A fault early warning method, device, equipment, medium and program product of an automation device

Abstract

This invention belongs to the technical field of fault early warning, specifically relating to a fault early warning method, device, medium, equipment, and program product for automated equipment. This application provides a fault early warning method for automated equipment, comprising: acquiring real-time operating data of each component of a target device and control commands issued to the target device; determining the actual operating characteristics of each component of the target device based on the real-time operating data; predicting the predicted operating characteristics of each component of the target device based on the control commands; and providing fault early warning for the target device based on the actual operating characteristics and the predicted operating characteristics of each component. This solves the problem of difficulty in determining the cause and faulty component when existing automated equipment malfunctions, achieving early warning before a fault occurs. Workers can promptly identify the faulty component and the cause of the fault based on the fault early warning information, greatly improving the maintenance efficiency of automated equipment.

Description

Technical Field

[0001] This invention belongs to the technical field of fault early warning, specifically relating to a fault early warning method, device, equipment, medium, and program product for automated equipment. Background Technology

[0002] Automated equipment consists of numerous components, including mechanical parts, electrical components, and control systems. When automated equipment malfunctions, it is generally difficult to accurately locate the faulty component and the cause of the failure. At most, the area of ​​the failure can be identified, but further manual inspection is still required to pinpoint the faulty component and the cause before an appropriate repair strategy can be developed. This entire process is inefficient and can significantly impact production. Summary of the Invention

[0003] This application discloses a fault early warning method for automated equipment, comprising: acquiring real-time operating data of each component of a target equipment and control commands issued to the target equipment; determining the actual operating characteristics of each component of the target equipment based on the real-time operating data; predicting the predicted operating characteristics of each component of the target equipment based on the control commands; and providing fault early warning for the target equipment based on the actual operating characteristics and the predicted operating characteristics of each component. This method solves the problem of difficulty in determining the cause and faulty component when existing automated equipment malfunctions, achieving early warning before a fault occurs. This allows staff to inspect components that may fail before a fault occurs, and also enables staff to promptly identify the faulty component and cause based on the fault early warning information when a fault occurs, greatly improving the maintenance efficiency of automated equipment.

[0004] To solve the above-mentioned technical problems, the technical solution provided by the present invention includes five aspects.

[0005] In a first aspect, this application provides a fault early warning method for automated equipment, comprising: acquiring real-time operating data of each component of a target equipment and control commands issued to the target equipment; determining the actual operating characteristics of each component of the target equipment based on the real-time operating data; predicting the predicted operating characteristics of each component of the target equipment based on the control commands; and providing fault early warning for the target equipment based on the actual operating characteristics and the predicted operating characteristics of each component.

[0006] In some embodiments, the step of providing fault warnings to the target device based on the actual operating characteristics and the predicted operating characteristics of each component includes: determining characteristic anomalies of each component based on the actual operating characteristics and the predicted operating characteristics; determining abnormal components and causes of anomalies based on the characteristic anomalies; and providing fault warnings to the target device based on the abnormal components and causes of anomalies.

[0007] In some embodiments, determining the abnormal component and the cause of the abnormality based on the characteristic anomaly points includes: determining the corresponding relevant control instructions based on the occurrence time of each characteristic anomaly point and the component that caused it; determining the component response chain that responds to each of the relevant control instructions, wherein the component response chain is used to characterize the sequential relationship or association between the various components when responding to the relevant control instructions; dividing the characteristic anomaly points into multiple first sets based on the relevant control instructions, wherein each relevant control instruction corresponds to one first set; determining the abnormal component based on the component response chain and the first set; determining the operational anomaly characteristics of each component based on the actual operational characteristics and the corresponding predicted operational characteristics of each characteristic anomaly point in the first set; and determining the cause of the abnormality based on the operational anomaly characteristics of each component and the abnormal component.

[0008] In some embodiments, determining the abnormal component based on the component response link and the first set includes: forming multiple second sets in the first set based on the occurrence time of each of the characteristic abnormal points in the first set, wherein the second set includes multiple characteristic abnormal points whose occurrence time points to the same related control instruction, and the same related control instruction refers to a related control instruction at a certain moment; generating abnormal response links corresponding to each second set based on the component response link and the component that caused each characteristic abnormal point in the second set; and determining the abnormal component based on each of the abnormal response links and the component response link.

[0009] In some embodiments, determining the cause of the anomaly based on the operational anomaly characteristics of each component and the anomaly component includes: fusing the operational anomaly characteristics of each component to form operational anomaly fusion characteristics; associating the anomaly fusion characteristics of each component according to the component response chain to form association characteristics; and determining the cause of the anomaly of the anomaly component based on the association characteristics.

[0010] In some embodiments, the step of providing fault warning to the target device based on the abnormal component and the abnormal cause includes: acquiring historical warning information containing the abnormal component and the abnormal cause; determining historical abnormal features related to the historical warning information based on the historical warning information; determining an abnormal development trend based on the historical abnormal features and the operational abnormal features; and providing fault warning to the target device based on the abnormal development trend. Secondly, this application proposes a fault early warning device for automated equipment, comprising: a first acquisition module, configured to acquire real-time operating data of each component of a target equipment and control commands issued to the target equipment; a first determination module, configured to determine the actual operating characteristics of each component of the target equipment based on the real-time operating data; a second determination module, configured to predict the predicted operating characteristics of each component of the target equipment based on the control commands; and a first execution module, configured to provide fault early warning for the target equipment based on the actual operating characteristics and the predicted operating characteristics of each component.

[0011] Thirdly, this application proposes a computer electronic production apparatus, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of any of the methods described in the first aspect.

[0012] Fourthly, this application proposes a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in any one of the first aspects of the claims.

[0013] Fifthly, this application proposes a computer program product comprising a computer program that, when executed by a processor, implements the steps of the method described in any of the first aspects.

[0014] This application discloses a fault early warning method for automated equipment, comprising: acquiring real-time operating data of each component of a target equipment and control commands issued to the target equipment; determining the actual operating characteristics of each component of the target equipment based on the real-time operating data; predicting the predicted operating characteristics of each component of the target equipment based on the control commands; and providing fault early warning for the target equipment based on the actual operating characteristics and the predicted operating characteristics of each component. This method solves the problem of difficulty in determining the cause and faulty component when existing automated equipment malfunctions, achieving early warning before a fault occurs. This allows staff to inspect components that may fail before a fault occurs, and also enables staff to promptly identify the faulty component and cause based on the fault early warning information when a fault occurs, greatly improving the maintenance efficiency of automated equipment. Attached Figure Description

[0015] One or more embodiments are illustrated by way of example with reference to the accompanying drawings, and these illustrative descriptions do not constitute a limitation on the embodiments.

[0016] Figure 1 This is a main flowchart of a fault early warning method for automated equipment provided in an embodiment of this application; Figure 2 This application provides a main structural block diagram of a fault early warning device for automated equipment. Figure 3 This is a structural block diagram of a computer electronic production equipment provided in an embodiment of this application. Detailed Implementation

[0017] Automated equipment consists of numerous components, including mechanical parts, electrical components, and control systems. When automated equipment malfunctions, it is generally difficult to accurately locate the faulty component and the cause of the failure. At most, the area of ​​the failure can be identified, but further manual inspection is still required to pinpoint the faulty component and the cause before an appropriate repair strategy can be developed. This entire process is inefficient and can significantly impact production.

[0018] To address the aforementioned technical problems, this invention proposes a fault early warning method for automated equipment. The implementation details of this embodiment of the fault early warning method for automated equipment are described below. The following implementation details are provided for ease of understanding and are not essential for implementing this solution.

[0019] Example 1: like Figure 1 As shown, this application provides a fault early warning method for automated equipment. The method is applicable to electronic production equipment, which can be a server, mobile terminal, computer, cloud platform, etc. The data processing function of the production equipment provided in this application embodiment can be implemented by the processor of the electronic production equipment calling program code, wherein the program code can be stored in a computer storage medium. The fault early warning method for automated equipment includes: Step S1: Obtain real-time operating data of each component of the target device and control commands issued to the target device.

[0020] An automated device can be divided into a mechanical system and a control system. The control system can be further divided into a host computer and a drive circuit. The mechanical system consists of mechanical components, and the drive circuit consists of electrical components. During operation, the automated device sends control commands from the host computer, which then drive the mechanical components to perform actions via the drive circuit. If a fault occurs in the electrical components of the drive circuit, the mechanical components will be unable to perform the corresponding actions. Similarly, when mechanical components malfunction, some actions will be impossible to complete. Furthermore, when some electrical components malfunction, the output power, voltage, or current may deviate from the preset values, preventing the mechanical components from performing their corresponding actions correctly. It's also possible that abnormal output from electrical components may not affect production, but it could still impact the lifespan of mechanical components and cause damage to the electrical components. Generally, most failures are caused by components operating under abnormal conditions for extended periods. That is, if components are in an abnormal state or working under abnormal conditions for a long time, although it may not affect production, it will damage the components and eventually lead to failure. Therefore, to achieve early warning of faults, it is necessary to monitor the actual operating data of each component. This data is used to identify abnormal components and their causes, enabling staff to promptly adjust or repair components when abnormalities occur. Furthermore, when a fault occurs, staff can quickly locate the faulty component and its cause based on pre-existing abnormalities. Therefore, this application requires real-time acquisition of the operating data of each component. The actual operating data in this application can include motion data, vibration data, pressure data, voltage, current, power, and temperature. For mechanical components, the primary focus is on collecting motion, vibration, pressure, and temperature data. For electrical components, more attention needs to be paid to voltage, current, power, and temperature. However, not all electrical components are power components; therefore, temperature and power data may not be collected for non-power electrical components.

[0021] However, detecting whether a component is operating abnormally is a challenge. While theoretically, each component should have standard operating data when working correctly, components age over time. The operating data of an aged component under normal operating conditions will inevitably differ from that of a brand-new component. Relying solely on standard operating data to determine component proper functioning will inevitably generate numerous false alarms. Furthermore, for automated equipment, the operating status of each component is controlled by control commands. Different control commands result in different operating conditions and data, making it even more difficult to determine if a component is malfunctioning. However, for a given control command, changes in the operating data of the components associated with that command are predictable. Therefore, to achieve fault warning in this application, it is necessary to acquire the control commands issued by the host computer.

[0022] Step S2: Determine the actual operating characteristics of each component of the target device based on the real-time operating data.

[0023] Real-time operational data includes the operational data of each component under various commands. Taking the vibration data of a mechanical component as an example, under certain operating conditions, the mechanical component will vibrate at a certain frequency and amplitude. When the operating conditions change, the vibration frequency and amplitude of the mechanical component will change. The process of the vibration data changing is the process of the mechanical component responding to control commands. The same applies to electrical components. When an electrical component executes a certain control command, its input, voltage, current, and power will be within a certain characteristic range. When the electrical component responds to a new control command, its input and output voltage, current, and power will change. Because this application involves real-time data collection, the volume of operational data is enormous, leading to a large computational burden and numerous interference factors during analysis. Therefore, this application selects to extract actual operational features from the real-time operational data. These actual operational features primarily express the changes in the real-time operational data of each component. By using actual operational features, the amount of data to be analyzed is reduced, and interference factors are also minimized. For example, data amplitude changes due to aging can occur. For instance, a new mechanical component vibrates at frequency a and amplitude b when executing control command A. As the component ages, it may vibrate at frequency c and amplitude d when executing control command A. However, as long as the vibration data remains stable during execution, the component is not abnormal. Without feature extraction from the real-time operational data, these data changes caused by aging would affect the determination of whether each component is abnormal. Therefore, extracting actual operational features from the real-time operational data in this application can shield against interference and reduce the amount of data to be processed and stored.

[0024] Step S3: Predict the predicted operating characteristics of each component of the target device according to the control command. For the designers and maintenance personnel of this automated equipment, it is crucial to understand the state changes of each component when executing various control commands. They can also clearly identify the differences in operational data between the execution of different control commands, i.e., predict operational characteristics. Since designers and maintenance personnel can predict the operational characteristics when each control command is responded to, these prediction rules can be embedded in programs or databases. This allows electronic equipment or fault warning systems for automated equipment to generate corresponding predicted operational characteristics based on the control commands issued to the target equipment.

[0025] Step S4: Provide fault warning for the target equipment based on the actual operating characteristics and predicted operating characteristics of each component.

[0026] In some embodiments, step S4, "providing fault warning for the target device based on the actual operating characteristics and the predicted operating characteristics of each component," includes: Step S41: Determine the characteristic anomalies of each component based on the actual operating characteristics and the predicted operating characteristics.

[0027] Because this application compares actual operating characteristics with predicted operating characteristics, interference caused by component aging has been eliminated. Therefore, theoretically, if all components are functioning normally, the actual operating characteristics and predicted operating characteristics should be consistent. Of course, consistency here means that the differences between the actual operating characteristics and predicted operating characteristics at different times are within a certain allowable range. Therefore, by comparing the actual operating characteristics with the predicted operating characteristics, the points in the actual operating characteristics that differ significantly from the predicted operating characteristics can be identified, i.e., the feature anomalies in this application.

[0028] Step S42: Determine the abnormal component and the cause of the abnormality based on the described abnormal feature points.

[0029] In some embodiments, step S42, "determining the abnormal component and the cause of the abnormality based on the characteristic abnormality points," includes: Step S421: Determine the corresponding control commands based on the occurrence time of each of the aforementioned abnormal feature points and the occurrence component.

[0030] Since the issuance of control commands is time-sequential, a component can only respond to one control command at the same time or within a certain time period. Therefore, the control command that the component responds to when the abnormality occurs can be determined based on the time of occurrence of the characteristic abnormal point and the component that occurs.

[0031] Step S422: Determine the component response chain for each of the relevant control commands, wherein the component response chain is used to characterize the sequential relationship or association between the various components when responding to the relevant control commands.

[0032] From the issuance of a control command by the host computer to its completion, multiple components need to cooperate and coordinate. Therefore, these components are not completely independent but rather interconnected. However, this interconnection is not necessarily fixed; it can arise with the issuance of the control command and end with its completion. Thus, in the process of responding to a control command, the operational data of the related components are correlated. An anomaly in one component may cause anomalies in the actual operational characteristics of its upstream or downstream components in the correlation. Therefore, to investigate which component has malfunctioned, it is necessary to analyze the correlations between components. This application needs to clarify the component response chain for relevant control commands, that is, which components need to cooperate and in what manner when a control command related to a characteristic anomaly point is executed—this is the component response chain in this application.

[0033] Step S423: Divide the feature anomaly points into multiple first sets according to the relevant control instructions, wherein each relevant control instruction corresponds to a first set.

[0034] Because there is a certain correlation between the various feature anomalies in this application, this application divides the feature anomalies into multiple first sets based on control commands. Each feature anomaly in a first set corresponds to the same control command, where the control command in the first set refers to the specific control command and, in some cases, the time at which the control command was issued. Since the automated equipment operates cyclically, a single control command may appear multiple times within a given time period. However, because the component response chain remains consistent regardless of when the control command is issued, feature anomalies generated by the same control command at different times can be grouped into the same set.

[0035] Step S424: Determine the abnormal component based on the component response link and the first set.

[0036] In some embodiments, step S424, "determining the abnormal component based on the component response link and the first set," includes: Step S4241: Based on the occurrence time of each of the characteristic anomalies in the first set, form multiple second sets in the first set. The second sets include multiple characteristic anomalies whose occurrence time points to the same related control command. The same related control command refers to the related control command at a certain moment.

[0037] Because the occurrence of anomalies can lead to anomalies in related components, but when a component malfunctions, the affected components are not necessarily fixed. The number of affected components may increase with the frequency of anomalies. Therefore, this application further divides the first set into multiple second sets. Each second set corresponds to one response to a relevant control command. That is, the characteristic anomaly points in the first set are divided according to the time of occurrence. Characteristic anomaly points that occur in the same time period are grouped into one set. The time period is determined by the response time of each response of the component response link to the relevant control command. Different control commands have different component response links and different response times. Therefore, the time range of the same time period divided into the second set in different first sets is different.

[0038] Step S4242: Generate an anomaly response link corresponding to each second set based on the component response link and the component that caused each characteristic anomaly point in the second set.

[0039] Since each second set represents a characteristic anomaly point caused by responding to a relevant control command, and each characteristic anomaly point corresponds to a specific component, anomaly response links can be generated based on the component response links of the relevant control commands and the components and times of occurrence of each characteristic anomaly point in the second set. Each anomaly response link can characterize the correlation between components with anomalies in the relevant control commands at the corresponding time of response. Because when one component in the same component response link experiences anomalies, it will inevitably lead to anomalies in the operational characteristics of related components. However, since components have design redundancy, these anomalies will be eliminated within a certain link range. Therefore, the components in the anomaly response link are not all components that have experienced anomalies; most are affected components. Therefore, further analysis of the anomaly response link is needed to identify the component that truly caused the anomaly, i.e., the anomalous component.

[0040] Step S4243: Determine the abnormal component based on each of the abnormal response links and the component response links.

[0041] The specific method for determining abnormal components based on abnormal response links in this application can be to compare the differences between abnormal response links generated by various second sets within the same first set. For example, components that exist in abnormal response links closer to the current time but not in abnormal response links from a time period further away can be eliminated. This confirms that such components are affected components. That is, by comparing the abnormal response links of various second sets within the first set, the link with the fewest components, and which is also included by other abnormal response links, is selected as the representative abnormal response link of that first set. Since most components in automated equipment share some functions, meaning a component does not only execute one control command but also executes other control commands, and since the component response links for different control commands are different, if a component is abnormal, it will affect the component response links of multiple control commands. In other words, it will appear in the representative abnormal response links of different control commands (multiple first sets). Therefore, by comparing the representative abnormal response links of multiple first sets, components that appear in multiple representative abnormal response links can be identified as abnormal components. Of course, if the abnormal responses of the two first sets represent links that do not have any overlapping components, then it means that the two abnormal responses represent links that each have their own abnormal components.

[0042] Step S425: Determine the operational anomaly characteristics of each component based on the actual operational characteristics and corresponding predicted operational characteristics of each feature anomaly point in the first set.

[0043] Step S426: Determine the cause of the abnormality based on the abnormal characteristics of each component and the abnormal component.

[0044] In some embodiments, step S426, "determining the cause of the anomaly based on the operational anomaly characteristics of each component and the anomaly component," includes: Step S4261: The operational anomaly features of each component are fused to form operational anomaly fused features.

[0045] Step S4262: Associate the abnormal fusion features of each component according to the component response chain to form associated features.

[0046] Step S4263: Determine the cause of the abnormality of the abnormal component based on the associated features.

[0047] After identifying the abnormal component, it is also necessary to determine the cause of the abnormality, or what kind of abnormality has occurred. Theoretically, different abnormalities in a component will have different manifestations. However, before a failure occurs, the differences caused by these abnormalities are difficult to distinguish, and the specific cause of the abnormality cannot be clearly identified. However, such abnormalities will be transmitted to lower or higher-level components, causing greater differences from the expected target in some components. Therefore, this application needs to obtain the difference characteristics between the actual operating characteristics and the predicted operating characteristics of each characteristic abnormal point, that is, the operating abnormality characteristics. The operating abnormality characteristics are used to describe the difference between the actual operating characteristics and the predicted operating characteristics. In this way, the abnormality caused by the first abnormal component can be amplified in subsequent components, making the abnormal effect of the abnormal component more obvious.

[0048] In order to obtain stable features, this application fuses the operational anomaly features of the same component in the first set to obtain stable operational anomaly features of each component, namely, anomaly fusion features. Then, according to the correlation between each component in the component response chain, the anomaly fusion features of each component are correlated to form correlation features. Correlation features can characterize the correlation between each anomaly fusion feature, that is, the flow of anomalies caused by an abnormal component between each component, or the impact of anomalies caused by an abnormal component on other components.

[0049] Since the abnormal components have been identified, their causes can be determined through correlation relationships. Specifically, because the response processes of each component to actions between its superior and subordinate components are known, the actual output of the abnormal component can be deduced based on the most obvious abnormal fusion characteristics combined with correlation relationships. Furthermore, given the correct output of the abnormal component to the corresponding control command, the abnormality, or cause, can be identified based on the correct and actual output. To prevent the obtained causes from being too one-sided or difficult to identify the cause from a single set of abnormal features, this application can also cross-verify the correlation features in multiple sets. Similarly, since a component responds to multiple control commands, the abnormal component will appear in multiple sets. Each set containing the abnormal component can then form a correlation feature for that component. By analyzing multiple correlation features, the cause of the abnormal component's abnormality can be determined. If only one definite cause can be identified from the multiple correlation features, then that cause is the cause of the abnormal component's abnormality. If multiple causes are obtained, further analysis of their commonalities or correlations is needed to further determine the root cause of the abnormality.

[0050] Step S43: Provide a fault warning for the target device based on the abnormal component and the cause of the abnormality.

[0051] In some embodiments, step S43, "providing a fault warning for the target device based on the abnormal component and the cause of the abnormality," includes: Step S431: Obtain historical warning information containing the abnormal component and the cause of the abnormality.

[0052] Step S432: Determine the historical anomaly features related to the historical warning information based on the historical warning information.

[0053] Step S433: Determine the anomaly development trend based on the historical anomaly characteristics and the operational anomaly characteristics.

[0054] Step S434: Provide a fault warning for the target device based on the abnormal development trend. Once the abnormal component and its cause are identified, an anomaly warning can be issued. This warning primarily alerts users or relevant personnel that a specific component of the target equipment has malfunctioned for a particular reason, thus providing a fault warning. For better warning capabilities, this application can further present the user with the probability of the fault, i.e., the likelihood of the fault occurring, or the urgency of handling the anomaly. This depends on the user's attitude; this application only provides a quantitative result or reference result regarding the relationship between anomaly and fault.

[0055] This application can obtain historical early warning information for the same abnormal component and the same cause of abnormality within a preset time range. This early warning information includes the operational abnormality characteristics at the time the early warning information was issued. For ease of distinction, the operational abnormality characteristics corresponding to the historical early warning information are referred to as historical abnormality characteristics. By comparing the historical abnormality characteristics in the historical early warning information at different times with the operational abnormality characteristics at the current time, the development trend of the abnormality in the abnormal component can be analyzed. The probability of the abnormality causing a failure can be determined by the development area of ​​the abnormality, or the time after which the abnormality will cause a clear failure in the target equipment, i.e., a failure that affects production, can be predicted. Of course, the urgency of the abnormality can also be determined based on the development area. This application does not impose further limitations on the specific manifestation of the fault early warning.

[0056] This application discloses a fault early warning method for automated equipment, comprising: acquiring real-time operating data of each component of a target equipment and control commands issued to the target equipment; determining the actual operating characteristics of each component of the target equipment based on the real-time operating data; predicting the predicted operating characteristics of each component of the target equipment based on the control commands; and providing fault early warning for the target equipment based on the actual operating characteristics and the predicted operating characteristics of each component. This method solves the problem of difficulty in determining the cause and faulty component when existing automated equipment malfunctions, achieving early warning before a fault occurs. This allows staff to inspect components that may fail before a fault occurs, and also enables staff to promptly identify the faulty component and cause based on the fault early warning information when a fault occurs, greatly improving the maintenance efficiency of automated equipment.

[0057] Example 2: Based on the foregoing embodiments, this application provides a fault early warning device for automated equipment. The various modules and units included in the device can be implemented by a processor in a computer device; of course, they can also be implemented by specific logic circuits. In the implementation process, the processor can be a central processing unit (CPU), a microprocessor (MPU), a digital signal processor (DSP), or a field programmable gate array (FPGA), etc.

[0058] like Figure 2 As shown, a fault early warning device for automated equipment includes: a first acquisition module 1, a first determination module 2, a second determination module 3, and a first execution module 4.

[0059] The first acquisition module 1 is used to acquire real-time operating data of each component of the target device and control commands issued to the target device. The first determination module 2 is used to determine the actual operating characteristics of each component of the target device based on the real-time operating data. The second determination module 3 is used to predict the predicted operating characteristics of each component of the target device based on the control commands. The first execution module 4 is used to provide fault warnings for the target device based on the actual operating characteristics and the predicted operating characteristics of each component.

[0060] The various modules in the aforementioned fault early warning device for automated equipment can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of the device in hardware form or independently of it, or stored in the memory of the processing device in software form, so that the processor can call and execute the operations corresponding to each module. It should be noted that the module division in this embodiment is illustrative and only represents a logical functional division; in actual implementation, there may be other division methods.

[0061] Example 3: Thirdly, this application provides a computer electronic production device, such as... Figure 3 As shown, it includes: at least one processor 901; and a memory 902 communicatively connected to the at least one processor 901; wherein the memory 902 stores instructions executable by the at least one processor 901, the instructions being executed by the at least one processor 901 to enable the at least one processor 901 to execute a fault warning method for an automated device in the above embodiments.

[0062] The memory and processor are connected via a bus, which can include any number of interconnecting buses and bridges, connecting various circuits of one or more processors and memories. The bus can also connect various other circuits, such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and will not be described further herein. The bus interface provides an interface between the bus and the transceiver. The transceiver can be a single element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium. Data processed by the processor is transmitted over the wireless medium via an antenna, which further receives data and transmits it to the processor.

[0063] The processor manages the bus and general processing, and also provides various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. Memory is used to store data used by the processor during operation.

[0064] In addition, the computer device may include (but is not limited to) a data bus, an input / output (I / O) bus, a display, and input / output devices (e.g., keyboard, mouse, speakers, etc.).

[0065] The processor can communicate with external devices via the I / O bus through wired or wireless networks.

[0066] In one embodiment, the at least one computer-executable instruction may also be compiled into or comprise a software product / computer program product, wherein one or more computer-executable instructions are executed by a processor to perform the steps of the various functions and / or methods in the embodiments described herein.

[0067] Example 4: Fourthly, this application proposes a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in any of the first aspects.

[0068] Computer-readable storage media can be implemented by any type of volatile or non-volatile storage device or a combination thereof. Computer-readable storage media may include, but are not limited to, random access memory (RAM), read-only memory (ROM), flash memory, EPROM memory, EEPROM memory, registers, and computer storage media (e.g., hard disks, floppy disks, solid-state drives, removable disks, CD-ROMs, DVD-ROMs, Blu-ray discs, etc.).

[0069] Computer-readable storage media may also store at least one computer-executable program / instruction, such as computer-readable instructions. Computer-readable storage media include, but are not limited to, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory. Computer-readable storage media may include, for example, read-only memory (ROM), hard disk, flash memory, etc. For example, a non-transitory computer-readable storage medium may be connected to a computing device such as a computer, and then, when the computing device executes the computer-readable instructions stored on the computer-readable storage medium, the various methods described above can be performed.

[0070] Example 5: Fifthly, this application proposes a computer program product comprising a computer program / instructions that, when executed by a processor, implement the steps of the method described in any of the first aspects.

[0071] Those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. This program is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0072] It should be understood that the phrase "one embodiment" or "an embodiment" throughout the specification means that a specific feature, structure, or characteristic related to the embodiment is included in at least one embodiment of this application. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. Furthermore, these specific features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. It should be understood that in the various embodiments of this application, the sequence numbers of the above-described processes do not imply a sequential order of execution; the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application. The sequence numbers of the above-described embodiments are merely descriptive and do not represent the superiority or inferiority of the embodiments.

[0073] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0074] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0075] In the embodiments provided in this disclosure, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative; for example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive 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 and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0076] Alternatively, if the integrated units described above are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a controller to execute all or part of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROMs, magnetic disks, or optical disks.

[0077] It should be noted that the terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this disclosure described herein can be implemented in orders other than those illustrated or described herein.

[0078] Those skilled in the art will understand that the above embodiments are specific embodiments for implementing this application, and in practical applications, various changes can be made to them in form and detail without departing from the spirit and scope of this application.

Claims

1. A fault early warning method for automated equipment, characterized in that, include: Acquire real-time operating data of each component of the target device and control commands issued to the target device; The actual operating characteristics of each component of the target device are determined based on the real-time operating data. The predicted operating characteristics of each component of the target device are predicted based on the control commands. The target equipment is given a fault warning based on the actual operating characteristics and predicted operating characteristics of each component.

2. The method according to claim 1, characterized in that, The method of providing fault warnings for the target equipment based on the actual operating characteristics and predicted operating characteristics of each component includes: Based on the actual operating characteristics and the predicted operating characteristics, the characteristic anomalies of each component are determined; The abnormal components and causes of the abnormality are determined based on the aforementioned characteristic anomalies. The target device is given a fault warning based on the abnormal component and the cause of the abnormality.

3. The method according to claim 2, characterized in that, The step of determining the abnormal component and the cause of the abnormality based on the characteristic anomaly points includes: The corresponding control commands are determined based on the occurrence time of each of the aforementioned characteristic anomalies and the component that caused them. Determine the component response chain for each of the relevant control commands, wherein the component response chain is used to characterize the sequential or associated relationship of each component when responding to the relevant control commands; According to the relevant control instructions, the feature anomalies are divided into multiple first sets, wherein each relevant control instruction corresponds to a first set. The abnormal component is determined based on the component response chain and the first set; The operational anomaly characteristics of each component are determined based on the actual operational characteristics of each feature anomaly point in the first set and the corresponding predicted operational characteristics. The cause of the anomaly is determined based on the abnormal operating characteristics of each component and the abnormal component.

4. The method according to claim 3, characterized in that, The step of determining the abnormal component based on the component response chain and the first set includes: Based on the occurrence time of each of the characteristic anomalies in the first set, multiple second sets are formed in the first set. The second set includes multiple characteristic anomalies whose occurrence time points to the same related control command. The same related control command refers to the related control command at a certain moment. Based on the component response chain and the component that caused each characteristic anomaly point in the second set, generate an anomaly response chain corresponding to each second set; The abnormal component is determined based on each of the abnormal response links and the component response links.

5. The method according to claim 3, characterized in that, The step of determining the cause of the anomaly based on the abnormal characteristics of each component and the abnormal component includes: The operational anomaly characteristics of each component are fused to form an operational anomaly fusion characteristic; The abnormal fusion features of each component are correlated according to the component response chain to form correlation features; The cause of the abnormality of the abnormal component is determined based on the associated characteristics.

6. The method according to claim 3, characterized in that, The method of providing fault warning for the target device based on the abnormal component and the cause of the abnormality includes: Obtain historical early warning information containing the abnormal component and the cause of the abnormality; Based on the historical early warning information, determine the historical abnormal features related to the historical early warning information; The anomaly development trend is determined based on the historical anomaly characteristics and the operational anomaly characteristics; The target device is given a fault warning based on the abnormal development trend.

7. A fault early warning device for automated equipment, characterized in that, include: The first acquisition module is used to acquire real-time operating data of each component of the target device and control commands issued to the target device; The first determining module is used to determine the actual operating characteristics of each component of the target device based on the real-time operating data. The second determining module is used to predict the predicted operating characteristics of each component of the target device based on the control instructions; The first execution module is used to provide fault warnings for the target device based on the actual operating characteristics and the predicted operating characteristics of each component.

8. A computer electronic production equipment, characterized in that, The method includes a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the steps of the method according to any one of claims 1-6.

Images