Data monitoring method and device for digital workshop, storage medium and electronic equipment

Abstract

The invention relates to a data monitoring method and device for a digital workshop, a storage medium and electronic equipment, and relates to the technical field of data processing.The method comprises the steps that if all first real-time operation data are normal, the first real-time operation data are monitored on the basis of target processing equipment which is prone to abnormal operation in all the processing equipment and corresponding target abnormal time periods; determining at least one key processing device which is easy to run abnormally after the current time; based on each second real-time operation data, determining an operation data change curve of the corresponding key processing equipment; and fitting the operation data change curve with a corresponding operation data standard curve to obtain a fitting rate, and if the fitting rate is lower than a preset fitting rate threshold, determining the corresponding key processing equipment as concerned processing equipment, and carrying out top processing on a visual data set of the concerned processing equipment in a terminal of a production manager. The method has the effect of improving the timeliness of finding the abnormal operation of the equipment.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a data monitoring method, device, storage medium and electronic equipment for a digital workshop. Background Art

[0002] Digital workshop refers to the data integration and analysis of information systems in the process of production management, as well as the visualization of product manufacturing process. Specifically, it establishes an industrial communication network between equipment, fully collects information on the production site, realizes data integration, analysis and visualization management of product manufacturing process, which can run through the design, manufacturing, quality, logistics and other links, realize the full life cycle management of products, and achieve significant results in reducing operating costs, shortening product development cycle, improving production efficiency, reducing defective product rate, and improving energy utilization. With the development of information technology, digital transformation has become an important way for manufacturing industry to improve efficiency and reduce costs. At present, most manufacturing enterprises have adopted various automation equipment to improve the efficiency of production lines. However, there are still many challenges in the data monitoring of a series of production-related data in the production process of the production line, such as equipment operation data.

[0003] At present, the way that enterprises usually use to monitor the data in the production process of the production line in the workshop is: collect the operating data of each device in the workshop production line through various sensors and display it visually on the terminal of the personnel, so that the personnel can view it in real time. Once an abnormality occurs, a corresponding warning will be issued. However, under this method, due to the large amount of complex data displayed visually, it is difficult for personnel to find the precursor of abnormal equipment operation, and the equipment usually has already failed when the warning is issued, resulting in poor timeliness in discovering abnormal equipment operation. Summary of the invention

[0004] In order to improve the timeliness of discovering abnormal equipment operation, the present application provides a data monitoring method, device, storage medium and electronic equipment for a digital workshop.

[0005] In a first aspect of the present application, a data monitoring method for a digital workshop is provided, which specifically includes: Acquire first real-time operation data of processing equipment corresponding to each processing step in a target production line of a digital workshop at the current time; If all of the first real-time operation data are normal, then based on the target processing equipment that is prone to operation abnormality among the processing equipment and the corresponding target abnormality time period, determine at least one key processing equipment that is prone to operation abnormality after the current time, and the target abnormality time period is the time period during which the target processing equipment is prone to operation abnormality; Based on at least one set of second real-time operation data within a preset time before the current time of each of the key processing equipment, when each of the second real-time operation data is normal, determining the operation data change curve of the corresponding key processing equipment based on each of the second real-time operation data; The operation data change curve is fitted with the corresponding operation data standard curve to obtain a fitting rate. If the fitting rate is lower than a preset fitting rate threshold, the corresponding key processing equipment is determined as the focus processing equipment, and the visualization data set of the focus processing equipment in the production management personnel's terminal is placed on the top. The visualization data set reflects the operation status of the corresponding processing equipment and the quality of the processed semi-finished products in real time. The terminal also includes visualization data sets of processing equipment other than the focus processing equipment.

[0006] By adopting the above technical solution, if all the first real-time operation data are normal, it means that all the processing equipment on the current target production line are operating normally. Then, on this basis, the target processing equipment that is prone to abnormal operation and the corresponding target abnormal time period are determined, and the possibility of abnormal operation of the target processing equipment after the current time is analyzed accordingly, and then the key processing equipment is determined. Further, the operation data of the key processing equipment within the preset time before the current time is analyzed in a targeted manner. If the fitting rate is lower than the fitting rate threshold, it means that the similarity between the operation data change curve and the corresponding operation data standard curve is poor, indicating that although the second real-time operation data of the corresponding key processing equipment are normal, it is likely to be a precursor to abnormality. Then it is determined as the processing equipment of concern and the corresponding visualization data set is placed on the top of the terminal, so as to facilitate production management personnel to monitor the operation data of the corresponding processing equipment more targetedly, thereby improving the timeliness of the discovery of equipment operation abnormalities.

[0007] Optionally, the determining, based on the target processing equipment that is prone to operating abnormalities among the processing equipment and the corresponding target abnormality time period, at least one key processing equipment that is prone to operating abnormalities after the current time specifically includes: Counting the first occurrence times of the historical operation abnormality of each processing equipment, and selecting the processing equipment with the first occurrence times from each processing equipment in descending order as the target processing equipment that is prone to the operation abnormality; Obtaining the historical time period in which each of the target processing equipment has a historical operation abnormality, counting the second occurrence number of each of the historical time periods, and selecting the second number of historical time periods from each of the historical time periods in descending order of the second occurrence number to determine as the target abnormality time period of the corresponding target processing equipment; Calculate a first weight of each target processing equipment and a second weight of each corresponding target abnormal time period, wherein the first weight is a ratio of a first occurrence number of each target processing equipment to a sum of first occurrence numbers of all target processing equipment, and the second weight is a ratio of a second occurrence number of a single target abnormal time period corresponding to the target processing equipment to a sum of second occurrence numbers of all corresponding target abnormal time periods; At least one key processing equipment that is likely to experience operational abnormality after the current time is determined based on the first weight and the corresponding second weights.

[0008] By adopting the above technical solution, the larger the number of first occurrences, the more likely the corresponding processing equipment is to have an abnormal operation, and the target processing equipment that is prone to abnormal operation is determined; the larger the number of second occurrences, the more likely the target processing equipment is to have an abnormal operation in the corresponding historical period, and the target abnormal period in which the target processing equipment is prone to abnormal operation is determined. Finally, the first weight and the corresponding second weight are combined to analyze the possibility of abnormalities in each target processing equipment after the current time, and then determine the key processing equipment, so as to facilitate subsequent targeted data monitoring.

[0009] Optionally, determining at least one key processing equipment that is likely to have an operational abnormality after the current time according to the first weight and the corresponding second weights specifically includes: Determine the target processing equipment including the current time in each corresponding target abnormal time period as an important processing equipment, determine the target abnormal time period including the current time as an important abnormal time period, and calculate the first product of the first weight of each important processing equipment and the second weight of the corresponding important abnormal time period; comparing each of the first products with a preset product threshold; If the first product is greater than the product threshold, the corresponding important processing equipment is determined as key processing equipment that is prone to abnormal operation after the current time.

[0010] By adopting the above technical solution, the larger the first product is, the more likely the corresponding important processing equipment is to have an abnormal operation after the current time. If the first product is greater than the preset product threshold, it means that the corresponding important processing equipment is more likely to have an abnormal operation after the current time, and the corresponding important processing equipment is determined as a key processing equipment that is prone to abnormal operation after the current time, so as to facilitate subsequent targeted data monitoring.

[0011] Optionally, the method further includes: If the first real-time operation data is abnormal, the corresponding processing equipment is determined as abnormal processing equipment, the processing step in which the abnormal processing equipment is located is determined as an abnormal processing step, and when the quality of the semi-finished product processed by the abnormal processing step is normal, the target abnormal type of the abnormal processing equipment is determined; Counting the first occurrence frequencies of historical operation abnormalities of the historical abnormality types of the next processing equipment, and selecting the third historical abnormality type from each of the historical abnormality types in descending order of the first occurrence frequencies as a prone abnormality type, the next processing equipment being a processing equipment in the next processing procedure of the abnormal processing procedure, and the historical abnormality type being an abnormality type in which the quality of the semi-finished product processed by the next processing equipment is normal; Obtaining historical production batches in which only the next-step processing equipment has a single abnormality of the prone abnormality type and the abnormal processing equipment has an abnormality of the target abnormality type, counting the second occurrence frequency of each product defect in the historical production batches, and selecting a fourth number of product defects from each of the product defects in descending order of the second occurrence frequency to determine as the prone defects of the corresponding prone abnormality type; Calculate the third weight of each prone-to-abnormal type and the corresponding fourth weight of each prone-to-defect, wherein the third weight is the ratio of the first occurrence frequency of each prone-to-abnormal type to the sum of the first occurrence frequencies of all prone-to-abnormal types, and the fourth weight is the ratio of the second occurrence frequency of a single prone-to-defect corresponding to the prone-to-abnormal type to the sum of the second occurrence frequencies of all corresponding prone-to-defects; Based on the third weight and the corresponding fourth weights, the visualization data set of the next processing equipment in the terminal is adjusted.

[0012] By adopting the above technical solution, the greater the first occurrence frequency, the more likely the next processing equipment will have the corresponding historical abnormality type, and then determine the prone abnormality type; the greater the second occurrence frequency, the greater the possibility of the corresponding product defect, and then determine the prone defect corresponding to the prone abnormality type. Finally, combined with the third weight and the corresponding fourth weights, the defect risk of the final processed product when the abnormal processing equipment and the next processing equipment are abnormal at the same time is analyzed, and then the visualized data set of the next processing equipment is adjusted, so as to facilitate the subsequent personnel to conduct targeted data monitoring.

[0013] Optionally, adjusting the visualization data set of the next processing equipment in the terminal based on the third weight and the corresponding fourth weights specifically includes: Calculate and sum the second products of each of the third weights and the corresponding fourth weights to obtain the sum of the corresponding first products; Summing the sums of the first products to obtain a final product sum, and if the final product sum is greater than a preset first product sum threshold, placing the visualization data set of the next processing equipment on top to obtain a processed data set; Comparing the sum of each of the first products with a preset second product sum threshold; The prone abnormality type whose sum of the first products exceeds the sum threshold of the second products is determined as an associated abnormality type, and the relevant dimensional operation data corresponding to each of the associated abnormality types in the processed data set are sorted and adjusted to obtain a final data set.

[0014] By adopting the above technical solution, the larger the sum of the first products is, when the abnormal processing equipment has an abnormality of the target abnormal type, the more likely the finished product defects will occur when the next processing equipment has an abnormality of the corresponding abnormal type. The larger the sum of the final products is, the greater the overall possibility of defects in the finished product when the next processing equipment has an abnormality of the abnormal type. If the sum of the final products is greater than the preset threshold of the sum of the first products, then the visualization data set of the next processing equipment in the terminal is placed on top, so that the production management personnel can better track the real-time operation of the next processing equipment and avoid the abnormality of the abnormal type in the next processing equipment to a certain extent; if the sum of the first products is greater than the preset threshold of the sum of the second products, it means that the next processing equipment is more likely to have product defects when the corresponding abnormal type is abnormal, then the relevant dimension operation data corresponding to each associated abnormal type is sorted and adjusted, so as to facilitate the production management personnel to conduct targeted data monitoring.

[0015] Optionally, the step of sorting and adjusting the relevant dimension operation data corresponding to each of the associated abnormality types in the processed data set to obtain a final data set specifically includes: When the next processing equipment is the target processing equipment, the target abnormal time period after the current time in each target abnormal time period corresponding to the next processing equipment is determined as the key abnormal time period, and the historical occurrence number of abnormalities of each associated abnormality type of the next processing equipment in the key abnormal time period is counted; Calculate the fifth weight of each of the key abnormal time periods and the sixth weight of each of the corresponding associated abnormal types, the fifth weight being the ratio of the second occurrence number of each key abnormal time period to the sum of the second occurrence numbers of all target abnormal time periods corresponding to the next processing equipment, and the sixth weight being the ratio of the historical occurrence number of a single associated abnormal type corresponding to the key abnormal time period to the sum of the historical occurrence numbers of all corresponding associated abnormal types; Based on the fifth weight and the corresponding sixth weights, the relevant dimensional operation data corresponding to each associated abnormality type in the processed data set are sorted and adjusted to obtain a final data set.

[0016] By adopting the above technical solution, the greater the number of historical occurrences, the greater the possibility of an abnormality of the corresponding associated abnormal type occurring within the key abnormal period. Further, the fifth weight representing the possibility of an operating abnormality occurring within the key abnormal period and the sixth weight representing the possibility of an abnormality of the associated abnormal type occurring within the key abnormal period are determined. Combined with the fifth weight and the corresponding sixth weight, the possibility of an abnormality of the associated abnormal type occurring in the next processing equipment after the current time is analyzed and determined, so as to reasonably perform sorting adjustments, which is beneficial for personnel to conduct targeted equipment abnormality monitoring.

[0017] Optionally, the step of sorting and adjusting the relevant dimension operation data corresponding to each of the associated abnormality types in the processed data set based on the fifth weight and the corresponding sixth weights to obtain a final data set specifically includes: Determine a third product of the fifth weight of each of the key abnormal time periods and the corresponding sixth weight of each of the associated abnormal types; Summing the third products corresponding to the same associated exception type to obtain the sum of the corresponding second products; According to the sum of the second products, the relevant dimensional operation data corresponding to each of the associated abnormality types in the processed data set are sorted and adjusted to obtain a final data set. The larger the sum of the second products, the higher the relevant dimensional operation data of the corresponding associated abnormality type is sorted.

[0018] By adopting the above technical solution, the larger the sum of the second products, the more likely the next processing equipment will have an abnormality of the corresponding associated abnormality type after the current time, and the greater the possibility of defects in the final product. Then, according to the sum of the second products, the relevant dimension operation data corresponding to each associated abnormality type in the processed data set are sorted and adjusted to obtain the final data set. The larger the sum of the second products, the higher the ranking of the relevant dimension operation data of the corresponding associated abnormality type, which is more conducive to real-time monitoring by personnel. Once the precursor of the abnormality is found, timely intervention can be carried out, which is conducive to avoiding the occurrence of abnormalities of the associated abnormality type.

[0019] In a second aspect of the present application, a data monitoring device for a digital workshop is provided, specifically comprising: A data acquisition module, used to acquire first real-time operation data of processing equipment corresponding to each processing step in a target production line of a digital workshop at a current time; an equipment screening module, for determining, if all the first real-time operation data are normal, at least one key processing equipment that is prone to operation abnormality after the current time based on the target processing equipment that is prone to operation abnormality and the corresponding target abnormality time period among the processing equipment, wherein the target abnormality time period is the time period during which the target processing equipment is prone to operation abnormality; A curve determination module, for determining an operation data change curve of a corresponding key processing equipment based on at least one set of second real-time operation data within a preset time before the current time of each of the key processing equipment, when each of the second real-time operation data is normal; The data monitoring module is used to fit the operation data change curve with the corresponding operation data standard curve to obtain a fitting rate. If the fitting rate is lower than a preset fitting rate threshold, the corresponding key processing equipment is determined as the focus processing equipment, and the visualization data set of the focus processing equipment in the production management personnel's terminal is placed on the top. The visualization data set reflects the operation status of the corresponding processing equipment and the quality of the processed semi-finished products in real time. The terminal also includes visualization data sets of processing equipment other than the focus processing equipment.

[0020] By adopting the above technical solution, the data acquisition module obtains the first real-time operation data of the processing equipment corresponding to each processing step, the equipment screening module determines at least one key processing equipment from each processing equipment, and then the curve determination module determines the operation data change curve of the key processing equipment. Finally, when the fitting rate is lower than the fitting rate threshold, the data monitoring module determines the corresponding key processing equipment as the focus processing equipment, and pins the visualization data set of the focus processing equipment in the production manager's terminal.

[0021] In a third aspect of the present application, a computer-readable storage medium is provided, in which a computer program is stored. When the computer program is loaded and executed by a processor, the method steps described in any one of the first aspects are performed.

[0022] In a fourth aspect of the present application, an electronic device is provided, specifically comprising: A processor, a memory, and a computer program stored in the memory and capable of running on the processor, wherein the processor is used to load and execute the computer program stored in the memory so that the electronic device performs the method as described in any one of the first aspects.

[0023] In summary, the present application includes at least one of the following beneficial technical effects: if all first real-time operation data are normal, it means that all processing equipment on the current target production line are operating normally, then on this basis, the target processing equipment that is prone to abnormal operation and the corresponding target abnormal time period are determined, and the possibility of abnormal operation of the target processing equipment after the current time is analyzed accordingly, and then the key processing equipment is determined. Further, the operation data of the key processing equipment within the preset time before the current time is analyzed in a targeted manner. If the fitting rate is lower than the fitting rate threshold, it means that the similarity between the operation data change curve and the corresponding operation data standard curve is poor, indicating that although the second real-time operation data of the corresponding key processing equipment are normal, they are probably abnormal precursors, then it is determined as the processing equipment of concern and the corresponding visualization data set is pinned to the top in the terminal, so as to facilitate production management personnel to monitor the operation data of the corresponding processing equipment more targetedly, thereby improving the timeliness of the discovery of equipment operation abnormalities. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 It is a flow chart of a data monitoring method for a digital workshop provided in an embodiment of the present application; Figure 2 It is a flow chart of another data monitoring method for a digital workshop provided in an embodiment of the present application; Figure 3 It is a structural schematic diagram of a data monitoring device for a digital workshop provided in an embodiment of the present application; Figure 4 It is a structural schematic diagram of another data monitoring device for a digital workshop provided in an embodiment of the present application.

[0025] Explanation of the accompanying drawings: 11. Data acquisition module; 12. Equipment screening module; 13. Curve determination module; 14. Data monitoring module; 15. Defect warning module. DETAILED DESCRIPTION

[0026] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0027] In the description of the embodiments of the present application, words such as "illustrative", "for example" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "illustrative", "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "illustrative", "for example" or "for example" is intended to present related concepts in a concrete way.

[0028] In the description of the embodiments of the present application, the term "and / or" is only a kind of association relationship describing the associated objects, indicating that there may be three kinds of relationships, for example, A and / or B, which can represent: A exists alone, B exists alone, and A and B exist at the same time. In addition, unless otherwise specified, the meaning of the term "multiple" refers to two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" can explicitly or implicitly include one or more of the features. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0029] See also Figure 1 The present application embodiment discloses a flow chart of a data monitoring method for a digital workshop, which can be implemented by a computer program or run on a data monitoring device for a digital workshop based on a server of a von Neumann system. The computer program can be integrated into an application or run as an independent tool application, specifically including: S101: Acquire first real-time operation data of processing equipment corresponding to each processing step in a target production line of a digital workshop at the current time.

[0030] Specifically, in an embodiment of the present application, the digital workshop is a production workshop of the assembly line type, which includes at least one production line. The production line refers to a system that processes, assembles and generates final products through a series of processing procedures and operations of raw materials or semi-finished products in an automated or semi-automated manner, wherein each processing procedure corresponds to a processing equipment to realize the processing of the product by the process. The target production line is a production line that is currently running in the digital workshop and needs to monitor product production-related data. The number of target production lines can be single, and in other embodiments, the number of target production lines can also be multiple. In addition, the first real-time operating data of the processing equipment corresponding to each processing procedure is a collection of operating data of at least one dimension of the corresponding processing equipment. Different processing equipment has different corresponding first real-time operating data. The first real-time operating data includes but is not limited to operating data of dimensions such as temperature, current, and voltage that can determine whether the processing equipment is abnormal.

[0031] Furthermore, the execution subject of a data monitoring method for a digital workshop disclosed in an embodiment of the present application is a server, and the server is connected to a terminal via a wireless network. The terminal may be a smart phone or a personal computer, and a data monitoring client or applet is installed in the terminal. The server is the background server of the client or applet, and may be an independent physical server, or a server cluster or distributed system composed of multiple physical servers. In addition, an implementation scenario of a data monitoring method for a digital workshop is as follows: when the target production line starts to run, the production manager sends a start instruction for data monitoring in the production process to the server through the client or applet of the terminal, and the server obtains the operating data of different dimensions of the processing equipment in each processing step through wirelessly connected temperature sensors, current and voltage sensors and other sensors, and finally obtains the first real-time operating data of the processing equipment corresponding to each processing step.

[0032] S102: If all the first real-time operating data are normal, based on the target processing equipment that is prone to operating abnormalities among the processing equipment and the corresponding target abnormal time period, determine at least one key processing equipment that is prone to operating abnormalities after the current time.

[0033] Specifically, after the first real-time operation data of each processing equipment is obtained, each first real-time operation data is updated in real time to the terminal of the production manager for visual display, so that during the production process, the production manager can grasp the operation status of each processing equipment in real time. Further, the operation data of each dimension in the first real-time operation data is compared with the normal data range of the corresponding dimension. If it is within the normal data range of the corresponding dimension, then the operation data of the corresponding dimension is determined to be normal. If the operation data of all dimensions are normal, then the corresponding first real-time operation data is determined to be normal. When all the first real-time operation data are normal, it means that all the processing equipment on the current target production line are operating normally. Then, based on the historical abnormality records of this target production line, the historical abnormality records include but are not limited to the time when each processing equipment has an abnormal operation and the single-dimensional operation data with abnormalities, etc., the first occurrence number of each processing equipment's historical operation abnormality is counted. The larger the first occurrence number, the more likely the corresponding processing equipment is to have an abnormal operation. Then, according to the order of the first occurrence number from large to small, the processing equipment with the first number is selected from each processing equipment to be determined as the target processing equipment that is prone to abnormal operation.

[0034] Furthermore, based on historical abnormality records, the historical time period in which each target processing equipment had historical operating abnormalities is determined, and the second number of occurrences in each historical time period is counted. The larger the second number of occurrences, the more likely the target processing equipment is to have operating abnormalities in the corresponding historical time period. Then, in order of the second number of occurrences from large to small, the historical time period with the second number is selected from each historical time period to be determined as the target abnormality time period of the corresponding target processing equipment, that is, the time period in which operating abnormalities are likely to occur.

[0035] Further, the first weight of each target processing equipment and the second weight of each corresponding target abnormal time period are calculated, the first weight is the ratio of the first occurrence number of each target processing equipment to the sum of the first occurrence number of all target processing equipment, and the second weight is the ratio of the second occurrence number of a single target abnormal time period corresponding to the target processing equipment to the sum of the second occurrence number of all corresponding target abnormal time periods. Further, based on the first weight and the corresponding second weights, at least one key processing equipment that is prone to abnormal operation after the current time is determined. A feasible determination method is: determine the target processing equipment that contains the current time in the corresponding target abnormal time period as an important processing equipment, determine the target abnormal time period that contains the current time as an important abnormal time period, and then calculate the first product of the first weight of each important processing equipment and the second weight of the corresponding important abnormal time period. The larger the first product, the more likely the corresponding important processing equipment is to have an abnormal operation after the current time. If the first product is greater than the preset product threshold, it means that the corresponding important processing equipment is more likely to have an abnormal operation after the current time, and then the corresponding important processing equipment is determined as a key processing equipment that is prone to abnormal operation after the current time.

[0036] S103: Based on at least one set of second real-time operating data of each key processing equipment within a preset time before the current time, when each second real-time operating data is normal, determine the operating data change curve of the corresponding key processing equipment based on each second real-time operating data.

[0037] Specifically, after the key processing equipment is determined, since the operating data of each dimension of each processing equipment is obtained in real time, at least one set of second real-time operating data within the preset time before the current time of the single key processing equipment is obtained. When each second real-time operating data is normal, it means that there is no abnormality in the operation of the corresponding key processing equipment within the preset time. Then, the operating data of the same dimension in each second real-time operating data is selected, and the curve is drawn through the MATLAB tool to obtain the operating data change curve of this single key processing equipment.

[0038] S104: Fit the operation data change curve with the corresponding operation data standard curve to obtain a fitting rate. If the fitting rate is lower than a preset fitting rate threshold, the corresponding key processing equipment is determined as the focus processing equipment, and the visualization data set of the focus processing equipment in the production manager's terminal is pinned to the top.

[0039] Specifically, the operation data change curve corresponding to each dimension is fitted with the corresponding operation data standard curve through the MATLAB tool to obtain the fitting rate, and the operation data standard curve is the operation data change curve of the processing equipment that has not experienced abnormal operation. If the fitting rate is lower than the preset fitting rate threshold, it means that the similarity between the operation data change curve and the corresponding operation data standard curve is poor, indicating that although the second real-time operation data of the corresponding key processing equipment are normal, there is a high possibility that abnormal operation will occur. Then the corresponding key processing equipment is determined as the processing equipment of concern, that is, the processing equipment whose operation needs to be focused on. Further, the visualization data set of the processing equipment of concern displayed in the terminal of the production manager is placed on top, and the visualization data set of the processing equipment other than the processing equipment of concern is also displayed in the terminal, wherein the visualization data set reflects the operation status of the corresponding processing equipment and the quality of the semi-finished products processed in real time. In this embodiment, the visualization data set of the processing equipment of concern is placed on top, and when there are many processing equipment in the target production line, the processing equipment with abnormal fluctuation precursors in the operation data can be found in a timely and rapid manner, so as to intervene and adjust the abnormal operation of the equipment in advance to avoid affecting the quality of the processed products.

[0040] See also Figure 2 The present application embodiment discloses a flow chart of another method for monitoring data in a digital workshop, which can be implemented by a computer program or run on a data monitoring device for a digital workshop based on a server of a von Neumann system. The computer program can be integrated into an application or run as an independent tool application, specifically including: S201: Acquire first real-time operation data of processing equipment corresponding to each processing step in a target production line of a digital workshop at the current time.

[0041] S202: If all the first real-time operating data are normal, based on the target processing equipment that is prone to operating abnormalities among the processing equipment and the corresponding target abnormal time period, determine at least one key processing equipment that is prone to operating abnormalities after the current time.

[0042] S203: Based on at least one set of second real-time operating data of each key processing equipment within a preset time before the current time, when each second real-time operating data is normal, determine the operating data change curve of the corresponding key processing equipment based on each second real-time operating data.

[0043] S204: Fit the operation data change curve with the corresponding operation data standard curve to obtain a fitting rate. If the fitting rate is lower than a preset fitting rate threshold, the corresponding key processing equipment is determined as the focus processing equipment, and the visualization data set of the focus processing equipment in the production manager's terminal is pinned to the top.

[0044] For details, please refer to steps S101-S104, which will not be described in detail here.

[0045] S205: If the first real-time operation data is abnormal, the corresponding processing equipment is determined as abnormal processing equipment, the processing procedure in which the abnormal processing equipment is located is determined as an abnormal processing procedure, and when the quality of the semi-finished product processed by the abnormal processing procedure is normal, the target abnormal type of the abnormal processing equipment is determined.

[0046] S206: Count the first occurrence frequencies of historical operation abnormalities of the historical abnormality types of the next processing equipment, and select the third historical abnormality type from each historical abnormality type in descending order of the first occurrence frequencies as the easy abnormality type.

[0047] S207: Obtain historical production batches in which only a single abnormality of the prone abnormality type occurs in the next-step processing equipment and a target abnormality type occurs in the abnormal processing equipment, count the second occurrence frequency of product defects in the historical production batches, and select the fourth number of product defects from each product defect in descending order of the second occurrence frequency to determine as a prone defect.

[0048] Specifically, if there is abnormal first real-time operation data in each of the first real-time operation data, the corresponding processing equipment is determined as abnormal processing equipment, and the processing step in which the abnormal processing equipment is located is determined as an abnormal processing step, and an abnormal reminder of the abnormal processing equipment is sent to the terminal. If there is no problem with the quality of the semi-finished product processed by the current abnormal processing equipment, it means that the current operation abnormality of the abnormal processing equipment does not directly affect the quality of the semi-finished product, then based on the dimension in which the operation data of each dimension in the first real-time operation data is abnormal, the type of abnormal operation of the abnormal processing equipment is determined, that is, the target abnormality type. Exemplarily, the dimension in which the abnormality exists is temperature, indicating that the corresponding target abnormality type is temperature abnormality.

[0049] Furthermore, based on the above historical abnormality records, the first occurrence frequency of the historical abnormality type that occurred in the next processing equipment is counted. The greater the first occurrence frequency, the more likely the next processing equipment is to have the corresponding historical abnormality type, wherein the next processing equipment is the processing equipment in the next processing step of the abnormal processing step, and the historical abnormality type is the abnormality type in which the quality of the semi-finished product processed by the next processing equipment is normal. Then, in the order of the first occurrence frequency from large to small, the historical abnormality type with the third number is selected from each historical abnormality type to be determined as the easy abnormality type, that is, the historical abnormality type that is easy to occur.

[0050] Furthermore, based on the historical production batch records, historical production batches in which only a single abnormality of the prone abnormality type occurred in the next-step processing equipment and an abnormality of the target abnormality type occurred in the abnormal processing equipment were screened out, and the second occurrence frequency of each product defect in the historical production batches was counted. The greater the second occurrence frequency, the greater the possibility of the corresponding product defect. In the order of the second occurrence frequency from large to small, the fourth number of product defects is selected from each product defect to determine as the prone defect of the corresponding prone abnormality type, that is, when the corresponding prone abnormality type and the target abnormality type abnormality occur at the same time, the finished product is more likely to have a defect.

[0051] S208: Calculate the third weight of each prone-to-abnormality type and the corresponding fourth weight of each prone-to-defect type.

[0052] S209: Based on the third weight and the corresponding fourth weights, the visualization data set of the next processing equipment in the terminal is adjusted.

[0053] Specifically, the third weight of each prone to abnormality type and the corresponding fourth weight of each prone to defect are calculated, the third weight is the ratio of the first occurrence frequency of each prone to abnormality type to the sum of the first occurrence frequencies of all prone to abnormality types, and the fourth weight is the ratio of the second occurrence frequency of a single prone defect corresponding to the prone to abnormality type to the sum of the second occurrence frequencies of all corresponding prone defects.

[0054] Furthermore, the second product of each third weight and the corresponding fourth weight is calculated and summed to obtain the corresponding sum of the first products of the prone abnormal type. The larger the sum of the first products, the more likely it is that the finished product will be defective when the next processing equipment has an abnormality of the target abnormal type. The sum of the first products is then summed to obtain the final sum of the products. The larger the final sum of the products, the greater the overall possibility of defects in the finished product when the next processing equipment has an abnormality of the prone abnormal type. If the final sum of the products is greater than the preset threshold of the sum of the first products, the visualization data set of the next processing equipment in the terminal is pinned to obtain a processed data set, so that the production management personnel can better track the real-time operation of the next processing equipment, avoid the prone abnormal type of abnormality in the next processing equipment to a certain extent, and reduce the possibility of defects in the final product.

[0055] If the sum of the first products is greater than the preset second product sum threshold, it means that when the next processing equipment has an abnormality of the corresponding prone abnormal type, it is more likely to produce product defects, and the corresponding prone abnormal type is determined as the associated abnormal type. Further, when the next processing equipment is the target processing equipment, the target abnormal time periods corresponding to the next processing equipment that are after the current time are determined as key abnormal time periods, and the historical occurrence times of each associated abnormal type of the next processing equipment in the key abnormal time period are counted. The greater the historical occurrence times, the greater the possibility of the occurrence of the corresponding associated abnormal type in the key abnormal time period.

[0056] Calculate the fifth weight of each key abnormal period and the sixth weight of each associated abnormal type, the fifth weight is the ratio of the second occurrence of each key abnormal period to the sum of the second occurrence of all target abnormal periods corresponding to the next processing equipment, and the sixth weight is the ratio of the historical occurrence of a single associated abnormal type corresponding to the key abnormal period to the sum of the historical occurrence of all associated abnormal types. Further, calculate the third product of the fifth weight of each key abnormal period and the sixth weight of each associated abnormal type, and then sum the third products corresponding to the same associated abnormal type to obtain the corresponding sum of the second products. The larger the sum of the second products, the easier it is for the next processing equipment to have an abnormality of the corresponding associated abnormal type after the current time, and the greater the possibility of defects in the final product. Then, according to the sum of the second products, sort and adjust the relevant dimension operation data corresponding to each associated abnormal type in the processed data set to obtain the final data set. The larger the sum of the second products, the higher the ranking of the relevant dimension operation data of the corresponding associated abnormal type, which is more conducive to personnel to carry out targeted equipment abnormality monitoring. Once the abnormal precursor is found, timely intervention can be carried out, which is conducive to avoiding the occurrence of abnormalities of associated abnormal types.

[0057] The implementation principle of a data monitoring method for a digital workshop in an embodiment of the present application is as follows: if all first real-time operation data are normal, it means that all processing equipment on the current target production line are operating normally. Then, on this basis, the target processing equipment that is prone to abnormal operation and the corresponding target abnormal time period are determined, and the possibility of abnormal operation of the target processing equipment after the current time is analyzed accordingly, and then the key processing equipment is determined. Further, the operation data of the key processing equipment within the preset time before the current time is analyzed in a targeted manner. If the fitting rate is lower than the fitting rate threshold, it means that the similarity between the operation data change curve and the corresponding operation data standard curve is poor, indicating that the second real-time operation data of the corresponding key processing equipment is normal, but it is likely to be an abnormal precursor. Then it is determined as a processing equipment of concern and the corresponding visualization data set is placed on top in the terminal, so as to facilitate production management personnel to monitor the operation data of the corresponding processing equipment more targetedly, thereby improving the timeliness of abnormal equipment operation discovery.

[0058] The following are device embodiments of the present application, which can be used to execute the method embodiments of the present application. For details not disclosed in the device embodiments of the present application, please refer to the method embodiments of the present application.

[0059] See also Figure 3 , is a schematic diagram of the structure of the data monitoring device for a digital workshop provided in an embodiment of the present application. The data monitoring device applied to a digital workshop can be implemented as all or part of the device through software, hardware or a combination of both. The device includes a data acquisition module 11, an equipment screening module 12, a curve determination module 13 and a data monitoring module 14.

[0060] The data acquisition module 11 is used to acquire first real-time operation data of the processing equipment corresponding to each processing step in the target production line of the digital workshop at the current time; The equipment screening module 12 is used to determine at least one key processing equipment that is prone to abnormal operation after the current time based on the target processing equipment that is prone to abnormal operation and the corresponding target abnormal time period among the processing equipment if all the first real-time operation data are normal, and the target abnormal time period is the time period when the target processing equipment is prone to abnormal operation; The curve determination module 13 is used to determine the operation data change curve of the corresponding key processing equipment based on at least one set of second real-time operation data within a preset time before the current time of each key processing equipment, when each second real-time operation data is normal; The data monitoring module 14 is used to fit the operation data change curve with the corresponding operation data standard curve to obtain a fitting rate. If the fitting rate is lower than a preset fitting rate threshold, the corresponding key processing equipment is determined as the focus processing equipment, and the visualization data set of the focus processing equipment in the production management personnel's terminal is placed on the top. The visualization data set reflects the operation status of the corresponding processing equipment and the quality of the processed semi-finished products in real time. The terminal also includes visualization data sets of processing equipment other than the focus processing equipment.

[0061] Optionally, the device screening module 12 is specifically used for: Count the first occurrence times of the historical operation abnormality of each processing equipment, and select the processing equipment with the first occurrence times from large to small from each processing equipment to determine it as the target processing equipment that is prone to operation abnormality; Obtain the historical time period in which each target processing equipment has historically run abnormally, count the number of second occurrences in each historical time period, and select the second historical time period from each historical time period in descending order of the number of second occurrences as the target abnormal time period of the corresponding target processing equipment; Calculate a first weight of each target processing equipment and a second weight of each corresponding target abnormal time period, wherein the first weight is a ratio of a first occurrence number of each target processing equipment to a sum of first occurrence numbers of all target processing equipment, and the second weight is a ratio of a second occurrence number of a single target abnormal time period corresponding to the target processing equipment to a sum of second occurrence numbers of all corresponding target abnormal time periods; At least one key processing equipment that is likely to experience operational abnormality after the current time is determined based on the first weight and the corresponding second weights.

[0062] Optionally, the device screening module 12 is specifically used for: Determine the target processing equipment including the current time in each corresponding target abnormal time period as an important processing equipment, determine the target abnormal time period including the current time as an important abnormal time period, and calculate the first product of the first weight of each important processing equipment and the second weight of the corresponding important abnormal time period; comparing each first product with a preset product threshold; If the first product is greater than the product threshold, the corresponding important processing equipment is determined as key processing equipment that is prone to abnormal operation after the current time.

[0063] Optional, such as Figure 4 As shown, the device also includes a defect warning module 15, which is specifically used to: If the first real-time operation data is abnormal, the corresponding processing equipment is determined as abnormal processing equipment, the processing step in which the abnormal processing equipment is located is determined as an abnormal processing step, and when the quality of the semi-finished product processed by the abnormal processing step is normal, the target abnormal type of the abnormal processing equipment is determined; The first occurrence frequency of the historical operation abnormality of the historical abnormality type of the next processing equipment is counted, and the historical abnormality type with the third number is selected from each historical abnormality type in descending order of the first occurrence frequency as the easy abnormality type, the next processing equipment is the processing equipment in the next processing procedure of the abnormal processing procedure, and the historical abnormality type is the abnormality type of the semi-finished product processed by the next processing equipment with normal quality; Obtain historical production batches in which only a single abnormality of a prone abnormality type occurs in the next-step processing equipment and an abnormality of a target abnormality type occurs in the abnormal processing equipment, count the second occurrence frequency of each product defect in the historical production batches, and select the fourth number of product defects from each product defect in descending order of the second occurrence frequency to determine as the prone defect of the corresponding prone abnormality type; Calculate the third weight of each prone-to-abnormality type and the corresponding fourth weight of each prone-to-defect, where the third weight is the ratio of the first occurrence frequency of each prone-to-abnormality type to the sum of the first occurrence frequencies of all prone-to-abnormality types, and the fourth weight is the ratio of the second occurrence frequency of a single prone-to-defect corresponding to the prone-to-abnormality type to the sum of the second occurrence frequencies of all corresponding prone-to-defects; Based on the third weight and the corresponding fourth weights, the visualization data set of the next processing equipment in the terminal is adjusted.

[0064] Optionally, the defect warning module 15 is specifically used for: Calculate the second product of each third weight and the corresponding fourth weight and sum them to obtain the sum of the corresponding first products; The sum of the first products is summed to obtain the final product sum. If the final product sum is greater than a preset first product sum threshold, the visualization data set of the next processing equipment is placed on top to obtain a processed data set. Comparing the sum of each first product with a preset second product sum threshold; The prone anomaly type whose sum of the first product exceeds the threshold of the sum of the second product is determined as the associated anomaly type, and the relevant dimension operation data corresponding to each associated anomaly type in the processed data set are sorted and adjusted to obtain the final data set.

[0065] Optionally, the defect warning module 15 is specifically used for: When the next processing equipment is the target processing equipment, the target abnormal time period after the current time in each target abnormal time period corresponding to the next processing equipment is determined as the key abnormal time period, and the historical occurrence number of abnormalities of each associated abnormal type of the next processing equipment in the key abnormal time period is counted; Calculate the fifth weight of each key abnormal period and the sixth weight of each corresponding associated abnormal type, the fifth weight is the ratio of the second occurrence number of each key abnormal period to the sum of the second occurrence numbers of all target abnormal periods corresponding to the next processing equipment, and the sixth weight is the ratio of the historical occurrence number of a single associated abnormal type corresponding to the key abnormal period to the sum of the historical occurrence numbers of all corresponding associated abnormal types; Based on the fifth weight and the corresponding sixth weights, the relevant dimension operation data corresponding to each associated abnormality type in the processed data set are sorted and adjusted to obtain a final data set.

[0066] Optionally, the defect warning module 15 is specifically used for: Determine a third product of the fifth weight of each key abnormal time period and the corresponding sixth weight of each associated abnormal type; The third products corresponding to the same associated anomaly type are summed to obtain the sum of the corresponding second products; According to the sum of the second products, the relevant dimension operation data corresponding to each associated abnormality type in the processed data set are sorted and adjusted to obtain the final data set. The larger the sum of the second products, the higher the ranking of the relevant dimension operation data of the corresponding associated abnormality type.

[0067] It should be noted that the data monitoring device for a digital workshop provided in the above embodiment only uses the division of the above functional modules as an example when executing the data monitoring method for a digital workshop. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the equipment can be divided into different functional modules to complete all or part of the functions described above. In addition, the data monitoring device for a digital workshop provided in the above embodiment and the data monitoring method embodiment for a digital workshop belong to the same concept. The implementation process is detailed in the method embodiment and will not be repeated here.

[0068] The embodiment of the present application further discloses a computer-readable storage medium, and the computer-readable storage medium stores a computer program, wherein when the computer program is executed by a processor, a data monitoring method for a digital workshop of the above embodiment is adopted.

[0069] Among them, the computer program can be stored in a computer-readable medium, the computer program includes computer program code, the computer program code can be in the form of source code, object code, executable file or certain middleware, etc. The computer-readable medium includes any entity or device that can carry the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the computer-readable medium includes but is not limited to the above-mentioned components.

[0070] Among them, through this computer-readable storage medium, a data monitoring method for a digital workshop in the above embodiment is stored in a computer-readable storage medium, and is loaded and executed on a processor to facilitate the storage and application of the above method.

[0071] An embodiment of the present application further discloses an electronic device, wherein a computer program is stored in a computer-readable storage medium, and when the computer program is loaded and executed by a processor, the above-mentioned data monitoring method for a digital workshop is adopted.

[0072] The electronic device may be a desktop computer, a laptop computer, a cloud server or other electronic device, and the electronic device includes but is not limited to a processor and a memory. For example, the electronic device may also include input and output devices, a network access device, and a bus.

[0073] Among them, the processor can adopt a central processing unit (CPU). Of course, according to actual usage, other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. can also be adopted. The general-purpose processor can adopt a microprocessor or any conventional processor, etc., and this application does not impose any restrictions on this.

[0074] Among them, the memory can be an internal storage unit of the electronic device, such as a hard disk or memory of the electronic device, or it can be an external storage device of the electronic device, such as a plug-in hard disk, a smart memory card (SMC), a secure digital card (SD) or a flash memory card (FC) equipped on the electronic device. Moreover, the memory can also be a combination of an internal storage unit and an external storage device of the electronic device. The memory is used to store computer programs and other programs and data required by the electronic device. The memory can also be used to temporarily store data that has been output or is to be output, and this application does not impose any restrictions on this.

[0075] Among them, through this electronic device, a data monitoring method for a digital workshop in the above embodiment is stored in the memory of the electronic device, and is loaded and executed on the processor of the electronic device for easy use.

[0076] The above is only an exemplary embodiment of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. This application is intended to cover any variation, use or adaptive change of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the technical field that are not recorded in the present disclosure. The description and examples are only regarded as exemplary, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A data monitoring method for a digital workshop, characterized in that: The method comprises: Acquire first real-time operation data of processing equipment corresponding to each processing step in a target production line of a digital workshop at the current time; If all of the first real-time operation data are normal, then based on the target processing equipment that is prone to operation abnormality among the processing equipment and the corresponding target abnormality time period, determine at least one key processing equipment that is prone to operation abnormality after the current time, and the target abnormality time period is the time period during which the target processing equipment is prone to operation abnormality; Based on at least one set of second real-time operation data of each of the key processing equipment within a preset time before the current time, when each of the second real-time operation data is normal, determining the operation data change curve of the corresponding key processing equipment based on each of the second real-time operation data; The operation data change curve is fitted with the corresponding operation data standard curve to obtain a fitting rate. If the fitting rate is lower than a preset fitting rate threshold, the corresponding key processing equipment is determined as the focus processing equipment, and the visualization data set of the focus processing equipment in the production management personnel's terminal is placed on the top. The visualization data set reflects the operation status of the corresponding processing equipment and the quality of the processed semi-finished products in real time. The terminal also includes visualization data sets of processing equipment other than the focus processing equipment.

2. The data monitoring method of a digital workshop according to claim 1 is characterized in that: The determining, based on the target processing equipment that is prone to abnormal operation among the processing equipment and the corresponding target abnormal time period, at least one key processing equipment that is prone to abnormal operation after the current time specifically includes: Counting the first occurrence times of the historical operation abnormality of each processing equipment, and selecting the processing equipment with the first occurrence times from each processing equipment in descending order as the target processing equipment that is prone to the operation abnormality; Obtaining the historical time period in which each of the target processing equipment has a historical operation abnormality, counting the second occurrence number of each of the historical time periods, and selecting the second number of historical time periods from each of the historical time periods in descending order of the second occurrence number to determine as the target abnormality time period of the corresponding target processing equipment; Calculate a first weight of each target processing equipment and a second weight of each corresponding target abnormal time period, wherein the first weight is a ratio of a first occurrence number of each target processing equipment to a sum of first occurrence numbers of all target processing equipment, and the second weight is a ratio of a second occurrence number of a single target abnormal time period corresponding to the target processing equipment to a sum of second occurrence numbers of all corresponding target abnormal time periods; At least one key processing equipment that is likely to experience operational abnormality after the current time is determined based on the first weight and the corresponding second weights.

3. The data monitoring method for a digital workshop according to claim 2 is characterized in that: The determining, based on the first weight and the corresponding second weights, at least one key processing equipment that is likely to have an operational abnormality after the current time specifically includes: Determine the target processing equipment including the current time in each corresponding target abnormal time period as an important processing equipment, determine the target abnormal time period including the current time as an important abnormal time period, and calculate the first product of the first weight of each important processing equipment and the second weight of the corresponding important abnormal time period; comparing each of the first products with a preset product threshold; If the first product is greater than the product threshold, the corresponding important processing equipment is determined as key processing equipment that is prone to abnormal operation after the current time.

4. The data monitoring method of a digital workshop according to claim 2 is characterized in that: The method further comprises: If the first real-time operation data is abnormal, the corresponding processing equipment is determined as abnormal processing equipment, the processing step in which the abnormal processing equipment is located is determined as an abnormal processing step, and when the quality of the semi-finished product processed by the abnormal processing step is normal, the target abnormal type of the abnormal processing equipment is determined; Counting the first occurrence frequencies of historical operation abnormalities of the historical abnormality types of the next processing equipment, and selecting the third historical abnormality type from each of the historical abnormality types in descending order of the first occurrence frequencies as a prone abnormality type, the next processing equipment being a processing equipment in the next processing procedure of the abnormal processing procedure, and the historical abnormality type being an abnormality type in which the quality of the semi-finished product processed by the next processing equipment is normal; Obtaining historical production batches in which only the next-step processing equipment has a single abnormality of the prone abnormality type and the abnormal processing equipment has an abnormality of the target abnormality type, counting the second occurrence frequency of each product defect in the historical production batches, and selecting a fourth number of product defects from each of the product defects in descending order of the second occurrence frequency to determine as the prone defects of the corresponding prone abnormality type; Calculate the third weight of each prone-to-abnormal type and the corresponding fourth weight of each prone-to-defect, wherein the third weight is the ratio of the first occurrence frequency of each prone-to-abnormal type to the sum of the first occurrence frequencies of all prone-to-abnormal types, and the fourth weight is the ratio of the second occurrence frequency of a single prone-to-defect corresponding to the prone-to-abnormal type to the sum of the second occurrence frequencies of all corresponding prone-to-defects; Based on the third weight and the corresponding fourth weights, the visualization data set of the next processing equipment in the terminal is adjusted.

5. The data monitoring method for a digital workshop according to claim 4 is characterized in that: The adjusting the visualization data set of the next processing equipment in the terminal based on the third weight and the corresponding fourth weights specifically includes: Calculate and sum the second products of each of the third weights and the corresponding fourth weights to obtain the sum of the corresponding first products; Summing the sums of the first products to obtain a final product sum, and if the final product sum is greater than a preset first product sum threshold, placing the visualization data set of the next processing equipment on top to obtain a processed data set; Comparing the sum of each of the first products with a preset second product sum threshold; The prone abnormality type whose sum of the first products exceeds the sum threshold of the second products is determined as an associated abnormality type, and the relevant dimensional operation data corresponding to each of the associated abnormality types in the processed data set are sorted and adjusted to obtain a final data set.

6. The data monitoring method for a digital workshop according to claim 5 is characterized in that: The sorting and adjusting of the relevant dimension operation data corresponding to each of the associated abnormality types in the processed data set to obtain the final data set specifically includes: When the next processing equipment is the target processing equipment, the target abnormal time period after the current time in each target abnormal time period corresponding to the next processing equipment is determined as the key abnormal time period, and the historical occurrence number of abnormalities of each associated abnormality type of the next processing equipment in the key abnormal time period is counted; Calculate the fifth weight of each of the key abnormal time periods and the sixth weight of each of the corresponding associated abnormal types, the fifth weight being the ratio of the second occurrence number of each key abnormal time period to the sum of the second occurrence numbers of all target abnormal time periods corresponding to the next processing equipment, and the sixth weight being the ratio of the historical occurrence number of a single associated abnormal type corresponding to the key abnormal time period to the sum of the historical occurrence numbers of all corresponding associated abnormal types; Based on the fifth weight and the corresponding sixth weights, the relevant dimensional operation data corresponding to each associated abnormality type in the processed data set are sorted and adjusted to obtain a final data set.

7. The data monitoring method for a digital workshop according to claim 6 is characterized in that: The step of sorting and adjusting the relevant dimension operation data corresponding to each of the associated abnormality types in the processed data set based on the fifth weight and the corresponding sixth weights to obtain a final data set specifically includes: Determine a third product of the fifth weight of each of the key abnormal time periods and the corresponding sixth weight of each of the associated abnormal types; Summing the third products corresponding to the same associated exception type to obtain the sum of the corresponding second products; According to the sum of the second products, the relevant dimensional operation data corresponding to each of the associated abnormality types in the processed data set are sorted and adjusted to obtain a final data set. The larger the sum of the second products, the higher the relevant dimensional operation data of the corresponding associated abnormality type is sorted.

8. A data monitoring device for a digital workshop, characterized in that: include: A data acquisition module (11) is used to acquire first real-time operation data of processing equipment corresponding to each processing step in a target production line of a digital workshop at a current time; an equipment screening module (12), configured to, if all of the first real-time operating data are normal, determine at least one key processing equipment that is likely to have an operating abnormality after the current time based on a target processing equipment that is likely to have an operating abnormality among the processing equipment and a corresponding target abnormality time period, wherein the target abnormality time period is a time period when the target processing equipment is likely to have an operating abnormality; A curve determination module (13) is used to determine the operation data change curve of the corresponding key processing equipment based on at least one set of second real-time operation data within a preset time before the current time of each key processing equipment, when each of the second real-time operation data is normal; The data monitoring module (14) is used to fit the operation data change curve with the corresponding operation data standard curve to obtain a fitting rate. If the fitting rate is lower than a preset fitting rate threshold, the corresponding key processing equipment is determined as the processing equipment of interest, and the visualization data set of the processing equipment of interest in the terminal of the production manager is placed on top, the visualization data set reflects the operation status of the corresponding processing equipment and the quality status of the processed semi-finished products in real time, and the terminal also includes the visualization data set of processing equipment other than the processing equipment of interest.

9. A computer-readable storage medium having a computer program stored therein, characterized in that: When the computer program is loaded and executed by a processor, the method according to any one of claims 1 to 7 is adopted.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that: When the processor loads and executes the computer program, the method according to any one of claims 1 to 7 is adopted.

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