CN114692903A - Method for equipment fault detection and terminal equipment - Google Patents
Method for equipment fault detection and terminal equipment Download PDFInfo
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Abstract
The invention provides a method for equipment fault detection and terminal equipment, wherein the method comprises the following steps: s10: establishing a fault learning model; s20: acquiring real-time equipment operation parameters of each data node; s30: and if the real-time equipment operation parameter and the (i + 1) th equipment operation predicted value are not smaller than a preset deviation value, sending an equipment fault signal. The method for detecting the equipment fault can detect the equipment fault in time so as to increase the efficiency of fault detection.
Description
Technical Field
The invention belongs to the technical field of linear regression, and particularly relates to a method for equipment fault detection and terminal equipment.
Background
PySyft is an algorithm framework of the Federal learning open source community, provides communication between data nodes and PyTorch algorithm library, but has not been implemented for linear regression.
In the existing technical scheme, another framework, namely linear regression realized by FATE, mainly relies on direct communication between data nodes, rather than relying on trusted server data nodes. This scheme requires frequent use of homomorphic encryption to ensure privacy protection, which may result in consumption efficiency of encryption calculation, and thus low efficiency of device failure detection.
Disclosure of Invention
In view of this, embodiments of the present invention provide a method for device fault detection and a terminal device, so as to solve the problem of low fault detection efficiency in the prior art.
A first aspect of an embodiment of the present invention provides a method for device fault detection, where the method includes:
s10: establishing a fault learning model, wherein the establishing of the fault learning model comprises: s101: selecting a first data node of a plurality of data nodes as an initiator and selecting data nodes other than the first data node as respondents; s102: acquiring the ith equipment operation parameter corresponding to the ith moment and the (i + 1) th equipment operation parameter corresponding to the (i + 1) th moment of the initiator and the responder, wherein i is a natural number greater than 0; s103: calculating to obtain an ith carving response value through a first function based on the ith carving equipment operation parameter of the responder, and calculating to obtain an i +1 th carving response value through the first function based on the i +1 th carving equipment operation parameter of the responder; s104: calculating to obtain an ith carving equipment operation parameter predicted value through a second function based on the ith carving equipment operation parameter and the ith carving response value of the initiator, and calculating to obtain an ith +1 carving equipment operation predicted value through the second function based on the ith +1 carving equipment operation parameter and the ith +1 carving response value of the initiator; s105: if the difference value between the operation predicted value of the ith +1 th equipment and the operation predicted value of the ith equipment is not greater than a preset value, sending a termination signal and stopping training;
s20: acquiring real-time equipment operation parameters of each data node;
s30: and if the real-time equipment operation parameter and the (i + 1) th equipment operation predicted value are not smaller than a preset deviation value, sending an equipment fault signal.
The method for detecting device faults as described above, wherein the device operation parameter of the ith implementation and the device operation parameter of the (i + 1) th implementation each include a weight of each of the plurality of data nodes and device operation data.
The method for equipment failure detection as described above, wherein the first function is a response sum of products of weights of the respondents and equipment operation data.
The method for equipment fault detection as described above, wherein the second function is the sum of the response sum and the product of the weight of the initiator and the equipment operational data minus a target value.
The method for equipment failure detection as described above, wherein step S105 includes:
distributing the operation predicted value of the ith carving equipment and the operation predicted value of the (i + 1) th carving equipment to each data node;
obtaining an ith scale gradient value of each data node through a third function based on the ith scale equipment operation predicted value and the data quantity in the ith scale equipment operation parameter of each data node;
obtaining an i +1 th scale gradient value of each data node through the third function based on the i +1 th scale equipment operation predicted value and equipment operation data in the i +1 th scale parameter of each data node;
and if the difference value between the i +1 th moment gradient value of each data node and the i th moment gradient value of the corresponding data node is not greater than the preset gradient value, sending a termination signal and stopping training.
The method for detecting the equipment fault as described above, wherein if the difference between the predicted operation value of the equipment at the i +1 th time and the predicted operation value of the equipment at the i th time is greater than a preset value, the steps S102 to S104 are repeated until the difference between the predicted operation value of the equipment at the i +1 th time and the predicted operation value of the equipment at the i th time is not greater than the preset value.
A third aspect of an embodiment of the present invention provides an apparatus for device fault detection, where the apparatus includes:
the establishing module is used for establishing a fault learning model, wherein the establishing of the fault learning model comprises the following steps: s101: selecting a first data node of a plurality of data nodes as an initiator and selecting data nodes other than the first data node as respondents; s102: acquiring the ith equipment operation parameter corresponding to the ith moment and the (i + 1) th equipment operation parameter corresponding to the (i + 1) th moment of the initiator and the responder, wherein i is a natural number greater than 0; s103: calculating to obtain an ith carving response value through a first function based on the ith carving equipment operation parameter of the responder, and calculating to obtain an i +1 th carving response value through the first function based on the i +1 th carving equipment operation parameter of the responder; s104: calculating to obtain an ith carving equipment operation parameter predicted value through a second function based on the ith carving equipment operation parameter and the ith carving response value of the initiator, and calculating to obtain an ith +1 carving equipment operation predicted value through the second function based on the ith +1 carving equipment operation parameter and the ith +1 carving response value of the initiator; s105: if the difference value between the operation predicted value of the ith +1 th equipment and the operation predicted value of the ith equipment is not greater than a preset value, sending a termination signal and stopping training;
the acquisition module is used for acquiring real-time equipment operation parameters of each data node;
a determination module: and the device fault signal is sent out in response to the fact that the real-time device operation parameter and the i +1 th equipment operation predicted value are not smaller than a preset deviation value.
The apparatus for device fault detection as described above, wherein the establishing module is further configured to: and in response to that the difference value between the operation predicted value of the equipment at the i +1 th stage and the operation predicted value of the equipment at the i th stage is larger than the preset value, repeating the steps S102 to S104 until the difference value between the operation predicted value of the equipment at the i +1 th stage and the operation predicted value of the equipment at the i th stage is not larger than the preset value.
A third aspect of the embodiments of the present invention provides a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method for device fault detection as described above when executing the computer program.
A fourth aspect of embodiments of the present invention provides a computer-readable storage medium, which stores a computer program, characterized in that the computer program, when being executed by a processor, implements the steps of the method for device fault detection as described above.
Compared with the prior art, the embodiment of the invention has the following beneficial effects: and encryption calculation in the training process is reduced, and the fault detection efficiency is improved.
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In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed for the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
FIG. 1 is a flow chart of a method for device fault detection provided by an embodiment of the present invention;
FIG. 2 is a schematic diagram of an implementation process for establishing a fault learning model according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of an apparatus for equipment fault detection provided by an embodiment of the invention;
fig. 4 is a schematic diagram of a terminal device according to an embodiment of the present invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.
In order to explain the technical means of the present invention, the following description will be given by way of specific examples.
As shown in fig. 1, an embodiment of the present invention provides a method for device fault detection, wherein the method for device fault detection of the present invention includes:
s10: establishing a fault learning model, wherein the establishing of the fault learning model comprises the following steps:
s101: a first data node of the plurality of data nodes is selected as an initiator and data nodes other than the first data node are selected as respondents.
S102: acquiring the ith-time equipment operation parameter (which can also be referred to as the ith-time parameter for short) corresponding to the ith time and the ith + 1-time equipment operation parameter (which can also be referred to as the (i + 1) -time parameter for short) corresponding to the ith time of the initiator and the responder, wherein i is a natural number greater than 0.
Specifically, the ith-stage device operation parameter and the (i + 1) th-stage device operation parameter both include a weight of each of the plurality of data nodes and device operation data (which may also be referred to as a data volume and include multiple types of device operation data parameters), where the types included in the device operation parameter corresponding to any time are the same and both include the weight of each of the plurality of data nodes and the device operation data.
S103: and calculating to obtain an ith moment response value through a first function based on the ith moment equipment operation parameter of the responder, and calculating to obtain an ith +1 th moment response value through the first function based on the ith +1 th moment equipment operation parameter of the responder.
Specifically, the first function is a response sum of products of weights of the respondents and the equipment operation data.
S104: and calculating to obtain an ith carving equipment operation parameter predicted value through a second function based on the ith carving equipment operation parameter and the ith carving response value of the initiator, and calculating to obtain an ith +1 carving equipment operation predicted value through the second function based on the ith +1 carving equipment operation parameter and the ith +1 carving response value of the initiator.
Specifically, the second function is the sum of the response sum and the product of the weight of the initiator and the device operation data minus a target value, wherein the target value is a specific device operation target value set by a person skilled in the art according to the application environment, and the target value is different in different application environments and is not limited herein.
S105: if the difference value between the operation predicted value of the ith +1 th equipment and the operation predicted value of the ith equipment is not greater than the preset value, sending a termination signal and stopping training;
specifically, if the difference value between the operation predicted value of the ith +1 th equipment and the operation predicted value of the ith equipment is not greater than a preset value, sending a termination signal and stopping training; if the difference value between the operation predicted value of the equipment at the i +1 th stage and the operation predicted value of the equipment at the i th stage is larger than the preset value, the steps S102 to S104 are repeated until the difference value between the operation predicted value of the equipment at the i +1 th stage and the operation predicted value of the equipment at the i th stage is not larger than the preset value.
Further, in the process of establishing the fault learning model, step S105 includes:
distributing the operation predicted value of the ith carving equipment and the operation predicted value of the (i + 1) th carving equipment to each data node;
obtaining the ith moment gradient value of each data node through a third function based on the ith moment equipment operation predicted value and equipment operation data in the ith moment parameter of each data node;
obtaining the i +1 th scale gradient value of each data node through a third function based on the operation predicted value of the i +1 th scale equipment and the data quantity in the i +1 th scale equipment operation parameter of each data node;
and if the difference value between the i +1 th moment gradient value of each data node and the i th moment gradient value of the corresponding data node is not greater than the preset gradient value, sending a termination signal and stopping training.
Specifically, if the difference value between the i +1 th moment gradient value of each data node and the i th moment gradient value of the corresponding data node is not greater than the preset gradient value, sending a termination signal and stopping training; if the difference value between the i +1 th moment gradient value of each data node and the i th moment gradient value of the corresponding data node is larger than the preset gradient value, repeating the steps S102 to S104 until the difference value between the i +1 th moment predicted value and the i th moment predicted value is not larger than the preset value.
S20: and acquiring real-time equipment operation parameters of each data node.
Specifically, the device operating parameters are obtained in real time at one or more locations of the device by the detection module.
S30: and if the real-time equipment operation parameter and the (i + 1) th equipment operation predicted value are not less than the preset deviation value, sending an equipment fault signal.
Specifically, if the difference value between the operation predicted value of the equipment at the i +1 th stage and the operation predicted value of the equipment at the i th stage is not greater than a preset value, selecting the operation predicted value of the equipment at the i +1 th stage as an output value or a critical value of fault simulation calculation of a fault learning model; if the difference value between the predicted value of the equipment at the i +1 th moment and the predicted value of the equipment at the i th moment is larger than the preset value, taking the current i +1 th moment as the i th moment, and taking the i +2 th moment as the i +1 th moment, repeating the steps S102 to S104 until the difference value between the predicted value at the i +1 th moment and the predicted value at the i th moment is not larger than the preset value. And if the real-time equipment operation parameter and the (i + 1) th equipment operation predicted value are not smaller than the preset deviation value, sending an equipment fault signal, and if the real-time equipment operation parameter and the (i + 1) th equipment operation predicted value are smaller than the preset deviation value, indicating that the equipment normally operates.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
An embodiment of the present invention for building a fault learning model will now be described in detail with reference to fig. 2, which is provided for clarity and is not intended to limit the present invention.
As shown in fig. 2, the establishing of the fault learning model of the present invention includes the following steps:
1) among the plurality of data nodes (e.g., A, B, C data nodes), a is selected as the initiator and B and C are selected as the respondents. Meanwhile, A, B, C three client data nodes are communicated with a Server (Server) in a unified manner, of course, any data node in B, C data nodes may be selected as an initiator, if B is the initiator, a and C are selected as responders, and it is not limited which data node is used as the initiator, and of course, two data nodes may be selected as the initiators, and no specific limitation is made here.
2) The three client data nodes calculate W · X based on their own weights Wa/Wb/Wc and their own sample data Xa/Xb/Xc, the responder B, C sends the calculation result to the Server, and the Server calculates and sums the sum by the following formula to obtain sum, that is, sum is Wb · Xb + Wb · Xc, and forwards the sum to the initiator a. As shown in Step 1 of fig. 2.
3) The a data node receives sum, and calculates a prediction error case d by the following formula, i.e., d is Wb · Xb + sum-y, where y in the process of calculating d is a target value, and the target value is a specific target value set by a person skilled in the art according to an application environment, and the target value is different from one another in different application environments, and the target value is not limited herein, and d is forwarded to B, C data nodes through a Server. A. B, C after obtaining d, the gradient Ga, Gb, Gc corresponding to the respective weights are calculated, as shown by Step 2.
4) As shown in Step3 in the figure, all client data nodes upload respective gradients to the Server, and the Server determines whether the global model has converged or not by combining the last time and the current gradient situation, if so, sends a termination signal and stops training. Otherwise, continuing the step 2) and carrying out iterative training.
Specifically, in step 4), the last gradient condition refers to a result calculated after the parameters corresponding to each data node at the last time are calculated by using steps 2) and 3), and when determining whether convergence occurs, if a difference between the current gradient condition and the last gradient condition is not greater than a preset value, the convergence is determined, where the preset value may be a value such as 0.01, 0.001, and the like, and in different application environments, the preset values may be different or the same, and the preset value is not limited herein.
As shown in fig. 3, the present invention also provides an apparatus 3 for detecting device failure, wherein the apparatus comprises:
an establishing module 301, configured to establish a fault learning model, where establishing the fault learning model includes: s101: selecting a first data node of the plurality of data nodes as an initiator and selecting data nodes other than the first data node as respondents; s102: acquiring an ith equipment operation parameter corresponding to the ith moment and an ith +1 equipment operation parameter corresponding to the (i + 1) th moment of an initiator and the responder, wherein i is a natural number greater than 0; s103: calculating to obtain an ith carving response value through a first function based on the ith carving equipment operation parameter of the responder, and calculating to obtain an i +1 th carving response value through the first function based on the i +1 th carving equipment operation parameter of the responder; s104: calculating to obtain an ith carving equipment operation parameter predicted value through a second function based on an ith carving equipment operation parameter and an ith carving response value of an initiator, and calculating to obtain an ith +1 carving equipment operation predicted value through the second function based on an ith +1 carving equipment operation parameter and an ith +1 carving response value of the initiator; s105: if the difference value between the operation predicted value of the ith +1 th equipment and the operation predicted value of the ith equipment is not greater than a preset value, sending a termination signal and stopping training;
an obtaining module 302, configured to obtain real-time device operating parameters of each data node;
and the judging module 303 is configured to send an equipment fault signal in response to that the real-time equipment operation parameter and the i +1 th equipment operation predicted value are not less than a preset deviation value.
Each module of the apparatus for device fault detection in this embodiment corresponds to each step of the above method, and is not described herein again.
Fig. 4 is a schematic diagram of a terminal device according to an embodiment of the present invention. As shown in fig. 4, the terminal device 4 of this embodiment includes: a processor 40, a memory 41 and a computer program 42 stored in said memory 41 and executable on said processor 40, such as a program for device failure detection. The processor 40, when executing the computer program 42, implements the steps in the various method embodiments for device failure detection described above, such as the steps 10 to 30 shown in fig. 1. Alternatively, the processor 40, when executing the computer program 42, implements the functions of each module/unit in each device embodiment described above, for example, the functions of the modules 301 to 303 shown in fig. 3.
Illustratively, the computer program 42 may be partitioned into one or more modules/units, which are stored in the memory 41 and executed by the processor 40 to implement the present invention. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution process of the computer program 42 in the terminal device 4.
The terminal device 4 may be a desktop computer, a notebook, a palm computer, a cloud server, or other computing devices. The terminal device may include, but is not limited to, a processor 40, a memory 41. Those skilled in the art will appreciate that fig. 4 is merely an example of a terminal device 4 and does not constitute a limitation of terminal device 4 and may include more or fewer components than shown, or some components may be combined, or different components, e.g., the terminal device may also include input-output devices, network access devices, buses, etc.
The Processor 40 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 41 may be an internal storage unit of the terminal device 4, such as a hard disk or a memory of the terminal device 4. The memory 41 may also be an external storage device of the terminal device 4, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the terminal device 4. Further, the memory 41 may also include both an internal storage unit and an external storage device of the terminal device 4. The memory 41 is used for storing the computer program and other programs and data required by the terminal device. The memory 41 may also be used to temporarily store data that has been output or is to be output.
It should be clear to those skilled in the art that, for convenience and simplicity of description, the foregoing division of the functional units and modules is only used for illustration, and in practical applications, the above function distribution may be performed by different functional units and modules as needed, that is, the internal structure of the apparatus may be divided into different functional units or modules to perform all or part of the above described functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the technical solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus/terminal device and method may be implemented in other ways. For example, the above-described embodiments of the apparatus/terminal device are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one position, or may be distributed on multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit may be implemented in the form of hardware, or may also be implemented in the form of a software functional unit.
The integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U.S. disk, removable hard disk, magnetic diskette, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signal, telecommunications signal, and software distribution medium, etc. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present invention, and are intended to be included within the scope of the present invention.
Claims (10)
1. A method for device fault detection, the method comprising:
s10: establishing a fault learning model, wherein the establishing of the fault learning model comprises: s101: selecting a first data node of a plurality of data nodes as an initiator and selecting data nodes other than the first data node as respondents; s102: acquiring the ith equipment operation parameter corresponding to the ith moment and the i +1 th equipment operation parameter corresponding to the i +1 th moment of the initiator and the responder, wherein i is a natural number greater than 0; s103: calculating to obtain a response value of the ith moment through a first function based on the operation parameter of the equipment of the i th moment of the responder, and calculating to obtain a response value of the i +1 th moment through the first function based on the operation parameter of the equipment of the i +1 th moment of the responder; s104: calculating to obtain an ith carving equipment operation parameter predicted value through a second function based on the ith carving equipment operation parameter and the ith carving response value of the initiator, and calculating to obtain an ith +1 carving equipment operation predicted value through the second function based on the ith +1 carving equipment operation parameter and the ith +1 carving response value of the initiator; s105: if the difference value between the operation predicted value of the ith + 1-th equipment and the operation predicted value of the ith-th equipment is not greater than a preset value, sending a termination signal and stopping training;
s20: acquiring real-time equipment operation parameters of each data node;
s30: and if the real-time equipment operation parameter and the (i + 1) th equipment operation predicted value are not smaller than a preset deviation value, sending an equipment fault signal.
2. The method for device fault detection as in claim 1, wherein the device-at-ith-time operational parameters and the device-at-i + 1-th operational parameters each comprise a weight and device operational data for each of a plurality of data nodes.
3. The method for equipment fault detection according to claim 2, wherein the first function is a response sum of a product of a weight of the responder and equipment operational data.
4. The method for device fault detection as in claim 3, wherein the second function is a sum of a product of the weight of the initiator and device operational data and the sum of the responses minus a target value.
5. The method for device failure detection according to any of claims 2 to 4, wherein step S105 comprises:
distributing the operation predicted value of the ith carving equipment and the operation predicted value of the (i + 1) th carving equipment to each data node;
obtaining an ith scale gradient value of each data node through a third function based on the ith scale equipment operation predicted value and the data quantity in the ith scale equipment operation parameter of each data node;
obtaining an i +1 th scale gradient value of each data node through the third function based on the i +1 th scale equipment operation predicted value and equipment operation data in the i +1 th scale parameter of each data node;
and if the difference value between the i +1 th moment gradient value of each data node and the i th moment gradient value of the corresponding data node is not greater than the preset gradient value, sending a termination signal and stopping training.
6. The method according to claim 1, wherein if the difference between the predicted operation value of the device at the i +1 th time and the predicted operation value of the device at the i th time is greater than a predetermined value, the steps S102 to S104 are repeated until the difference between the predicted operation value of the device at the i +1 th time and the predicted operation value of the device at the i th time is not greater than the predetermined value.
7. An apparatus for equipment fault detection, comprising:
the fault learning system comprises an establishing module and a fault learning module, wherein the establishing module is used for establishing a fault learning model, and comprises the following steps: s101: selecting a first data node of a plurality of data nodes as an initiator and selecting data nodes other than the first data node as respondents; s102: acquiring the ith equipment operation parameter corresponding to the ith moment and the (i + 1) th equipment operation parameter corresponding to the (i + 1) th moment of the initiator and the responder, wherein i is a natural number greater than 0; s103: calculating to obtain a response value of the ith moment through a first function based on the operation parameter of the equipment of the i th moment of the responder, and calculating to obtain a response value of the i +1 th moment through the first function based on the operation parameter of the equipment of the i +1 th moment of the responder; s104: calculating to obtain an ith carving equipment operation parameter predicted value through a second function based on the ith carving equipment operation parameter and the ith carving response value of the initiator, and calculating to obtain an ith +1 carving equipment operation predicted value through the second function based on the ith +1 carving equipment operation parameter and the ith +1 carving response value of the initiator; s105: if the difference value between the operation predicted value of the ith +1 th equipment and the operation predicted value of the ith equipment is not greater than a preset value, sending a termination signal and stopping training;
the acquisition module is used for acquiring real-time equipment operation parameters of each data node;
a determination module: and the device fault signal is sent out in response to the fact that the real-time device operation parameters and the i +1 th equipment operation predicted values are not smaller than preset deviation values.
8. The apparatus for device fault detection as recited in claim 7, wherein the setup module is further to: and in response to that the difference value between the operation predicted value of the equipment at the i +1 th stage and the operation predicted value of the equipment at the i th stage is larger than the preset value, repeating the steps S102 to S104 until the difference value between the operation predicted value of the equipment at the i +1 th stage and the operation predicted value of the equipment at the i th stage is not larger than the preset value.
9. A terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the method for device failure detection according to any of claims 1 to 6 when executing the computer program.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method for device failure detection according to any one of claims 1 to 6.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116599766A (en) * | 2023-07-11 | 2023-08-15 | 深圳友讯达科技股份有限公司 | Smart electric meter detection method, device, equipment and storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116599766A (en) * | 2023-07-11 | 2023-08-15 | 深圳友讯达科技股份有限公司 | Smart electric meter detection method, device, equipment and storage medium |
| CN116599766B (en) * | 2023-07-11 | 2023-09-29 | 深圳友讯达科技股份有限公司 | Smart electric meter detection method, device, equipment and storage medium |
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