes a collection device, a specific test device, and a control device that is connected to the collection device and the specific test device. Figure 7 As shown, the control device 6 of the ball valve leakage detection device of this embodiment includes: at least one processor 60 ( Figure 7 Only one is shown), at least one memory 61 ( Figure 7 Only one is shown in the figure) and a computer program 62 stored in the at least one memory 61 and executable on the at least one processor 60. When the processor 60 executes the computer program 62, the ball valve leakage detection device implements the steps in any of the above-mentioned ball valve leakage detection method embodiments, or the ball valve leakage detection device implements the functions of the units in the above-mentioned device embodiments.

[0220] Exemplarily, the computer program 62 may be divided into one or more modules / units, which are stored in the memory 61 and executed by the processor 60 to complete the present application. The one or more modules / units may be a series of computer program instruction segments capable of completing specific functions, which are used to describe the execution process of the computer program 62 in the control device 6 of the ball valve leakage detection device.

[0221] The control device 6 of the ball valve leakage detection device can be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The ball valve leakage detection device may include, but is not limited to, a processor 60 and a memory 61. Those skilled in the art can understand that Figure 7This is only an example of a ball valve leakage detection device and does not constitute a limitation of the ball valve leakage detection device. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, it may also include input and output devices, network access devices, buses, etc.

[0222] The processor 60 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0223] In some embodiments, the memory 61 may be an internal storage unit of the control device 6 of the ball valve leakage detection device, such as a hard disk or memory of the ball valve leakage detection device. In other embodiments, the memory 61 may also be an external storage device of the ball valve leakage detection device, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the ball valve leakage detection device. Further, the memory 61 may also include both an internal storage unit and an external storage device of the ball valve leakage detection device. The memory 61 is used to store an operating system, an application program, a boot loader (BootLoader), data, and other programs, such as the program code of the computer program, etc. The memory 61 may also be used to temporarily store data that has been output or is to be output.

[0224] An embodiment of the present application further provides a computer-readable storage medium, wherein the 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.

[0225] An embodiment of the present application provides a computer program product. When the computer program product is run on a ball valve leakage detection device, the ball valve leakage detection device implements the steps in any of the above method embodiments.

[0226] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may at least include: any entity or device that can carry the computer program code to the ball valve leakage detection device, recording medium, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk or an optical disk. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electric carrier signals and telecommunication signals.

[0227] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0228] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0229] In the embodiments provided in the present application, it should be understood that the disclosed ball valve leakage detection device, equipment and method can be implemented in other ways. For example, the ball valve leakage detection device and equipment embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0230] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0231] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A ball valve leakage detection method, characterized in that: include: Acquire a detection signal; wherein the detection signal includes a detection point identifier of the ball valve and detection point data of the ball valve; Generate detection information according to the detection signal; wherein the detection information includes the detection point position of the ball valve, the detection time of the detection point of the ball valve, and the detection point data of the ball valve; Determine the risk point position information of the ball valve according to the detection information, and generate an information collection instruction corresponding to the risk point position information; wherein the risk point of the ball valve is used to characterize the detection point of the ball valve with leakage risk and / or leakage anomaly, and the information collection instruction is used to instruct the collection device to collect the risk point detection data corresponding to the risk point of the ball valve; According to the information collection instruction corresponding to the risk point location information, control the collection device to collect target information; wherein the target information includes risk point detection data corresponding to the risk point of the ball valve; Determining the leakage point of the ball valve from the risk points of the ball valve according to the target information; Wherein, determining the risk point position information of the ball valve according to the detection information includes: Obtaining historical information of each detection point and a safety threshold of each detection point; wherein the historical information of each detection point is obtained through a ball valve database; Compare the detection data of each detection point in the detection sub-information of each detection point with the safety threshold of each detection point, determine whether the detection data of each detection point is abnormal, and obtain a determination result of each detection point; wherein the detection information includes the detection sub-information of each detection point, the detection sub-information of each detection point includes the position of each detection point, the detection time of each detection point, and the detection data of each detection point, and the determination result includes an abnormal result or a normal result; Determine the risk point location information of the ball valve according to the judgment result of each detection point, the detection sub-information of each detection point and the historical information of each detection point; The step of determining the risk point position information of the ball valve according to the judgment result of each detection point, the detection sub-information of each detection point and the historical information of each detection point includes: Performing data analysis on the detection sub-information of each detection point whose result is judged to be a normal result and the historical information of each detection point whose result is judged to be a normal result, to obtain the first risk point location information; Based on the detection sub-information of each detection point whose judgment result is an abnormal result, statistical operations are performed on the historical judgment results in the historical information of each detection point whose judgment result is an abnormal result to obtain the second risk point position information; The first risk point location information and the second risk point location information are integrated to obtain the risk point location information of the ball valve.

2. The ball valve leakage detection method according to claim 1, characterized in that: The step of performing data analysis on the detection sub-information of each detection point whose judgment result is a normal result and the historical information of each detection point whose judgment result is a normal result to obtain the first risk point location information includes: Based on the historical detection data in the historical information of the detection point of each normal result, a baseline model corresponding to the detection point of each normal result is constructed; Analyze the data trend corresponding to each detection point of the normal result according to the baseline model corresponding to each detection point of the normal result and the detection data of each detection point of the normal result to obtain the trend result corresponding to each detection point of the normal result; Each detection point whose trend result is an increasing trend is determined as a first risk point, and the position of each detection point whose trend result is an increasing trend is determined as the position of the first risk point to obtain first risk point position information.

3. The ball valve leakage detection method according to claim 1, characterized in that: The step of performing statistical operations on the historical judgment results in the historical information of each detection point whose judgment result is an abnormal result based on the detection sub-information of each detection point whose judgment result is an abnormal result, to obtain the second risk point location information, includes: Calculate the number of times the historical judgment results in the historical information of each abnormal result detection point are abnormal results, and obtain the number of abnormalities corresponding to each abnormal result detection point; Determine whether the number of abnormalities corresponding to the detection point of each abnormal result exceeds the number threshold corresponding to the detection point of each abnormal result, and determine each detection point whose number of abnormalities exceeds the corresponding number threshold as a second risk point; The position of each detection point where the number of abnormalities exceeds the corresponding number threshold is determined as the position of the second risk point, and the second risk point position information is obtained.

4. The ball valve leakage detection method according to claim 1, characterized in that: The method further comprises: Compare each sub-data in the detection data of each detection point with the safety threshold corresponding to each sub-data, determine whether each sub-data in the detection data of each detection point exceeds the safety threshold corresponding to each sub-data, and obtain the determination result of each sub-data of each detection point; Determine the detection point where the judgment result of each sub-data indicates that each sub-data exceeds the safety threshold corresponding to each sub-data as the leakage point of the ball valve; The position of the detection point where the judgment result of each sub-data indicates that each sub-data exceeds the safety threshold corresponding to each sub-data is determined as the position of the leakage point of the ball valve, and the leakage point position information of the ball valve is obtained.

5. The ball valve leakage detection method according to claim 4, characterized in that: The generating of the information collection instruction corresponding to the risk point location information includes: Get specific test information; Generate a control instruction corresponding to the specific test according to the specific test information; wherein the control instruction corresponding to the specific test is used to control the specific test equipment to perform the test operation; According to the control instruction corresponding to the specific test and the risk point location information, an information collection instruction corresponding to the risk point location information is generated.

6. The ball valve leakage detection method according to claim 5, characterized in that: The step of generating, according to the control instruction corresponding to the specific test and the risk point location information, an information collection instruction corresponding to the risk point location information comprises: Determine the data collection duration according to the test duration information in the control instruction corresponding to the specific test, and obtain the data collection duration information; Determine the data collection frequency according to the specific test information in the control instruction corresponding to the specific test, and obtain data collection frequency information; wherein the data collection frequency information includes the data collection frequency corresponding to the sub-data of each detection point in the risk point location information; According to the data collection duration information, the data collection frequency information and the risk point location information, an information collection instruction corresponding to the risk point location information is generated.

7. The ball valve leakage detection method according to claim 6, characterized in that: Determining the leakage point of the ball valve from the risk points of the ball valve according to the target information includes: Performing data trend analysis on each sub-data of each detection point in the risk point detection data in the target information, determining whether each sub-data of each detection point in the risk point detection data is an increasing trend, and obtaining an analysis result of each sub-data; wherein the risk point detection data is collected within the collection time in the data collection time information; Comparing each sub-data of each detection point in the risk point detection data with the safety threshold corresponding to each sub-data, determining whether each sub-data of each detection point in the risk point detection data exceeds the safety threshold corresponding to each sub-data, and obtaining a comparison result of each sub-data; The detection point corresponding to the sub-data whose analysis result is an increasing trend and whose comparison result is exceeding the corresponding safety threshold is determined as the leakage point of the ball valve.

8. A ball valve leakage detection device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

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