Method, device and electronic device for determining remaining useful life of equipment

Abstract

The present application proposes a method, device, electronic device and storage medium for determining the remaining service life of a device, wherein the method includes: in the process of determining the remaining service life corresponding to the designated device at the current moment, obtaining the parameter values ​​of multiple parameters corresponding to the designated device at the current moment, and the standard values ​​of multiple parameters corresponding to the designated device, and determining the health index value of the designated device at the current moment based on the parameter values ​​and standard values ​​of the multiple parameters, so as to determine the remaining service life corresponding to the designated device at the current moment. Thus, based on the parameter values ​​and standard values ​​of multiple parameters corresponding to the designated device at the current moment, the health index value corresponding to the designated device at the current moment is determined to accurately determine the remaining service life corresponding to the designated device at the current moment, thereby achieving accurate determination of the remaining service life corresponding to the designated device at the current moment, thereby improving the safety and reliability of the operation of the equipment.

Description

Technical Field

[0001] The present application relates to the technical field of equipment life prediction, and in particular to a method, device, electronic device and storage medium for determining the remaining useful life of equipment. Background Art

[0002] In the new energy system, a large number of devices may damage their health due to long-term operation, environmental changes, frequent start and stop, etc., and even fail when the scheduled maintenance time is not due. These phenomena may cause problems in the entire new energy system. Therefore, in the new energy system, a method for evaluating the health of equipment is urgently needed. Summary of the invention

[0003] The present application proposes a method, apparatus, electronic device and storage medium for determining the remaining useful life of a device.

[0004] The first aspect of the present application proposes a method for determining the remaining service life of a device, the method comprising: obtaining parameter values ​​of multiple parameters corresponding to a specified device at a current moment; obtaining standard values ​​of multiple parameters corresponding to the specified device; determining a health index value corresponding to the specified device at the current moment based on the parameter values ​​and standard values ​​of the multiple parameters; determining the remaining service life corresponding to the specified device at the current moment based on the health index value.

[0005] In one embodiment of the present application, determining the health index value corresponding to the designated device at the current moment based on the parameter values ​​and standard values ​​of the multiple parameters includes: inputting the parameter values ​​and standard values ​​of the multiple parameters into a health indicator model to obtain the health index value corresponding to the designated device at the current moment.

[0006] In one embodiment of the present application, the health indicator model includes an association analysis layer and a health indicator determination layer, and the parameter values ​​and standard values ​​of the multiple parameters are input into the health indicator model to obtain the health index value corresponding to the designated device at the current moment, including: performing association analysis on the parameter values ​​and standard values ​​of the multiple parameters through the association analysis layer to obtain the similarity between the parameter values ​​of the multiple parameters and the standard values; and inputting the similarity into the health indicator determination layer to obtain the health index value corresponding to the designated device at the current moment.

[0007] In one embodiment of the present application, determining the remaining service life of the designated device at the current moment based on the health index value includes: inputting the health index into a remaining service life regression model to obtain the remaining service life of the designated device at the current moment.

[0008] In one embodiment of the present application, the remaining useful life regression model includes a degradation value prediction layer and a remaining useful life determination layer, and the inputting of the health index into the remaining useful life regression model to obtain the remaining useful life corresponding to the designated device at the current moment includes: inputting the health index value into the degradation value prediction layer to predict the degradation value time series of the designated device, wherein the degradation value time series includes multiple degradation values ​​and corresponding moments; obtaining the target moment corresponding to the time when the degradation value reaches the degradation alarm threshold from the degradation value time series through the remaining useful life determination layer, and determining the remaining useful life corresponding to the designated device at the current moment based on the duration between the target moment and the current moment.

[0009] The present application proposes a method for determining the remaining service life of a device. In the process of determining the remaining service life corresponding to a designated device at the current moment, the parameter values ​​of multiple parameters corresponding to the designated device at the current moment and the standard values ​​of multiple parameters corresponding to the designated device are obtained, and based on the parameter values ​​and standard values ​​of the multiple parameters, the health index value of the designated device at the current moment is determined, and then the remaining service life corresponding to the designated device at the current moment is accurately determined based on the health index value. Thus, based on the parameter values ​​and standard values ​​of multiple parameters corresponding to the designated device at the current moment, the health index value corresponding to the designated device at the current moment is determined to accurately determine the remaining service life corresponding to the designated device at the current moment, and the remaining service life corresponding to the designated device at the current moment is accurately predicted, thereby improving the safety and reliability of the operation of the equipment.

[0010] The second aspect embodiment of the present application proposes a device for determining the remaining service life of a device, the device comprising: a first acquisition module, used to obtain parameter values ​​of multiple parameters corresponding to a specified device at a current moment; a second acquisition module, used to obtain standard values ​​of multiple parameters corresponding to the specified device; a first determination module, used to determine a health index value corresponding to the specified device at the current moment based on the parameter values ​​and standard values ​​of the multiple parameters; a second determination module, used to determine the remaining service life corresponding to the specified device at the current moment based on the health index value.

[0011] In one embodiment of the present application, the first determination module is specifically used to: input the parameter values ​​and standard values ​​of the multiple parameters into the health index model to obtain the health index value corresponding to the specified device at the current moment.

[0012] In one embodiment of the present application, the health indicator model includes an association analysis layer and a health indicator determination layer, and the first determination module is specifically used to: perform association analysis on the parameter values ​​and standard values ​​of the multiple parameters through the association analysis layer to obtain the similarity between the parameter values ​​and the standard values ​​of the multiple parameters; input the similarity into the health indicator determination layer to obtain the health index value corresponding to the specified device at the current moment.

[0013] In one embodiment of the present application, the second determination module is specifically used to: input the health index into a remaining useful life regression model to obtain the remaining useful life corresponding to the designated device at the current moment.

[0014] In one embodiment of the present application, the remaining useful life regression model includes a degradation value prediction layer and a remaining useful life determination layer, and the second determination module is specifically used to: input the health index value into the degradation value prediction layer to predict the degradation value time series of the specified equipment, wherein the degradation value time series includes multiple degradation values ​​and corresponding moments; obtain the target moment corresponding to when the degradation value reaches the degradation alarm threshold from the degradation value time series through the remaining useful life determination layer, and determine the remaining useful life of the specified equipment corresponding to the current moment based on the duration between the target moment and the current moment.

[0015] The present application proposes a device for determining the remaining service life of a device. In the process of determining the remaining service life corresponding to a designated device at the current moment, the parameter values ​​of multiple parameters corresponding to the designated device at the current moment and the standard values ​​of multiple parameters corresponding to the designated device are obtained, and based on the parameter values ​​and standard values ​​of the multiple parameters, the health index value of the designated device at the current moment is determined, and then the remaining service life corresponding to the designated device at the current moment is accurately determined based on the health index value. Thus, based on the parameter values ​​and standard values ​​of multiple parameters corresponding to the designated device at the current moment, the health index value corresponding to the designated device at the current moment is determined to accurately determine the remaining service life corresponding to the designated device at the current moment, thereby achieving accurate prediction of the remaining service life corresponding to the designated device at the current moment, thereby improving the safety and reliability of equipment operation.

[0016] The third aspect of the present application proposes an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, a method for determining the remaining service life of the device in the embodiment of the present application is implemented.

[0017] The fourth aspect embodiment of the present application proposes a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, a method for determining the remaining useful life of a device in the embodiment of the present application is provided.

[0018] Other effects of the above optional manner will be described below in conjunction with specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a flow chart of a method for determining the remaining useful life of a device provided in an embodiment of the present application;

[0020] Figure 2 It is a flow chart of another method for determining the remaining useful life of a device provided in an embodiment of the present application;

[0021] Figure 3 is a schematic diagram of a joint learning framework provided in an embodiment of the present application;

[0022] Figure 4 It is a structural schematic diagram of a device for determining the remaining useful life of a device provided in an embodiment of the present application;

[0023] Figure 5 It is a block diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0024] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0025] The following describes a method, apparatus, and electronic device for determining the remaining useful life of a device according to an embodiment of the present application with reference to the accompanying drawings.

[0026] Figure 1 It is a flow chart of a method for determining the remaining useful life of a device provided in an embodiment of the present application. It should be noted that the execution subject of the method for determining the remaining useful life of a device provided in this embodiment is a device for determining the remaining useful life of the device, and the device for determining the remaining useful life of the device can be implemented by software and / or hardware. The device for determining the remaining useful life of the device in this embodiment can be configured in an electronic device. The electronic device in this embodiment can include a terminal device, a server, etc., and this embodiment does not specifically limit the electronic device.

[0027] Figure 1 It is a flow chart of a method for determining the remaining useful life of a device provided in an embodiment of the present application.

[0028] like Figure 1 As shown, the method for determining the remaining useful life of the equipment may include:

[0029] Step 101, obtaining parameter values ​​of multiple parameters corresponding to a specified device at the current moment.

[0030] In some embodiments, the designated device may be any device in the new energy system, such as a rotating device, but is not limited thereto.

[0031] In some embodiments, parameter values ​​of multiple parameters corresponding to a specified device at the current moment are obtained. The multiple parameters may include but are not limited to equipment operating years, historical maintenance data, current operating data (such as temperature, pressure, etc.), energy consumption data, etc. This embodiment does not make specific limitations on this.

[0032] The parameter values ​​of the multiple parameters corresponding to the specified device at the current moment may be data recorded by a reliable system, such as a data acquisition system and a monitoring control system, but is not limited thereto.

[0033] Step 102, obtaining standard values ​​of multiple parameters corresponding to the specified device.

[0034] In some embodiments, the standard values ​​of the multiple parameters corresponding to the designated device may be normal standard values ​​obtained when the designated device operates normally, but is not limited thereto.

[0035] Step 103, determining the health index value corresponding to the designated device at the current moment according to the parameter values ​​and standard values ​​of the multiple parameters.

[0036] In some embodiments, an implementation method for determining the health index value corresponding to a specified device at the current moment based on parameter values ​​and standard values ​​of multiple parameters is as follows: the parameter values ​​and standard values ​​of multiple parameters are input into a health indicator model to obtain the health index value corresponding to the specified device at the current moment, thereby, based on the parameter values ​​and standard values ​​of multiple parameters corresponding to the specified device at the current moment, an association is performed to accurately determine the health index value corresponding to the specified device at the current moment.

[0037] Specifically, the above-mentioned health indicator model includes an association analysis layer and a health indicator determination layer. The association analysis layer performs association analysis on the parameter values ​​and standard values ​​of multiple parameters to obtain the similarity between the parameter values ​​and the standard values ​​of multiple parameters, and then inputs the similarity into the health indicator determination layer to obtain the health index value corresponding to the specified device at the current moment, thereby realizing the accurate determination of the health index value corresponding to the specified device at the current moment.

[0038] Step 104: Determine the remaining service life of the designated device at the current moment according to the health index value.

[0039] In some embodiments, one implementation method for determining the remaining service life of a specified device at the current moment based on a health index value is to input the health index into a remaining service life regression model to obtain the remaining service life of the specified device at the current moment, thereby accurately predicting the remaining service life of the specified device at the current moment based on the remaining service life regression model.

[0040] Specifically, the above-mentioned remaining useful life regression model includes a degradation value prediction layer and a remaining useful life determination layer. The health index value can be input into the degradation value prediction layer to predict the degradation value time series of the specified equipment, wherein the degradation value time series includes multiple degradation values ​​and corresponding moments. The remaining useful life determination layer obtains the target moment corresponding to the time when the degradation value reaches the degradation alarm threshold from the degradation value time series, and determines the remaining useful life corresponding to the specified equipment at the current moment according to the time length between the target moment and the current moment, thereby realizing accurate prediction of the remaining useful life corresponding to the specified equipment at the current moment.

[0041] The present application proposes a method for determining the remaining service life of a device. In the process of determining the remaining service life corresponding to a designated device at the current moment, the parameter values ​​of multiple parameters corresponding to the designated device at the current moment and the standard values ​​of multiple parameters corresponding to the designated device are obtained, and based on the parameter values ​​and standard values ​​of the multiple parameters, the health index value of the designated device at the current moment is determined, and then the remaining service life corresponding to the designated device at the current moment is accurately determined based on the health index value. Thus, based on the parameter values ​​and standard values ​​of multiple parameters corresponding to the designated device at the current moment, the health index value corresponding to the designated device at the current moment is determined to accurately determine the remaining service life corresponding to the designated device at the current moment, and the remaining service life corresponding to the designated device at the current moment is accurately predicted, thereby improving the safety and reliability of the operation of the equipment.

[0042] Figure 2 It is a flow chart of another method for determining the remaining useful life of a device provided in an embodiment of the present application.

[0043] like Figure 2 As shown, the method for determining the remaining useful life of the equipment may include:

[0044] Step 201, collect data.

[0045] In some embodiments, a current moment for evaluating a designated device can be selected, and then parameter values ​​of multiple parameters corresponding to the designated device at the current moment collected from the data acquisition and monitoring control system can be obtained. The multiple parameters can include equipment operating years, historical maintenance data, temperature, pressure, energy consumption data, etc., and the multiple parameters are checked. If the multiple parameters are missing or have abnormal values, expert experience can be combined to use regression methods to interpolate values ​​and remove abnormal values, and combined with pre-set thresholds, it can be determined whether the collected multiple parameters exceed the thresholds, thereby directly determining whether the equipment parameters are faulty. If it is determined that there is a fault, directly report it for repair. If it is determined that there is no fault, enter step 202.

[0046] Step 202: Establish a health indicator model.

[0047] In some embodiments, a correlation analysis can be performed between reference values ​​of multiple parameters of actual working conditions and normal standard values ​​obtained in the laboratory without any abnormality, and based on the similarity between the reference values ​​and the standard values, for example, when the reference values ​​of multiple parameters of actual working conditions are closest to the normal standard values, it is proved that the actual situation of the equipment is the same as the standard and is in a normal state. Otherwise, the equipment representing the actual data is in an abnormal state and deviates from the standard, so as to accurately establish a health indicator model, wherein the calculation method for performing correlation analysis on the reference values ​​of multiple parameters of actual working conditions and the normal standard values ​​can be:

[0048]

[0049] Among them, x i ,y i are the values ​​in the two sets of data vectors, is the mean of the corresponding data. Here we can take x i is the reference value of multiple parameters under actual working conditions, y i It is the normal standard value of the laboratory.

[0050] Step 203: RUL regression model.

[0051] In some embodiments, after correlation analysis is performed on parameter values ​​of multiple parameters of actual working conditions and normal laboratory standard values ​​based on a health indicator model, the output HI of the health indicator model is used as the input of a remaining useful life regression model (Remaining Useful Life, RUL), wherein the remaining useful life regression model includes a degradation value prediction layer and a remaining useful life determination layer, and the regression result is obtained by inputting HI into the degradation value prediction layer and performing regression according to the characteristics of the HI data.

[0052] Among them, the method of regressing the characteristics of HI data may also include but is not limited to a linear regression method, a polynomial regression method and a neural network model regression method.

[0053] For example, exponential regression can be used, and the regression result can be:

[0054]

[0055] The corresponding HI value and the time are matched one by one, so as to obtain the degradation value time series of the specified device, which is:

[0056] RUL(t)=t final -HI -1 (t)

[0057] The obtained degradation value time series of the specified equipment is then input into the remaining service life determination layer to determine the target time corresponding to when the degradation value reaches the degradation alarm threshold, and the remaining service life corresponding to the specified equipment at the current time is determined based on the time between the target time and the current time.

[0058] Among them, t final The time when the device fails.

[0059] Step 204: output the remaining service life.

[0060] It should be noted that, in order to accurately determine the remaining service life, in some embodiments, the health indicator model and the remaining service life regression model can be jointly trained. The exemplary process of the joint training of the health indicator model and the remaining service life regression model is as follows: constructing an initial joint model, the joint model includes: an initial health indicator model and a corresponding initial remaining service life regression model, wherein the output of the initial health indicator model is the input of the initial remaining service life regression model, determining training data, wherein the training data includes: parameter values ​​of multiple parameters corresponding to the specified device at the sample moment and the sample remaining service life corresponding to the specified device at the sample moment, taking the parameter values ​​of multiple parameters corresponding to the sample moment as the input of the joint model, taking the sample remaining service life as the output of the joint model, adjusting the coefficients of the initial health indicator model and the initial remaining service life regression model in the joint model to achieve training.

[0061] Based on the above embodiment, an exemplary implementation of training the joint training model can be to distribute all data to different machines, each machine downloads the model from the server, and then uses the local data to train the model, and then returns the parameters that need to be updated to the server. The server aggregates the returned parameters on each machine, updates the model, and then feeds the latest model back to each machine. Figure 3As shown, the example is as follows: each participant downloads the latest model from server A, and uses local data to train the model, and uploads the encrypted gradient to server A. Server A aggregates the gradients of each user to update the model parameters, and server A returns the updated model to each participant. The more participants there are, the more samples of the model in the server, and the more adaptable the model is. Finally, each participant updates its own model.

[0062] The present application proposes a method for determining the remaining service life of a device. In the process of determining the remaining service life corresponding to a specified device at the current moment, by collecting parameter values ​​of multiple parameters corresponding to the current moment and standard values ​​of multiple parameters, and then performing correlation analysis on the parameter values ​​and standard values, a health index model is established, and then the health index value obtained according to the health index model is input into the remaining service life regression model to obtain the degradation value of the specified device and the corresponding moment, thereby determining the remaining service life of the specified device at the current moment. Thus, based on the degradation value of the specified device and the corresponding moment, the moment when the specified device fails is accurately determined to determine the remaining service life of the specified device, thereby preventing the specified device from operating under unsafe conditions.

[0063] Figure 4 It is a structural schematic diagram of a device for determining the remaining useful life of a device provided in an embodiment of the present application.

[0064] like Figure 4 As shown, the device 400 for determining the remaining useful life of the equipment includes:

[0065] The first acquisition module 401 is used to acquire parameter values ​​of multiple parameters corresponding to a specified device at a current moment.

[0066] The second acquisition module 402 is used to acquire standard values ​​of multiple parameters corresponding to the specified device.

[0067] The first determination module 403 is used to determine the health index value corresponding to the designated device at the current moment according to the parameter values ​​and standard values ​​of multiple parameters.

[0068] The second determination module 404 is used to determine the remaining service life of the designated device at the current moment according to the health index value.

[0069] The present application proposes a device for determining the remaining service life of a device. In the process of determining the remaining service life corresponding to a designated device at the current moment, the parameter values ​​of multiple parameters corresponding to the designated device at the current moment and the standard values ​​of multiple parameters corresponding to the designated device are obtained, and based on the parameter values ​​and standard values ​​of the multiple parameters, the health index value of the designated device at the current moment is determined, and then the remaining service life corresponding to the designated device at the current moment is accurately determined based on the health index value. Thus, based on the parameter values ​​and standard values ​​of multiple parameters corresponding to the designated device at the current moment, the health index value corresponding to the designated device at the current moment is determined to accurately determine the remaining service life corresponding to the designated device at the current moment, thereby achieving accurate prediction of the remaining service life corresponding to the designated device at the current moment, thereby improving the safety and reliability of equipment operation.

[0070] In one embodiment of the present application, the first determining module 403 is specifically configured to:

[0071] The parameter values ​​and standard values ​​of the multiple parameters are input into the health index model to obtain the health index value corresponding to the specified device at the current moment.

[0072] In one embodiment of the present application, the health indicator model includes an association analysis layer and a health indicator determination layer, and the first determination module 403 is specifically used to:

[0073] The parameter values ​​of the multiple parameters and the standard values ​​are subjected to association analysis through the association analysis layer to obtain the similarity between the parameter values ​​of the multiple parameters and the standard values.

[0074] The similarity is input into the health index determination layer to obtain the health index value corresponding to the specified device at the current moment.

[0075] In one embodiment of the present application, the second determining module 404 is specifically configured to:

[0076] The health index is input into the remaining useful life regression model to obtain the remaining useful life corresponding to the specified equipment at the current moment.

[0077] In one embodiment of the present application, the remaining useful life regression model includes a degradation value prediction layer and a remaining useful life determination layer, and the second determination module 404 is specifically used to:

[0078] The health index value is input into the degradation value prediction layer to predict the degradation value time series of the specified device, wherein the degradation value time series includes multiple degradation values ​​and corresponding time points.

[0079] The remaining service life determination layer obtains the target time corresponding to when the degradation value reaches the degradation alarm threshold from the degradation value time series, and determines the remaining service life corresponding to the specified equipment at the current time according to the time between the target time and the current time.

[0080] like Figure 5 , which is a block diagram of an electronic device according to an embodiment of the present application.

[0081] like Figure 5 As shown, the electronic device includes:

[0082] Memory 501 , processor 502 , and computer instructions stored in the memory 501 and executable on the processor 502 .

[0083] When the processor 502 executes the instruction, the method for determining the remaining service life of the device provided in the above embodiment is implemented.

[0084] Furthermore, the electronic device further comprises:

[0085] The communication interface 503 is used for communication between the memory 501 and the processor 502 .

[0086] The memory 501 is used to store computer instructions that can be executed on the processor 502 .

[0087] The memory 501 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0088] The processor 502 is configured to implement the method for determining the remaining useful life of the device of the above embodiment when executing a program.

[0089] If the memory 501, the processor 502 and the communication interface 503 are implemented independently, the communication interface 503, the memory 501 and the processor 502 can be connected to each other through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0090] Optionally, in a specific implementation, if the memory 501, the processor 502 and the communication interface 503 are integrated on a chip, the memory 501, the processor 502 and the communication interface 503 can communicate with each other through an internal interface.

[0091] The processor 502 may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0092] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0093] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0094] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for determining the remaining useful life of equipment, characterized in that: The method comprises: Get the parameter values ​​of multiple parameters corresponding to the specified device at the current moment; Obtaining standard values ​​of multiple parameters corresponding to the specified device; Determining a health index value corresponding to the designated device at the current moment according to the parameter values ​​and standard values ​​of the multiple parameters; Determining the remaining service life of the designated device at the current moment according to the health index value; The determining, according to the parameter values ​​and standard values ​​of the multiple parameters, the health index value corresponding to the designated device at the current moment includes: Inputting the parameter values ​​and standard values ​​of the multiple parameters into a health index model to obtain a health index value corresponding to the specified device at the current moment; The health index model includes an association analysis layer and a health index determination layer, and the parameter values ​​and standard values ​​of the multiple parameters are input into the health index model to obtain the health index value corresponding to the designated device at the current moment, including: Performing association analysis on the parameter values ​​and standard values ​​of the multiple parameters through the association analysis layer to obtain similarities between the parameter values ​​and the standard values ​​of the multiple parameters; Inputting the similarity into a health index determination layer to obtain a health index value corresponding to the designated device at the current moment; The calculation method for performing correlation analysis on the parameter values ​​and standard values ​​of the multiple parameters is: Among them, x i ,y i are the values ​​in the two sets of data vectors, is the mean of the corresponding data, and x i is the reference value of multiple parameters under actual working conditions, y i It is the normal standard value of the laboratory; After performing correlation analysis on parameter values ​​of multiple parameters of actual working conditions and normal laboratory standard values ​​based on the health index model, the output HI of the health index model is used as the input of the remaining service life regression model, wherein the remaining service life regression model includes a degradation value prediction layer and a remaining service life determination layer, and the regression result is obtained by inputting HI into the degradation value prediction layer and performing regression according to the characteristics of HI data; The determining, according to the health index value, the remaining service life of the designated device corresponding to the current moment includes: Inputting the health index into a remaining useful life regression model to obtain the remaining useful life of the designated device at the current moment; The remaining service life regression model includes a degradation value prediction layer and a remaining service life determination layer, and the health index is input into the remaining service life regression model to obtain the remaining service life corresponding to the designated equipment at the current moment, including: Inputting the health index value into the degradation value prediction layer to predict the degradation value time series of the designated device, wherein the degradation value time series includes a plurality of degradation values ​​and corresponding moments; The remaining service life determination layer obtains the target time corresponding to when the degradation value reaches the degradation alarm threshold from the degradation value time series, and determines the remaining service life of the designated device corresponding to the current time according to the time length between the target time and the current time.

2. A device for determining the remaining useful life of equipment, characterized in that: The device comprises: A first acquisition module, used to acquire parameter values ​​of multiple parameters corresponding to a specified device at a current moment; A second acquisition module, used to acquire standard values ​​of multiple parameters corresponding to the specified device; A first determination module, configured to determine a health index value corresponding to the designated device at the current moment according to the parameter values ​​and standard values ​​of the plurality of parameters; A second determination module is used to determine the remaining service life of the designated device at the current moment according to the health index value; The first determining module is specifically configured to: Inputting the parameter values ​​and standard values ​​of the multiple parameters into a health index model to obtain a health index value corresponding to the specified device at the current moment; The health indicator model includes an association analysis layer and a health indicator determination layer, and the first determination module is specifically used to: Performing association analysis on the parameter values ​​and standard values ​​of the multiple parameters through the association analysis layer to obtain similarities between the parameter values ​​and the standard values ​​of the multiple parameters; Inputting the similarity into a health index determination layer to obtain a health index value corresponding to the designated device at the current moment; The calculation method for performing correlation analysis on the parameter values ​​and standard values ​​of the multiple parameters is: Among them, x i ,y i are the values ​​in the two sets of data vectors, is the mean of the corresponding data, and x i is the reference value of multiple parameters under actual working conditions, y i It is the normal standard value of the laboratory; After performing correlation analysis on parameter values ​​of multiple parameters of actual working conditions and normal laboratory standard values ​​based on the health index model, the output HI of the health index model is used as the input of the remaining service life regression model, wherein the remaining service life regression model includes a degradation value prediction layer and a remaining service life determination layer, and the regression result is obtained by inputting HI into the degradation value prediction layer and performing regression according to the characteristics of HI data; The second determining module is specifically used to: Inputting the health index into a remaining useful life regression model to obtain the remaining useful life of the designated device at the current moment; The remaining useful life regression model includes a degradation value prediction layer and a remaining useful life determination layer, and the second determination module is specifically used to: Inputting the health index value into the degradation value prediction layer to predict the degradation value time series of the designated device, wherein the degradation value time series includes a plurality of degradation values ​​and corresponding moments; The remaining service life determination layer obtains the target time corresponding to when the degradation value reaches the degradation alarm threshold from the degradation value time series, and determines the remaining service life of the designated device corresponding to the current time according to the time length between the target time and the current time.

3. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the method according to claim 1 is implemented when the processor executes the program.

4. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to claim 1 is implemented.

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