WO2023279846A1 - Procédé et appareil de génération de données de production traçables, et dispositif, support et programme - Google Patents

Procédé et appareil de génération de données de production traçables, et dispositif, support et programme Download PDF

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Publication number
WO2023279846A1
WO2023279846A1 PCT/CN2022/092410 CN2022092410W WO2023279846A1 WO 2023279846 A1 WO2023279846 A1 WO 2023279846A1 CN 2022092410 W CN2022092410 W CN 2022092410W WO 2023279846 A1 WO2023279846 A1 WO 2023279846A1
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Prior art keywords
action
abnormal
execution
action sequence
sequence
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PCT/CN2022/092410
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English (en)
Chinese (zh)
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王飞
王磊
林君仪
周嘉明
白登峰
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上海商汤智能科技有限公司
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Publication of WO2023279846A1 publication Critical patent/WO2023279846A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/018—Certifying business or products
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G06Q10/06395—Quality analysis or management
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/04—Manufacturing
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/30—Computing systems specially adapted for manufacturing

Definitions

  • the present disclosure relates to the field of computer technology, and relates to but not limited to a generation method, device, electronic equipment, computer readable storage medium and computer program product of traceable production data.
  • Embodiments of the present disclosure at least provide a method, device, electronic device, computer-readable storage medium, and computer program product for generating traceable production data, so as to trace data related to product quality problems and improve product qualification rate.
  • An embodiment of the present disclosure provides a method for generating traceable production data, the method including:
  • the abnormal production data is used to reflect the abnormal actions performed by the target object in the operation area on the target product;
  • the abnormal production data made by the target object in the operation area for the target product can be determined based on the acquired video data.
  • the abnormal production data here can include abnormal actions and make abnormal actions.
  • the traceable production data of product quality traceability can be determined. It can be seen that the embodiment of the present disclosure can determine abnormal production data through video analysis, and based on this abnormal production data, product quality can be traced, especially in the case of product defects, targeted solutions can be provided through abnormal production data , in order to improve the product qualification rate.
  • the obtaining abnormal production data based on the video data includes:
  • the abnormal actions in the execution action sequence and the problem type of the abnormal action can be determined based on the comparison results between the execution action sequence of the target object and the reference action sequence, which provides effective data support for subsequent product quality traceability.
  • the abnormal actions in the execution action sequence and the problem types of the abnormal actions are obtained, including:
  • the abnormal actions include at least one of actions added, missing actions in the execution action sequence, and actions with the same execution order and action content similarity less than a threshold value compared with the reference action sequence,
  • the problem types that describe abnormal actions include abnormal actions.
  • the abnormal actions in the execution action sequence and the problem types of the abnormal actions are obtained, including:
  • the abnormal action includes an action whose execution sequence is wrong in the execution action sequence compared with the reference action sequence, and the problem type of the abnormal action includes a step exception.
  • the actions in the executed action sequence that are executed in the wrong sequence are determined according to the following steps:
  • the first target action does not match the first reference action, it is determined that the first target action is an incorrectly executed action in the execution action sequence.
  • the actions in the executed action sequence that are executed in the wrong sequence are determined according to the following steps:
  • the second order is the first step in the execution action sequence
  • the consistency between the action sequences of the two action sequences can be verified from multiple dimensions, that is, the consistency comparison can not only be carried out according to the individual comparison method, but also the consistency comparison can be carried out in combination with the action sequence, thus taking into account more The quality inspection requirements of many application scenarios.
  • the method further includes:
  • the target device can be used to output the first result containing the abnormal action in the action sequence and the second result containing the problem type of the abnormal action, so that the target device can take corresponding actions on the above results. Responses.
  • the method also includes:
  • the sending the first result containing the abnormal action in the execution action sequence to the target device, and the second result containing the question type of the abnormal action includes:
  • the corresponding output results can also have different reminder strengths, so it can be displayed and/or broadcast in combination with the importance level, so that when problems occur in different execution actions , for adaptive solutions.
  • the sending to the target device the first result including the abnormal action in the execution action sequence, and the second result including the question type of the abnormal action include:
  • the method also includes:
  • the target abnormal production data matching the target detection result is determined from the traceable production data.
  • An embodiment of the present disclosure also provides a device for generating traceable production data, the device comprising:
  • the obtaining module is configured to obtain video data corresponding to the operation area collected by the camera equipment;
  • the determination module is configured to obtain abnormal production data based on the video data, and the abnormal production data is used to reflect the abnormal actions performed by the target object in the operation area on the target product;
  • the generation module is configured to correlate the quality inspection results of the target product with the abnormal production data to obtain traceable production data for product quality traceability.
  • An embodiment of the present disclosure also provides an electronic device, including: a processor, a memory, and a bus.
  • the memory stores machine-readable instructions executable by the processor.
  • the processor and the The memory communicates with each other through a bus, and when the machine-readable instructions are executed by the processor, the method for generating traceable production data described in any one of the above-mentioned items is executed.
  • An embodiment of the present disclosure also provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and the computer program is executed by a processor as described in any one of the first aspect and its various implementation modes The generation method of traceable production data.
  • An embodiment of the present disclosure also provides a computer program product, the computer program product carries a program code, and the instructions included in the program code can be used to execute the method for generating traceable production data described in any one of the above items.
  • FIG. 1A is a flowchart of a method for generating traceable production data provided by an embodiment of the present disclosure
  • Figure 1B is a schematic flow diagram of determining abnormal production data based on traceable production data provided by the embodiment of the present application;
  • Fig. 1C is a schematic flow diagram of determining abnormal production data provided by the embodiment of the present application.
  • FIG. 1D is a schematic flow diagram of determining an action in the execution sequence that is executed in the wrong order provided by the embodiment of the present application;
  • FIG. 1E is another schematic flow diagram for determining actions performed in the wrong order in the action sequence provided by the embodiment of the present application.
  • FIG. 2 is a schematic diagram of a generation device for traceable production data provided by an embodiment of the present disclosure
  • Fig. 3 is a schematic diagram of an electronic device provided by an embodiment of the present disclosure.
  • the embodiments of the present disclosure provide a method, device, electronic device, computer readable storage medium, and computer program product for generating traceable production data, so as to trace the relevant data of product quality problems and assist in finding The root cause of quality problems, and provide targeted adjustment strategies or solutions from the root cause, so as to improve the product qualification rate.
  • the execution subject of the method for generating traceable production data provided by the embodiments of the present disclosure is generally a Capable electronic equipment, the electronic equipment includes, for example: terminal equipment, server or other processing equipment; wherein, the terminal equipment can be user equipment (User Equipment, UE), mobile equipment, user terminal, cellular phone, cordless phone, personal digital assistant (Personal Digital Assistant, PDA), handheld devices, computing devices, vehicle-mounted devices, wearable devices, etc.
  • the method for generating traceable production data may be implemented by a processor invoking computer-readable instructions stored in a memory.
  • FIG. 1A it is a flow chart of a method for generating traceable production data provided by an embodiment of the present disclosure.
  • the method includes steps S101 to S103, wherein:
  • Step S101 Obtain video data corresponding to the work area collected by the camera equipment
  • Step S102 Obtain abnormal production data based on the video data, and the abnormal production data is used to reflect the abnormal actions performed by the target object in the operation area for the target product;
  • Step S103 Associating the quality inspection results of the target product with abnormal production data to obtain traceable production data for product quality traceability.
  • the application scenarios of the generation method may be introduced in detail below.
  • the generation method in the embodiments of the present disclosure can be mainly applied to various related fields that require product quality inspection, and in practical applications in these related fields, the capture of video data during the product production process is used as a reference basis instead of Based on the product obtained as a reference.
  • an embodiment of the present disclosure provides a method for generating traceable production data.
  • the method uses the analysis results of video data to determine the traceable production data corresponding to the quality inspection results, and then can provide targeted information based on the traceable production data. Solutions to improve product qualification rate.
  • the video data may be video clips collected by camera equipment deployed in the operation area, for example, it may be deployed at the production line personnel's position to ensure that the operation area where the personnel are located can be photographed.
  • the camera equipment here can turn on the screen capture function when production is started in the work area, and turn on the power saving mode when it is idle.
  • the abnormal production data can be the data relative to the normal production data
  • the normal production data can include normal production activities
  • In-progress production data such as reference actions pointed to by normative operations at the time of execution of actions, may also be referred to as standard actions.
  • abnormal production data can be abnormal actions that do not match the standard actions, or details of abnormal actions such as whether the abnormal action is slow or fast by how many seconds, the degree of abnormality of the abnormal action, and the person or person corresponding to the abnormal action.
  • the target object can be a production line worker in the operation area.
  • the worker here can be a real person or a robot.
  • the video data needs to ensure that the worker's operation action can be captured; in addition, the target object can also be It is an operation device that can work automatically in the work area. At this time, the video data needs to ensure that the operation actions of the operation device can be captured.
  • the traceable production data that caused the quality failure can be determined from the abnormal production data, which largely clarifies the cause of the quality failure . For example, if it is determined that the cause of the unqualified quality comes from the irregularity of a worker in a certain operation, then targeted guidance can be provided to the worker based on the irregularity of the action.
  • statistical analysis of data can also be performed based on traceable production data to determine the main cause of unqualified quality, so as to provide more popular solutions and further improve the pass rate of subsequent products.
  • the relevant quality detection results may be determined manually, or based on the trained quality detection network detection, or determined by other methods, and different methods may be used in specific applications , no specific restrictions are made here.
  • the generation method of traceable production data provided by the embodiment of the present disclosure can also trace the source of each quality problem, which can be realized through the steps shown in Figure 1B:
  • Step S1031 In response to receiving the product quality traceability request, obtain traceable production data associated with the target product indicated by the product quality traceability request;
  • Step S1032 Based on the target detection result in the quality detection results of the target product, determine the target abnormal production data matching the target detection result from the traceable production data.
  • the target detection result here may be one or several results of the quality detection results.
  • matching abnormal production data can be determined for each quality inspection result. For example, when it is determined that the weight of the target product is not up to the standard, it can be judged whether there are fewer pieces due to missing actions. The result is that the dustproof parts are too loose.
  • the above-mentioned product quality traceability request may be initiated on the target device, for example, it may be triggered by the product producer by triggering a relevant button of an application program (Application, APP) on the handheld user terminal.
  • the embodiments of the present disclosure can search the data associated with the target product from various traceable production data, so as to realize the acquisition of targeted abnormal production data for different quality problems.
  • the abnormal production data here mainly includes the abnormal actions performed by the target object in the operation area for the target product and the problem types of the abnormal actions, which can be obtained through The steps shown to identify the above abnormal production data:
  • Step S1021 identifying the execution action sequence corresponding to the target object in the video data; and acquiring a reference action sequence corresponding to the work area;
  • Step S1022 By comparing the execution action sequence with the reference action sequence, obtain the abnormal actions in the execution action sequence and the problem types of the abnormal actions.
  • the execution action sequence recognition here can be obtained based on the trained action recognition network recognition, that is, the acquired video data is input into the trained action recognition network, and the execution action made by the target object in the video data can be obtained sequence.
  • a plurality of video samples and the action annotation information marked for each video sample in the plurality of video samples can be obtained first, where the video sample corresponds to the action annotation corresponding to an action, or by An action label of a complete action composed of multiple actions.
  • the action label information here can be the operation action of pointing to the target object in the video sample to operate the target product. For example, it can be to unscrew the screw, install the accessory, and then screw on the screw this series of operations.
  • the video sample can be input into the action recognition network to be trained, the output result of the action recognition network can be determined, and the output result can be compared with the action labeling information marked on the video sample. If the comparison is inconsistent, the current The network parameter value cannot meet the training requirements.
  • the network parameter value of the action recognition network can be adjusted, and the video sample is input into the adjusted action recognition network, and the network training is performed again until the determined output result is consistent with the action marked by the video sample.
  • the trained action recognition network can be obtained by matching the marked information.
  • the methods for generating traceable production data can be determined based on other action recognition methods in addition to using the above-mentioned action recognition network to determine the sequence of execution actions made by the target object, which is not specifically limited here.
  • determining the execution action sequence of the target object it is possible to first determine the reference action sequence that matches the operation area corresponding to the current camera equipment from the reference action sequences contained in the template library that match multiple operation areas, and then based on The comparison result between the execution action sequence of the target object and the reference action sequence matching the operation area determines the abnormal actions with problems and the corresponding problem types.
  • different reference action sequences can be set for different operation areas.
  • the operation area here can be pre-bound with the product to be operated, the operation time, and the production line workers who operate the product; in the case of determining the operation area, It can be determined that the target product will be operated in the operation area, and there is a reference action sequence corresponding to the target product.
  • abnormal action in the embodiments of the present disclosure may point to a problem in the execution of the action itself, a problem in the sequence of executing the action, or other problems.
  • the problem type of abnormal action can point to the specific action abnormality, whether it is missing an action, or doing too much action, etc., and it can also point to the abnormal step.
  • the execution action sequence of the target object includes an execution action
  • directly compare the execution action with the corresponding reference execution action in the template library if the actions are consistent, there is no problem with the execution action; if the actions are inconsistent, it means There was a problem executing the action.
  • the execution action sequence of the target object includes multiple execution actions, it can be determined whether the execution action sequence of the target object is the same as the reference action sequence based on the execution action sequence of the target object and the reference action sequence matching the work area; There is a problem in determining the executed action of the target object when the executed action sequence of the target object is different from the reference action sequence.
  • the difference between the execution action sequence of the target object and the reference action sequence may be that the execution action in the execution action sequence of the target object is different from the execution action of the reference action sequence, or it may be that the execution action sequence of the target object is different from the reference action sequence
  • the execution sequence of the execution actions is different, that is, it is necessary to comprehensively evaluate whether there is a problem in combination with the number of execution actions, specific execution actions, and the order of each execution action.
  • the execution action sequence of the target object contains additional execution actions that are not in the reference action sequence, it can be regarded as The target object has done an action that should not be done, that is, it is determined that the target object has the problem of additional execution actions (that is, doing more); when the execution action sequence of the target object does not include all the execution actions in the reference action sequence, It can be determined that the target object has the problem of missing execution actions (that is, omission); in the execution action sequence of the target object, the execution action corresponding to the reference execution action included in the reference action sequence, and the included execution action is the same as the reference execution action In the case where the similarity between the content of the target object is less than the threshold value, that is, when it is determined that the reference execution action has actually been performed, but the actual operation action is not standardized, it can be determined that the target object has made a wrong action (that is, a wrong action); In the execution action sequence of
  • Step S10221 Take each action in the execution action sequence as the first target action, determine the first order of the first target action in the execution action sequence, and determine the first reference corresponding to the first order from the reference action sequence action;
  • Step S10222 In the case that the first target action does not match the first reference action, determine that the first target action is an incorrectly executed action in the execution action sequence.
  • the corresponding reference action can be searched from the reference action sequence based on the order of this action in the entire execution action sequence (any step), and judge whether the two actions match. In the case of , it can indicate that there is an incorrectly performed action.
  • the sequence of execution actions can be determined based on item-by-item comparison, that is, regardless of the action sequence, each action is compared independently of each other; for example, the template library specifies that the first item is action A, and the second item is action B , then when performing detection, associate the detected execution actions of the target object with the action sequence to obtain the actual first item is action C, and the actual second item is action D.
  • action C By comparing action C with action A, action D Compare with action B to determine whether the order is wrong. Whether action C does not match action A or action D does not match action B, it means that the order is wrong.
  • Step S10223 Obtain a second target action and a second reference action in the second order from the execution action sequence and the reference action sequence respectively according to the arrangement order of the actions in the execution action sequence;
  • Step S10224 In the case that the second target action does not match the second reference action, determine that the second target action is an incorrectly executed action in the execution action sequence.
  • steps S10221-S10222 and steps S10223-S10224 represent implementations of determining actions in the execution sequence that are executed in the wrong order compared with the reference action sequence from two different aspects.
  • the second order is the first step in the action sequence, take the next step of the second order as the second order and repeat the above steps until all actions in the action sequence are traversed; or, in the second If the order is the last step in the action sequence, take the previous step of the second order as the second order and repeat the above steps until all actions in the action sequence are traversed.
  • the second order here may be the initial state of the action comparison, for example, the first step or the last step.
  • the first step in the case that two actions (ie, the second target action and the second reference action) are obtained for the first step, directly compare whether the two actions match. Regardless of whether it matches or not, here you can select the next two actions for comparison in positive order, and so on, until the last action is traversed.
  • contextual comparison can be used to determine the sequence of actions to be executed, and to check sequentially, that is, to check in a forward or reverse order, that is, each action is related and not completely independent; for example, the first item in the template library is an action A, the next item adjacent to action A, that is, the second item is action B, then the adjacent relationship needs to be considered when detecting, for example, the first item actually detected is action C, and the action is executed after action C D, then in the case that action C is the same as action A, compare action D after action C with action B after action A to determine whether the order is wrong.
  • the method for generating traceable production data provided by the embodiments of the present disclosure can also send the first result including the abnormal action in the execution action sequence to the relevant target equipment, as well as the problem including the abnormal action
  • the second result of type thus, the product producer can use the target device to view the above results.
  • the target device may output one or more of the above results, and the specific output form may be display, broadcast, or other forms.
  • the generation method provided by the embodiment of the present disclosure can also obtain the equipment identification (IDentity, ID) of the production equipment corresponding to the operation area, and then use the target equipment to combine the equipment identification to output the above results.
  • equipment identification IDentity, ID
  • a production error alarm event can be generated, which includes the wrong action problem type (wrongly done, missed, over done, wrong sequence), action Importance level and corresponding device ID.
  • the target device in the embodiment of the present disclosure can be a user terminal, a front-end display interface (large screen), an audible and visual alarm, etc., so that product managers can find production problems in time, and it can also be an industrial system interface, such as data collection and monitoring Control (Supervisory Control And Data Acquisition, SCADA) system or Programmable Logic Controller (Programmable Logic Controller, PLC) to perform linkage operations based on adverse events, etc., can also be other associated devices, and will not be described here.
  • SCADA Supervisory Control And Data Acquisition
  • PLC Programmable Logic Controller
  • the writing order of each step does not mean a strict execution order and constitutes any limitation on the implementation process.
  • the specific execution order of each step should be based on its function and possible
  • the inner logic is OK.
  • the embodiments of the present disclosure also provide a traceable production data generation device corresponding to the generation method of the traceable production data. Since the problem-solving principle of the device in the embodiments of the present disclosure is the same as that described above in the embodiments of the present disclosure, The generation method of traceable production data is similar, so the implementation of the device can refer to the implementation of the method, and the repetition will not be repeated.
  • FIG. 2 it is a schematic diagram of a generation device for traceable production data provided by an embodiment of the present disclosure.
  • the device includes: an acquisition module 201, a determination module 202, and a generation module 203; wherein,
  • the obtaining module 201 is configured to obtain video data corresponding to the work area collected by the camera equipment;
  • the determination module 202 is configured to obtain abnormal production data based on the video data, and the abnormal production data is used to reflect the abnormal actions performed by the target object in the operation area for the target product;
  • the generation module 203 is configured to correlate the quality inspection results and abnormal production data of the target product to obtain traceable production data for product quality traceability.
  • the abnormal production data made by the target object in the operation area for the target product can be determined based on the acquired video data.
  • the abnormal production data here can include abnormal actions and abnormal actions.
  • the traceable production data of product quality traceability can be determined. It can be seen that the present disclosure can determine abnormal production data through video analysis, and based on this abnormal production data, product quality can be traced. Especially in the case of product defects, targeted solutions can be provided through abnormal production data to improve Rate of qualified products.
  • the determining module 202 is configured to obtain abnormal production data based on video data according to the following steps:
  • the determination module 202 is configured to obtain the abnormal action in the execution action sequence and the problem type of the abnormal action by comparing the execution action sequence with the reference action sequence according to the following steps:
  • Determining that the abnormal action includes at least one of the actions added in the action sequence, the missing action, and the action in the same order and the similarity of the action content is less than the threshold compared with the reference action sequence.
  • the problem type of the abnormal action includes action abnormal.
  • the determination module 202 is configured to obtain the abnormal action in the execution action sequence and the problem type of the abnormal action by comparing the execution action sequence with the reference action sequence according to the following steps:
  • Determining the abnormal actions includes actions in the wrong execution sequence in the execution action sequence compared with the reference action sequence, and the problem type of the abnormal actions includes step exceptions.
  • the determining module 202 is configured to perform the actions in the action sequence that are performed in the wrong order compared with the reference action sequence according to the following steps:
  • the first target action does not match the first reference action, it is determined that the first target action is an incorrectly executed action in the execution action sequence.
  • the determination module 202 is configured to perform the actions in the action sequence that are performed in the wrong order compared with the reference action sequence according to the following steps:
  • the second order is the first step in the action sequence
  • the above-mentioned device also includes:
  • the sending module 204 is configured to send the first result including the abnormal action in the execution action sequence to the target device after obtaining the abnormal action in the execution action sequence and the problem type of the abnormal action by comparing the execution action sequence with the reference action sequence, and a second result of the question type containing abnormal actions, causing the target device to output at least one of the first result and the second result.
  • the sending module 204 is configured to send to the target device the first result containing the abnormal action in the action sequence and the second result containing the question type of the abnormal action to the target device according to the following steps:
  • the target device Send to the target device the first result containing each abnormal action in the action sequence, the second result including the problem type of each abnormal action, and the importance level of each abnormal action, so that the target device will act on each abnormal action according to the importance level and the problem type of each abnormal action to display and/or broadcast.
  • the sending module 204 is configured to send to the target device the first result containing the abnormal action in the action sequence and the second result containing the question type of the abnormal action to the target device according to the following steps:
  • the above-mentioned device also includes:
  • the response module is configured to, in response to receiving the product quality traceability request, obtain traceable production data associated with the target product indicated by the product quality traceability request; Identify target anomalous production data that matches target detection results.
  • FIG. 3 is a schematic structural diagram of an electronic device provided by an embodiment of the present disclosure, including: a processor 301 , a memory 302 , and a bus 303 .
  • the memory 302 stores machine-readable instructions executable by the processor 301 (for example, execution instructions corresponding to the acquisition module 201, the determination module 202, and the generation module 203 in the device in FIG. 2 ), and when the electronic device is running, the processor 301 Communicate with the memory 302 through the bus 303, and when the machine-readable instructions are executed by the processor 301, the following processing is performed:
  • the abnormal production data is obtained based on the video data, and the abnormal production data is used to reflect the abnormal actions performed by the target object in the operation area for the target product;
  • An embodiment of the present disclosure also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is run by a processor, the method for generating traceable production data described in the above-mentioned method embodiments is executed.
  • the storage medium may be a volatile or non-volatile computer-readable storage medium.
  • An embodiment of the present disclosure also provides a computer program product, the computer program product carries a program code, and the instructions included in the program code can be used to execute the method for generating traceable production data described in the method embodiment above.
  • the computer program product carries a program code
  • the instructions included in the program code can be used to execute the method for generating traceable production data described in the method embodiment above.
  • the above-mentioned computer program product may be specifically implemented by means of hardware, software or a combination thereof.
  • the computer program product is embodied as a computer storage medium, and in another optional embodiment, the computer program product is embodied as a software product, such as a software development kit (Software Development Kit, SDK) etc. Wait.
  • the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.
  • each functional unit in each embodiment of the present disclosure may be integrated into one processing unit, each unit may exist separately physically, or two or more units may be integrated into one unit.
  • the functions are realized in the form of software function units and sold or used as independent products, they can be stored in a non-volatile computer-readable storage medium executable by a processor.
  • the technical solution of the present disclosure is essentially or the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including Several instructions are used to make an electronic device (which may be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the methods described in various embodiments of the present disclosure.
  • the aforementioned storage media include: U disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), magnetic disk or optical disc and other media that can store program codes. .
  • the present disclosure relates to a method, device, electronic device, computer readable storage medium and computer program product for generating traceable production data.
  • the method includes: acquiring the video data corresponding to the work area collected by the camera equipment; based on the video data Obtaining abnormal production data, the abnormal production data is used to reflect the abnormal actions performed by the target object in the operation area for the target product; associating the quality inspection results of the target product with the abnormal production data to obtain the Traceable production data for quality traceability. Therefore, the present disclosure can determine abnormal production data through video analysis, and based on this abnormal production data, product quality can be traced. Especially in the case of product defects, targeted solutions can be provided through abnormal production data, so as to Improve product qualification rate.

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Abstract

La présente divulgation concerne un procédé et un appareil de génération de données de production traçables, et un dispositif électronique, un support de stockage lisible par ordinateur et un produit programme d'ordinateur. Le procédé consiste : à acquérir des données vidéo, qui correspondent à une zone de fonctionnement et sont collectées par un dispositif de caméra ; à obtenir des données de production anormales sur la base des données vidéo, les données de production anormales étant utilisées pour refléter une action anormale, qui est exécutée par un objet cible dans la zone de fonctionnement pour un produit cible ; et à associer un résultat de détection de qualité du produit cible aux données de production anormales, de façon à obtenir des données de production traçables destinées au traçage de qualité de produit. Au moyen de la présente divulgation, des données de production anormales peuvent être déterminées au moyen d'une analyse vidéo, un traçage de qualité de produit peut être réalisé sur la base des données de production anormales, et en particulier, une solution ciblée peut être fournie au moyen des données de production anormales lorsqu'un produit présente un défaut, de telle sorte que le rapport de qualification de produit est amélioré.
PCT/CN2022/092410 2021-07-06 2022-05-12 Procédé et appareil de génération de données de production traçables, et dispositif, support et programme WO2023279846A1 (fr)

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