Industrial production supervision system, supervision method, computer equipment and medium
By integrating data collection, processing and analysis, monitoring and adjustment modules in the industrial production supervision system, and using the identification model to identify and control defects, the problem that traditional systems are difficult to monitor complex production processes is solved, and efficient production management and refined adjustments are achieved .
Patent Information
- Application Number
- CN202411365001.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-09-27
- Publication Date
- 2024-12-10
AI Technical Summary
Traditional monitoring and management systems are difficult to effectively monitor task batch methods, alarm needs and personnel work arrangements in the industrial production process, and cannot meet the complex needs of modern industrial production.
An industrial production supervision system was designed, including a data collection module, a data processing analysis module, a monitoring module and an adjustment module. The trained identification model is used for defect identification and regulation, and the identification model is trained and updated through the data reflow module to realize Intelligent monitoring and refined management.
It improves production efficiency and the accuracy of monitoring abnormalities, realizes clear production status display and timely adjustment of production plan, and improves the overall efficiency of process production.
Smart Images
Figure CN119105434A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of display technology, and more specifically, to an industrial production supervision system, a supervision method, a computer device and a medium. Background Art
[0002] With the continuous development of industrial production, the number of process steps is increasing, and the required production equipment is becoming more and more complex. Therefore, the demand for progress supervision in the production process is gradually increasing, and the traditional monitoring and management system can no longer meet the needs of modern industrial production. Although the existing monitoring and management system has tried to use various sensors and monitoring equipment to collect data in the industrial production process, there are still many challenges in terms of how to effectively monitor the different stations along the path of each task batch, whether there are alarms for tasks that require manual intervention, and personnel work arrangements. Summary of the invention
[0003] The object of the present invention is to provide an industrial production supervision system, a supervision method, a computer device and a medium to solve at least one of the problems existing in the prior art.
[0004] In order to achieve the above object, the present invention adopts the following technical solutions:
[0005] A first aspect of the present invention provides an industrial production supervision system, comprising:
[0006] The data collection module is used to obtain production data related to processing and production from various production equipment in the process production line;
[0007] A data processing and analysis module is used to process the production data and identify defects in the processed production data using a trained recognition model, obtain the identified production data and output it to the monitoring module;
[0008] A monitoring module, used to generate visualized production data according to the identified production data;
[0009] An adjustment module, used for receiving adjustment instructions for regulating processing production based on the visualized production data, and sending the adjustment instructions to relevant production equipment;
[0010] The data reflow module is used to take the identified production data, the visualized production data, and the adjustment data corresponding to the adjustment instruction as input, so that the data processing and analysis module can train the recognition model.
[0011] In an optional embodiment, the data collection module is used to obtain one or more of the equipment data of each production equipment, the production data of the process production line, the data of the personnel performing the production, and the defect data of the process production line. The defect data of the process production line includes one or more of defective equipment, defective sites, defective product information and defective pictures that have production problems in the process production line.
[0012] In an optional embodiment, the data processing and analysis module includes a data preprocessing module, which is used to perform one or more of data cleaning, format conversion and data association on the identified production data.
[0013] In an optional embodiment, the data processing and analysis module includes an AI image recognition module, which is used to identify and analyze the preprocessed production data transmitted by the preprocessing module according to a trained recognition model to obtain abnormal data and potential risk information and output them.
[0014] In an optional embodiment, the data processing and analysis module also includes a re-judgment module, which is used to display the abnormal data and potential risk information determined by the AI image judgment module to the judgment personnel and have the judgment personnel perform data judgment, and generate judgment data output based on the judgment results.
[0015] In an optional embodiment, the data return module is used to obtain abnormal data and potential risk information identified by the AI image judgment module, and / or obtain judgment data transmitted by the re-judgment module.
[0016] The data processing and analysis module also includes a model iteration module, which is used to use the abnormal data and potential risk information transmitted by the data reflow module, and / or the judgment data as input to iteratively train and update the recognition model.
[0017] In an optional embodiment, the model iteration module is further used to use a preset test question bank to determine the accuracy of the recognition model before iteration and the recognition model after iteration, and use the recognition model with high accuracy to perform defect recognition on production data.
[0018] In an optional embodiment, the monitoring module is further used to monitor the production data of the process line during the production process, and / or monitor the task result information of the process line after the production is completed, and present it to the user in the form of a graphical interface.
[0019] In an optional embodiment, the adjustment module is further used to output the adjustment instructions to one or more of the processing production equipment in production corresponding to the production data, the personnel production equipment used by the personnel performing production, the monitoring production equipment to which the monitoring module belongs, and the defective equipment with production problems and the production equipment used by the defective site.
[0020] In an optional embodiment, the data reflux module is also used to obtain abnormal data and potential risk information identified by the AI image judgment module, and one or more of the judgment data transmitted by the re-judgment module, and output them to the data collection module for data synchronization and recording.
[0021] In an optional embodiment, one or more of the AI image judgment module, the re-judgment module, and the monitoring module are further configured to output alarm information.
[0022] The data backflow module is also used to obtain the alarm information and output the alarm information to the data collection module and / or the model iteration module.
[0023] In an optional embodiment, the production supervision system further includes a staff training module for generating training content based on the production data stored in the data collection module to train personnel who perform production.
[0024] A second aspect of the present invention provides an industrial production supervision method applied to a process production line, the method comprising:
[0025] Obtain production data related to processing and production from each production equipment in the process production line;
[0026] Processing the production data, and using the trained recognition model to perform defect recognition on the processed production data, obtaining the recognized production data and outputting it to the monitoring module;
[0027] generating visualized production data according to the identified production data;
[0028] Receiving adjustment instructions for regulating processing production based on the visualized production data, and sending the adjustment instructions to relevant production equipment;
[0029] The identified production data, the visualized production data, and the adjustment data corresponding to the adjustment instruction are used as input to train a recognition model.
[0030] A third aspect of the present invention provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method described in the second aspect of the present invention when executing the program.
[0031] A fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in the second aspect of the present invention.
[0032] The beneficial effects of the present invention are as follows:
[0033] The industrial production supervision system of the embodiment of the present invention utilizes the data processing and analysis module to perform intelligent defect identification on the production data obtained by the data collection module, and utilizes the monitoring module to display the visualized production data to the user, thereby providing clear production status and production problems. The adjustment module timely adjusts the production plan and personnel allocation according to the feedback from the monitoring module, realizes refined production management, and improves production efficiency. The data reflux module refluxes the data output by each module in the data processing and analysis module, the monitoring module, and the adjustment module to the data processing and analysis module, and trains and updates the recognition model, thereby improving the accuracy of the recognition model, improving the production efficiency of the process production and the accuracy of monitoring to detect abnormalities. BRIEF DESCRIPTION OF THE DRAWINGS
[0034] The specific implementation modes of the present invention are further described in detail below in conjunction with the accompanying drawings.
[0035] Figure 1 A schematic diagram showing the architecture of an industrial production monitoring system according to an embodiment of the present invention;
[0036] Figure 2 A schematic diagram showing the architecture of a data processing and analysis module according to an optional embodiment of the present invention;
[0037] Figure 3 A schematic diagram showing the architecture of an industrial production monitoring system according to an optional embodiment of the present invention;
[0038] Figure 4 A schematic diagram showing the architecture of an industrial production monitoring system according to an optional embodiment of the present invention;
[0039] Figure 5 A schematic diagram showing the architecture of an industrial production monitoring system according to an optional embodiment of the present invention;
[0040] Figure 6 A schematic diagram showing the architecture of an industrial production monitoring system according to an optional embodiment of the present invention;
[0041] Figure 7 A schematic diagram showing an interface of a test question bank according to an embodiment of the present invention;
[0042] Figure 8 A test interface view of an embodiment of the present invention is shown;
[0043] Fig. 9A schematic diagram showing the framework of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION
[0044] In order to explain the present invention more clearly, the present invention is further described below in conjunction with embodiments and drawings. Similar components in the drawings are represented by the same reference numerals. It should be understood by those skilled in the art that the content specifically described below is illustrative rather than restrictive, and the protection scope of the present invention should not be limited thereto.
[0045] The embodiment of the present invention provides an industrial production supervision system, a supervision method, a computer device and a medium to improve the supervision efficiency of industrial production and the timeliness of maintenance equipment.
[0046] like Figure 1 As shown, the first embodiment of the present invention is an industrial production supervision system, and the industrial production supervision system includes:
[0047] A data processing and analysis module is used to process the production data and identify defects in the processed production data using a trained recognition model, obtain the identified production data and output it to the monitoring module;
[0048] A monitoring module, used to generate visualized production data according to the identified production data;
[0049] An adjustment module, used for receiving adjustment instructions for regulating processing production based on the visualized production data, and sending the adjustment instructions to relevant production equipment;
[0050] The data reflow module is used to take the identified production data, the visualized production data, and the adjustment data corresponding to the adjustment instruction as input, so that the data processing and analysis module can train the recognition model.
[0051] like Figure 1 The industrial production supervision system of the embodiment of the present invention shown uses a data processing and analysis module to perform intelligent defect identification on the production data obtained by the data collection module, and uses a monitoring module to display the visualized production data, thereby providing a clear production status and production problems. The adjustment module timely adjusts the production plan and personnel allocation according to the feedback from the monitoring module, realizes refined production management, and improves production efficiency. The data reflux module refluxes the data output by each module in the data processing and analysis module, the monitoring module, and the adjustment module to the data processing and analysis module, and iteratively trains the recognition model, thereby improving the accuracy of the recognition model, improving the production efficiency of the process production and the accuracy of monitoring to detect abnormalities.
[0052] Based on the above embodiments, the embodiments of the present invention exemplarily illustrate the production supervision system.
[0053] The production equipment in this embodiment not only refers to the equipment directly involved in production and processing, but also refers to the equipment used by production personnel in the production line, such as personnel production equipment used by personnel involved in material feeding, monitoring production equipment belonging to the monitoring module, and one or more of the site production equipment where a certain production site is located. These devices are directly or indirectly used in process production to provide the required production data for process production.
[0054] The site in this embodiment refers to a process point that provides workers with resources such as tools, equipment, materials, etc. and provides operation guidance and monitoring for a specific process or procedure.
[0055] In an optional embodiment, the data collection module is used to obtain one or more of the equipment data of each production equipment, the production data of the process production line, the data of the personnel performing the production, and the defect data of the process production line. The defect data of the process production line includes one or more of defective equipment, defective sites, defective product information and defective pictures that have production problems in the process production line.
[0056] In this embodiment, the equipment data of the production equipment includes the equipment model, producible process parameters, equipment number, operating status, etc., which are required for the current process production line production, so as to facilitate determining the location and status of each production equipment during the production process and to facilitate determining problems in the production process.
[0057] The production data of the process production line described in this embodiment includes the process production mode, personnel arrangement, processing speed, and arrangement design of each site of different products. For example, the same production line can process different products after different personnel configurations, site arrangements, and raw material designs. Therefore, each process production line may have different production arrangements in different time periods. In order to improve the production efficiency of production and processing, the production data of the process production line is also collected by the data collection module.
[0058] The personnel data for executing production in this embodiment includes one or more of the number of personnel, personnel position number, and personnel on-site status, as well as personnel data in other situations. The defective equipment in this embodiment includes, for example, equipment that has abnormal operation and needs to be repaired, or equipment that is temporarily unable to process due to lack of raw materials in the production process. The defective site in this embodiment refers to a site where problems occur during the production process that affect processing. The defective product information in this embodiment includes the quantity, model, defect content, etc. of the defective products. The defective pictures in this embodiment include physical pictures of the products at the defective locations, enlarged pictures, thumbnails, etc.
[0059] The production data described above in this embodiment is not limited to the above examples, and all data required for production and processing are the production data described in this embodiment, which will not be repeated here.
[0060] In an optional embodiment, if Figure 2 As shown, the data processing and analysis module includes a data preprocessing module, which is used to perform one or more of data cleaning, format conversion and data association on the identified production data.
[0061] In this embodiment, the data cleaning step is the process of cleaning and processing the production data collected by the data collection module, which is used to remove invalid and duplicate data and solve the problem of missing or erroneous data. Common data cleaning operations include removing duplicate records, filling missing values, processing outliers and noise, etc. The goal of data cleaning is to ensure the consistency and accuracy of the data.
[0062] In this embodiment, format conversion is to convert data of different formats into a unified format using methods including data parsing, extraction and conversion. In the data collection process, different data formats and structures may be involved, such as text files, databases, sensor data, etc. In the format conversion stage, data of different formats are converted into a unified format for subsequent processing, which may include data parsing, extraction and conversion, such as converting text data into structured data, converting timestamps into standard time formats, etc.
[0063] In this embodiment, data association matches and connects data in different data sets according to specific association rules or key fields, associates production progress with actual progress data, associates material supply information with consumption data, etc., and connects related data items so that they can be comprehensively analyzed and processed.
[0064] The specific methods and tools for integrating and processing data depend on the actual needs of the system and the technical choices. Programming languages and related data processing libraries, database management systems, ETL (Extract, Transform, Load) tools, etc. can be used to implement data cleaning, format conversion, and data association.
[0065] In an optional embodiment, if Figure 2 As shown, the data processing and analysis module includes an AI image judgment module, which is used to identify and analyze the preprocessed production data transmitted by the preprocessing module according to the trained recognition model to obtain abnormal data and potential risk information and output them.
[0066] Exemplarily, the recognition model of this embodiment can be trained using a variety of network models such as machine learning models, deep learning models, and neural network models, and is applicable. In addition, the use of an AI image judgment module for intelligent image judgment can improve the accuracy of abnormality judgment.
[0067] In an optional embodiment, if Figure 2As shown, the data processing and analysis module also includes a re-judgment module, which is used to display the abnormal data and potential risk information determined by the AI image judgment module to the judge, and the judge makes a data judgment by the judge, and generates a judgment data output according to the judgment result. Exemplarily, after the AI image judgment module completes the intelligent recognition, it is manually re-judged. When the AI image judgment module makes an incorrect judgment, the production data with the incorrect judgment is marked and corresponding operations are performed to further ensure the accuracy of the judgment.
[0068] In an optional embodiment, if Figure 3 As shown, the data reflux module is used to obtain abnormal data and potential risk information identified by the AI image judgment module, and / or obtain the judgment data transmitted by the re-judgment module. That is to say, the data reflux module has multiple data input terminals, which can not only obtain the abnormal data and potential risk information identified by the AI image judgment module, but also obtain the judgment data determined by the re-judgment module, thereby improving the diversity of data. Moreover, since the judgment data is manually re-judged, the data reflux module uses the obtained judgment data and abnormal data with a higher accuracy.
[0069] In an optional embodiment, if Figure 3 As shown, the data processing and analysis module also includes a model iteration module, which is used to iteratively train and update the recognition model using the abnormal data and potential risk information transmitted by the data reflow module, and / or the judgment data as input.
[0070] In other words, the input of the model iteration module can come from both the abnormal data determined by the AI image judgment module and the judgment data of the re-judgment module. These data are input into the model iteration module to re-learn and train the recognition model.
[0071] In a specific example, when the AI image judgment module performs automatic recognition, there is a possibility that the automatic recognition result will be wrong. Therefore, this embodiment outputs the recognition result to the re-judgment module for manual re-judgment. At this time, the executor can use the re-judgment module to re-mark its defect location and defect category information, and after generating the judgment data, output the judgment data to the monitoring module and the data reflow module. The data reflow module reflows these judgment data to the model iteration module to update the recognition model.
[0072] In another example, when the AI image recognition module directly determines that the data is abnormal after automatic recognition, the abnormal data is transmitted to the data reflux module, and the data reflux module inputs the abnormal data into the model iteration module to update the recognition model.
[0073] Therefore, each time the production supervision system of this embodiment receives a new production task, based on the production data generated by each production task, the production data of each production task is iterated using the data reflux module and the model iteration module, which will improve the supervision and identification capabilities of the entire supervision system, and ultimately reduce the probability of risks, prevent risks in advance, and improve production efficiency.
[0074] In an optional example, the model iteration module is also used to use a preset test question bank to determine the accuracy of the recognition model before iteration and the recognition model after iteration, and use the recognition model with high accuracy to identify defects in production data, thereby improving the accuracy of defect identification and the timeliness of monitoring anomalies.
[0075] In an optional embodiment, if Figure 4 As shown, the data reflux module is also used to obtain abnormal data and potential risk information identified by the AI image judgment module, and one or more of the judgment data transmitted by the re-judgment module, and output them to the data collection module for data synchronization and recording.
[0076] On the basis of the data reflow module and the model iteration module forming a first data reflow path, this embodiment further sets a second data reflow path between the data reflow module and the data collection module for data synchronization. That is to say, the data collection module not only collects the production data of the processing equipment, but is also used to synchronize and record the abnormal data and judgment data output by the AI image judgment module and the re-judgment module and transmitted by the data reflow module, so as to realize data recording for production management using the production supervision system, which is convenient for data storage and call.
[0077] In an optional embodiment, the monitoring module is further used to monitor the production data of the process line during the production process, and / or monitor the task result information of the process line after the production is completed, and present it to the user in the form of a graphical interface.
[0078] The monitoring module of this embodiment uses a graphical interface to display the operating status of the processing equipment, the dynamic information of the executors, the defect information of the production task batches, etc. in real time based on the results output by the data processing and analysis module. It monitors the personnel, equipment and production tasks in the entire processing process in real time, so that the operators can clearly understand the status and problems of each task batch, so as to better adjust the production plan and personnel allocation.
[0079] The monitoring module in this embodiment can monitor the production process and monitor the completion of tasks after the production is completed, throughout the entire production process, to facilitate timely dynamic adjustments during production and abnormal analysis after production is completed.
[0080] In this embodiment, the production data of the process production line during the production process includes equipment directly involved in the production and processing equipment and equipment indirectly involved in the production, such as personnel production equipment used by personnel involved in material feeding and site production equipment at a certain production site. The monitoring module monitors the working status of different production equipment in the production process and presents it to engineers and managers, realizing real-time monitoring and dynamic adjustment of the production process.
[0081] In this embodiment, the task result information of the process production line after the production is completed includes the task result information given by the AI image judgment module, the re-judgment module, and the adjustment module that may cause some equipment or sites to require alarms or additional manual intervention, and also includes the task result information after the normal completion of the process production given by the AI image judgment module, the re-judgment module, and the adjustment module. The monitoring module presents it to the user more intuitively with a visual interface, which is convenient for the allocation and supervision of personnel work, so that the user can clearly understand the status and existing problems of each task batch, so as to better adjust the production plan and personnel allocation.
[0082] In an optional embodiment, the adjustment module is further used to output the adjustment instructions to one or more of the processing production equipment in production corresponding to the production data, the personnel production equipment used by the personnel performing production, the monitoring production equipment to which the monitoring module belongs, and the defective equipment with production problems and the production equipment used by the defective site.
[0083] If there are problems with production equipment, the equipment with problems can be adjusted and optimized, such as repairing or replacing faulty equipment, suspending faulty equipment, etc. If personnel adjustments are required during production, managers can find out whether there are problems in personnel allocation based on the real-time visual interface displayed by the monitoring module, and use the adjustment module to configure the personnel operating the processing equipment. For example, if all employees are very busy but there is still a need for manual intervention, or if some problems are not promptly arranged for manual processing, etc., the production plan and personnel allocation can be dynamically adjusted to achieve refined production management.
[0084] In an optional embodiment, one or more of the AI image judgment module, the re-judgment module, and the monitoring module are also used to output alarm information to promptly alarm abnormalities in the production process and notify corresponding personnel to perform relevant maintenance operations, such as repairing or replacing faulty equipment, adjusting personnel configuration, etc., to avoid more serious accidents and ensure the safety of the production and processing process.
[0085] In an optional embodiment, if Figure 5As shown, the data reflow module is also used to obtain the alarm information and output the alarm information to the data collection module and / or the model iteration module. Exemplarily, when the alarm information is output to the data collection module through the data reflow module, it is used for synchronization of data transmission of each module, which is convenient for calling and recording production data. When the alarm information is output to the model iteration module through the data reflow module, the alarm information is used as the input of the model iteration module to learn and train the recognition model, thereby improving the accuracy of the recognition model in identifying abnormalities and defects.
[0086] In an optional embodiment, if Figure 6 As shown, the production supervision system also includes a staff training module, which is used to generate training content based on the production data stored in the data collection module to train the personnel who perform production.
[0087] For example, the training content may be a test question bank generated based on production data, such as Figure 7 As shown in FIG, the training module includes information such as the question bank number, the tester, the number of test questions, and the employee's test accuracy rate for the test question bank. Figure 8 As shown, the test interface displays various types of defect images, such as thumbnails, real images, enlarged images, simulation images, parameter areas, and judgment areas, etc. The user judges the defect images in the judgment area to conduct the test. Based on the design of the above employee training module, it can help the employee to better conduct growth training and monitoring.
[0088] A second embodiment of the present invention provides an industrial production supervision method applied to a process production line, the method comprising:
[0089] Obtain production data related to processing and production from each production equipment in the process production line;
[0090] Processing the production data, and using the trained recognition model to perform defect recognition on the processed production data, obtaining the recognized production data and outputting it to the monitoring module;
[0091] generating visualized production data according to the identified production data;
[0092] receiving adjustment instructions for regulating processing production based on the visualized production data, and sending the adjustment instructions to relevant production equipment;
[0093] The identified production data, the visualized production data, and the adjustment data corresponding to the adjustment instruction are used as input to train a recognition model.
[0094] The present embodiment utilizes a trained recognition model to intelligently identify defects in the acquired production data, and displays the visualized production data to the user, thereby providing a clear production status and production defects. According to the feedback of the visualized data, the production plan and staffing are adjusted in a timely manner to achieve refined production management and improve production efficiency. In addition, the supervision method of the present embodiment recirculates the production data of each step, and utilizes the recirculated production data to train and update the recognition model, thereby improving the accuracy of the recognition model, improving the production efficiency of the process production and the accuracy of monitoring to detect abnormalities.
[0095] Another embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements: obtaining production data related to processing production from each production equipment of a process production line; processing the production data, and using a trained recognition model to perform defect identification on the processed production data to obtain identified production data and output it to a monitoring module; generating visualized production data based on the identified production data; receiving adjustment instructions for regulating processing production based on the visualized production data, and sending the adjustment instructions to related production equipment; using the identified production data, the visualized production data, and the adjustment data corresponding to the adjustment instructions as input to train the recognition model.
[0096] In practical applications, the computer-readable storage medium can adopt any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present embodiment, the computer-readable storage medium can be any tangible medium containing or storing a program, which can be used by an instruction execution system, device or device or used in combination with it.
[0097] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program codes. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, device, or device. The program code contained on the computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, wire, optical cable, RF, etc., or any suitable combination of the above.
[0098] Computer program code for performing the operations of the present invention may be written in one or more programming languages or a combination thereof, including object-oriented programming languages such as Java, Smalltalk, C++, and conventional procedural programming languages such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).
[0099] like Fig. 9 As shown, a structural schematic diagram of a computer device provided by another embodiment of the present invention. Fig. 9 The computer device 12 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.
[0100] like Fig. 9 As shown, the computer device 12 is in the form of a general-purpose computing device. The components of the computer device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that connects various system components (including the system memory 28 and the processing unit 16).
[0101] Bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor or a local bus using any of a variety of bus architectures. By way of example, these architectures include, but are not limited to, an Industry Standard Architecture (ISA) bus, a Micro Channel Architecture (MAC) bus, an Enhanced ISA bus, a Video Electronics Standards Association (VESA) local bus, and a Peripheral Component Interconnect (PCI) bus.
[0102] The computer device 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the computer device 12, including volatile and non-volatile media, removable and non-removable media.
[0103] The system memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. The computer device 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the storage system 34 may be used to read and write non-removable, non-volatile magnetic media ( Fig. 9 not shown, usually called a "hard drive"). Although Fig. 9 Not shown in the figure, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk"), and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, a DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to the bus 18 via one or more data medium interfaces. The memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to perform the functions of the various embodiments of the present invention.
[0104] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in the memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment. The program modules 42 generally perform the functions and / or methods of the embodiments described herein.
[0105] The computer device 12 may also communicate with one or more external devices 14 (e.g., keyboards, pointing devices, displays 24, etc.), one or more devices that enable a user to interact with the computer device 12, and / or any device that enables the computer device 12 to communicate with one or more other computing devices (e.g., network cards, modems, etc.). Such communication may be performed through an input / output (I / O) interface 22. Furthermore, the computer device 12 may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) through a network adapter 20. Fig. 9 As shown, the network adapter 20 communicates with other modules of the computer device 12 via the bus 18. It should be understood that although Fig. 9 Not shown, other hardware and / or software modules may be used in conjunction with computer device 12, including but not limited to microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.
[0106] The processor unit 16 executes various functional applications and data processing by running programs stored in the system memory 28, such as implementing an industrial production supervision method applied to a process production line provided by an embodiment of the present invention.
[0107] It is worth noting that the specific implementation of the industrial production supervision method of the embodiment of the present invention can refer to the industrial production supervision system of the aforementioned embodiment, which will not be repeated here.
[0108] In the description of the present invention, relational terms such as first and second, etc. are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "comprise", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the statement "comprises a ..." does not exclude the presence of other identical elements in the process, method, article or device including the element.
[0109] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not limitations on the embodiments of the present invention. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is impossible to list all the embodiments here. All obvious changes or modifications derived from the technical solution of the present invention are still within the protection scope of the present invention.
Claims
1. An industrial production supervision system, characterized in that: The industrial production supervision system comprises: The data collection module is used to obtain production data related to processing and production from various production equipment in the process production line; A data processing and analysis module is used to process the production data and identify defects in the processed production data using a trained recognition model, obtain the identified production data and output it to the monitoring module; A monitoring module, used to generate visualized production data according to the identified production data; An adjustment module, used for receiving adjustment instructions for regulating processing production based on the visualized production data, and sending the adjustment instructions to relevant production equipment; The data reflow module is used to take the identified production data, the visualized production data, and the adjustment data corresponding to the adjustment instruction as input, so that the data processing and analysis module can train the recognition model.
2. The production supervision system according to claim 1, characterized in that: The data collection module is used to obtain one or more of the equipment data of each production equipment, the production data of the process production line, the personnel data of the production execution, and the defect data of the process production line. The defect data of the process production line includes one or more of defective equipment, defective sites, defective product information and defective pictures that have production problems in the process production line.
3. The production supervision system according to claim 2, characterized in that: The data processing and analysis module includes a data preprocessing module, which is used to perform one or more of data cleaning, format conversion and data association on the identified production data.
4. The production supervision system according to claim 3, characterized in that: The data processing and analysis module includes an AI image recognition module, which is used to identify and analyze the preprocessed production data transmitted by the preprocessing module according to the trained recognition model to obtain abnormal data and potential risk information and output them.
5. The production supervision system according to claim 4, characterized in that: The data processing and analysis module also includes a re-judgment module, which is used to display the abnormal data and potential risk information determined by the AI image judgment module to the judge, and the judge makes a data judgment by the judge, and generates a judgment data output according to the judgment result.
6. The production supervision system according to any one of claims 4 or 5, characterized in that: The data return module is used to obtain abnormal data and potential risk information identified by the AI image judgment module, and / or obtain judgment data transmitted by the re-judgment module. The data processing and analysis module also includes a model iteration module, which is used to use the abnormal data and potential risk information transmitted by the data reflow module, and / or the judgment data as input to iteratively train and update the recognition model.
7. The production supervision system according to claim 6, characterized in that: The model iteration module is also used to use a preset test question bank to determine the accuracy of the recognition model before iteration and the recognition model after iteration, and use the recognition model with high accuracy to perform defect recognition on production data.
8. The production supervision system according to claim 2, characterized in that: The monitoring module is further used to monitor the production data of the process line during the production process, and / or monitor the task result information of the process line after the production is completed, and present it to the user in the form of a graphical interface.
9. The production supervision system according to claim 2, characterized in that: The adjustment module is further used to output the adjustment instructions to one or more of the processing production equipment in production corresponding to the production data, the personnel production equipment used by the personnel performing production, the monitoring production equipment to which the monitoring module belongs, and the defective equipment with production problems and the production equipment used by the defective site.
10. The production supervision system according to claim 6, characterized in that: The data reflux module is also used to obtain one or more of the abnormal data and potential risk information identified by the AI image judgment module and the judgment data transmitted by the re-judgment module, and output them to the data collection module for data synchronization and recording.
11. The production supervision system according to claim 6, characterized in that: One or more of the AI image judgment module, the re-judgment module, and the monitoring module are also used to output alarm information. The data backflow module is also used to obtain the alarm information and output the alarm information to the data collection module and / or the model iteration module.
12. An industrial production supervision method applied to the production supervision system described in any one of claims 1 to 11, characterized in that: The method comprises: Obtain production data related to processing and production from each production equipment in the process production line; Processing the production data, and using the trained recognition model to perform defect recognition on the processed production data, obtaining the recognized production data and outputting it to the monitoring module; generating visualized production data according to the identified production data; receiving adjustment instructions for regulating processing production based on the visualized production data, and sending the adjustment instructions to relevant production equipment; The identified production data, the visualized production data, and the adjustment data corresponding to the adjustment instruction are used as input to train a recognition model.
13. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the method according to claim 12 is implemented.
14. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to claim 12 is implemented.