Device management method and system based on production data operation platform

Abstract

The device management method and system based on the production data operation platform provided in the application, in the technical solution represented in the disclosure, on the one hand, the method can call the number of production line devices in the production event description content set through AI thread number analysis, and the number of production line devices in the production event description content set can be intelligently determined by the calling thread, so that even if there are interference problems in the automatic production operation data to be improved for device state analysis, the accurate number of production line devices can be analyzed, thereby improving the completeness of the device resource occupation analysis. On the other hand, based on the production line device call number analysis information and the production line device state semantic analysis information, the accuracy of the description content can be analyzed, the production line device state semantics and the production line device call number are referred to when the accuracy is analyzed, and the credibility of the device resource occupation analysis for different semantic production line device states is improved.

Description

Technical Field

[0001] This application relates to the field of data management technology, and more specifically, to a method and system for equipment management based on a production data-driven operation platform. Background Technology

[0002] The rapid development of new technologies such as the Internet of Things (IoT) and big data has spurred rapid economic growth, leading to increasingly fierce market competition in the manufacturing sector. With changing demographics, cheap labor is no longer a competitive advantage in manufacturing, and the extensive development model of the processing and manufacturing industry has reached its end. Many drawbacks of traditional manufacturing are gradually becoming apparent. Therefore, digital production technologies combining IoT and big data are gradually replacing traditional manufacturing technologies, thus addressing their shortcomings. However, when digital production is applied in practice, the inventors have discovered that the unreasonable use of equipment resources is a significant factor limiting productivity. To address this issue, it is necessary to analyze and process equipment resource usage; however, existing technologies struggle to efficiently analyze equipment resource usage. Summary of the Invention

[0003] In view of this, this application provides a method and system for equipment management based on a production data-driven operation platform.

[0004] Firstly, a method for equipment management based on a production data-driven operation platform is provided, which applies an equipment management system. The method includes:

[0005] Identify the automated production operation data for which the equipment status analysis needs to be improved; identify the potential production line equipment status description content set that matches the production event description content set in the automated production operation data for which the equipment status analysis needs to be improved, wherein the production event description content set includes the production line equipment status and at least one production event identifier.

[0006] The production event description content set is parsed to obtain production line equipment call count parsing information, and the potential production line equipment status description content set is parsed to obtain production line equipment status semantic parsing information.

[0007] Based on the parsing information of the number of production line equipment calls and the semantic parsing information of the production line equipment status, it is determined whether the target production event in the production event description content set has equipment resource occupancy status.

[0008] In one standalone embodiment, the potential production line equipment status description set that matches the set of production event descriptions in the automated production operation data containing the equipment status analysis to be improved, and includes:

[0009] Mining the automated production operation data of the equipment status analysis to be improved yields the set of production line equipment status descriptions and the set of production event descriptions.

[0010] The target production line equipment status description set that best compares and evaluates the obtained production event description set with the production equipment status description set is determined, and the target production line equipment status description set is identified as a potential production line equipment status description set that has a matching relationship with the production event description set.

[0011] In one standalone embodiment, the potential production line equipment status description set that matches the set of production event descriptions in the automated production operation data containing the equipment status analysis to be improved, and includes:

[0012] Mining the automated production operation data of the equipment status analysis to be improved yields the set of production line equipment status descriptions and the set of production event descriptions.

[0013] The quantization matching data recognition network that has been debugged beforehand is invoked to determine the quantization matching data between the production line equipment status description content set and the production event description content set.

[0014] The target production line equipment status description set that best matches the quantitative matching data of the production event description set is determined as the potential production line equipment status description set that has a matching relationship with the production event description set.

[0015] In one independently implemented embodiment, the step of parsing the production line equipment call count of the production event description content set to obtain production line equipment call count parsing information includes: parsing the production line equipment call count of the production event description content set to obtain the production line equipment call count and the corresponding first probability evaluation; and determining the production line equipment call count as the production line equipment call count parsing information of the production event description content set based on the first probability evaluation reaching the first probability evaluation determination value.

[0016] The step of performing production line equipment state semantic parsing on the potential production line equipment state description content set to obtain production line equipment state semantic parsing information includes: performing production line equipment state semantic parsing on the potential production line equipment state description content set to obtain production line equipment state semantics and corresponding second probability evaluation; based on the second probability evaluation reaching the second probability evaluation judgment value, determining the production line equipment state semantics as the production line equipment state semantic parsing information of the production line equipment state description content set.

[0017] In one standalone embodiment, determining whether a target production event in the production event description set has equipment resource occupancy based on the production line equipment call count parsing information and the production line equipment status semantic parsing information includes one of the following:

[0018] Based on the parsing information of the number of production line equipment calls as the first parsing information, it is determined that the target production event has equipment resource occupancy; the first parsing information indicates that the number of production line equipment calls has reached a first set number.

[0019] Based on the production line equipment call count parsing information as second parsing information, and the production line equipment status semantics expressed by the semantic parsing information as the set production line collaboration rejection semantics, it is determined that the target production event has equipment resource occupancy; the second parsing information indicates that the production line equipment call count has reached a second set number, and the second set number is less than the first set number;

[0020] Based on the production line equipment call count parsing information, the production line equipment call count is the second parsing information, and the production line equipment status semantics expressed by the semantic parsing information is not the set production line collaboration rejection semantics, it is determined that the target production event does not have equipment resource occupation.

[0021] Based on the production line equipment call count parsing information as third parsing information, it is determined that the target production event does not have equipment resource occupancy; the third parsing information expresses that the production line equipment call count is a third set number, and the third set number is less than the second set number.

[0022] Based on the fact that the production line equipment call count parsing information is the fourth parsing information, it is determined that there is an anomaly in the equipment resource usage parsing for the target production event.

[0023] In one standalone embodiment, the fourth parsed information expresses that the automated production operation data for which the equipment status analysis to be improved corresponds to at least one of the following target tasks: a task of production event delay; a task of production event suspension; a task where several production events have upstream and downstream relationships; a task where production demand indicators do not meet the set indicators; a task where the status of production line equipment is deviated.

[0024] In one standalone implementation, the method further includes: generating an equipment management and debugging strategy based on the equipment resource occupancy status of the target production event.

[0025] In one standalone embodiment, the production line equipment call count parsing information is obtained by mining the production event description content set using a resource occupancy parsing network. The debugging steps for the resource occupancy parsing network are as follows:

[0026] A first debugging template is determined. The first debugging template includes template automated production operation data for multiple production events and first indication data for the number of production line equipment calls corresponding to each set of automated production operation data. The first indication data includes one of the following keywords: one production line equipment is occupied, two production line equipment are occupied, three production line equipment are occupied, and there are abnormal keywords. The abnormal keywords include at least one of the following situations: production event delay, production event pause, several production events have upstream and downstream relationships, production demand indicators do not meet the set indicators, and there is a deviation in the status of production line equipment.

[0027] Load the first debug template into the first undebugged thread to obtain the template production line equipment call count parsing information for each group of template automated production operation data;

[0028] Based on the template production line equipment call count parsing information and the first thread cost determined by the first indication data, the first un-debugged thread is debugged to obtain the resource usage parsing network.

[0029] In one standalone implementation, the production line equipment status resolution information is obtained by mining the production line equipment status description content set through a production line equipment status resolution network. The debugging steps of the production line equipment status resolution network are as follows:

[0030] A second debugging template is determined, which includes template automated production operation data of multiple production line equipment statuses and second indication data of production line equipment status semantics corresponding to each set of automated production operation data.

[0031] Load the second debug template into the set second undebugged thread to obtain the template production line equipment status semantic parsing information of each group of template automated production operation data;

[0032] Based on the semantic parsing information of the template production line equipment status and the second thread cost determined by the second indication data, the second un-debugged thread is debugged to obtain the production line equipment status parsing network.

[0033] Secondly, an equipment management system based on a production data operation platform is provided, including a processor and a memory that communicate with each other. The processor is used to retrieve a computer program from the memory and implement the above-mentioned method by running the computer program.

[0034] The equipment management method and system based on a production data-driven operation platform provided in this application, in the technical solution disclosed in this disclosure, on the one hand, the method uses an AI thread to parse the production event description content set to determine the number of production line equipment calls. The calling thread can intelligently determine the number of production line equipment calls in the production event description content set. Thus, even if there are interference problems in the automated production operation data where equipment status analysis needs to be improved, the accurate number of production line equipment calls can still be parsed, thereby improving the completeness of equipment resource usage analysis.

[0035] On the other hand, the accuracy of the description can be analyzed based on the parsing information of the number of production line equipment calls and the semantic parsing information of the production line equipment status. By referring to the semantics of the production line equipment status and the number of production line equipment calls when performing accuracy analysis, the reliability of equipment resource usage analysis can be improved for different semantic production line equipment statuses. Attached Figure Description

[0036] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0037] Figure 1 A flowchart illustrating an equipment management method based on a production data-driven operation platform, provided as an embodiment of this application.

[0038] Figure 2 This is a block diagram of an equipment management device based on a production data-driven operation platform, provided as an embodiment of this application.

[0039] Figure 3 This is an architecture diagram of an equipment management system based on a production data-driven operation platform, provided as an embodiment of this application. Detailed Implementation

[0040] To better understand the above technical solutions, the technical solutions of this application will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments of this application and the specific features in the embodiments are detailed descriptions of the technical solutions of this application, rather than limitations on the technical solutions of this application. In the absence of conflict, the embodiments of this application and the technical features in the embodiments can be combined with each other.

[0041] Please see Figure 1 This paper illustrates a method for equipment management based on a production data-driven operation platform, which may include the technical solutions described in S101-S104 below.

[0042] S101, Identify automated production operation data for which equipment status analysis needs to be improved.

[0043] S102, determine the potential production line equipment status description content set that has a matching relationship with the production event description content set in the automated production operation data of the equipment status analysis to be improved, which is contained in the set of production line equipment status description content of the automated production operation data of the equipment status analysis to be improved. The production event description content set includes production line equipment status and at least one production event identifier.

[0044] S103, perform production line equipment call count parsing on the production event description content set to obtain production line equipment call count parsing information, and perform production line equipment status semantic parsing on the potential production line equipment status description content set to obtain production line equipment status semantic parsing information.

[0045] S104, based on the production line equipment call count parsing information and the production line equipment status semantic parsing information, determine whether the target production event in the production event description content set has equipment resource occupancy status.

[0046] In the equipment management method based on a production data-driven operation platform disclosed in this disclosure, on the one hand, the method uses an AI thread to parse the production event description content set to determine the number of production line equipment calls. The thread can intelligently determine the number of production line equipment calls in the production event description content set. Thus, even if there are interference problems in the automated production operation data where equipment status analysis needs to be improved, the accurate number of production line equipment calls can still be parsed, thereby improving the completeness of equipment resource usage analysis.

[0047] On the other hand, the accuracy of the description can be analyzed based on the parsing information of the number of production line equipment calls and the semantic parsing information of the production line equipment status. By referring to the semantics of the production line equipment status and the number of production line equipment calls when performing accuracy analysis, the reliability of equipment resource usage analysis can be improved for different semantic production line equipment statuses.

[0048] After determining the automated production operation data for which equipment status analysis needs to be improved, the production event description content set specified in this disclosure is used to characterize the mining of target production events in the automated production operation data for which equipment status analysis needs to be improved. The target production event can be implemented based on production instructions. For example, the target production event can be understood as any production event selected from the production events contained in the automated production operation data for which equipment status analysis needs to be improved. Another example is that the target production event can be understood as the production event with the best accuracy among the production events contained in the automated production operation data for which equipment status analysis needs to be improved. Yet another example is that each production event contained in the automated production operation data for which equipment status analysis needs to be improved can be implemented as a target production event. The production event description content set may include production line equipment status and at least one production event identifier.

[0049] The production line equipment status description content set disclosed herein is used to characterize the mining composition of production line equipment status in automated production operation data for which equipment status analysis needs to be improved.

[0050] In one possible embodiment, the target production line equipment status can be determined by comparing and evaluating the production event description content set with the production line equipment status description content set.

[0051] This disclosure discloses a method for determining a potential production line equipment status description content set. During execution S102, S202 can be executed to mine the automated production operation data of the equipment to be improved to obtain the production line equipment status description content set and the production event description content set. Then, S204 can be executed to determine the target production line equipment status description content set that best compares and evaluates the obtained production line equipment status description content set with the production event description content set, and to determine the target production line equipment status description content set as a potential production line equipment status description content set that matches the production event description content set.

[0052] Therefore, the target production line equipment status description set that best compares and evaluates the production event description content set is identified as the potential production line equipment status description content set that matches the production event description content set. This leverages the potential locational relationships between production line equipment status and production events to determine the precise potential production line equipment status description content set. This facilitates accurate determination of the production line equipment status semantics mined from the target production event, improving the completeness of equipment resource usage analysis. In one possible embodiment, during S202, an object mining thread can be invoked to perform object mining to obtain the mining corresponding to the production event and production line equipment status in the automated production operation data to be analyzed for improved equipment status. Then, the description content set composed of the target mining corresponding to the target production event in the automated production operation data to be analyzed for improved equipment status can be determined as the production event description content set; and the description content set composed of the mining corresponding to the production line equipment status in the automated production operation data to be analyzed for improved equipment status can be determined as the production line equipment status description set.

[0053] During execution of S204, the comparison and evaluation between each production line equipment status description set and the production event description set can be determined. Then, according to the order of the obtained comparison and evaluation, the production line equipment status description sets can be distributed, and the production line equipment status description sets ranked higher are determined as the target production line equipment status description sets. Subsequently, the target production line equipment status description sets can be determined as potential production line equipment status description sets that have a matching relationship with the production event description sets.

[0054] In one possible embodiment, the comparison evaluation may include the ratio of the description content set where the production line equipment status description content set intersects with the production event description content set, to the description content set formed by the union of the production line equipment status description content set and the production event description content set. That is, it expresses the comparison evaluation by invoking the correlation between the production line equipment status description content set and the production event description content set.

[0055] This disclosure discloses a method for determining a potential production line equipment status description content set. During execution S102, S402 can be executed to mine the automated production operation data for which the equipment status analysis to be improved is performed to obtain the production line equipment status description content set and the production event description content set. Then, S404 is executed to call a pre-tested quantitative matching data recognition network to determine the quantitative matching data between the production line equipment status description content set and the production event description content set. Then, S406 can be executed to determine the target production line equipment status description content set with the best quantitative matching data between the production line equipment status description content set and the production event description content set as the potential production line equipment status description content set that has a matching relationship with the production event description set.

[0056] This allows for the precise expression of the potential relationship between the production event description set and the production line equipment status description set by calling quantitative matching data. In this way, the potential production line equipment status description set with the optimal potential degree with the production event description set can be determined, which helps to accurately determine the production line equipment status semantics mined from the target production event, thereby improving the completeness of equipment resource usage analysis.

[0057] The quantization matching data algorithm can be understood as an algorithm built on artificial intelligence. When debugging this algorithm, firstly, automated production operation data including multiple sets of production line equipment status description content sets and production event description content sets can be identified. Then, the quantization matching data between each pair of production line equipment status description content sets and production event description sets is marked to obtain many debugging templates. Specifically, if the production event description content set and the production line equipment status description content set are incompatible, the quantization matching data is marked as 2; otherwise, it is marked as -2. Afterwards, the algorithm can be tested and debugged using the debugging templates. Once debugging is complete, the quantization matching data algorithm can be used to detect the quantization matching data between the production line equipment status description content sets and the production event description sets in the automated production operation data for which equipment status analysis needs to be improved.

[0058] After determining the potential production line equipment status description set, step S103 can be continued. The description set in this step (including the production event description set and the production line equipment status description set) can be understood as a set of descriptions composed of automated production operation data from which the equipment status analysis needs to be improved. The description set may be accompanied by corresponding automated production operation data descriptions.

[0059] The production event description set disclosed in this disclosure may contain a first automated production operation data description corresponding to the production event description content. For example, the first automated production operation data description may include the production line equipment status mined from the production event, and the automated production operation data description corresponding to the production event identifier associated with that production line equipment status. Calling the first automated production operation data description can determine the number of production line equipment calls.

[0060] The production line equipment status description set disclosed in this disclosure may contain a second automated production operation data description related to the semantics of the production line equipment status. For example, the second automated production operation data description may include an automated production operation data description corresponding to the production line equipment status. Calling the second automated production operation data description can determine the semantics of the production line equipment status.

[0061] In one possible embodiment, when executing S103, S1032 can be executed to parse the production event description content set to obtain production line equipment call count parsing information. And S1034 can be executed to parse the potential production line equipment status description content set to obtain production line equipment status semantic parsing information.

[0062] In one possible embodiment, during execution of S1032, the production event description content set corresponding to the production event description content set can be determined first. In another possible embodiment, the production event corresponding to the target production event and the collected automated production operation data for improving equipment status analysis can be loaded into the description content set description mining layer to obtain the production event description content set corresponding to the target production event.

[0063] After obtaining the production event description content set, the production line equipment call count can be parsed to obtain production line equipment call count parsing information.

[0064] In one possible embodiment, a production line equipment call count parsing thread that has already completed debugging can be invoked for parsing. The production line equipment call count parsing information output by this thread may include first parsing information, second parsing information, and third parsing information, as well as probability evaluations corresponding to each type of parsing information. Specifically, the first parsing information indicates that the production line equipment call count has reached a first set number. The second parsing information indicates that the production line equipment call count has reached a second set number. The third parsing information indicates that the production line equipment call count is a third set number. The first, second, and third set numbers can be set based on production instructions. For example, the first set number can be understood as 6, the second set number as 4, and the third set number as 2.

[0065] When determining the final production line equipment call count parsing information, the parsing information bound to the best probability evaluation can be selected. In this disclosure, this task can be understood as the target task. Furthermore, a fourth parsing information indicating an anomaly in the current parsing can be added to the production line equipment call count parsing information obtained after parsing the production event description content set. If the production line equipment call count parsing information for the production event description content set is the fourth parsing information, it indicates that the task in the production event description content set is the target task and cannot improve equipment resource usage parsing; therefore, it is not necessary to improve equipment resource usage parsing for the production event description content set.

[0066] Based on the above description, the production line equipment call count parsing information output by the production line equipment call count parsing thread can include first parsing information, second parsing information, third parsing information and fourth parsing information, as well as the probability evaluation corresponding to each type of parsing information.

[0067] The fourth parsing information expresses that the automated production operation data for which the equipment status analysis needs to be improved corresponds to at least one of the following target tasks: a task where production events are delayed; a task where production events are suspended; a task where several production events have upstream and downstream relationships; a task where production demand indicators do not meet the set indicators; and a task where the status of production line equipment is deviated.

[0068] When determining the final number of production line equipment calls, the analysis information corresponding to the best probability evaluation can be selected.

[0069] In one possible embodiment, the debugging method of the resource occupancy resolution network may include S11-S13.

[0070] S11, determine the first debugging template. The first debugging template includes template automated production operation data for multiple production events and first indication data of the number of production line equipment calls corresponding to each group of automated production operation data. The first indication data includes one of the following keywords: one production line equipment is occupied, two production line equipment are occupied, three production line equipment are occupied, or there is an abnormal keyword. The abnormal keyword includes at least one of the following: production event delay, production event pause, several production events have upstream and downstream relationships, production demand indicators do not meet the set indicators, or there is a deviation in the status of production line equipment. S12, load the first debugging template into the set first un-debugged thread to obtain the template production line equipment call number parsing information for each group of template automated production operation data.

[0071] S13, based on the template production line equipment call count parsing information and the first thread cost determined by the first indication data, debug the first un-debugged thread to obtain the resource usage parsing network.

[0072] When the aforementioned debugging method is invoked to parse the number of production line equipment calls, it can, on the one hand, reduce the target task of not being able to perform description content mining and equipment resource usage parsing, thereby improving the effect of description content mining and equipment resource usage parsing; on the other hand, it can accurately parse the number of production line equipment calls, thereby improving the equipment resource usage parsing capability.

[0073] In one possible embodiment, after parsing the production line equipment call count of the production event description content set to obtain the production line equipment call count and the corresponding first probability evaluation, the production line equipment call count can be determined as the production line equipment call count parsing information of the production event description content set based on the first probability evaluation reaching the first probability evaluation determination value.

[0074] In one possible embodiment, when executing S1034, a corresponding production line equipment status description set diagram can be obtained first based on the potential production line equipment status description content set. In another possible embodiment, the production line equipment status corresponding to the potential production line equipment status description content set and the target description set corresponding to the automated production operation data for improving the equipment status analysis can be used.

[0075] Then, production line equipment state semantic parsing can be performed on the production line equipment state description content set graph to obtain production line equipment state semantic parsing information. In one possible embodiment, a production line equipment state parsing thread that has already completed debugging can be invoked to perform production line equipment state semantic parsing. This may include parsing the production line equipment states in the production line equipment state description content set into probability evaluations for each set of production line equipment state semantics. When determining the final production line equipment state semantics, the production line equipment state semantics corresponding to the best probability evaluation can be selected, that is, the production line equipment state semantics corresponding to the best probability evaluation can be determined as the production line equipment state semantic parsing information.

[0076] In one possible embodiment, the debugging method for the production line equipment status parsing thread may include S21-S23.

[0077] Specifically, in step S21, a second debugging template is determined. The second debugging template includes template automated production operation data of multiple production line equipment states and second indication data corresponding to the semantics of the production line equipment states for each set of automated production operation data.

[0078] S22, load the second debug template into the set second undebugged thread to obtain the template production line equipment status semantic parsing information of each group of template automated production operation data.

[0079] S23, based on the semantic parsing information of the template production line equipment status and the second thread cost determined by the second indication data, debug the second un-debugged thread to obtain the production line equipment status parsing thread.

[0080] In one possible embodiment, after performing production line equipment state semantic parsing on the potential production line equipment state description content set to obtain production line equipment state semantics and the corresponding second probability evaluation, the production line equipment state semantics can be determined as the production line equipment state semantic parsing information of the production line equipment state description content set based on the second probability evaluation reaching a second probability evaluation determination value.

[0081] After obtaining the production event production line equipment call count parsing information and the production line equipment status semantic parsing information, the equipment can execute S104.

[0082] In one possible embodiment, efficient production parsing information that meets real-time requirements can be output for different production line equipment status semantic tasks.

[0083] When executing S104, firstly, the target production event can be determined to have equipment resource occupancy based on the parsing information of the number of production line equipment calls as first parsing information; the first setting parsing information indicates that the number of production line equipment calls has reached a first setting number.

[0084] Secondly, based on the number of production line equipment calls, the information can be parsed as second parsing information, and the semantic parsing information expresses the production line equipment status semantics as the set production line collaboration rejection semantics, to determine that the target production event has equipment resource occupancy; the second parsing information expresses that the number of production line equipment calls has reached a second set number, and the second set number is lower than the first set number.

[0085] Thirdly, based on the production line equipment call count parsing information being the second parsing information, and the production line equipment status semantics being the set production line collaboration rejection semantics, it can be determined that the target production event does not have equipment resource occupancy.

[0086] Fourthly, based on the parsing information of the number of production line equipment calls as the third parsing information, it can be determined that the target production event does not have equipment resource occupancy.

[0087] The third parsing information expresses the number of production line equipment calls as a third set number, which is lower than the second set number.

[0088] Fifthly, based on the parsing information of the number of production line equipment calls as the fourth parsing information, it can be determined that there is an anomaly in the parsing of equipment resource usage for the target production event.

[0089] By invoking the accuracy judgment methods mentioned above, we can output efficient production parsing information that meets real-time requirements for different production line equipment status semantic tasks, thereby improving the accuracy of parsing.

[0090] In one possible embodiment, a device management and debugging strategy can be generated if it is determined that the target production event has device resource occupancy.

[0091] This disclosure discloses a method for parsing descriptive content. After the parsing system collects automated production operation data for improving equipment status analysis, step S501 can be executed. A first calling thread parses the production events corresponding to production events and the production line equipment status corresponding to production line equipment status appearing in the automated production operation data for improving equipment status analysis. Target production events corresponding to target production events are then filtered. The descriptive content set composed of the target production events in the automated production operation data for improving equipment status analysis is determined as the production event descriptive content set, and the descriptive content set composed of the production line equipment status in the automated production operation data for improving equipment status analysis is determined as the production line equipment status descriptive content set.

[0092] Then, step S502 can be executed to determine the comparison evaluation between the status description content set of each production line equipment and the production event description content set, and to determine the target production line equipment status description content set corresponding to the maximum comparison evaluation as the potential production line equipment status description content set that has a locational matching relationship with the production event description content set. By comparing the locational matching between production events and the mined production line equipment statuses, the potential production line equipment status description content sets that have a matching relationship with the production event description content set can be accurately determined, which helps to improve the completeness of semantic parsing of production line equipment status and obtain accurate description content parsing information.

[0093] This disclosure discloses a method for mining equipment resource occupancy. First, step S602 is executed to determine the parsing information expressed by the production line equipment call count parsing information. If the production line equipment call count parsing information is abnormal, then the equipment resource occupancy parsing for the target production event can be omitted.

[0094] If the number of production line equipment calls expressed in the production line equipment call count parsing information reaches the point where three production line equipment are occupied, it is determined that the target production event has equipment resource occupancy.

[0095] If the production line equipment call count parsing information indicates that two production line equipment have been occupied, then S604 can be further executed to determine whether the production line equipment status semantics expressed by the semantic parsing information are abnormal.

[0096] If the production line equipment status semantics are abnormal, then the target production event is determined to have equipment resource occupancy; otherwise, the target production event is determined not to have equipment resource occupancy.

[0097] If the production line equipment call count parsing information indicates that one production line equipment is already in use, then it is determined that the target production event does not have equipment resource occupancy.

[0098] Therefore, on the one hand, it eliminates the need for parsing descriptions for specific target tasks, thus improving the accuracy of description parsing; on the other hand, it can output efficient production parsing information that meets real-time requirements for different production line equipment status semantic tasks, thereby improving parsing accuracy.

[0099] Based on the above, please refer to the following: Figure 2 A device 200 based on a production data-driven operation platform is provided, which is applied to a device management system based on a production data-driven operation platform. The device includes:

[0100] Content determination module 210 is used to determine the automated production operation data of the equipment status analysis to be improved; determine the potential production line equipment status description content set that has a matching relationship with the production event description content set in the automated production operation data of the equipment status analysis to be improved, which is contained in the production line equipment status description content set of the automated production operation data of the equipment status analysis to be improved. The production event description content set includes the production line equipment status and at least one production event identifier.

[0101] The information acquisition module 220 is used to perform production line equipment call count parsing on the production event description content set to obtain production line equipment call count parsing information, and to perform production line equipment status semantic parsing on the potential production line equipment status description content set to obtain production line equipment status semantic parsing information.

[0102] The situation determination module 230 is used to determine whether the target production event in the production event description content set has equipment resource occupancy based on the production line equipment call number parsing information and the production line equipment status semantic parsing information.

[0103] Based on the above, please refer to the following: Figure 3 The present invention illustrates an equipment management system 300 based on a production data operation platform, comprising a processor 310 and a memory 320 that communicate with each other. The processor 310 is used to read computer programs from the memory 320 and execute them to implement the above-described method.

[0104] Based on the above, a computer-readable storage medium is also provided, on which a computer program stored implements the above method during runtime.

[0105] In summary, based on the above-mentioned solution, in the technical solution disclosed in this disclosure, on the one hand, the method uses an AI thread to parse the production event description content set to determine the number of production line equipment calls. The calling thread can intelligently determine the number of production line equipment calls in the production event description content set. Thus, even if there are interference problems in the automated production operation data where equipment status analysis needs to be improved, the accurate number of production line equipment calls can still be parsed, thereby improving the completeness of equipment resource usage analysis.

[0106] On the other hand, the accuracy of the description can be analyzed based on the parsing information of the number of production line equipment calls and the semantic parsing information of the production line equipment status. By referring to the semantics of the production line equipment status and the number of production line equipment calls when performing accuracy analysis, the reliability of equipment resource usage analysis can be improved for different semantic production line equipment statuses.

[0107] It should be understood that the systems and modules described above can be implemented in various ways. For example, in some embodiments, the systems and modules can be implemented by hardware, software, or a combination of both. The hardware portion can be implemented using dedicated logic; the software portion can be stored in memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated-design hardware. Those skilled in the art will understand that the methods and systems described above can be implemented using computer-executable instructions and / or included in processor control code, for example, such code provided on a carrier medium such as a disk, CD, or DVD-ROM, a programmable memory such as read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The systems and modules of this application can be implemented not only by hardware circuits such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field-programmable gate arrays, programmable logic devices, etc., but also by software executed by various types of processors, or by a combination of the aforementioned hardware circuits and software (e.g., firmware).

[0108] It should be noted that different embodiments may produce different beneficial effects. In different embodiments, the beneficial effects may be any one or a combination of the above, or any other possible beneficial effects.

[0109] The basic concepts have been described above. Obviously, for those skilled in the art, the detailed disclosure above is merely illustrative and does not constitute a limitation of this application. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements, and corrections to this application. Such modifications, improvements, and corrections are suggested in this application, and therefore remain within the spirit and scope of the exemplary embodiments of this application.

[0110] Furthermore, this application uses specific terms to describe embodiments of the application. For example, "an embodiment," "one embodiment," and / or "some embodiments" refer to a particular feature, structure, or characteristic associated with at least one embodiment of the application. Therefore, it should be emphasized and noted that "an embodiment," "one embodiment," or "an alternative embodiment" mentioned twice or more in different locations in this specification do not necessarily refer to the same embodiment. In addition, certain features, structures, or characteristics in one or more embodiments of the application can be appropriately combined.

[0111] Furthermore, those skilled in the art will understand that aspects of this application can be described and illustrated through several patentable types or situations, including any new and useful combination of processes, machines, products, or substances, or any new and useful improvements thereof. Accordingly, aspects of this application can be implemented entirely by hardware, entirely by software (including firmware, resident software, microcode, etc.), or by a combination of hardware and software. All of the above hardware or software may be referred to as a “data block,” “module,” “engine,” “unit,” “component,” or “system.” Furthermore, aspects of this application may manifest as a computer product located on one or more computer-readable media, the product including computer-readable program code.

[0112] Computer storage media may contain a propagated data signal containing computer program code, for example, on baseband or as part of a carrier wave. This propagated signal may take various forms, including electromagnetic, optical, and suitable combinations thereof. Computer storage media can be any computer-readable medium other than a computer-readable storage medium, which can be connected to an instruction execution system, apparatus, or device to enable communication, propagation, or transmission of a program for use. The program code located on the computer storage medium can be propagated through any suitable medium, including radio, cable, fiber optic cable, RF, or similar media, or any combination of the above media.

[0113] The computer program code required for the operation of each part of this application can be written in any one or more programming languages, including object-oriented programming languages ​​such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB.NET, Python, etc., conventional procedural programming languages ​​such as C, Visual Basic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP, dynamic programming languages ​​such as Python, Ruby, and Groovy, or other programming languages. This program code can run entirely on the user's computer, or as a standalone software package on the user's computer, or partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the latter case, the remote computer can be connected to the user's computer through any network, such as a local area network (LAN) or wide area network (WAN), or connected to an external computer (e.g., via the Internet), or in a cloud computing environment, or used as a service such as Software as a Service (SaaS).

[0114] Furthermore, unless expressly stated in the claims, the order of processing elements and sequences, the use of numbers and letters, or other names described in this application are not intended to limit the order of the processes and methods of this application. Although the foregoing disclosure has discussed some currently considered useful embodiments of the invention through various examples, it should be understood that such details are for illustrative purposes only, and the appended claims are not limited to the disclosed embodiments; rather, the claims are intended to cover all modifications and equivalent combinations that conform to the substance and scope of the embodiments of this application. For example, while the system components described above can be implemented using hardware devices, they can also be implemented solely through software solutions, such as installing the described system on existing servers or mobile devices.

[0115] Similarly, it should be noted that, in order to simplify the description of the present application and thus aid in the understanding of one or more embodiments of the invention, the foregoing description of the embodiments of the present application sometimes combines multiple features into a single embodiment, drawing, or description thereof. However, this disclosure method does not imply that the subject matter of the application requires more features than those mentioned in the claims. In fact, the embodiments contain fewer features than all the features of the single embodiments disclosed above.

[0116] In some embodiments, numbers describing the quantity of components and attributes are used. It should be understood that such numbers used in the description of embodiments are modified in some examples with the terms "approximately," "approximately," or "generally." Unless otherwise stated, "approximately," "approximately," or "generally" indicates that the numbers are open to adaptive variation. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which may be changed depending on the characteristics required by individual embodiments. In some embodiments, numerical parameters are taken into account a specified number of significant digits and employ a general method of digit reservation. Although the numerical ranges and parameters used to confirm their breadth of application in some embodiments of this application are approximate values, in specific embodiments, such values ​​are set as precisely as feasible.

[0117] For each patent, patent application, patent application publication, and other material such as articles, books, specifications, publications, and documents referenced in this application, the entire contents of that patent are incorporated herein by reference. This excludes historical application documents that are inconsistent with or conflict with the content of this application, as well as documents that limit the broadest scope of the claims in this application (currently or subsequently appended to this application). It should be noted that if there are any inconsistencies or conflicts between the descriptions, definitions, and / or terminology used in the supplementary materials of this application and the content of this application, the descriptions, definitions, and / or terminology used in this application shall prevail.

[0118] Finally, it should be understood that the embodiments described in this application are merely illustrative of the principles of the embodiments of this application. Other modifications may also fall within the scope of this application. Therefore, alternative configurations of the embodiments of this application are considered as examples and not limitations, and are regarded as consistent with the teachings of this application. Accordingly, the embodiments of this application are not limited to the embodiments explicitly described and illustrated in this application.

[0119] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for equipment management based on a production data-driven operation platform, characterized in that, The application device management system, the method includes: Identify the automated production operation data for which the equipment status analysis needs to be improved; identify the potential production line equipment status description content set that matches the production event description content set in the automated production operation data for which the equipment status analysis needs to be improved, wherein the production event description content set includes the production line equipment status and at least one production event identifier. The production event description content set is parsed to obtain production line equipment call count parsing information, and the potential production line equipment status description content set is parsed to obtain production line equipment status semantic parsing information. Based on the production line equipment call count parsing information and the production line equipment status semantic parsing information, determine whether the target production event in the production event description content set has equipment resource occupancy status. The potential production line equipment status description set that matches the set of production event descriptions in the automated production operation data containing the equipment status analysis to be improved includes: Mining the automated production operation data of the equipment status analysis to be improved yields the set of production line equipment status descriptions and the set of production event descriptions. The quantization matching data recognition network that has been debugged beforehand is invoked to determine the quantization matching data between the production line equipment status description content set and the production event description content set. The target production line equipment status description set that best matches the quantitative matching data of the production event description set is determined as the potential production line equipment status description set that has a matching relationship with the production event description set.

2. The method as described in claim 1, characterized in that, The step of parsing the production line equipment call count of the production event description content set to obtain production line equipment call count parsing information includes: parsing the production line equipment call count of the production event description content set to obtain the production line equipment call count and the corresponding first probability evaluation; based on the first probability evaluation reaching the first probability evaluation judgment value, determining the production line equipment call count as the production line equipment call count parsing information of the production event description content set. The step of performing production line equipment state semantic parsing on the potential production line equipment state description content set to obtain production line equipment state semantic parsing information includes: performing production line equipment state semantic parsing on the potential production line equipment state description content set to obtain production line equipment state semantics and corresponding second probability evaluation; based on the second probability evaluation reaching the second probability evaluation judgment value, determining the production line equipment state semantics as the production line equipment state semantic parsing information of the production line equipment state description content set.

3. The method as described in claim 1, characterized in that, Based on the parsing information of the number of production line equipment calls and the semantic parsing information of the production line equipment status, it is determined whether the target production event in the production event description content set has equipment resource occupancy, including one of the following: Based on the parsing information of the number of production line equipment calls as the first parsing information, it is determined that the target production event has equipment resource occupancy; the first parsing information indicates that the number of production line equipment calls has reached a first set number. Based on the production line equipment call count parsing information as second parsing information, and the production line equipment status semantics expressed by the semantic parsing information as the set production line collaboration rejection semantics, it is determined that the target production event has equipment resource occupancy; the second parsing information indicates that the production line equipment call count has reached a second set number, and the second set number is less than the first set number; Based on the production line equipment call count parsing information, the production line equipment call count is the second parsing information, and the production line equipment status semantics expressed by the semantic parsing information is not the set production line collaboration rejection semantics, it is determined that the target production event does not have equipment resource occupation. Based on the production line equipment call count parsing information as third parsing information, it is determined that the target production event does not have equipment resource occupancy; the third parsing information expresses that the production line equipment call count is a third set number, and the third set number is less than the second set number. Based on the fact that the production line equipment call count parsing information is the fourth parsing information, it is determined that there is an anomaly in the equipment resource usage parsing for the target production event.

4. The method as described in claim 3, characterized in that, The fourth parsing information expresses that the automated production operation data for which the equipment status analysis needs to be improved corresponds to at least one of the following target tasks: a task where production events are delayed; a task where production events are suspended; a task where several production events have upstream and downstream relationships; a task where production demand indicators do not meet the set indicators; and a task where the status of production line equipment is deviated.

5. The method as described in claim 1, characterized in that, The method further includes: generating an equipment management and debugging strategy based on the equipment resource occupancy status of the target production event.

6. The method as described in claim 1, characterized in that, The production line equipment call count parsing information is obtained by mining the production event description content set using the resource usage parsing network. The debugging steps of the resource usage parsing network are as follows: A first debugging template is determined. The first debugging template includes template automated production operation data for multiple production events and first indication data for the number of production line equipment calls corresponding to each set of automated production operation data. The first indication data includes one of the following keywords: one production line equipment is occupied, two production line equipment are occupied, three production line equipment are occupied, and there are abnormal keywords. The abnormal keywords include at least one of the following situations: production event delay, production event pause, several production events have upstream and downstream relationships, production demand indicators do not meet the set indicators, and there is a deviation in the status of production line equipment. Load the first debug template into the first undebugged thread to obtain the template production line equipment call count parsing information for each group of template automated production operation data; Based on the template production line equipment call count parsing information and the first thread cost determined by the first indication data, the first un-debugged thread is debugged to obtain the resource usage parsing network.

7. The method as described in claim 1, characterized in that, The production line equipment status resolution information is obtained by mining the production line equipment status description content set through the production line equipment status resolution network. The debugging steps of the production line equipment status resolution network are as follows: A second debugging template is determined, which includes template automated production operation data of multiple production line equipment statuses and second indication data of production line equipment status semantics corresponding to each set of automated production operation data. Load the second debug template into the set second undebugged thread to obtain the template production line equipment status semantic parsing information of each group of template automated production operation data; Based on the semantic parsing information of the template production line equipment status and the second thread cost determined by the second indication data, the second un-debugged thread is debugged to obtain the production line equipment status parsing network.

8. An equipment management system based on a production data-driven operation platform, characterized in that, The method includes a processor and a memory that communicate with each other, the processor being configured to retrieve a computer program from the memory and to implement the method of any one of claims 1-7 by running the computer program.

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