Valve detection method, system, equipment and medium

Abstract

The invention relates to a valve detection method, system, equipment and medium, the valve detection method comprises the following steps: first operation parameters and second operation parameters of a valve are obtained, the first operation parameters at least comprise the temperature, pressure, flow and medium viscosity of the valve, and the second operation parameters at least comprise the actual flow velocity of the valve; the first operation parameter is input into a prediction model, and the predicted flow velocity of the valve is output; determining health degree information of the valve according to a comparison result of the predicted flow velocity and the actual flow velocity; and whether the valve breaks down or not is determined according to the health degree information. The predicted flow velocity of the valve is calculated by collecting the temperature, the pressure, the flow and the medium viscosity of the valve in real time, the predicted flow velocity is compared with the actual flow velocity to determine the health state of the valve, accurate overhaul of the fault valve is achieved, and long-period stable operation of production is guaranteed.

Description

Technical Field

[0001] The present invention belongs to the field of valve technology, and in particular relates to a valve detection method, system, equipment and medium. Background Art

[0002] Valves are widely used in the inlets and outlets of factory pipelines and equipment. They are one of the most numerous equipment. The large number of valves also leads to the most valve failures, which will affect the production and operation of the factory. Therefore, it is very important to inspect and repair valves in time.

[0003] In the related art, passive repairs are usually carried out after a valve fails, or the valves are replaced or repaired uniformly at regular intervals. However, the former may cause production fluctuations or even production stoppages, resulting in production losses, while the latter may lead to over-repair or disrepair, increasing costs. Summary of the invention

[0004] The purpose of the present invention is to provide a valve detection method, system, equipment and medium in order to solve the above problems.

[0005] The present invention achieves the above-mentioned purpose through the following technical solutions:

[0006] A valve detection method comprises the following steps:

[0007] Acquire a first operating parameter and a second operating parameter of the valve, wherein the first operating parameter at least includes a temperature, a pressure, a flow rate, and a medium viscosity of the valve, and the second operating parameter at least includes an actual flow rate of the valve;

[0008] Inputting the first operating parameter into a prediction model and outputting a predicted flow rate of the valve;

[0009] Determining valve health information based on a comparison result between the predicted flow rate and the actual flow rate;

[0010] Determine whether the valve is faulty according to the health information.

[0011] As a further optimization scheme of the present invention, the prediction model is a trained multivariate linear model, and before the first operating parameter is input into the prediction model, the method further includes:

[0012] Acquire a first historical operating parameter and a second historical operating parameter of the valve in a normal working state, wherein the first historical operating parameter at least includes a historical temperature, a historical pressure, a historical flow rate, and a historical medium viscosity of the valve, and the second historical operating parameter at least includes a historical flow rate of the valve;

[0013] The multivariate linear model to be trained is trained based on the first historical operating parameters and the second historical operating parameters to obtain the prediction model.

[0014] As a further optimization scheme of the present invention, the training of the multivariate linear model to be trained based on the first historical operating parameter and the second historical operating parameter includes:

[0015] constructing a goodness of fit of a multivariate linear model, wherein the goodness of fit is determined based on a mean square error between the predicted flow rate and the actual flow rate;

[0016] The multivariate linear model to be trained is trained based on the first historical operating parameter and the second historical operating parameter until the goodness of fit meets a preset condition.

[0017] As a further optimization scheme of the present invention, it is characterized in that the determining of the health information of the valve according to the comparison result of the predicted flow rate and the actual flow rate includes:

[0018] Determining a correlation coefficient between the predicted flow rate and the actual flow rate;

[0019] determining a health score of the valve according to the correlation coefficient;

[0020] Determining whether the valve is faulty according to the health information includes:

[0021] When the health score is less than a preset score, an alarm message of valve failure is output.

[0022] As a further optimization solution of the present invention, the valve detection method further includes:

[0023] Setting a corresponding preset score for each valve in the system to be tested;

[0024] Determine the health score of each valve in the system to be tested;

[0025] According to the health score of each valve and the corresponding preset score, it is determined whether each valve of the system to be tested is faulty.

[0026] As a further optimization scheme of the present invention, the method of determining the health information of the valve according to the comparison result of the predicted flow rate and the actual flow rate includes:

[0027] Determining a correlation coefficient between the predicted flow rate and the actual flow rate;

[0028] Determine the health level corresponding to the correlation coefficient based on a preset comparison table, wherein the preset comparison table records the health levels corresponding to different correlation coefficients;

[0029] Determining whether the valve is faulty according to the health information includes:

[0030] When the health level is at a preset level, an alarm message of valve failure is output.

[0031] As a further optimization scheme of the present invention, the method further includes:

[0032] Set a corresponding preset comparison table for each valve in the system to be tested;

[0033] Determine the correlation coefficient of each valve in the system to be tested, and determine the health level of each valve based on the corresponding preset comparison table;

[0034] According to the health level of each valve, it is determined whether each valve of the system to be tested is faulty.

[0035] A valve detection system, comprising:

[0036] An acquisition module, used for acquiring a first operating parameter and a second operating parameter of the valve, wherein the first operating parameter at least includes a temperature, a pressure, a flow rate, and a medium viscosity of the valve, and the second operating parameter at least includes an actual flow rate of the valve;

[0037] A prediction module, configured to input the first operating parameter into a prediction model and output a predicted flow rate of the valve;

[0038] A comparison module, used to determine the health information of the valve according to the comparison result of the predicted flow rate and the actual flow rate;

[0039] The detection module is used to determine whether the valve is faulty according to the health information.

[0040] An electronic device comprises a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus;

[0041] Memory, for storing computer programs;

[0042] The processor is used to implement the valve detection method when executing the program stored in the memory.

[0043] A computer-readable storage medium stores a computer program, wherein the computer program implements a valve detection method when executed by a processor.

[0044] The beneficial effects of the present invention are:

[0045] The present invention calculates the predicted flow rate of the valve by real-time collection of the temperature, pressure, flow rate and medium viscosity of the valve, and compares the predicted flow rate with the actual flow rate to determine the health status of the valve, thereby achieving accurate maintenance of faulty valves and ensuring long-term stable operation of production. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1A flow chart of a valve detection method provided by an embodiment of the present invention;

[0047] Figure 2 A schematic flow chart of another valve detection method provided by an embodiment of the present invention;

[0048] Figure 3 A schematic flow chart of another valve detection method provided by an embodiment of the present invention;

[0049] Figure 4 A schematic flow chart of another valve detection method provided by an embodiment of the present invention;

[0050] Figure 5 A schematic diagram of the structure of a valve detection system provided by an embodiment of the present invention;

[0051] Figure 6 A schematic diagram of the hardware structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0052] The present application is further described in detail below in conjunction with the accompanying drawings. It is necessary to point out here that the following specific implementation methods are only used to further illustrate the present application and cannot be understood as limiting the scope of protection of the present application. Technical personnel in this field can make some non-essential improvements and adjustments to the present application based on the above application content.

[0053] like Figure 1 As shown, a valve detection method comprises the following steps:

[0054] Step S101, obtaining first operating parameters and second operating parameters of the valve, wherein the first operating parameters at least include temperature, pressure, flow rate, and medium viscosity of the valve, and the second operating parameters at least include actual flow rate of the valve.

[0055] Specifically, a temperature sensor is provided on the valve for collecting the temperature parameters of the valve in real time; a pressure sensor is provided on the valve for collecting the pressure parameters of the valve in real time; a flow sensor is provided on the valve for collecting the flow parameters of the valve in real time; the actual flow rate of the valve is determined based on the flow rate and flow time collected by the flow sensor; the viscosity of the medium flowing through the valve is measured by a viscometer.

[0056] Step S102: input the first operating parameter into a prediction model, and output a predicted flow rate of the valve.

[0057] Specifically, the prediction model has an input of X=[temperature, pressure, flow, medium viscosity], and an output of Y=[flow rate]. The temperature, pressure, flow, and medium viscosity of the valve collected in real time in step S101 are input into the prediction model to obtain the predicted flow rate of the valve.

[0058] Step S103: determining valve health information according to the comparison result between the predicted flow rate and the actual flow rate.

[0059] Specifically, in order to improve the maintenance accuracy, the first operating parameters and actual flow rate of the valve over a period of time can be collected, and the predicted flow rate within the period of time can be calculated based on the first operating parameters; if the predicted flow rate and the actual flow rate of the valve within the period of time are always very different, it means that the valve is in an unhealthy operating state; if the predicted flow rate and the actual flow rate of the valve within the period of time are always slightly different, it means that the valve is in a healthy operating state.

[0060] Step S104: Determine whether the valve is faulty according to the health information.

[0061] Specifically, if the valve is in an unhealthy operating state, valve fault information can be output to remind the staff to repair the valve in time; if the valve is in a healthy operating state, it means that the valve has not failed, and steps S101-S104 can be continued, that is, the valve can continue to be monitored.

[0062] The valve detection method provided by the embodiment of the present invention obtains the first operating parameter and the second operating parameter of the valve, wherein the first operating parameter includes at least the temperature, pressure, flow rate, and medium viscosity of the valve, and the second operating parameter includes at least the actual flow rate of the valve; the first operating parameter is input into a prediction model to output the predicted flow rate of the valve; the health information of the valve is determined based on the comparison result between the predicted flow rate and the actual flow rate; and whether the valve is faulty is determined based on the health information; that is, this embodiment calculates the predicted flow rate by real-time acquisition of the temperature, pressure, flow rate, and medium viscosity of the valve, and compares the predicted flow rate with the actual flow rate to determine the health status of the valve, thereby achieving accurate maintenance of faulty valves and ensuring long-term stable operation of production.

[0063] Based on the above embodiments, Figure 2 A flow chart of another valve detection method provided by an embodiment of the present invention is shown as follows: Figure 2 As shown, before step S102, the following steps are also included:

[0064] Step S201: Acquire a first historical operating parameter and a second historical operating parameter of a valve under a normal working state.

[0065] The first historical operating parameter at least includes the historical temperature, historical pressure, historical flow rate, and historical medium viscosity of the valve, and the second historical operating parameter at least includes the historical flow rate of the valve.

[0066] Step S202: training the multivariate linear model to be trained based on the first historical operating parameters and the second historical operating parameters to obtain the trained multivariate linear model.

[0067] Specifically, firstly, batch historical data of the valve is collected, including multiple parameters such as temperature, pressure, flow, medium viscosity, flow rate, etc. under normal working conditions; then, the multivariate linear regression method is used to fit the multiple parameters, and its input includes temperature, pressure, flow, medium viscosity, and the output is the predicted flow rate.

[0068] Furthermore, after collecting batches of historical data, the historical data will be cleaned, that is, the historical data with obvious abnormalities will be deleted; then the cleaned historical data will be split, 80% as a training set and 20% as a test set, and the model will be trained to obtain a trained multivariate linear model.

[0069] In some embodiments, step S202 includes: constructing the goodness of fit of the multivariate linear model, wherein the goodness of fit is determined based on the mean square error between the predicted flow rate and the actual flow rate; training the multivariate linear model to be trained based on the first historical operating parameters and the second historical operating parameters until the goodness of fit meets the preset conditions.

[0070] Specifically, the mean square error between the predicted flow rate and the actual flow rate is shown in formula (1):

[0071] MSE = mean((y pred -y) 2 ) (1)

[0072] Among them, MSE represents the mean square error, y pred represents the predicted flow rate, y represents the actual flow rate, and mean represents the mean.

[0073] The goodness of fit of the constructed multivariate linear model is shown in formula (2):

[0074]

[0075] Among them, R 2 It represents the goodness of fit, also known as the coefficient of determination, and VAR represents the variance between the predicted flow rate and the actual flow rate.

[0076] On the basis of the above-mentioned embodiment, a multivariate linear model is constructed by mining the historical operating parameters of the valve. The multivariate linear model can accurately predict the flow rate of the valve under normal working conditions based on input parameters such as temperature, pressure, flow, and medium viscosity.

[0077] Based on the above embodiments, Figure 3A flow chart of another valve detection method provided by an embodiment of the present invention is as follows: Figure 3 As shown, the valve detection method includes:

[0078] Step S301, obtaining first operating parameters and second operating parameters of the valve, wherein the first operating parameters at least include temperature, pressure, flow rate, and medium viscosity of the valve, and the second operating parameters at least include actual flow rate of the valve.

[0079] Step S302: input the first operating parameter into the prediction model, and output the predicted flow rate of the valve.

[0080] Step S303: Determine the correlation coefficient between the predicted flow rate and the actual flow rate.

[0081] Step S304: Determine the health score of the valve according to the correlation coefficient.

[0082] Step S305: When the health score is less than a preset score, output valve failure alarm information.

[0083] The implementation methods of steps S301 and S302 in this embodiment are similar to those of steps S101 and S102 in the aforementioned embodiments, and are not described again here.

[0084] The difference from the above-mentioned embodiment is that this embodiment defines a method for implementing the health information. In this embodiment, the correlation coefficient between the predicted flow rate and the actual flow rate is determined; the health score of the valve is determined according to the correlation coefficient; and when the health score is less than a preset score, the alarm information of the valve failure is output.

[0085] Specifically, the correlation coefficient between the actual flow rate and the predicted flow rate is as shown in formula (3):

[0086] ρ=cov(y pred ,y)σ(y pred )σy (3)

[0087] Among them, ρ represents the correlation coefficient, cov(y pred ,y) represents the covariance between the predicted flow rate and the actual flow rate, σ(y pred) and σy represent the standard deviation of the predicted flow rate and the actual flow rate, respectively. When ρ is a positive number, it indicates that there is a positive correlation between the predicted value and the true value, that is, the closer the predicted value is to the true value; when ρ is a negative number, it indicates that there is a negative correlation between the predicted value and the true value, that is, the larger the gap between the predicted value and the true value. When ρ is 0, it indicates that there is no linear relationship between the predicted value and the true value. The health score can be from 0 to 100 points. The higher the score, the higher the health. The correlation coefficient -1 to 1 can be mapped to the health score of 0 to 100 points, so that the health score of the valve can be determined based on the correlation coefficient. The preset score can be set by a technician in this field based on experience. For example, if the preset score is 60 points, when the health score of the valve is lower than 60 points, it means that the valve may be faulty. When the health score of the valve is higher than 60 points, it means that the valve is operating normally.

[0088] In some embodiments, the method further comprises: setting a corresponding preset score for each valve in the system to be detected;

[0089] Determine the health score of each valve in the system to be tested; and determine whether each valve in the system to be tested is faulty based on the health score of each valve and the corresponding preset score.

[0090] Specifically, the system includes multiple valves, each of which may have a different fault tolerance rate, that is, a corresponding preset score can be accurately set for each valve in the system; then the health score of each valve is compared with the corresponding preset score to determine whether each valve in the system is faulty. Furthermore, if the system includes too many valves, the valves can be divided into different types, such as switch valves, regulating valves, check valves, vent valves, etc., and corresponding preset scores are set for different types of valves.

[0091] On the basis of the above-mentioned embodiment, the health score of the valve is determined according to the correlation coefficient between the predicted flow rate and the actual flow rate, and by comparing with the preset score, accurate detection of whether the valve is faulty is achieved.

[0092] Based on the above embodiments, Figure 4 A flow chart of another valve detection method provided by an embodiment of the present invention is as follows: Figure 4 As shown, the valve detection method includes:

[0093] Step S401, obtaining first operating parameters and second operating parameters of the valve, wherein the first operating parameters at least include temperature, pressure, flow rate, and medium viscosity of the valve, and the second operating parameters at least include actual flow rate of the valve.

[0094] Step S402: input the first operating parameter into the prediction model, and output the predicted flow rate of the valve.

[0095] Step S403: Determine the correlation coefficient between the predicted flow rate and the actual flow rate.

[0096] Step S404: determining the health level corresponding to the correlation coefficient based on a preset comparison table, wherein the preset comparison table records the health levels corresponding to different correlation coefficients.

[0097] Step S405: When the health level is at a preset level, output valve failure alarm information.

[0098] The implementation methods of steps S401 and S402 in this embodiment are similar to those of steps S101 and S102 in the aforementioned embodiment, and are not described again here.

[0099] The difference from the above embodiment is that this embodiment defines another implementation method of health information. In this embodiment, the correlation coefficient between the predicted flow rate and the actual flow rate is determined; the health level corresponding to the correlation coefficient is determined based on a preset comparison table, and the preset comparison table records the health levels corresponding to different correlation coefficients; when the health level is at a preset level, the alarm information of valve failure is output.

[0100] Specifically, the health information can be divided into multiple levels, such as healthy, sub-healthy, and faulty, where healthy means that the valve is operating normally, sub-healthy means that the valve is basically operating normally but has some minor problems, and faulty means that the valve needs to be repaired; different correlation coefficients correspond to different health levels, which can be recorded in a preset comparison table. In this embodiment, after calculating the correlation coefficient between the predicted flow rate and the actual flow rate of the valve, the health level of the valve can be determined according to the preset comparison table, and if the valve is at the fault level, the alarm information of the valve fault is output.

[0101] In some embodiments, the method further includes: setting a corresponding preset comparison table for each valve in the system to be detected; determining a correlation coefficient corresponding to each valve in the system to be detected, and determining a health level corresponding to each valve based on the corresponding preset comparison table; and determining whether each valve in the system to be detected is faulty according to the health level of each valve.

[0102] Specifically, the system includes multiple valves, each of which may have a different fault tolerance rate, that is, a corresponding preset comparison table can be accurately set for each valve in the system, that is, for different valves, the same correlation coefficient may belong to different health levels; after calculating the correlation coefficient of each valve, its health level is determined according to the preset comparison table of the valve; when the health level of the valve is the fault level, the corresponding valve fault of the system is determined. Furthermore, if the system includes too many valves, the valves can be divided into different types, such as switch valves, regulating valves, check valves, vent valves, etc., and corresponding preset comparison tables are set for different types of valves.

[0103] On the basis of the above-mentioned embodiment, the health level of the valve is determined according to the correlation coefficient between the predicted flow rate and the actual flow rate, and according to the health level, accurate detection of whether the valve is faulty is achieved.

[0104] Figure 5 A schematic diagram of the structure of a valve detection system provided by an embodiment of the present invention is shown in FIG. Figure 5 As shown, the valve detection system includes:

[0105] An acquisition module 501 is used to acquire a first operating parameter and a second operating parameter of a valve, wherein the first operating parameter includes at least the temperature, pressure, flow rate, and medium viscosity of the valve, and the second operating parameter includes at least the actual flow rate of the valve; a prediction module 502 is used to input the first operating parameter into a prediction model and output the predicted flow rate of the valve; a comparison module 503 is used to determine the health information of the valve based on the comparison result between the predicted flow rate and the actual flow rate; and a detection module 504 is used to determine whether the valve is faulty based on the health information.

[0106] In some embodiments, the prediction model is a trained multivariate linear model, and the prediction module 502 further includes:

[0107] Acquire first historical operating parameters and second historical operating parameters of the valve under normal working conditions, wherein the first historical operating parameters at least include historical temperature, historical pressure, historical flow rate, and historical medium viscosity of the valve, and the second historical operating parameters at least include historical flow rate of the valve; train a multivariate linear model to be trained based on the first historical operating parameters and the second historical operating parameters to obtain the trained multivariate linear model.

[0108] In some embodiments, the prediction module 502 is specifically used to: construct the goodness of fit of the multivariate linear model, wherein the goodness of fit is determined based on the mean square error between the predicted flow rate and the actual flow rate; and train the multivariate linear model to be trained based on the first historical operating parameters and the second historical operating parameters until the goodness of fit meets a preset condition.

[0109] In some embodiments, the comparison module 503 is specifically used to: determine the correlation coefficient between the predicted flow rate and the actual flow rate; determine the health score of the valve based on the correlation coefficient; the detection module 504 is specifically used to: output valve failure alarm information when the health score is less than a preset score.

[0110] In some embodiments, the comparison module 503 is also used to: set a corresponding preset score for each valve in the system to be detected; determine the health score of each valve in the system to be detected; the detection module 504 is specifically used to: determine whether each valve in the system to be detected is faulty based on the health score of each valve and the corresponding preset score.

[0111] In some embodiments, the comparison module 503 is specifically used to: determine the correlation coefficient between the predicted flow rate and the actual flow rate; determine the health level corresponding to the correlation coefficient based on a preset comparison table, and the preset comparison table records the health levels corresponding to different correlation coefficients; the detection module 504 is specifically used to: when the health level is at a preset level, output an alarm message of valve failure.

[0112] In some embodiments, the comparison module 503 is also used to: set a corresponding preset comparison table for each valve in the system to be detected; determine the correlation coefficient of each valve in the system to be detected, and determine the health level of each valve based on the corresponding preset comparison table; the detection module 504 is specifically used to: determine whether each valve in the system to be detected is faulty according to the health level of each valve.

[0113] The implementation process of the functions and effects of each module in the above system is specifically described in the implementation process of the corresponding steps in the above method, which will not be repeated here.

[0114] For the system embodiment, since it basically corresponds to the method embodiment, the relevant parts can refer to the partial description of the method embodiment. The system embodiment described above is only schematic, wherein the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, that is, they may be located in one place, or they may be distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of the present invention. Those of ordinary skill in the art can understand and implement it without paying creative labor.

[0115] In the above embodiments, any number of all modules can be combined into one module for implementation, or any one of the modules can be split into multiple modules. Alternatively, at least part of the functions of one or more of these modules can be combined with at least part of the functions of other modules and implemented in one module. At least one of all modules can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application-specific integrated circuit (ASIC), or can be implemented by hardware or firmware such as any other reasonable way of integrating or packaging the circuit, or by any one of the three implementation methods of software, hardware and firmware or by an appropriate combination of any of them. Alternatively, at least one of all modules can be at least partially implemented as a computer program module, and when the computer program module is run, the corresponding function can be executed. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working process and corresponding beneficial effects of the valve detection system described above can refer to the corresponding process in the aforementioned method example, and will not be repeated here.

[0116] Figure 6 A schematic diagram of the hardware structure of an electronic device provided by an embodiment of the present invention is shown in FIG. Figure 6 As shown, the electronic device includes: a processor 601, a communication interface 602, a memory 603 and a communication bus 604, wherein the processor 601, the communication interface 602, and the memory 603 communicate with each other through the communication bus 604.

[0117] Memory 603, used for storing computer programs;

[0118] In one embodiment of the present application, the processor 601 is used to implement the steps of the valve detection method provided by any one of the aforementioned method embodiments when executing the program stored in the memory 603.

[0119] The implementation principle and technical effects of the electronic device provided in the embodiment of the present application are similar to those of the above embodiment and will not be repeated here.

[0120] The above-mentioned memory 603 can be an electronic memory such as a flash memory, an EEPROM (electrically erasable programmable read-only memory), an EPROM, a hard disk or a ROM. The memory 603 has a storage space for program codes for executing any method steps in the above-mentioned method. For example, the storage space for program codes may include various program codes for implementing the various steps in the above method respectively. These program codes can be read from or written into one or more computer program products. These computer program products include program code carriers such as hard disks, compact disks (CDs), memory cards or floppy disks. Such computer program products are usually portable or fixed storage units. The storage unit may have a storage segment or storage space arranged similarly to the memory 603 in the above-mentioned electronic device. The program code can be compressed, for example, in an appropriate form. Generally, the storage unit includes a program for executing the method steps according to the embodiment of the present application, that is, a code that can be read by a processor such as 601, which, when run by an electronic device, causes the electronic device to execute the various steps in the method described above.

[0121] The embodiment of the present application further provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the valve detection method described above are implemented.

[0122] The computer-readable storage medium may be included in the device / apparatus described in the above embodiment; or it may exist independently without being assembled into the device / apparatus. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed, the method according to the embodiment of the present application is implemented.

[0123] According to an embodiment of the present application, the computer-readable storage medium may be a non-volatile computer-readable storage medium, for example, may include but is not limited to: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, apparatus, or device.

[0124] It should be noted that, in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0125] The above description is only a specific implementation of the present application, so that those skilled in the art can understand or implement the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest range consistent with the principles and novel features applied for herein.

Claims

1. A valve detection method, characterized in that: The following steps are involved: Acquire a first operating parameter and a second operating parameter of the valve, wherein the first operating parameter at least includes a temperature, a pressure, a flow rate, and a medium viscosity of the valve, and the second operating parameter at least includes an actual flow rate of the valve; Inputting the first operating parameter into a prediction model and outputting a predicted flow rate of the valve; Determining valve health information based on a comparison result between the predicted flow rate and the actual flow rate; Determine whether the valve is faulty according to the health information.

2. A valve detection method according to claim 1, characterized in that: The prediction model is a trained multivariate linear model. Before inputting the first operating parameter into the prediction model, the method further includes: Acquire a first historical operating parameter and a second historical operating parameter of the valve in a normal working state, wherein the first historical operating parameter at least includes a historical temperature, a historical pressure, a historical flow rate, and a historical medium viscosity of the valve, and the second historical operating parameter at least includes a historical flow rate of the valve; The multivariate linear model to be trained is trained based on the first historical operating parameters and the second historical operating parameters to obtain the prediction model.

3. A valve detection method according to claim 2, characterized in that: The training of the multivariate linear model to be trained based on the first historical operating parameter and the second historical operating parameter includes: constructing a goodness of fit of a multivariate linear model, wherein the goodness of fit is determined based on a mean square error between the predicted flow rate and the actual flow rate; The multivariate linear model to be trained is trained based on the first historical operating parameter and the second historical operating parameter until the goodness of fit meets a preset condition.

4. A valve detection method according to any one of claims 1 to 3, characterized in that: The determining of the health information of the valve according to the comparison result of the predicted flow rate and the actual flow rate includes: Determining a correlation coefficient between the predicted flow rate and the actual flow rate; determining a health score of the valve according to the correlation coefficient; Determining whether the valve is faulty according to the health information includes: When the health score is less than a preset score, an alarm message of valve failure is output.

5. A valve detection method according to claim 4, characterized in that: The valve detection method further comprises: Setting a corresponding preset score for each valve in the system to be tested; Determine the health score of each valve in the system to be tested; According to the health score of each valve and the corresponding preset score, it is determined whether each valve of the system to be tested is faulty.

6. A valve detection method according to any one of claims 1 to 3, characterized in that: The determining of the health information of the valve according to the comparison result of the predicted flow rate and the actual flow rate includes: Determining a correlation coefficient between the predicted flow rate and the actual flow rate; Determine the health level corresponding to the correlation coefficient based on a preset comparison table, wherein the preset comparison table records the health levels corresponding to different correlation coefficients; Determining whether the valve is faulty according to the health information includes: When the health level is at a preset level, an alarm message of valve failure is output.

7. A valve detection method according to claim 6, characterized in that: The method further comprises: Set a corresponding preset comparison table for each valve in the system to be tested; Determine the correlation coefficient of each valve in the system to be tested, and determine the health level of each valve based on the corresponding preset comparison table; According to the health level of each valve, it is determined whether each valve of the system to be tested is faulty.

8. A valve detection system, characterized in that: include: An acquisition module, used for acquiring a first operating parameter and a second operating parameter of the valve, wherein the first operating parameter at least includes a temperature, a pressure, a flow rate, and a medium viscosity of the valve, and the second operating parameter at least includes an actual flow rate of the valve; A prediction module, configured to input the first operating parameter into a prediction model and output a predicted flow rate of the valve; A comparison module, used to determine the health information of the valve according to the comparison result of the predicted flow rate and the actual flow rate; The detection module is used to determine whether the valve is faulty according to the health information.

9. An electronic device, characterized in that: It includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus; Memory, for storing computer programs; The processor is used to implement the valve detection method according to any one of claims 1 to 7 when executing the program stored in the memory.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the valve detection method according to any one of claims 1 to 7 is implemented.

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