Fault prediction methods, devices, media and equipment applied to the Industrial Internet

Abstract

This application provides a fault prediction method, apparatus, computer-readable medium, and electronic device applied to the Industrial Internet. The fault prediction method for the Industrial Internet includes: acquiring operational data from the current industrial production process and associated component information; determining the percentage of abnormal operation based on operational parameters and current runtime in the operational data, as well as preset parameter thresholds; acquiring the service life corresponding to the component identifier, and determining the effective lifespan percentage of the component based on the total runtime and the service life; and determining the probability of the component failing based on the percentage of abnormal operation and the effective lifespan percentage. By determining the percentage of abnormal operation based on the actual operational parameter values ​​of the component, and simultaneously considering the runtime and service life of the component in the fault prediction, the comprehensiveness and accuracy of fault prediction are ensured, improving the efficiency and reliability of equipment monitoring in industrial production.

Description

[0001] This application is a divisional application of Chinese application No. 2022103534984, filed on April 6, 2022, entitled "Fault Prediction Method, Device and Electronic Equipment Applied to Industrial Internet". Technical Field

[0002] This application relates to the field of computer technology, and more specifically, to a fault prediction method, apparatus, computer-readable medium, and electronic device applied to the Industrial Internet. Background Technology

[0003] The Industrial Internet is a new type of infrastructure, application model, and industrial ecosystem that deeply integrates next-generation information and communication technologies with the industrial economy. Through comprehensive connectivity of people, machines, things, and systems, it constructs a new manufacturing and service system covering the entire industrial chain and value chain. In actual industrial production, potential equipment anomalies can be identified through certain signs, even before they occur. This identification can be initially screened using models or algorithms, then manually confirmed before warnings are issued to equipment engineers. Finally, on-site verification confirms whether a fault or a fault trend exists. However, this method often fails to meet the needs of producers in terms of accuracy and efficiency in fault prediction. Summary of the Invention

[0004] The embodiments of this application provide a fault prediction method, apparatus, computer-readable medium, and electronic device applied to the Industrial Internet, thereby ensuring, at least to a certain extent, the comprehensiveness and accuracy of fault prediction and improving the efficiency and reliability of equipment monitoring in industrial production.

[0005] Other features and advantages of this application will become apparent from the following detailed description, or may be learned in part from practice of this application.

[0006] According to one aspect of the embodiments of this application, a fault prediction method applied to the Industrial Internet is provided, comprising:

[0007] Acquire operational data from the current industrial production process and associated component information; the operational data includes operational parameters, current cycle duration, and total cycle duration, and the component information includes component identifiers;

[0008] The percentage of abnormal operations is determined based on the operating parameters and current runtime in the operating data, as well as preset parameter thresholds.

[0009] Obtain the service life corresponding to the accessory identifier, and determine the effective service life percentage of the accessory based on the total running time and the service life.

[0010] Based on the percentage of abnormal operation and the percentage of effective lifespan, the probability of the component failing is determined.

[0011] In some embodiments of this application, based on the foregoing scheme, obtaining the operating data of the current industrial production process and the component information associated with the operating data includes:

[0012] Based on the set data acquisition cycle, the system acquires the operating data of the current industrial production process through preset sensors, and retrieves the accessory information associated with the operating data from the database.

[0013] In some embodiments of this application, based on the foregoing scheme, determining the percentage of abnormal operation according to the operation parameters and current run duration in the operation data, as well as a preset parameter threshold, includes:

[0014] Calculate the parameter difference between the operating parameter and the parameter threshold;

[0015] The percentage of abnormal operations is determined based on the ratio between the parameter difference and the parameter threshold, and the current running time.

[0016] In some embodiments of this application, based on the foregoing scheme, determining the effective lifespan percentage of the components according to the total running time and the service life includes:

[0017] The percentage of time a component has been used is determined based on the ratio between its service life and total operating time.

[0018] The effective lifespan percentage of the components is determined based on the percentage of time already used.

[0019] In some embodiments of this application, based on the foregoing scheme, determining the probability of the component failing based on the percentage of abnormal operation and the percentage of effective lifespan includes:

[0020] The probability of component failure is determined based on the ratio between the percentage of abnormal operation and the percentage of effective lifespan.

[0021] In some embodiments of this application, based on the foregoing scheme, the method further includes:

[0022] Based on the probability of failure of the component and its service life, the time of failure of the component is predicted.

[0023] In some embodiments of this application, based on the foregoing scheme, predicting the time of failure of the component according to the probability of failure and the service life includes:

[0024] Based on the probability of the component malfunctioning, Por_fai, the probability of the component operating normally, Por_fuc, is determined as: Por_fuc = 1 - Por_fai;

[0025] The normal operating time Tim_fuc of the component is determined by multiplying the probability of normal operation Por_fuc of the component with its service life Tim_tal as: Tim_fuc = μ·Por_fuc·Tim_tal; where μ is a preset operating factor.

[0026] Determine the time when a component fails based on its normal operating time.

[0027] According to one aspect of the embodiments of this application, a fault prediction device for industrial internet is provided, comprising:

[0028] The data acquisition unit is used to acquire the operating data of the current industrial production process and the accessory information associated with the operating data; the operating data includes operating parameters, the duration of the current run, and the total running time, and the accessory information includes accessory identifiers;

[0029] An anomaly determination unit is used to determine the percentage of abnormal operations based on the operating parameters and current runtime in the operating data, as well as preset parameter thresholds.

[0030] A time determination unit is used to obtain the service life corresponding to the accessory identifier, and determine the effective service life ratio of the accessory based on the total running time and the service life.

[0031] The fault prediction unit is used to determine the probability of the component failing based on the percentage of abnormal operation and the percentage of effective lifespan.

[0032] In some embodiments of this application, based on the foregoing scheme, obtaining the operating data of the current industrial production process and the component information associated with the operating data includes:

[0033] Based on the set data acquisition cycle, the system acquires the operating data of the current industrial production process through preset sensors, and retrieves the accessory information associated with the operating data from the database.

[0034] In some embodiments of this application, based on the foregoing scheme, determining the percentage of abnormal operation according to the operation parameters and current run duration in the operation data, as well as a preset parameter threshold, includes:

[0035] Calculate the parameter difference between the operating parameter and the parameter threshold;

[0036] The percentage of abnormal operations is determined based on the ratio between the parameter difference and the parameter threshold, and the current running time.

[0037] In some embodiments of this application, based on the foregoing scheme, determining the effective lifespan percentage of the components according to the total running time and the service life includes:

[0038] The percentage of time a component has been used is determined based on the ratio between its service life and total operating time.

[0039] The effective lifespan percentage of the components is determined based on the percentage of time already used.

[0040] In some embodiments of this application, based on the foregoing scheme, determining the probability of the component failing based on the percentage of abnormal operation and the percentage of effective lifespan includes:

[0041] The probability of component failure is determined based on the ratio between the percentage of abnormal operation and the percentage of effective lifespan.

[0042] In some embodiments of this application, based on the foregoing scheme, the method further includes:

[0043] Based on the probability of failure of the component and its service life, the time of failure of the component is predicted.

[0044] In some embodiments of this application, based on the foregoing scheme, predicting the time of failure of the component according to the probability of failure and the service life includes:

[0045] Based on the probability of the component malfunctioning, Por_fai, the probability of the component operating normally, Por_fuc, is determined as: Por_fuc = 1 - Por_fai;

[0046] The normal operating time Tim_fuc of the component is determined by multiplying the probability of normal operation Por_fuc of the component with its service life Tim_tal as: Tim_fuc = μ·Por_fuc·Tim_tal; where μ is a preset operating factor.

[0047] Determine the time when a component fails based on its normal operating time.

[0048] According to one aspect of the embodiments of this application, a computer-readable medium is provided having a computer program stored thereon, which, when executed by a processor, implements the fault prediction method for the Industrial Internet as described in the above embodiments.

[0049] According to one aspect of the embodiments of this application, an electronic device is provided, including: one or more processors; and a storage device for storing one or more programs, which, when executed by the one or more processors, cause the one or more processors to implement the fault prediction method for the Industrial Internet as described in the above embodiments.

[0050] According to one aspect of the embodiments of this application, a computer program product or computer program is provided, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the fault prediction method for the Industrial Internet provided in the various optional implementations described above.

[0051] In some embodiments of this application, the technical solutions involve acquiring operational data from the current industrial production process and associated component information; determining the percentage of abnormal operation based on the operational parameters and current runtime in the operational data, as well as preset parameter thresholds; acquiring the service life corresponding to the component identifier, and determining the effective lifespan percentage of the component based on the total runtime and the service life; and determining the probability of component failure based on the percentage of abnormal operation and the effective lifespan percentage. In this embodiment, by determining the percentage of abnormal operation based on the actual operational parameter values ​​of the component, and simultaneously considering the runtime and service life of the component in failure prediction, the comprehensiveness and accuracy of failure prediction are ensured, thereby improving the efficiency and reliability of equipment monitoring in industrial production.

[0052] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0053] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0054] Figure 1 A flowchart illustrating a fault prediction method applied to the Industrial Internet according to an embodiment of this application is shown schematically.

[0055] Figure 2 A flowchart illustrating the prediction of accessory failure time according to one embodiment of this application is shown schematically.

[0056] Figure 3 The illustration shows a schematic diagram of a fault prediction device applied to the Industrial Internet according to an embodiment of this application.

[0057] Figure 4 A schematic diagram of the structure of a computer system suitable for implementing the electronic device of the present application is shown. Detailed Implementation

[0058] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided to make this application more comprehensive and complete, and to fully convey the concept of the exemplary embodiments to those skilled in the art.

[0059] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a thorough understanding of embodiments of this application. However, those skilled in the art will recognize that the technical solutions of this application can be practiced without one or more of the specific details, or other methods, components, apparatuses, steps, etc., can be employed. In other instances, well-known methods, apparatuses, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of this application.

[0060] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0061] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be broken down, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0062] The implementation details of the technical solutions in the embodiments of this application are described in detail below:

[0063] Figure 1 A flowchart illustrating a fault prediction method applied to the Industrial Internet according to an embodiment of this application is shown. (Refer to...) Figure 1 As shown, the fault prediction method applied to the Industrial Internet includes at least steps S110 to S140, which are described in detail below:

[0064] In step S110, the operating data of the current industrial production process and the accessory information associated with the operating data are obtained; the operating data includes operating parameters, current running time and total running time, and the accessory information includes accessory identifiers.

[0065] In one embodiment of this application, during actual industrial production, current operating data and component information of equipment or components are acquired. Specifically, the operating data acquired in this embodiment may include operating parameters, current cycle running time, and total running time. Operating parameters may include temperature, rotational speed, or moving speed, etc. The current cycle running time represents the duration of continuous operation of the component during this operation, and the total running time represents the sum of the running time of the component in all operation processes.

[0066] In one embodiment of this application, based on a set data acquisition cycle, operational data of the current industrial production process can be acquired through a preset sensor, and accessory information associated with the operational data can be retrieved from a database. This improves the accuracy of data acquisition through sensors.

[0067] In step S120, the percentage of abnormal operation is determined based on the operation parameters and current run duration in the operation data, as well as preset parameter thresholds.

[0068] In one embodiment of this application, the percentage of abnormal operations is determined based on the operating parameters and current runtime in the operating data, as well as a preset parameter threshold, including:

[0069] Calculate the parameter difference between the operating parameter and the parameter threshold;

[0070] The percentage of abnormal operations is determined based on the ratio between the parameter difference and the parameter threshold, and the current running time.

[0071] In one embodiment of this application, the parameter difference Par_dif between the running parameter Par_fun and the parameter threshold Par_thr is calculated as follows:

[0072] Par_dif = Par_thr - Par_fun

[0073] The longer the current run time, the higher the probability of a failure. Therefore, in this embodiment, when determining the percentage of abnormal operation, the actual running parameters and parameter thresholds are taken into account in the anomaly assessment, and combined with the current run time for comprehensive consideration. Based on the ratio between the parameter difference Par_dif and the parameter threshold Par_thr, and the current run time Tim_rod, the percentage of abnormal operation Por_abn is determined as follows:

[0074]

[0075] Wherein, γ represents the preset abnormality factor. In this embodiment, when determining the proportion of abnormal operation, the actual operating parameters and parameter thresholds are taken into account in the abnormality assessment, and the duration of this round of operation is combined for comprehensive consideration, which improves the comprehensiveness and accuracy of fault and abnormality assessment.

[0076] In step S130, the service life corresponding to the accessory identifier is obtained, and the effective service life ratio of the accessory is determined based on the total running time and the service life.

[0077] In one embodiment of this application, the service life of a component is obtained from a database to determine the effective lifespan percentage of the component based on its total operating time and service life. In this embodiment, the effective lifespan percentage represents the proportion of the component's usable time (based on lifespan prediction) within its total lifespan, i.e., the available time for the component to operate normally.

[0078] In one embodiment of this application, determining the effective lifespan percentage of a component based on the total runtime and the service life includes:

[0079] The percentage of time a component has been used is determined based on the ratio between its service life and total operating time.

[0080] The effective lifespan percentage of the components is determined based on the percentage of time already used.

[0081] In one embodiment of this application, the percentage of time a component has been used, Por_use, is determined based on the ratio between the service life (Tim_tal) and the total runtime (Tim_use).

[0082] Por_use = α·Tim_use / Tim_tal

[0083] Where α represents the preset time factor.

[0084] Based on the percentage of time already used, the effective lifespan percentage of the component, Por_val, is determined as follows:

[0085] Por_val=1-α·Tim_use / Tim_tal

[0086] The above method can be used to calculate the current effective lifespan percentage of the parts. The larger the value, the longer the usable time, which means the lower the probability of failure; the smaller the value, the shorter the usable time, which means the higher the probability of failure.

[0087] In step S140, the probability of the component failing is determined based on the percentage of abnormal operation and the percentage of effective lifespan.

[0088] In one embodiment of this application, determining the probability of a component malfunctioning based on the percentage of abnormal operation and the percentage of effective lifespan includes:

[0089] The probability of component failure is determined based on the ratio between the percentage of abnormal operation and the percentage of effective lifespan.

[0090] In one embodiment of this application, the probability of a component failure, Por_fai, is determined based on the ratio between the percentage of abnormal operation, Por_abn, and the percentage of effective lifespan, Por_val:

[0091] Por_fai=η·Por_abn / Por_val

[0092] Where η represents the preset fault factor. The above method determines the proportion of abnormal operation based on the actual operating parameter values ​​of the parts, and also takes into account the running time and service life of the parts in the fault prediction, thus ensuring the comprehensiveness and accuracy of fault prediction.

[0093] In one embodiment of this application, the method further includes:

[0094] Based on the probability of failure of the component and its service life, the time of failure of the component is predicted.

[0095] In one embodiment of this application, predicting the time of failure of the accessory based on the probability of accessory failure and the service life includes:

[0096] Based on the probability of the component malfunctioning, determine the probability of the component operating normally;

[0097] The duration of normal operation of the component is determined by multiplying the probability of normal operation of the component with its service life.

[0098] Determine the time when a component fails based on its normal operating time.

[0099] In one embodiment of this application, the probability of normal operation of the component, Por_fuc, is determined based on the probability of the component malfunctioning, Por_fai:

[0100] Por_fuc=1-Por_fai

[0101] Based on the product of the probability of the component operating normally, Por_fuc, and its service life, Tim_tal, the duration of normal operation of the component, Tim_fuc, is determined as follows:

[0102] Tim_fuc=μ·Por_fuc·Tim_tal

[0103] Where μ is a preset operating factor. In this embodiment, the normal operating time is used to represent the estimated probability that the component can maintain normal operation without abnormalities. After calculating the normal operating time, the time when the component will fail can be determined, that is, an abnormality may occur after the normal operating time ends.

[0104] The above solution acquires operational data from the current industrial production process and associated component information; determines the percentage of abnormal operation based on operational parameters and current runtime in the operational data, as well as preset parameter thresholds; acquires the service life corresponding to the component identifier, and determines the effective lifespan percentage of the component based on the total runtime and the service life; and determines the probability of component failure based on the percentage of abnormal operation and the effective lifespan percentage. In this embodiment, by determining the percentage of abnormal operation based on the actual operational parameter values ​​of the components, and simultaneously considering the runtime and service life of the components in failure prediction, the comprehensiveness and accuracy of failure prediction are ensured, improving the efficiency and reliability of equipment monitoring in industrial production.

[0105] The following describes an embodiment of the apparatus described in this application, which can be used to execute the fault prediction method for the Industrial Internet described in the above embodiments of this application. It is understood that the apparatus may be a computer program (including program code) running on a computer device, for example, the apparatus may be application software; the apparatus may be used to execute the corresponding steps in the method provided in the embodiments of this application. For details not disclosed in the apparatus embodiments of this application, please refer to the embodiments of the fault prediction method for the Industrial Internet described above.

[0106] Figure 3 A block diagram of a fault prediction device for the Industrial Internet according to an embodiment of this application is shown.

[0107] Reference Figure 3 As shown, a fault prediction device 300 for industrial internet according to an embodiment of this application includes:

[0108] The data acquisition unit 310 is used to acquire the operating data of the current industrial production process and the accessory information associated with the operating data; the operating data includes operating parameters, the current running time and the total running time, and the accessory information includes accessory identifiers;

[0109] The anomaly determination unit 320 is used to determine the percentage of abnormal operation based on the operation parameters and current run duration in the operation data, as well as a preset parameter threshold.

[0110] The time determination unit 330 is used to obtain the service life corresponding to the accessory identifier and determine the effective service life ratio of the accessory based on the total running time and the service life.

[0111] The fault prediction unit 340 is used to determine the probability of the component failing based on the percentage of abnormal operation and the percentage of effective lifespan.

[0112] In some embodiments of this application, based on the foregoing scheme, obtaining the operating data of the current industrial production process and the component information associated with the operating data includes:

[0113] Based on the set data acquisition cycle, the system acquires the operating data of the current industrial production process through preset sensors, and retrieves the accessory information associated with the operating data from the database.

[0114] In some embodiments of this application, based on the foregoing scheme, determining the percentage of abnormal operation according to the operation parameters and current run duration in the operation data, as well as a preset parameter threshold, includes:

[0115] Calculate the parameter difference between the operating parameter and the parameter threshold;

[0116] The percentage of abnormal operations is determined based on the ratio between the parameter difference and the parameter threshold, and the current running time.

[0117] In some embodiments of this application, based on the foregoing scheme, determining the effective lifespan percentage of the components according to the total running time and the service life includes:

[0118] The percentage of time a component has been used is determined based on the ratio between its service life and total operating time.

[0119] The effective lifespan percentage of the components is determined based on the percentage of time already used.

[0120] In some embodiments of this application, based on the foregoing scheme, determining the probability of the component failing based on the percentage of abnormal operation and the percentage of effective lifespan includes:

[0121] The probability of component failure is determined based on the ratio between the percentage of abnormal operation and the percentage of effective lifespan.

[0122] In some embodiments of this application, based on the foregoing scheme, the method further includes:

[0123] Based on the probability of failure of the component and its service life, the time of failure of the component is predicted.

[0124] In some embodiments of this application, based on the foregoing scheme, predicting the time of failure of the component according to the probability of failure and the service life includes:

[0125] Based on the probability of the component malfunctioning, determine the probability of the component operating normally;

[0126] The duration of normal operation of the component is determined by multiplying the probability of normal operation of the component with its service life.

[0127] Determine the time when a component fails based on its normal operating time.

[0128] The above solution acquires operational data from the current industrial production process and associated component information; determines the percentage of abnormal operation based on operational parameters and current runtime in the operational data, as well as preset parameter thresholds; acquires the service life corresponding to the component identifier, and determines the effective lifespan percentage of the component based on the total runtime and the service life; and determines the probability of component failure based on the percentage of abnormal operation and the effective lifespan percentage. In this embodiment, by determining the percentage of abnormal operation based on the actual operational parameter values ​​of the components, and simultaneously considering the runtime and service life of the components in failure prediction, the comprehensiveness and accuracy of failure prediction are ensured, improving the efficiency and reliability of equipment monitoring in industrial production.

[0129] Figure 4 A schematic diagram of the structure of a computer system suitable for implementing the electronic device of the present application is shown.

[0130] It should be noted that, Figure 4 The computer system 400 of the electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0131] like Figure 4 As shown, the computer system 400 includes a Central Processing Unit (CPU) 401, which can perform various appropriate actions and processes based on programs stored in Read-Only Memory (ROM) 402 or programs loaded from Storage Unit 408 into Random Access Memory (RAM) 403, such as performing the methods described in the above embodiments. The RAM 403 also stores various programs and data required for system operation. The CPU 401, ROM 402, and RAM 403 are interconnected via a bus 404. An Input / Output (I / O) interface 405 is also connected to the bus 404.

[0132] The following components are connected to I / O interface 405: an input section 406 including a keyboard, mouse, etc.; an output section 407 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 408 including a hard disk, etc.; and a communication section 409 including a network interface card such as a LAN (Local Area Network) card, modem, etc. The communication section 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to I / O interface 405 as needed. A removable medium 411, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on drive 410 as needed so that computer programs read from it can be installed into storage section 408 as needed.

[0133] Specifically, according to embodiments of this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program including a computer program for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 409, and / or installed from removable medium 411. When the computer program is executed by central processing unit (CPU) 401, it performs various functions defined in the system of this application.

[0134] It should be noted that the computer-readable medium shown in the embodiments of this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying a computer-readable computer program. The transmitted data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The computer program contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to wireless, wired, etc., or any suitable combination thereof.

[0135] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. Each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0136] The units described in the embodiments of this application can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the specific unit itself.

[0137] According to one aspect of this application, a computer program product or computer program is provided, comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the methods provided in the various alternative implementations described above.

[0138] In another aspect, this application also provides a computer-readable medium, which may be included in the electronic device described in the above embodiments; or it may exist independently and not assembled into the electronic device. The computer-readable medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to perform the methods described in the above embodiments.

[0139] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to the embodiments of this application, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0140] Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, touch terminal, or network device, etc.) to execute the method according to the embodiments of this application.

[0141] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the embodiments disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein.

[0142] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A failure prediction method applied to an industrial internet, characterized by, The method comprises: obtaining running data in a current industrial production process and accessory information associated with the running data; the running data comprises running parameters, a current running duration and a total running duration, and the accessory information comprises accessory identification; determining an abnormal running proportion according to the running parameters and the current running duration in the running data and a preset parameter threshold; acquire the service life corresponding to the accessory identifier , determine the effective life proportion of the accessory according to the total running time and the service life , determine the effective life proportion of the accessory according to the total running time and the service life determining a probability of failure of the accessory based on the abnormal running proportion and the effective life proportion; wherein the method further comprises: According to the probability of failure of the accessory and the useful life , the time of failure of the accessory is predicted; wherein the time to failure of the accessory is predicted based on a probability of failure of the accessory and the useful life , the probability of failure of the accessory, and the useful life According to the probability of failure of the accessory , the probability of normal operation of the accessory is determined as: ; a product between a probability of normal operation of the accessory and a service life , determines a duration of normal operation of the accessory is: ; wherein, is a preset operation factor, and the duration of normal operation of the accessory represents a duration in which the accessory remains in normal operation and does not occur abnormity. determining a time of failure of the accessory according to a duration of normal operation of the accessory.

2. The method of claim 1, wherein, The method comprises: obtaining running data in a current industrial production process and accessory information associated with the running data, comprising:

3. The method of claim 1, wherein, obtaining the running data in the current industrial production process through a preset sensor based on a set data acquisition period, and obtaining the accessory information associated with the running data from a database. determining an abnormal running proportion according to the running parameters and the current running duration in the running data and a preset parameter threshold, comprising: calculating a parameter difference value between the running parameters and the parameter threshold; 4.A failure prediction device applied to an industrial internet, characterized by, determining an abnormal running proportion according to a ratio between the parameter difference value and the parameter threshold and the current running duration. The method comprises: a data acquisition unit configured to obtain running data in a current industrial production process and accessory information associated with the running data; the running data comprises running parameters, a current running duration and a total running duration, and the accessory information comprises accessory identification; A time determination unit is configured to acquire a service life corresponding to the accessory identifier , determine an effective life proportion of the accessory according to the total running time and the service life . an abnormality determination unit configured to determine an abnormal running proportion according to the running parameters and the current running duration in the running data and a preset parameter threshold; a failure prediction unit configured to determine a probability of failure of the accessory based on the abnormal running proportion and the effective life proportion; According to the probability of failure of the accessory and the useful life , the time of failure of the accessory is predicted; wherein the time to failure of the accessory is predicted based on a probability of failure of the accessory and the useful life , the probability of failure of the accessory, and the useful life The probability of failure of the accessory The probability of normal operation of the accessory is determined as: ;​ a product between a probability of normal operation of the accessory and a service life , determines a duration of normal operation of the accessory is: ; wherein, is a preset operation factor, and the duration of normal operation of the accessory represents a duration in which the accessory remains in normal operation and does not occur abnormity. wherein the failure prediction device applied to the industrial internet is further configured to:

5. A computer readable medium having stored thereon a computer program, characterized in that, determine a time of failure of the accessory according to a duration of normal operation of the accessory.

6. An electronic device, comprising: The computer program is executed by a processor to implement the failure prediction method applied to the industrial internet as claimed in any one of claims 1 to 3. The method comprises: one or more processors; a storage device configured to store one or more programs, which, when executed by the one or more processors, cause the one or more processors to implement the failure prediction method applied to the industrial internet as claimed in any one of claims 1 to 3.

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