Abnormal positioning method, device and equipment of automation module and medium

Abstract

Embodiments of the present application disclose an abnormal positioning method, device, equipment and medium of an automation module. The method comprises: obtaining module image data, module action data and execution state data of the automation module; generating an action log of the automation module according to the module image data and the module action data; generating a state log of the automation module according to the execution state data; and extracting an abnormal action log of the automation module in an abnormal working state from the action log of the automation module according to the state log of the automation module. Embodiments of the present application realize rapid positioning of sudden abnormalities of the automation module.

Description

Technical Field

[0001] This invention relates to the field of automation technology, and in particular to an anomaly location method, apparatus, device, and medium for an automation module. Background Technology

[0002] Automation technology is a comprehensive science and technology involving many disciplines and with wide applications. Therefore, during the commissioning phase of automation modules, a large number of professionals, including commissioning engineers, electrical control engineers, vision engineers, and design engineers, are needed to follow up on the automation modules until they are in stable production.

[0003] However, when an abnormality occurs after the production of an automated module, causing an abnormal shutdown, experienced technicians are required to be on-site. Otherwise, it is impossible to quickly locate the abnormality and handle it, which could cause significant losses to the safe production of the factory. Summary of the Invention

[0004] This invention provides a method, apparatus, device, and storage medium for locating anomalies in automated modules, enabling rapid location of sudden anomalies in automated modules.

[0005] According to one aspect of the present invention, an anomaly localization method for an automated module is provided, the method comprising:

[0006] Acquire module image data, module action data, and execution status data of the automation module;

[0007] Based on the module image data and the module action data, an action log of the automation module is generated;

[0008] Based on the execution status data, generate the status log of the automation module;

[0009] Based on the status log of the automation module, extract the abnormal action log of the automation module in the abnormal working state from the action log of the automation module.

[0010] According to another aspect of the present invention, an anomaly location device for an automated module is provided, the device comprising:

[0011] The data acquisition module is used to acquire module image data, module action data, and execution status data of the automation module;

[0012] An action log generation module is used to generate an action log for the automation module based on the module image data and the module action data.

[0013] The status log generation module is used to generate the status log of the automation module based on the execution status data.

[0014] The abnormal action log extraction module is used to extract abnormal action logs of the automation module in abnormal working state from the action log of the automation module based on the status log of the automation module.

[0015] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0016] At least one processor; and

[0017] A memory communicatively connected to the at least one processor; wherein,

[0018] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the anomaly localization method of the automated module according to any embodiment of the present invention.

[0019] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the anomaly location method of the automated module according to any embodiment of the present invention.

[0020] The present invention generates an action log for an automated module based on the acquired module image data and module action data; generates a status log for the automated module based on the execution status data; and extracts abnormal action logs of the automated module in abnormal working states from the action logs of the automated module based on the status logs of the automated module. This enables rapid location of sudden anomalies in the automated module, thereby enabling backtracking of anomalies in the automated module. This facilitates relevant technical personnel to resolve abnormal problems of the automated module in a timely manner through anomaly backtracking.

[0021] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a flowchart of an anomaly localization method for an automated module according to Embodiment 1 of the present invention;

[0024] Figure 2 This is a flowchart of an anomaly localization method for an automated module according to Embodiment 2 of the present invention;

[0025] Figure 3 This is a schematic diagram of the structure of an abnormality location device for an automated module according to Embodiment 3 of the present invention;

[0026] Figure 4 This is a schematic diagram of the structure of an electronic device that implements the anomaly location method of the automated module in this embodiment of the invention. Detailed Implementation

[0027] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0028] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention 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 the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0029] Example 1

[0030] Figure 1 This is a flowchart of an anomaly localization method for an automated module provided in Embodiment 1 of the present invention. This embodiment is applicable to situations involving anomaly backtracking of an automated module. The method can be executed by an anomaly localization device for the automated module, which can be implemented in hardware and / or software. This anomaly localization device can be configured in an electronic device, such as... Figure 1 As shown, the method includes:

[0031] S110. Obtain module image data, module action data, and execution status data of the automation module.

[0032] Here, an automation module can be a module or component in an automated device that performs a certain function. Module image data can be video or image data of the automated module's operation; module action data can be action data of each component in the automated module during execution; execution status data can be data such as changes or alterations in the module's execution status during the execution process.

[0033] The module image data of the automation module can include surveillance image data and industrial control computer image data. The surveillance image data can be acquired by image acquisition devices pre-installed throughout the automation module, such as surveillance cameras; the industrial control computer image data can be recorded by software on the industrial control computer to which the automation module belongs.

[0034] For example, surveillance cameras deployed on an automation module can collect key actions at each workstation in real time in a group format. For instance, they can capture images of cylinder or servo movements such as loading / unloading, robotic arm picking / unloading, and screw driving. The surveillance image data can be collected and transmitted in real time via a USB 3.0 (Universal Serial Bus 3.0) interface.

[0035] For example, the industrial control computer (ICC) image data is recorded through the interface of the ICC software program. The ICC software program may include MES (Manufacturing Execution System) communication software, vision programs, and programming programs. The ICC image data can be acquired and transmitted in real time via TCP / IP (Transmission Control Protocol / Internet Protocol) communication services.

[0036] It should be noted that due to the large amount of data in the module image data of the automation module, a significant amount of memory space is required. Therefore, to reduce the memory space occupied by the module image data, the acquired module image data of the automation module can be processed by H.264 (video compression coding standard) lossy compression to achieve a smaller image storage space. This reduces storage space while ensuring good image quality and fast low-bandwidth image transmission.

[0037] The module's motion data can include hardware-executed motion data and software-executed motion data. For example, hardware-executed motion data can be obtained by establishing communication connections with workstation components such as robotic arms and servo motors on the automation module and acquiring the execution actions of each component. Software-executed motion data can be obtained through software programs running on the industrial control computer of the automation module.

[0038] The execution status data can include status change data and anomaly alarm data. Status change data refers to changes or switches in the status of the automation module during execution. For example, status change data can include "in production," "awaiting materials," "operating abnormally," "in engineering shutdown," and "in planned shutdown." Anomaly alarm data can include anomaly type and warning type. Execution status data can be obtained by real-time collection of the automation module's execution status.

[0039] S120. Generate the action log of the automated module based on the module image data and module action data.

[0040] For example, an action log for an automated module can be generated based on the acquisition time corresponding to the module image data and module action data, respectively.

[0041] Specifically, the acquired module image data and module action data can be organized and combined, and the data can be encapsulated in JSON format to obtain action logs indexed primarily by acquisition time. These action logs can include module image logs, hardware action logs, and software action logs. Module image logs can be obtained by integrating module image data; hardware and software action logs can be obtained by integrating module action data.

[0042] The module image log may also include monitoring image sub-logs and industrial control computer image sub-logs. Specifically, the monitoring image sub-logs can be obtained by integrating monitoring image data from the module image data; similarly, the industrial control computer image sub-logs can be obtained by integrating industrial control computer image data from the module image data.

[0043] The hardware action log may also include sub-logs of the module's robotic arm actions. Specifically, this can be obtained by integrating the robotic arm action data from the hardware execution action data within the module action data.

[0044] The software action log may also include visual action sub-logs and control action sub-logs. The visual action sub-log can be obtained by integrating visual program action data from the software execution action data in the module action data; the control action sub-log can be obtained by integrating control program action data from the software execution action data in the module action data.

[0045] It should be noted that the module image log, hardware action log, and software action log, as well as the sub-logs contained in each log, all record detailed time information including year, month, day, hour, minute, second, and millisecond.

[0046] S130. Generate the status log of the automation module based on the execution status data.

[0047] The status log includes a status change log and an anomaly alarm log. For example, a status change log can be generated based on status change data in the execution status, and an anomaly alarm log can be generated based on anomaly alarm data in the execution status. Both the status change log and the anomaly alarm log record detailed time information such as year, month, day, hour, minute, second, and millisecond.

[0048] It should be noted that the status change log can include five statuses: in production, waiting for materials, abnormal operation, engineering shutdown, and planned shutdown, and each status records the time information of the automation module in each status.

[0049] S140. Based on the status log of the automation module, extract the abnormal action log of the automation module in the abnormal working state from the action log of the automation module.

[0050] For example, the abnormal time when the automation module is in an abnormal working state can be determined based on the status log of the automation module; and the action log at that abnormal time can be extracted from the action log of the automation module as the abnormal action log.

[0051] For example, if the abnormal time when the automation module was in an abnormal working state is determined to be 19:00-19:05 on 2022 / 11 / 27 based on the status log of the automation module, then the action log of the time 19:00-19:05 on 2022 / 11 / 27 is extracted from the action log of the automation module as the abnormal action log.

[0052] Optionally, an anomaly backtracking dashboard can be generated based on the abnormal action logs, allowing relevant technical personnel to identify and resolve abnormal issues in the automation module.

[0053] The present invention generates an action log for an automated module based on the acquired module image data and module action data; generates a status log for the automated module based on the execution status data; and extracts abnormal action logs of the automated module in abnormal working states from the action logs of the automated module based on the status logs of the automated module. This enables rapid location of sudden anomalies in the automated module, thereby enabling backtracking of anomalies in the automated module. This facilitates relevant technical personnel to resolve abnormal problems of the automated module in a timely manner through anomaly backtracking.

[0054] It should be noted that module action data can include hardware execution action data and software execution action data, and the methods for obtaining hardware execution action data and software execution action data are different.

[0055] In one optional embodiment, the module motion data includes hardware execution motion data; the hardware execution motion data includes at least one of the following: robot arm motion, servo motor motion, cylinder motion, product production motion, industrial tri-color light motion, and hardware-defined motion; the hardware execution motion data is acquired in the following ways: via the robot arm, based on the TCP transmission control protocol, to acquire robot arm motion; via a PLC (Programmable Logic Controller), based on the Modbus communication protocol, to acquire servo motor motion, cylinder motion, and product production motion; or via a motion control card, to acquire servo motor motion, cylinder motion, and product production motion; via an industrial touch screen, to acquire hardware-defined motion; and via a PLC, to acquire industrial tri-color light motion.

[0056] It should be noted that before acquiring hardware execution action data, it is necessary to establish communication connections with the robotic arm, PLC, motion control card, and industrial touch screen in the automation module.

[0057] For example, a communication connection can be established between a TCPServer service and the robotic arm in the automation module via the TCP / IP protocol to collect the robotic arm's movements. The constructed TCPServer service implements multiplexing, aggregating multiplexers to handle a large number of client connections concurrently. The PLC acquires servo motor movements, cylinder movements, and production product movements based on the Modbus communication protocol. Alternatively, a motion control card can also acquire these movements. It should be noted that the motion control card can directly acquire servo motor movements, cylinder movements, and production product movements without relying on the TCP / IP communication protocol. Custom hardware movements are acquired via an industrial touchscreen; the PLC acquires the movements of industrial tri-color lights. Specifically, the industrial tri-color light movements refer to the color changes of the industrial tri-color lights.

[0058] In another optional embodiment, the module action data further includes software execution action data; the software execution action data includes vision program action data and control program action data; the vision program running on the industrial control computer is collected to obtain vision program action data; and the control program running on the industrial control computer is collected to obtain control program action data.

[0059] Among them, the visual program action data can be the visual program action information in the visual workstation of the automation module; the control program action data can be the software program action information in the software workstation of the automation module.

[0060] For example, visual program motion data can be obtained by collecting data from a visual program running on an industrial control computer; control program motion data can be obtained by collecting data from a control program running on an industrial control computer.

[0061] The visual program action data can include visual process triggering action data, light source control action data, camera image acquisition action data, visual processing action data, and result feedback action data. Specifically, the visual process triggering action includes attribute information such as the trigger visual process number, light source brightness (1-255), camera image exposure value, camera image gain value, and camera image gamma value.

[0062] The control program action data can include MES inspection actions, MES submission actions, and software-defined actions. The vision program action data and control program action data can be collected in real time via TCP / IP communication services.

[0063] This optional embodiment achieves comprehensive acquisition of hardware execution action data by establishing communication connections with the robot arm, PLC, motion control card and industrial control touch screen in the automation module; and achieves comprehensive acquisition of software execution action data by collecting the vision program and control program running on the industrial control computer.

[0064] Example 2

[0065] Figure 2 This is a flowchart of an abnormal location method for an automated module provided in Embodiment 2 of the present invention. This embodiment has been optimized, improved and refined in a deeper manner based on the above technical solutions.

[0066] Furthermore, the step "extracting abnormal action logs of the automation model in abnormal working state from the action log of the automation module based on the status log of the automation module" is refined to "determining the abnormal state time corresponding to when the automation module is in an abnormal working state based on the status log of the automation module; determining the target tracing time period based on the abnormal state time and the preset tracing time; and using the action logs associated with the target tracing time period as abnormal action logs" to improve the method of determining abnormal action logs.

[0067] Furthermore, the step "generating the action log of the automated module based on the module image data and module action data" is refined into "generating a module image log with a time stamp based on the action acquisition time corresponding to the module image data; generating software action logs and hardware action logs with time stamps based on the action acquisition time corresponding to the module action data; generating an automated module action log including the module image log, software action log, and hardware action log." This improves the method for generating the action log of the automated module.

[0068] Furthermore, the step "generate status logs for the automation module based on execution status data" is refined into "generate status change logs for the automation module based on status change data in the execution status data and the status change collection time of the status change data; generate abnormal alarm logs for the automation module based on abnormal alarm data in the execution status data and the abnormal alarm collection time of the abnormal alarm data; generate status logs for the automation module that include status change logs and abnormal alarm logs." This improves the method for generating status logs for the automation module.

[0069] like Figure 2 As shown, the method includes the following specific steps:

[0070] S210. Obtain module image data, module action data, and execution status data of the automation module.

[0071] S220. Generate a module image log with a time stamp based on the action acquisition time corresponding to the module image data.

[0072] For example, the time of action acquisition can be used as the timestamp of the module image data. For instance, if the module image data is acquired at 19:00 on 2022 / 11 / 27, then the time 2022 / 11 / 27 / 19:00 is the timestamp of the module image data in the module image log.

[0073] The module image log includes monitoring image sub-logs and industrial control computer image sub-logs, both of which are time-stamped image logs.

[0074] S230. Based on the action acquisition time corresponding to the module action data, generate software action logs and hardware action logs with time identifiers.

[0075] For example, the action acquisition time can be used as the timestamp of the module action data. For instance, if the module action data is acquired at 18:00 on 2022 / 11 / 27, then the time 2022 / 11 / 27 / 18:00 is the timestamp of the module action data in the module action log.

[0076] The module action log includes hardware action logs and software action logs. The hardware action logs further include sub-logs of the module's robotic arm actions and servo motor actions. The software action logs include sub-logs of vision actions and control actions. All sub-logs within both the hardware and software action logs are time-stamped action logs.

[0077] S240. Generate an action log for the automated module, including module image logs, software action logs, and hardware action logs.

[0078] For example, module image logs, software action logs, and hardware action logs are all used as action logs for the automation module.

[0079] S250. Generate the status change log of the automation module based on the status change data in the execution status data and the status change collection time of the status change data.

[0080] The status change data can include production, waiting for materials, abnormal operation, engineering shutdown, and planned shutdown. The automation module can switch between these five states.

[0081] The specific switching rules are as follows: When the automated module starts production, the status information is submitted as "Production in Progress"; when the automated module stops production due to material shortage or manual clicking of the stop button, the status information is submitted as "Waiting for Material"; when the automated module cannot continue production due to mechanical failure or abnormal incoming materials, the status information is submitted as "Abnormal Operation"; when the automated module needs to be paused due to changes in engineering processes or material switching, the engineer selects the "Engineering Stop" button on the industrial control touchscreen, and the status information is submitted as "Engineering Stop"; when the automated module needs to be paused due to consumable replacement, engineering maintenance, or cleaning, the equipment maintenance personnel select the "Planned Stop" button on the industrial control touchscreen, and the status information is submitted as "Planned Stop". Based on the status information submitted in these five states, data status change data is obtained.

[0082] It should be noted that when submitting status information in the five states, the submission time, i.e. the status change collection time, can be recorded. Based on the status change data and the status change collection time of the status change data, a status change log of the automated module with a time stamp can be generated.

[0083] S260. Generate an abnormal alarm log for the automation module based on the abnormal alarm data in the execution status data and the abnormal alarm collection time of the abnormal alarm data.

[0084] The abnormal alarm data can include abnormal type data and warning type data. Abnormal type data can be data related to the cause of the abnormality collected when the automation module is in an abnormal operating state. Warning type data can be shutdown warning data collected when the automation module is in a state of engineering shutdown or planned shutdown.

[0085] For example, while collecting abnormal alarm data, the collection time of abnormal alarm data, such as abnormal type data and warning type data, is recorded. Based on the abnormal alarm data and the abnormal alarm collection time, an abnormal alarm log with a time stamp is generated for the automated module.

[0086] S270. Generate status logs for the automation module, including status change logs and abnormal alarm logs.

[0087] For example, both the status change log and the abnormal alarm log can be used as the status logs of the automation module.

[0088] It should be noted that, in order to facilitate the rapid location and screening of the causes of abnormalities in the automation module by relevant technical personnel, the abnormal causes of the abnormality of the automation module during the abnormal state time can also be extracted from the abnormal alarm data.

[0089] In one optional embodiment, the abnormality type of the automation module under the corresponding abnormality time is extracted from the abnormal alarm data according to the abnormality time when the automation module is in an abnormal working state.

[0090] For example, after determining the abnormal state time corresponding to when the automation module is in an abnormal working state, the abnormality type of the automation module at that abnormal state time is extracted from the abnormal alarm data. The abnormality type, also known as the error type, is used to characterize the reason for the abnormality of the automation module.

[0091] S280. Based on the status log of the automation module, determine the abnormal status time corresponding to when the automation module is in an abnormal working state.

[0092] For example, the abnormal status events corresponding to when the automation module is in an abnormal working state can be determined from the status log of the automation module. The abnormal status time can be a single moment, a period of time, or it can be continuous or dispersed.

[0093] S290. Determine the target tracing time period based on the abnormal state time and the preset tracing time.

[0094] The traceability time can be preset by relevant technical personnel based on actual experience or experimental values. For example, the traceability time can be 5 minutes.

[0095] For example, if the abnormal state time is 2022 / 11 / 27 / 09:00 and the preset tracing time is 5 minutes, then the target tracing time period can be 2022 / 11 / 27 / 09:00-2022 / 11 / 27 / 09:05.

[0096] S2100. Treat the action logs associated with the target traceability time period as abnormal action logs.

[0097] For example, action logs that fall within the target tracing time period are considered abnormal action logs. For instance, if the target tracing time period is 2022 / 11 / 27 / 09:00-2022 / 11 / 27 / 09:05, then abnormal action logs can be action logs within that time period.

[0098] This embodiment determines the abnormal state time corresponding to the abnormal working state of the automation module based on the module's status log; determines the target traceability time period based on the abnormal state time and a preset traceability time; and uses the action logs associated with the target traceability time period as abnormal action logs, thus achieving accurate location of the abnormal state time and accurate extraction of abnormal action logs. Based on the action acquisition time corresponding to the module image data, time-stamped module image logs are generated; based on the action acquisition time corresponding to the module action data, time-stamped software action logs and hardware action logs are generated, improving the accuracy and comprehensiveness of action log generation. Based on the status change data in the execution status data and the status change acquisition time of the status change data, a status change log for the automation module is generated; based on the abnormal alarm data in the execution status data and the abnormal alarm acquisition time of the abnormal alarm data, an abnormal alarm log for the automation module is generated, improving the accuracy and comprehensiveness of status log generation.

[0099] Example 3

[0100] Figure 3 This is a schematic diagram of the structure of an anomaly location device for an automated module provided in Embodiment 3 of the present invention. The anomaly location device for an automated module provided in this embodiment of the present invention is applicable to situations involving anomaly backtracking of the automated module. This anomaly location device for the automated module can be implemented in hardware and / or software, such as... Figure 3 As shown, the device specifically includes: a data acquisition module 301, an action log generation module 302, a status log generation module 303, and an abnormal action log extraction module 304. Among them,

[0101] Data acquisition module 301 is used to acquire module image data, module action data and execution status data of the automation module;

[0102] Action log generation module 302 is used to generate action logs for the automation module based on the module image data and the module action data;

[0103] The status log generation module 303 is used to generate the status log of the automation module based on the execution status data.

[0104] The abnormal action log extraction module 304 is used to extract the abnormal action log of the automation module in an abnormal working state from the action log of the automation module according to the status log of the automation module.

[0105] Optionally, the abnormal action log extraction module 304 includes:

[0106] The abnormal status event determination unit is used to determine the abnormal status time corresponding to when the automation module is in an abnormal working state based on the status log of the automation module.

[0107] The target tracing time determination unit is used to determine the target tracing time period based on the abnormal state time and the preset tracing time.

[0108] An abnormal action log determination unit is used to identify action logs associated with the target traceability time period as abnormal action logs.

[0109] Optionally, the action log generation module 302 includes:

[0110] The module image log generation unit is used to generate a module image log with a time identifier based on the action acquisition time corresponding to the module image data.

[0111] The software and hardware action log generation unit is used to generate software action logs and hardware action logs with time identifiers based on the action acquisition time corresponding to the module action data.

[0112] An action log generation unit is used to generate action logs for the automated module, including the module image log, the software action log, and the hardware action log.

[0113] Optionally, the status log generation module 303 includes:

[0114] The status change log generation unit is used to generate the status change log of the automation module based on the status change data in the execution status data and the status change collection time of the status change data.

[0115] An abnormal alarm log generation unit is used to generate an abnormal alarm log for the automation module based on the abnormal alarm data in the execution status data and the abnormal alarm collection time of the abnormal alarm data.

[0116] The status log generation unit is used to generate status logs for the automation module, including the status change logs and the abnormal alarm logs.

[0117] Optionally, the device further includes:

[0118] The anomaly type determination module is used to extract the anomaly type of the automation module from the anomaly alarm data based on the anomaly state time corresponding to the anomaly state time when the automation module is in an abnormal working state.

[0119] Optionally, the module action data includes hardware execution action data; the hardware execution action data includes at least one of the following: robot arm action, servo motor action, cylinder action, product production action, industrial tri-color light action, and hardware-defined action; the hardware execution action data is obtained in the following way:

[0120] The robotic arm's movements are acquired using the TCP (Transmission Control Protocol) method.

[0121] The servo motor motion, cylinder motion, and product motion are acquired via a programmable logic controller (PLC) based on the Modbus communication protocol; or, the servo motor motion, cylinder motion, and product motion are acquired via a motion control card.

[0122] The custom actions of the hardware are obtained through the industrial control touch screen;

[0123] The PLC is used to obtain the operation of the industrial tri-color lights.

[0124] Optionally, the module action data includes software execution action data; the software execution action data includes vision program action data and control program action data;

[0125] The vision program running on the industrial control computer is collected to obtain the vision program's action data; and,

[0126] The control program running on the industrial computer is collected to obtain the control program action data.

[0127] The anomaly location device for the automated module provided in this embodiment of the invention can execute the anomaly location method for the automated module provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0128] Example 4

[0129] Figure 4A schematic diagram of an electronic device 40 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0130] like Figure 4 As shown, the electronic device 40 includes at least one processor 41 and a memory, such as a read-only memory (ROM) 42 or a random access memory (RAM) 43, communicatively connected to the at least one processor 41. The memory stores computer programs executable by the at least one processor. The processor 41 can perform various appropriate actions and processes based on the computer program stored in the ROM 42 or loaded into the RAM 43 from storage unit 48. The RAM 43 may also store various programs and data required for the operation of the electronic device 40. The processor 41, ROM 42, and RAM 43 are interconnected via a bus 44. An input / output (I / O) interface 45 is also connected to the bus 44.

[0131] Multiple components in electronic device 40 are connected to I / O interface 45, including: input unit 46, such as keyboard, mouse, etc.; output unit 47, such as various types of monitors, speakers, etc.; storage unit 48, such as disk, optical disk, etc.; and communication unit 49, such as network card, modem, wireless transceiver, etc. Communication unit 49 allows electronic device 40 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0132] Processor 41 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 41 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 41 performs the various methods and processes described above, such as the anomaly localization method of an automation module.

[0133] In some embodiments, the anomaly localization method for the automation module can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 48. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 40 via ROM 42 and / or communication unit 49. When the computer program is loaded into RAM 43 and executed by processor 41, one or more steps of the anomaly localization method for the automation module described above can be performed. Alternatively, in other embodiments, processor 41 can be configured to perform the anomaly localization method for the automation module by any other suitable means (e.g., by means of firmware).

[0134] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0135] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0136] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0137] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0138] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0139] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0140] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0141] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for anomaly localization in an automated module, characterized in that, include: The module image data, module action data, and execution status data of the automation module are acquired; wherein, the module image data of the automation module is image data that has undergone lossy compression encoding. Based on the module image data and the module action data, an action log of the automation module is generated; Based on the execution status data, generate the status log of the automation module; Based on the status log of the automation module, extract the abnormal action log of the automation module in the abnormal working state from the action log of the automation module. The step of extracting abnormal action logs of the automation model in abnormal working states from the action logs of the automation module based on the status logs of the automation module includes: Based on the status log of the automation module, determine the abnormal status time corresponding to when the automation module is in an abnormal working state; The target tracing time period is determined based on the abnormal state time and the preset tracing time. Action logs associated with the target traceability time period are treated as abnormal action logs.

2. The method according to claim 1, characterized in that, The step of generating the action log of the automated module based on the module image data and the module action data includes: Based on the action acquisition time corresponding to the module image data, a module image log with a time identifier is generated; Based on the action acquisition time corresponding to the module action data, generate software action logs and hardware action logs with time identifiers. Generate an action log for the automated module, including the module image log, the software action log, and the hardware action log.

3. The method according to claim 1, characterized in that, The step of generating the status log of the automation module based on the execution status data includes: Based on the status change data in the execution status data and the status change collection time of the status change data, a status change log of the automation module is generated; Based on the abnormal alarm data in the execution status data and the abnormal alarm collection time of the abnormal alarm data, an abnormal alarm log of the automation module is generated. Generate a status log for the automated module, including the status change log and the anomaly alarm log.

4. The method according to any one of claims 1-3, characterized in that, The method further includes: Based on the abnormal state time corresponding to when the automation module is in an abnormal working state, extract the abnormal type of the automation module under the corresponding abnormal state time from the abnormal alarm data.

5. The method according to any one of claims 1-3, characterized in that, The module motion data includes hardware execution motion data; the hardware execution motion data includes at least one of the following: robot arm motion, servo motor motion, cylinder motion, product production motion, industrial tri-color light motion, and hardware-defined motion; the hardware execution motion data is obtained in the following way: The robotic arm's movements are acquired using the TCP (Transmission Control Protocol) method. The servo motor motion, cylinder motion, and product motion are acquired via a programmable logic controller (PLC) based on the Modbus communication protocol; or, the servo motor motion, cylinder motion, and product motion are acquired via a motion control card. The custom actions of the hardware are obtained through the industrial control touch screen; The PLC is used to obtain the operation of the industrial tri-color lights.

6. The method according to any one of claims 1-3, characterized in that, The module action data includes software execution action data; the software execution action data includes visual program action data and control program action data. The vision program running on the industrial control computer is collected to obtain the action data of the vision program; and, The control program running on the industrial computer is collected to obtain the control program action data.

7. An anomaly location device for an automated module, characterized in that, include: The data acquisition module is used to acquire module image data, module action data, and execution status data of the automation module; An action log generation module is used to generate an action log for the automation module based on the module image data and the module action data. The status log generation module is used to generate the status log of the automation module based on the execution status data. An abnormal action log extraction module is used to extract abnormal action logs of the automation module in an abnormal working state from the action log of the automation module based on the status log of the automation module. The module image data of the automation module is image data that has undergone lossy compression encoding. The abnormal action log extraction module includes: The abnormal status event determination unit is used to determine the abnormal status time corresponding to when the automation module is in an abnormal working state based on the status log of the automation module. The target tracing time determination unit is used to determine the target tracing time period based on the abnormal state time and the preset tracing time. An abnormal action log determination unit is used to identify action logs associated with the target traceability time period as abnormal action logs.

8. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the anomaly localization method of the automation module according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the anomaly location method of the automated module according to any one of claims 1-6.

Images