Wire production line data acquisition control method and system

Abstract

The invention relates to the field of electric wire production line data acquisition control, and provides an electric wire production line data acquisition control method and system, which can realize refined acquisition, segmentation and semantic binding of energy consumption data by responding to PLC anchoring signals to segment energy consumption data and associating semantic information such as production events and work orders. The method has the advantages that the problem that in the prior art, energy consumption data is difficult to accurately synchronize and bind with information such as production events, work orders and products is solved, and refined segmentation and semantic association of the energy consumption data are achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of wire production line data acquisition control, in particular to a wire production line data acquisition control method and system. BACKGROUND

[0002] In a small batch, multi-species wire production environment, production plan and process parameter adjustment are frequent, resulting in rapid changes in energy consumption characteristics of each stage of the production line. When collecting energy consumption data, the prior art has difficulty in accurately synchronizing and binding these data with specific production work orders, product specifications, process parameter set values, and production events such as device start-stop, material switching, etc. This results in fuzzy sub-item energy consumption measurement results, which cannot be accurately attributed to specific work orders or products, affecting the accurate accounting of production costs. In addition, the existing method has deficiencies in synchronously collecting and correlating key process parameters and production result information, making it difficult to delve into specific process steps or operating behaviors for energy efficiency analysis. At the same time, the energy consumption collection of auxiliary energy-consuming units lacks effective linkage with the main production line state, making it difficult to accurately assess their energy consumption contribution. The prior art also lacks the ability to respond to specific production events to trigger targeted data segmentation collection and special situation identification, making it difficult to compare and analyze energy efficiency based on specific scenarios.

[0003] In view of the above problems, the prior art needs to be improved. SUMMARY

[0004] The purpose of the present application is to provide a wire production line data acquisition control method and system, which solves the problem that energy consumption data and production events, work orders, products, etc. cannot be accurately synchronized and bound in the prior art, and has the advantages of fine segmentation and semantic correlation of energy consumption data.

[0005] The present application provides a wire production line data acquisition control method, the technical scheme is as follows:

[0006] A wire production line data acquisition control method, the wire production line includes a programmable logic controller PLC and a manufacturing execution system MES, the method comprising:

[0007] Obtaining a PLC anchoring signal inside the programmable logic controller PLC; the PLC anchoring signal is at least one of a relay state, an input point state, a data register value, and a logic flag bit; the PLC anchoring signal is used to identify production device start, stop, process parameter adjustment, or material switching events;

[0008] In response to the state change of the PLC anchoring signal, the energy consumption data stream collected by the wire production line is segmented to obtain a plurality of energy consumption data segments; wherein any energy consumption data segment includes start and end time markers;

[0009] According to any energy consumption data segment, a semantic information is determined, and the energy consumption data segment and the corresponding semantic information are reported to perform encapsulated storage; the semantic information includes at least one of a PLC anchor signal name corresponding to a trigger of the energy consumption data segment start and end time range, a process, an MES work order number, a product code, and a process parameter setting value.

[0010] Further, the application also provides an electric wire production line data acquisition control method, and the method further comprises pre-establishing a mapping rule of MES work order information and a PLC anchor signal, including:

[0011] Correlating work order information issued by a manufacturing execution system (MES) with a PLC anchor signal, so as to obtain corresponding changed MES work order information in response to a state change of the PLC anchor signal; the MES work order information includes product specification information and / or process flow information.

[0012] Further, the application also provides an electric wire production line data acquisition control method, and according to any energy consumption data segment, a semantic information is determined, and the energy consumption data segment and the corresponding semantic information are reported to perform encapsulated storage, including:

[0013] Monitoring energy consumption characteristics in the energy consumption data segment; if the energy consumption characteristics present a preset characteristic change, a non-anchor running parameter in the PLC is obtained; the non-anchor running parameter is related to an electric wire production line operation state;

[0014] According to the characteristic change of the energy consumption characteristics and the non-anchor running parameter, an adjusted process meaning is determined from a pre-set working condition rule library, so as to supplement or correct a process meaning represented by the PLC anchor signal;

[0015] According to the adjusted process meaning and the MES work order information, a semantic information is determined, and the energy consumption data segment is encapsulated.

[0016] Further, the application also provides an electric wire production line data acquisition control method, and according to the characteristic change of the energy consumption characteristics and the non-anchor running parameter, an adjusted process meaning is determined from a pre-set working condition rule library, including:

[0017] The characteristic change of the energy consumption characteristics and the non-anchor running parameter are used to perform matching and searching in the pre-set working condition rule library, so as to obtain the adjusted process meaning.

[0018] Further, the application also provides an electric wire production line data acquisition control method, and according to the characteristic change of the energy consumption characteristics and the non-anchor running parameter, an adjusted process meaning is determined from a pre-set working condition rule library, further including:

[0019] If the adjustment process meaning is not found in the preset process condition rule library, the historical process meaning with a similarity satisfying a preset condition is determined as the adjustment process meaning according to the feature change of the energy consumption characteristic, the non-anchor running parameter, the product specification information and / or the process flow information in the MES work order information, and the historical working condition data.

[0020] Further, the application also provides an electric wire production line data acquisition control method, and the historical process meaning with a similarity satisfying a preset condition is determined as the adjustment process meaning according to the feature change of the energy consumption characteristic, the non-anchor running parameter, the product specification information and / or the process flow information in the MES work order information, and the historical working condition data, including:

[0021] According to the MES work order information, the contribution degree parameters corresponding to the feature change of the energy consumption characteristic, the non-anchor running parameter, the product specification information and the process flow information are matched and acquired from the contribution degree rule set;

[0022] According to the contribution degree parameters, the matching conditions of the feature change of the energy consumption characteristic, the non-anchor running parameter, the product specification information and the process flow information and the historical corresponding items are integrated to obtain a comprehensive similarity;

[0023] According to the comprehensive similarity, the corresponding historical process meaning is selected from the historical working condition data as the adjustment process meaning.

[0024] Further, the application also provides an electric wire production line data acquisition control method, and the energy consumption data stream is the electric parameter of each energy consumption device of the electric wire production line, including: current, voltage and / or power;

[0025] In response to the state change of the PLC anchor signal, the energy consumption data stream collected by the electric wire production line is processed in segments to obtain a plurality of energy consumption data segments, including: in response to the state change of the PLC anchor signal, the current time is determined as an energy consumption data segmentation point, so that the continuous energy consumption data stream is cut into a plurality of energy consumption data segments with start and end time marks.

[0026] Further, the application also provides an electric wire production line data acquisition control method, and according to the MES work order information, the contribution degree parameters corresponding to the feature change of the energy consumption characteristic, the non-anchor running parameter, the product specification information and the process flow information are matched and acquired from the contribution degree rule set, including:

[0027] The approximate degree of the MES work order information and each rule condition in the contribution degree rule set is evaluated to obtain an approximate degree evaluation result;

[0028] Based on the similarity evaluation results, at least one rule whose similarity meets a preset similarity standard is selected, and a historical contribution parameter corresponding to the at least one rule is obtained; the historical contribution parameter includes the historical contribution of characteristic changes in energy consumption characteristics, non-anchored operating parameters, product specification information, and process flow information in the similarity calculation;

[0029] Based on the similarity evaluation results and the historical contribution parameters obtained, the contribution parameters applicable to the current MES work order information are generated through parameter adjustment logic; the contribution parameters include the characteristic changes of energy consumption characteristics, non-anchored operating parameters, product specification information, and process flow information, and their respective contributions in the similarity calculation.

[0030] Furthermore, the present application also proposes a method for controlling data acquisition of a wire production line, which generates contribution parameters applicable to the current MES work order information based on the approximation evaluation results and the acquired historical contribution parameters through parameter adjustment logic, including:

[0031] Based on the approximation evaluation results, a weight value is set for the historical contribution parameter corresponding to each approximation rule; the weight value is determined by the approximation degree between the approximation rule and the MES work order information corresponding to the current working condition;

[0032] Using the preset aggregation calculation method, each historical contribution parameter is aggregated based on its corresponding weight value to generate a contribution parameter applicable to the current MES work order information.

[0033] Furthermore, the present application also proposes a data acquisition and control system for a wire production line, wherein the wire production line includes a programmable logic controller (PLC) and a manufacturing execution system (MES), and the system includes:

[0034] An acquisition module is used to obtain a PLC anchor signal inside a programmable logic controller (PLC); the PLC anchor signal is at least one of a relay state, an input point state, a data register value, and a logic flag; the PLC anchor signal is used to identify production equipment start-up, stop, process parameter adjustment, or material switching events;

[0035] The collection and segmentation module is used to segment the energy consumption data stream collected by the wire production line in response to the state change of the PLC anchor signal to obtain multiple energy consumption data segments; wherein each energy consumption data segment includes a start and end time mark;

[0036] The encapsulation module is used to determine a semantic information based on any energy consumption data segment, and to encapsulate and store the energy consumption data segment and its corresponding semantic information report; the semantic information includes the PLC anchor signal name corresponding to the start and end time range of the energy consumption data segment and at least one of its process, MES work order number, product code, and process parameter setting value.

[0037] From the above, the application provides a kind of electric wire production line data acquisition control method and system, by responding to the energy consumption data of PLC anchor signal section and associated production event, work order etc. Semantics information, realizes the fine collection of energy consumption data, segmentation and semantic binding, with the problem of energy consumption data and production event, work order, product etc. Information is difficult to accurately synchronize and bind in the prior art, realizes the fine segmentation and semantic association of energy consumption data The advantages of. BRIEF DESCRIPTION OF DRAWINGS

[0038] In order to more clearly illustrate the technical solutions of the embodiments of the application, the drawings needed in the embodiments will be briefly introduced below, and it should be understood that the following drawings only show some embodiments of the application, and therefore should not be regarded as limiting the scope, and for those skilled in the art, other related drawings can also be obtained without creative labor.

[0039] Figure 1 It is the flowchart of the electric wire production line data acquisition control method steps disclosed by the embodiments of the application;

[0040] Figure 2 It is the structure diagram of the electric wire production line data acquisition control system disclosed by the embodiments of the application. DETAILED DESCRIPTION

[0041] The technical solutions in the application will be described clearly and completely in the application combined with the drawings, obviously, the described embodiments are only part of the application, not all. The components of the application described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the application provided in the drawings is not intended to limit the scope of the claimed application, but only represents selected embodiments of the application. Based on the embodiments of the application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of the application.

[0042] It should be noted that: similar signs and letters represent similar items in the following drawings, therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of the application, the terms "first", "second" and the like are only used to distinguish description, and cannot be understood as indicating or implying relative importance.

[0043] Traditional data collection and control methods for wire production lines struggle to synchronize collected energy consumption data with specific work orders, product specifications, and process stages down to the equipment action level, navigating the frequent production plan adjustments, equipment startups and shutdowns, and operating parameter changes inherent in small-batch, high-variety production models. Furthermore, they struggle to synchronize and correlate key process parameters with production results. They fail to effectively coordinate and categorize the energy consumption data collected from auxiliary energy-consuming units based on the actual operating status of the main production line, and lack the ability to trigger targeted data segmentation and contextual identification for specific production events.

[0044] In this regard, the present application proposes a data acquisition and control method for a wire production line, wherein the wire production line includes a programmable logic controller (PLC) and a manufacturing execution system (MES). Figure 1 As shown, the method includes:

[0045] S101, obtaining a PLC anchor signal within a programmable logic controller (PLC); the PLC anchor signal is at least one of a relay state, an input point state, a data register value, and a logic flag; the PLC anchor signal is used to identify production equipment start, stop, process parameter adjustment, or material switching events;

[0046] S102, in response to a state change of the PLC anchor signal, segmenting the energy consumption data stream collected by the electric wire production line to obtain a plurality of energy consumption data segments; wherein each energy consumption data segment includes a start and end time stamp;

[0047] S103, determine a semantic information based on any energy consumption data segment, and encapsulate and store the energy consumption data segment and its corresponding semantic information report; the semantic information includes the PLC anchor signal name corresponding to the start and end time range of the energy consumption data segment, and at least one of its process, MES work order number, product code, and process parameter setting value.

[0048] The PLC anchor signal refers to a signal within the programmable logic controller (PLC) used to identify production equipment start-up, shutdown, process parameter adjustment, or material switching events. It can be implemented using at least one of relay status, input point status, data register value, and logic flags, such as the on / off state of an output relay, the high or low level of a sensor input signal, a value representing the equipment's operating mode within a storage area, or the result of an internal logic operation. Its primary purpose is to capture key state change points during the production process. The energy consumption data stream refers to the continuous energy consumption data collected by the wire production line. It can be implemented using the electrical parameters of each energy-consuming device in the wire production line, such as real-time measurements of current, voltage, and / or power. It primarily reflects the energy consumption of production equipment at different time points. Segmentation processing refers to the segmentation of the energy consumption data stream collected by the wire production line in response to changes in the state of the PLC anchor signal. This can be implemented by defining a time point at the moment of the signal state change as a segmentation marker for the data stream. Its primary purpose is to divide the continuous energy consumption data stream according to production events. An energy consumption data segment refers to a data set with an independent time range obtained after segmentation processing, wherein any energy consumption data segment includes a start and end time mark, which is mainly used to carry energy consumption data under a specific production stage or working condition. Semantic information refers to information describing the production status corresponding to the data segment determined based on the energy consumption data segment, which may include the name of the PLC anchor signal corresponding to the start and end time range of the energy consumption data segment and at least one of its process, MES work order number, product code, and process parameter setting value, which is mainly used to give business meaning and production background to the energy consumption data segment. Encapsulation storage refers to packaging and saving energy consumption data segments and their corresponding semantic information. It can be achieved by organizing the data segments and semantic information into a specific data structure and writing it into a database or file system. It is mainly to facilitate subsequent query, analysis, and utilization of energy consumption data.

[0049] In some preferred embodiments, the wire production line can employ a Siemens S7-1500 series PLC. The PLC anchor signal can be specifically set as the state of an output relay representing the running state of the device, the state of an optical sensor input point representing the workpiece in place, the value of a register storing the current pulling speed setting, or a logic flag representing the end of the changeover process. The energy consumption data stream can be collected by smart meters or power sensors installed on the power supply lines of major energy-consuming devices such as extruders and drawing machines, which upload real-time electrical parameter data through the Profinet network. The data collection and segmentation processing functions can be implemented on an industrial PC that communicates with the PLC to obtain anchor signal states through the OPC UA protocol, and obtains energy consumption data streams from smart meters through the Modbus TCP protocol. When the industrial PC detects a change in the state of the PLC anchor signal, it records the current system time as a segmentation point and cuts the energy consumption data stream before and after that time point into segments. The encapsulation module can integrate the cut energy consumption data segments (e.g., an array containing timestamps and power values) with the anchor signal name obtained from the PLC, the current MES work order number obtained from the MES system through the API interface, and the product code, etc. into a JSON object. The encapsulated JSON object can be stored in a MongoDB database for subsequent querying and analysis.

[0050] Through the above technical solution, the energy consumption data can be accurately synchronized and associated with key production events such as the start and stop of production equipment, process parameter adjustment, or material switching. Each energy consumption data segment has clear start and end time markers and associated semantic information, so that energy consumption data is no longer isolated values, but business-meaning, traceable data. This fine-grained, semantic data collection method solves the problem of misplacement and difficulty in accurate attribution of energy consumption data and production process information in the prior art. Based on accurate segmentation and semantic association of energy consumption data, more accurate unit product energy consumption accounting, energy consumption analysis of different processes or production stages, and accurate positioning of energy consumption anomalies can be performed. This helps enterprises to carry out fine energy efficiency management, optimize production scheduling, reduce energy costs, and provide a reliable basis for product cost accounting.

[0051] The present application further proposes that the method further comprises pre-establishing a mapping rule between MES work order information and PLC anchor signals, including:

[0052] Correlating the work order information issued by the manufacturing execution system (MES) with the PLC anchor signal, so as to obtain the corresponding changed MES work order information in response to the state change of the PLC anchor signal; the MES work order information includes product specification information and / or process flow information.

[0053] The pre-established mapping rule of the MES work order information and the PLC anchoring signal refers to defining a corresponding relationship between specific fields in the MES work order, such as a work order number, a product code, and a process route, and specific anchoring signals in the PLC that reflect production states or process stages, such as a device running / stop flag, a product switching completion flag, and a process parameter setting value change flag. The mapping rule can be implemented by using a configuration table, a database association, or software logic. The purpose is to provide a basis for subsequently associating energy consumption data with specific production tasks and process backgrounds.

[0054] The association of the work order information issued by the manufacturing execution system (MES) and the PLC anchoring signal refers to binding the current valid MES work order information and the real-time monitored PLC anchoring signal according to the pre-established mapping rule during system operation. Specifically, when a new MES work order or a PLC anchoring signal state change is received, the current association state is updated. The purpose is to ensure that accurate MES work order information corresponding to the change time can be obtained when the PLC anchoring signal change triggers energy consumption data segmentation.

[0055] The MES work order information includes product specification information and / or process flow information, which means that the MES work order information contains detailed specifications of the product being produced, such as model, size, material, and production process steps and parameter settings required for the product. The purpose is to provide more rich production background information for energy consumption data segments, so that energy consumption analysis can distinguish the energy consumption characteristics of different products and different process stages.

[0056] The scheme of the present application introduces MES work order information based on PLC anchoring signal-based energy consumption data segmentation and associates the MES work order information with the PLC anchoring signal through a pre-established mapping rule. When the state of the PLC anchoring signal changes and triggers the segmentation of the energy consumption data stream, the system can simultaneously obtain the MES work order information containing product specification information and / or process flow information corresponding to the signal change time according to this association mechanism. The obtained MES work order information combined with the preliminary semantic information determined by the PLC anchoring signal itself makes the semantic information of the energy consumption data segment more rich and accurate. For example, the same device start / stop signal may correspond to different products or different process stages under different work orders. By obtaining the corresponding MES work order information, the energy consumption data segment can be accurately labeled as the energy consumption of a specific work order, a specific product, or a specific process stage. This way effectively integrates device-level state changes with management-level production task information, solves the problem of not being able to fully reflect production tasks and process requirements by relying solely on device state signals, improves the accuracy and completeness of energy consumption data semantic information, and provides a more reliable data foundation for subsequent fine energy efficiency analysis and cost accounting based on products, work orders, or process stages.

[0057] In some embodiments, specifically, assuming that the wire production line is executing an MES work order that specifies the production of a specific model of cable, including the process steps of wire drawing, wire bundling, extrusion, etc. The system has previously established a mapping table in the database that defines the correspondence between the fields in the MES work order, such as work order number, product model, current process step, etc. and specific data register addresses or relay numbers in the PLC used to identify device status or process switching. When the production line completes the wire bundling process and begins to enter the extrusion process, a logical flag bit in the PLC representing the "start of extrusion process" changes from false to true. The system monitors the state change of this PLC anchor signal and immediately marks a segment point in the energy consumption data stream. At the same time, the system queries the current valid MES work order information according to the pre-established mapping table and obtains the detailed information of the work order, such as the work order number "WO20231201-001", the product model "ABC-123", and the current process step "extrusion". These obtained MES work order information, together with the PLC anchor signal information that triggered the segmentation, are used to determine the semantic information of the current energy consumption data segment, for example, marking the data segment as "work order WO20231201-001, product ABC-123, extrusion process energy consumption".

[0058] Through the above technical solution, the work order information issued by the manufacturing execution system MES is associated with the programmable logic controller PLC anchor signal, and the corresponding changed MES work order information is obtained in response to the state change of the PLC anchor signal, so that the semantic information of the energy consumption data segment can include product specification information and / or process flow information. This improves the accuracy and completeness of the energy consumption data semantic information, and can more accurately correspond the energy consumption data to specific production tasks and process background, providing a more reliable data basis for subsequent fine energy efficiency analysis and cost accounting based on products, work orders or process stages.

[0059] The present application further proposes determining a semantic information according to any energy consumption data segment, and packaging and storing the energy consumption data segment and its corresponding semantic information, including:

[0060] Monitoring the energy consumption characteristics within the energy consumption data segment; if the energy consumption characteristics present a preset characteristic change, obtaining a non-anchor running parameter inside the PLC; the non-anchor running parameter is related to the wire production line operation state;

[0061] According to the characteristic change of the energy consumption characteristics and the non-anchor running parameter, determining an adjusted process meaning from a pre-set working condition rule library to supplement or correct the process meaning represented by the PLC anchor signal;

[0062] According to the adjusted process meaning and the MES work order information, determining a semantic information, and packaging the energy consumption data segment.

[0063] wherein, the energy consumption characteristics refer to the change trend or mode of the electrical parameters reflecting the running state of the device in the energy consumption data segment, such as the average value, peak value, fluctuation amplitude, change rate of power, current, voltage, etc., the purpose of which is to infer the specific working condition of the device by analyzing the actual energy consumption performance; the preset characteristic change refers to the energy consumption characteristic mode that is predefined and can indicate that the state or working condition of the device may change, such as sudden increase or decrease of power, continuous fluctuation of current exceeding the threshold, abnormal peak of voltage, etc., the purpose of which is to serve as a condition for triggering further analysis (acquiring non-anchor running parameters); the non-anchor running parameters refer to the running data related to the operating state of the wire production line inside the PLC, but not as the main segmentation basis, such as the actual temperature of each heating zone of the device, screw speed, traction speed, melt pressure, cooling water flow, material consumption rate, etc., the purpose of which is to provide more detailed and comprehensive device running detail information than the PLC anchor signal; the preset working condition rule library refers to the knowledge base or lookup table that is pre-established and stores the corresponding relationship between the characteristic change of the energy consumption characteristics, the non-anchor running parameters and the specific working procedure meaning, the purpose of which is to automatically infer more accurate working procedure meaning by matching the energy consumption characteristics and non-anchor running parameters collected at present; the adjusted working procedure meaning refers to the more specific or accurate description of the working procedure obtained by analyzing the energy consumption characteristics and non-anchor running parameters, which is used to supplement or correct the working procedure indicated by the PLC anchor signal, such as refining the "extrusion running" indicated by the PLC anchor signal into "extrusion running-warming-up phase" or "extrusion running-stable phase", the purpose of which is to improve the granularity and accuracy of the working procedure information; encapsulation refers to the operation of associating and storing the energy consumption data segment with the determined semantic information, which can specifically be binding the energy consumption data segment as the main body and the semantic information as its metadata or label to form a structured data package, the purpose of which is to closely combine the energy consumption data with the production background information generating the energy consumption, which is convenient for subsequent query, analysis and utilization.

[0064] The solution of the present application captures subtle changes in the actual operating state of the equipment by monitoring the energy consumption characteristics within the energy consumption data segment. When the energy consumption characteristics show a preset characteristic change, it indicates that the equipment may be in a certain specific or transitional operating condition, and at this time, the non-anchored operating parameters inside the PLC are further obtained. These non-anchored operating parameters provide richer equipment operation details than the PLC anchor signal, such as specific temperature, speed, pressure, etc. Based on the characteristic changes of energy consumption characteristics and non-anchored operating parameters, the system queries the preset operating condition rule base, which presets the correspondence between these parameter combinations and more detailed process meanings. Through matching and searching, the meaning of the adjustment process can be determined, and the adjustment process meaning is used to supplement or correct the basic process information provided by the PLC anchor signal. For example, the PLC anchor signal may only indicate "extrusion operation", but combined with the energy consumption characteristics (such as the power continues to increase) and non-anchored parameters (such as the heating zone temperature does not reach the set value), it can be determined that the adjustment process meaning is "extrusion operation-heating stage". Finally, this more precise meaning of the adjustment process is combined with the MES work order information (including product, process, and other information) to form the complete semantic information of the energy consumption data segment, and the energy consumption data segment and the semantic information are encapsulated and stored. It is precisely because of the comprehensive utilization of energy consumption characteristics, non-anchored operating parameters, and preset rule bases that this solution can identify subtle differences in operating conditions that cannot be distinguished by PLC anchor signals, thereby giving energy consumption data segments more precise semantic labels and solving the problem of inaccurate semantic information caused by relying solely on PLC anchor signals. This more precise semantic information enables subsequent energy efficiency analysis to be more accurately traced back to specific production stages and process states, improving the sophistication of energy efficiency management.

[0065] In some preferred embodiments, when the extruder power curve within a specific energy consumption data segment shows a sustained upward trend, the system triggers the acquisition of non-anchored operating parameters related to the extruder from the PLC, such as the actual temperature of each barrel section, screw speed, and melt pressure. Assume that at this point, the PLC anchor signal still indicates "extrusion operation," but the acquired non-anchored operating parameters indicate that the barrel temperature has not yet reached the process setpoint and the screw speed is low. The system matches this information, such as the sustained power increase, the barrel temperature not reaching the setpoint, and the low screw speed, with a pre-set operating condition rule base. The rule base may define rising power, temperatures not reaching the setpoint, and low speed as corresponding to the extrusion operation - heating up phase. Therefore, the adjustment process is determined to be the extrusion operation - heating up phase. This adjustment process is combined with the current MES work order information (e.g., work order number 12345, product code ABC, process parameter setpoint: extrusion temperature 180°C) to form the semantic information for this energy consumption data segment: work order 12345, product ABC, extrusion operation - heating up phase, setpoint temperature 180°C. Finally, the energy consumption data segment (including power, current and other data within the start and end time) is encapsulated with the semantic information and stored in the database.

[0066] The application further proposes that the step of determining the adjustment process meaning from the preset working condition rule library according to the characteristic change of the energy consumption characteristic and the non-anchor operation parameter comprises:

[0067] The characteristic change of the energy consumption characteristic and the non-anchor operation parameter are matched and searched in the preset working condition rule library to obtain the adjustment process meaning.

[0068] In order to better understand the technical solutions of the application, the technical features involved therein are described in detail below. The characteristic change of the energy consumption characteristic refers to a specific mode or form of the change of the energy consumption in the energy consumption data segment with time, which can be realized by using signal processing technology, and the purpose is to capture the energy performance of the change of the device state or the process parameter in the production process. The non-anchor operation parameter refers to a parameter related to the operation state of the wire production line but not directly triggering the segmentation of the PLC anchor signal, which can be obtained from the PLC or other field instruments, and the purpose is to provide more fine-grained working condition information than the anchor signal. The preset working condition rule library refers to a knowledge base pre-established and storing the mapping relationship between the characteristic change of the energy consumption characteristic, the non-anchor operation parameter and the corresponding adjustment process meaning, which can be stored in the form of a database, a lookup table or an expert system rule set, and the purpose is to provide a basis for determining the process meaning according to the energy consumption and the non-anchor parameter. The matching search refers to the process of searching for the corresponding record in the preset working condition rule library according to the current characteristic change of the energy consumption characteristic and the non-anchor operation parameter, which can be realized by using algorithms such as exact matching, fuzzy matching, rule-based reasoning or machine learning classification, and the purpose is to find the corresponding adjustment process meaning from the rule library. The adjustment process meaning refers to a more fine-grained or more accurate process description used to supplement or correct the process meaning represented by the PLC anchor signal, which can be represented in the form of a text string, an enumeration value or a code, and the purpose is to improve the accuracy and granularity of process identification.

[0069] Based on the above technical features, the scheme of the present application uses the characteristic change of energy consumption characteristics and non-anchor operation parameters to perform matching search in the pre-set working condition rule library to obtain the adjustment process meaning. This enables further analysis of the detailed working conditions within each energy consumption data segment after the energy consumption data stream is segmented in response to the change in the state of the PLC anchor signal. Specifically, when the energy consumption characteristics exhibit a pre-set characteristic change and the non-anchor operation parameters related to the electric wire production line operation state are obtained, these information are used as query conditions to perform search in the pre-set working condition rule library. The rule library pre-stores the corresponding relationship between different energy consumption characteristic changes, non-anchor parameter combinations and specific process meanings. Through the matching process, the rule item that best matches the current working condition can be found, and the corresponding adjustment process meaning is obtained. This adjustment process meaning is used to supplement or correct the process meaning preliminarily represented by the PLC anchor signal, for example, the "running" indicated by the PLC signal is refined into more specific process states such as "high-speed drawing" and "low-temperature extrusion". This rule matching method based on energy consumption and non-anchor parameters makes up for the problem of insufficient granularity relying only on the PLC anchor signal, making the semantic labeling of the energy consumption data segment more accurate, thereby improving the accuracy of subsequent energy efficiency analysis and cost accounting. It is precisely due to this fine-grained working condition recognition capability that the energy consumption data can be accurately attributed to specific production stages and process states in a small-batch, multi-variety, and frequently changing working condition production environment, solving the problems of inaccurate energy consumption attribution and difficult energy efficiency analysis traceability mentioned in the background art.

[0070] The present application further proposes that the step of determining the adjustment process meaning from the pre-set working condition rule library according to the characteristic change of energy consumption characteristics and non-anchor operation parameters comprises:

[0071] If the adjustment process meaning is not matched and found in the pre-set working condition rule library, the product specification information and / or process flow information in the MES work order information are compared with the historical working condition data according to the characteristic change of energy consumption characteristics, non-anchor operation parameters, to determine the historical process meaning with a similarity satisfying a pre-set condition as the adjustment process meaning; wherein the historical working condition data includes historical energy consumption characteristics, historical non-anchor operation parameters and corresponding historical process meaning.

[0072] The characteristic change of the energy consumption characteristic refers to the trend, fluctuation, peak value, valley value, average value, variance, etc. of the power, current, voltage, etc. of the energy consumption data segment over time, which can be obtained by time series analysis, feature extraction algorithm, etc. The purpose is to capture the key behavior mode of the energy consumption data in a specific time period. The non-anchor running parameter refers to the parameter related to the operation state of the wire production line, but not directly used as the anchor signal of the PLC to trigger the segmentation. It can be the temperature, pressure, speed, flow, position, etc. of the internal sensor of the equipment, or some state word, counter value, etc. inside the PLC. The purpose is to provide auxiliary information related to the running state of the equipment in addition to the energy consumption. The product specification information and / or process flow information in the MES work order information refers to the product model, specification, material, color, structure, etc. descriptive information and the production process steps, parameter setting range, production sequence, etc. process information corresponding to the product corresponding to the current production task issued by the manufacturing execution system MES. The purpose is to provide background information of the current production task to help more accurately judge the working condition. The historical working condition data refers to the comprehensive data set related to the production working condition recorded and stored in the past production process, which can include historical energy consumption characteristics, historical non-anchor running parameters, historical MES work order information, and historical process meaning corresponding to these historical data and determined. The purpose is to accumulate production experience and provide reference for the identification of unknown working conditions. The similarity meets the preset condition refers to the matching degree of the features (energy consumption characteristics, non-anchor running parameters, MES work order information) of the current working condition and a certain entry in the historical working condition data in multiple dimensions reaching or exceeding the threshold value or standard set in advance. It can be evaluated by distance calculation, correlation analysis, machine learning model, etc. The preset condition can be a fixed numerical threshold or a dynamically adjusted rule. The purpose is to select the historical experience closest to the current working condition. The historical process meaning refers to the production process description corresponding to a certain historical energy consumption characteristic, historical non-anchor running parameter and historical MES work order information in the historical working condition data and has been confirmed or labeled. It can be a standard process name (such as "extrusion", "filament bundling", "cabling"), or a more detailed stage description (such as "extruder warming up", "filament bundling machine replacing material and cleaning", "cabling machine fault shutdown"). The purpose is to provide the process label corresponding to the historical experience.

[0073] The scheme of the present application determines the adjustment process meaning by comparing the historical working condition data when the preset working condition rule library is not hit. Specifically, when the adjustment process meaning cannot be found in the preset working condition rule library according to the characteristic change of the energy consumption characteristic and the non-anchor running parameter, the system will further utilize more comprehensive information, including the current energy consumption characteristic characteristic change, non-anchor running parameter and product specification information and / or process flow information in the MES work order information. These information together constitute a multi-dimensional description of the current production state. The system compares this multi-dimensional description with the stored historical working condition data. The historical working condition data contains the energy consumption, parameters, work order information and the actual process meaning at that time under various past production states. Through comparison, the system finds similar historical records of the current state. The similarity calculation comprehensively considers the matching degree of energy consumption, parameters, product specifications and process flow and other multiple dimensions. If one or more historical records that meet the preset conditions are found, the system will take the corresponding historical process meaning in these historical records as the adjustment process meaning of the current working condition. This method utilizes the experience knowledge of historical data, even in the face of new working conditions that are not covered by the rule library, it can also infer the process meaning by analogy with similar historical situations, thereby improving the robustness and accuracy of process recognition. This historical data-based fallback mechanism makes up for the limitations of the preset rule library, so that in the production environment of small batch, multi-species and variable working conditions, the energy consumption data segment can also be more effectively semantically annotated, laying a foundation for subsequent fine energy efficiency analysis.

[0074] In some preferred embodiments, when the characteristic change of the energy consumption characteristic and the non-anchor operating parameter do not match the adjustment process meaning in the preset working condition rule library, the system will start the historical data comparison process. For example, the current energy consumption characteristic presents a certain high-frequency fluctuation, the non-anchor operating parameter shows that the extruder screw speed is in a certain lower range and the barrel temperature has a slight drop, and the MES work order information indicates that a new model of flame-retardant cable is currently being produced, and the process flow thereof includes a special cooling step. The system will integrate these information (energy consumption fluctuation characteristics, low speed, temperature drop, new model of flame-retardant cable, special cooling step) into a query vector and search in the historical working condition database. The historical database stores the historical energy consumption characteristics, historical non-anchor operating parameters, historical MES work order information and corresponding historical process meanings recorded in the past production of various cables. The system will calculate the similarity between the current query vector and each record in the historical database. The similarity calculation can comprehensively consider the similarity of the energy consumption curve shape, the closeness of the parameter values, the matching degree of the product specifications and the process flow, etc. For example, the system may find that there is a record in the historical database, whose historical energy consumption characteristic also presents similar high-frequency fluctuation, the historical non-anchor parameter (such as screw speed, temperature) value is close, and the corresponding historical MES work order information is to produce another flame-retardant cable, and the historical process thereof also includes a similar cooling step. If the calculated similarity meets the preset threshold, the system will take the corresponding historical process meaning (for example, “special cooling after extrusion”) in the historical record as the adjustment process meaning of the current energy consumption data segment. In this way, even the special process of the new model product can be accurately identified by learning from the experience of similar historical working conditions.

[0075] Through the above technical solution, even if faced with new working conditions or abnormal working conditions not covered by the preset working condition rule library, reasonable process meanings can be inferred through comparison with similar historical working conditions. This improves the ability to accurately annotate energy consumption data segments under complex and variable working conditions in small-batch and multi-variety production mode, making energy consumption data analysis more detailed and comprehensive, and providing a more reliable data basis for energy efficiency management and cost accounting.

[0076] The present application further proposes a step of comparing the product specification information and / or the process flow information in the MES work order information with the historical working condition data to determine the historical process meaning with a similarity meeting a preset condition as the adjustment process meaning.

[0077] According to the MES work order information, the contribution degree parameters corresponding to the characteristic change of the energy consumption characteristic, the non-anchor operating parameter, the product specification information and the process flow information are matched and obtained from the contribution degree rule set;

[0078] According to the contribution degree parameter, the characteristic change of energy consumption characteristics, the non-anchor operation parameter, the product specification information, the process flow information and the matching condition of the historical corresponding items are integrated to obtain a comprehensive similarity;

[0079] According to the comprehensive similarity, a corresponding historical process meaning is selected from historical working condition data as an adjusted process meaning.

[0080] The contribution degree rule set refers to a knowledge base that stores the correlation between different MES work order information and the importance of each factor (characteristic change of energy consumption characteristics, non-anchor operation parameter, product specification information, process flow information) in similarity calculation, which can be realized by rule table, lookup table or machine learning model. The contribution degree parameter refers to the weight or importance of each factor (characteristic change of energy consumption characteristics, non-anchor operation parameter, product specification information, process flow information) in similarity calculation. The matching condition of the historical corresponding items refers to the similarity or matching degree between the characteristic change of energy consumption characteristics, the non-anchor operation parameter, the product specification information, the process flow information of the current working condition and the corresponding items (historical energy consumption characteristics, historical non-anchor operation parameter, historical product specification information, historical process flow information) in the historical working condition data, which can be realized by similarity algorithm or matching function. The comprehensive similarity refers to the overall similarity score obtained by weighting or integrating the matching condition of each factor based on the contribution degree parameter, which is used to measure the overall similarity between the current working condition and a certain historical working condition, which can be realized by weighted summation, weighted average or multi-dimensional similarity calculation model.

[0081] This solution builds on the previous solution and provides a specific method for more effectively leveraging MES work order information and historical process data to determine the meaning of an adjusted process when the pre-defined process rule base cannot directly match the meaning of the adjusted process. By introducing a similarity comparison method based on contribution parameters, this method comprehensively considers the influence of multiple factors and more accurately identifies process meanings similar to the current process from historical data. First, based on the current MES work order information, contribution parameters applicable to the current work order are searched or calculated from a pre-set contribution rule set. These parameters reflect the importance of energy consumption characteristics, non-anchor parameters, product specification information, and process flow information in determining the meaning of the process under the current product specifications and process flow. The contribution parameters are used to weight these different types of information. The energy consumption characteristics, non-anchor parameters, product specification information, and process flow information of the current process flow are then compared with the corresponding items in the historical process data to determine their respective degrees of match. Next, these degrees of match are weighted and integrated using the previously obtained contribution parameters to calculate a comprehensive similarity score. This score comprehensively reflects the overall similarity between the current working condition and each historical working condition in multiple dimensions, and takes into account the differences in the importance of information in different dimensions. Finally, based on the calculated comprehensive similarity, the historical process meanings whose similarity meets the preset threshold are selected from the historical working condition data and used as the adjusted process meanings of the current working condition. This method makes the similarity calculation more refined and accurate by introducing the contribution parameter, and can better reflect the actual value of different information under different working conditions, thereby improving the accuracy and reliability of determining the process meaning when the rule base is insufficient. This solves the problem that relying solely on energy consumption and non-anchor parameter comparisons may be inaccurate, and effectively utilizes MES work order information and historical experience, so that when the preset rule base does not match the result, it can still make effective process meaning judgments based on historical experience, thereby improving the robustness and accuracy of the system.

[0082] In some preferred embodiments, for example, when the production line is producing a certain type of cable, the MES work order information includes product specifications (for example, the conductor cross-section is X square millimeters, the insulation material is Y type) and process flow (for example, the extrusion temperature curve is set to Z, and the drawing speed is set to W). When the PLC anchor signal change triggers the segmentation of energy consumption data, and the preset rule base does not match the meaning of the adjustment process, the system starts a similarity matching process based on historical data. The system first queries the contribution rule set based on the current MES work order information. For example, for this type of cable, the rule set may indicate: the contribution of the energy consumption characteristic change in the insulation extrusion stage is a relatively high value, the contribution of the extruder screw speed (non-anchor parameter) is a medium value, the contribution of the product specification information is a low value, and the contribution of the process flow information is a medium value. These are the contribution parameters applicable to the current work order. Then, the system compares the energy consumption characteristics of the current energy consumption data segment (for example, the power curve presents a certain shape), the current screw speed, the current product specification description, the current process flow parameters with the corresponding items of each historical working condition recorded in the historical working condition data, and calculates their respective matching degrees (for example, curve similarity, numerical difference, text similarity). Then, using the contribution parameters obtained previously, these matching degrees are weighted and summed to obtain the comprehensive similarity between the current working condition and each historical working condition. For example, the comprehensive similarity is equal to the sum of the products of the matching degrees of each factor and their corresponding contribution parameters. Finally, the system searches for the record with the highest comprehensive similarity in the historical working condition data. If the highest similarity exceeds the preset threshold, the historical process meaning corresponding to the historical record (for example, "insulation extrusion-stable operation") is used as the adjustment process meaning of the current energy consumption data segment.

[0083] Through the above technical solution, when the preset working condition rule base does not match the meaning of the adjustment process, MES work order information and historical working condition data can be effectively utilized. By introducing the contribution parameter, the weights of different factors in the similarity calculation can be dynamically adjusted according to the current work order information, so that the similarity calculation results more accurately reflect the actual similarity between the current working condition and the historical working condition. Based on a more accurate comprehensive similarity, the most relevant historical process meaning can be selected from the historical data as the adjustment process meaning. This improves the accuracy and reliability of determining the meaning of the process under complex and changeable working conditions, makes up for the shortcomings of the preset rule base, makes full use of historical experience and MES information, and provides more accurate semantic information for subsequent energy consumption analysis and process tracing.

[0084] This application further proposes that the energy consumption data stream is the electrical parameters of each energy-consuming device in the wire production line, including: current, voltage and / or power;

[0085] In response to the state change of the PLC anchoring signal, the energy consumption data stream collected by the wire production line is segmented to obtain a plurality of energy consumption data segments, including: in response to the state change of the PLC anchoring signal, determining the current time as an energy consumption data segmentation point, so as to cut the continuous energy consumption data stream into a plurality of energy consumption data segments with start and end time markers.

[0086] Among them, the energy consumption data segmentation point refers to the time point used to define the boundary of different data segments in the continuous energy consumption data stream, which can be realized by using the precise time stamp recorded by the system clock;

[0087] Among them, the start and end time marker refers to the time stamp information used to identify the start and end time of an energy consumption data segment, which can be realized by using the time stamp field stored together with the energy consumption data.

[0088] The scheme of the present application clearly defines the specific content of the energy consumption data stream as the electrical parameters of each energy-consuming device of the wire production line, such as current, voltage and / or power, ensuring that the data processed directly reflects the energy consumption state of the device. On this basis, the scheme responds to the state change of the PLC anchoring signal and accurately determines the current time when the change occurs as a segmentation point of the energy consumption data stream. This means that every time the production state is switched (identified by the PLC anchoring signal), it will trigger the cutting of the data stream once. In this way, the continuous energy consumption data stream is cut into a plurality of data segments with clear start and end time markers. Each data segment accurately corresponds to a specific production phase or event indicated by the PLC anchoring signal. This way of basing on the electrical parameter data stream and taking the time when the PLC signal changes as the accurate segmentation point makes the division of energy consumption data segments highly synchronized with the key events in the actual production process, thereby solving the problem that relying only on signal changes for rough segmentation may cause the data to not match the actual working conditions. By associating the energy consumption data with the accurate time period, an accurate data basis is provided for subsequent determination of semantic information and encapsulation storage according to the energy consumption data segment, so that energy consumption analysis can be more finely traced to specific production links and states.

[0089] In some preferred embodiments, the data acquisition system of the wire production line continuously acquires current, voltage and power data of each energy-consuming device (such as an extruder, a drawing machine), forming a continuous energy consumption data stream. At the same time, the system monitors an anchor signal inside the PLC, such as a relay state bit indicating the running state of the extruder. When the relay state bit changes from "stop" to "run", the system immediately acquires the current system time and determines this time point as an energy consumption data segmentation point. This time point serves as both the end time marker of the previous energy consumption data segment (e.g. device downtime or standby phase) and the start time marker of the new energy consumption data segment (e.g. device startup or running phase). The system classifies the energy consumption data acquired from this time point into the new data segment until the next PLC anchor signal state change occurs. Each cut energy consumption data segment is attached with its precise start and end time markers.

[0090] The present application further proposes that according to the MES work order information, the contribution degree parameters corresponding to the characteristic changes of energy consumption characteristics, non-anchor running parameters, product specification information, and process flow information are matched and obtained from the contribution degree rule set, including:

[0091] The approximate degree of the MES work order information to each rule condition in the contribution degree rule set is evaluated, and an approximate degree evaluation result is obtained;

[0092] According to the approximate degree evaluation result, at least one rule whose approximate degree meets a preset approximate standard is selected, and the historical contribution degree parameters corresponding to the at least one rule are obtained; the historical contribution degree parameters include the historical contribution degrees of the characteristic changes of energy consumption characteristics, non-anchor running parameters, product specification information, and process flow information in the similarity calculation;

[0093] According to the approximate degree evaluation result and the obtained historical contribution degree parameters, a contribution degree parameter suitable for the current MES work order information is generated through parameter adjustment logic; the contribution degree parameter includes the contribution degrees of the characteristic changes of energy consumption characteristics, non-anchor running parameters, product specification information, and process flow information in the similarity calculation.

[0094] The approximate degree evaluation result refers to a quantitative result obtained after evaluating the approximate degree of the MES work order information and each rule condition in the contribution degree rule set, which can be represented in the form of a similarity score, a distance value, or a matching degree percentage, and the purpose is to provide a basis for subsequent rule selection and parameter adjustment; the preset approximate standard refers to a threshold or condition for screening approximate rules, which can be determined in the form of setting a minimum similarity threshold, a maximum distance threshold, or a minimum matching degree requirement, and the purpose is to ensure that the selected rule has sufficient relevance to the current working condition; the historical contribution degree parameter refers to the weight or influence factor of the characteristic change of the energy consumption characteristic, the non-anchor operating parameter, the product specification information, and the process flow information in the similarity calculation, which is obtained from the selected approximate rule and determined in historical data analysis, which can be achieved by directly reading the pre-stored parameter value associated with the selected rule from the contribution degree rule set, and the purpose is to use historical experience to provide basic weight information for current similarity calculation; the parameter adjustment logic refers to a calculation method or algorithm for generating a contribution degree parameter suitable for the current MES work order information according to the approximate degree evaluation result and the obtained historical contribution degree parameter, which can be achieved in the form of weighted average, linear interpolation, parameter prediction based on a machine learning model, or rule reasoning, and the purpose is to make the contribution degree parameter more accurately reflect the characteristics of the current MES work order information; the contribution degree parameter suitable for the current MES work order information refers to the weight or influence factor of the characteristic change of the energy consumption characteristic, the non-anchor operating parameter, the product specification information, and the process flow information, which is obtained after processing by the parameter adjustment logic and used for current similarity calculation, which can be represented in the form of an adjusted numerical set, and the purpose is to improve the accuracy and adaptability of similarity calculation.

[0095] The scheme of the present application quantifies the difference between the current working condition and the historical rules by first evaluating the approximation degree of the current MES work order information to each rule condition in the contribution degree rule set. Based on this evaluation result, rules that are close enough to the current working condition are selected, and the historical contribution degree parameters corresponding to these rules are obtained. These historical parameters represent the historical influence weight of each feature item (feature change of energy consumption characteristics, non-anchor operating parameters, product specification information, process flow information) on similarity calculation under similar working conditions. Subsequently, according to the approximation degree evaluation result and the obtained historical contribution degree parameters, the historical parameters are modified using parameter adjustment logic to generate contribution degree parameters that are more suitable for the current MES work order information. For example, for rules that are highly similar to the current work order information, their historical contribution degree parameters can be given higher weights or smaller adjustment amplitudes; for rules that have a low approximation degree but still meet the standard, their historical parameters can be given lower weights or larger amplitude adjustments. In this way, instead of directly using rule parameters that may not be completely applicable, historical experience is flexibly adjusted according to the actual situation of the current working condition. Applying the contribution degree parameters generated in this way to similarity calculation can more accurately measure the similarity between the current working condition and the historical working condition, thereby more reliably determining the adjustment process meaning. This method overcomes the limitations of direct rule matching, improves the accuracy of similarity calculation, and is especially suitable for the production environment of wire production lines with small batch, multiple varieties, and variable working conditions, making energy efficiency analysis and process semantics determination more accurate.

[0096] In some preferred embodiments, in particular, when receiving the MES work order information for a specific product model and process flow, the system can first calculate the similarity score of the work order information with the product model and process flow conditions preset in each rule in the contribution degree rule set. For example, a text similarity algorithm can be used to evaluate the approximation degree of the product model and process flow description. Then, a similarity threshold is set, for example, 0.7. The system selects all rules with a similarity score higher than 0.7. For each selected rule, the feature variation of the energy consumption characteristics, the non-anchor operating parameters, the product specification information, and the process flow information determined in the historical data analysis are obtained. Then, according to the similarity score of each selected rule with the current work order information, a weight value is set for the corresponding historical contribution degree parameter, and the higher the similarity score, the higher the weight value can be set. Finally, the historical contribution degree parameters of all selected rules and their corresponding weight values are aggregated and calculated using a weighted average method to generate the final contribution degree parameter applicable to the current MES work order information. For example, if two rules are selected, the similarity scores are S1 and S2, and the corresponding historical parameters are P1 and P2, the weights can be set as W1=S1 / (S1+S2), W2=S2 / (S1+S2), and the final parameter P=W1*P1+W2*P2. The parameter set generated in this way will be used for subsequent similarity calculation.

[0097] Through the above technical solution, by evaluating the approximation degree of the current MES work order information with the conditions of each rule in the contribution degree rule set, and adjusting the historical contribution degree parameters according to the evaluation results, the contribution degree parameter applicable to the current work order information is generated. This overcomes the problem that direct matching of rules may not be applicable, and improves the accuracy of the contribution degree parameter. The improvement of the accuracy of the contribution degree parameter makes the similarity calculation based on these parameters more reliable, so that the adjustment process meaning can be determined more accurately. This helps to more accurately identify and mark the energy consumption data under different working conditions in the production environment of small batch and multi-variety wire production lines, and provides a more reliable data basis for subsequent energy efficiency analysis and management.

[0098] The present application further proposes a step of generating a contribution degree parameter applicable to the current MES work order information through parameter adjustment logic according to the approximation degree evaluation result and the obtained historical contribution degree parameter.

[0099] According to the approximation degree evaluation result, a weight value is set for the historical contribution degree parameter corresponding to each approximation rule; the weight value is determined according to the approximation degree of the approximation rule and the MES work order information corresponding to the current working condition;

[0100] The historical contribution degree parameters are aggregated based on their corresponding weight values using a preset aggregation calculation method to generate a contribution degree parameter applicable to the current MES work order information.

[0101] wherein, the approximation degree evaluation result refers to a quantitative result of the matching or similarity degree of the MES work order information and each rule condition in the contribution degree rule set; the approximate rule refers to a rule in the contribution degree rule set that is determined to have a certain similarity with the MES work order information corresponding to the current working condition according to the approximation degree evaluation result; the historical contribution degree parameter refers to a contribution degree value of each of the feature change of the energy consumption characteristic, the non-anchor operation parameter, the product specification information, and the process flow information that is obtained from the approximate rule and used in the historical similarity calculation; the weight value refers to a numerical value set for each historical contribution degree parameter corresponding to the approximate rule, used to represent the importance or reliability of the historical contribution degree parameter in the aggregation calculation, and the weight value is determined according to the approximation degree of the approximate rule and the MES work order information corresponding to the current working condition, the higher the approximation degree, the greater the weight value; the preset aggregation calculation mode refers to a calculation method of synthesizing multiple historical contribution degree parameters into a final contribution degree parameter, which can be realized by weighted average, weighted summation, or an aggregation algorithm based on a machine learning model.

[0102] The scheme of the present application sets a weight value for each historical contribution degree parameter corresponding to the approximate rule according to the approximation degree evaluation result, and the weight value reflects the matching degree of the historical rule and the current working condition. The higher the approximation degree, the greater the weight value, indicating that the reference value of the historical parameter to the current working condition is higher. Then, a preset aggregation calculation mode is used to weight and aggregate each historical contribution degree parameter based on the corresponding weight value. This weighted aggregation method can effectively synthesize the information of multiple historical rules and give different influences according to their matching degrees with the current working condition, thereby generating a contribution degree parameter that can better represent the characteristics of the current MES work order information. This generated contribution degree parameter will be used in subsequent similarity calculations. Compared with directly using a certain historical parameter or simple average, the weighted aggregation method can more finely utilize historical data and overcome the inaccuracy caused by the difference between historical data and the current working condition. In this way, the accuracy of the similarity calculation is improved, and the accuracy of determining the adjustment process meaning through historical working condition data when the pre-set working condition rule library does not match the adjustment process meaning is improved, and finally the overall accuracy of the process meaning adjustment is improved.

[0103] In some preferred embodiments, for example, it is assumed that the approximation degree evaluation result shows that the approximation degrees of historical rule A, historical rule B, historical rule C and the current MES work order information are 0.8, 0.6, and 0.4, respectively. The corresponding historical contribution degree parameters (for example, the contribution degree of the change of the energy consumption characteristic feature) are PA=0.7, PB=0.5, and PC=0.3, respectively. The weight values can be set to be directly related to the approximation degrees, for example, the weight values WA=0.8, WB=0.6, and WC=0.4. The preset aggregation calculation manner can adopt weighted average. The generated contribution degree parameter P_current applicable to the current MES work order information can be obtained by calculating (WA*PA+WB*PB+WC*PC) / (WA+WB+WC), that is, (0.8*0.7+0.6*0.5+0.4*0.3) / (0.8+0.6+0.4) = (0.56+0.30+0.12) / 1.8 = 0.98 / 1.8≈0.544. In this way, the multiple historical contribution degree parameters are comprehensively combined according to the matching degrees of the historical contribution degree parameters and the current working condition, and a more representative contribution degree parameter is obtained.

[0104] Through the above technical solution, the historical contribution degree parameters can be weighted and combined according to the approximation degrees of the historical rules and the current working condition, and a contribution degree parameter closer to the current actual working condition is generated. This overcomes the deviation that may be caused by directly using the historical parameters, improves the accuracy of the similarity calculation, and further improves the accuracy of determining the process meaning through the historical data when no rule is directly matched.

[0105] In addition, the present application also provides a wire production line data acquisition control system, and the wire production line includes a programmable logic controller (PLC) and a manufacturing execution system (MES), as shown in Figure 2 The system includes:

[0106] The acquisition module 201 is configured to acquire a PLC anchoring signal inside the programmable logic controller (PLC). The PLC anchoring signal is at least one of a relay state, an input point state, a data register value, and a logic flag bit. The PLC anchoring signal is used to identify a production equipment start, stop, process parameter adjustment, or material switching event.

[0107] The acquisition and segmentation module 202 is configured to respond to the state change of the PLC anchoring signal and perform segmentation processing on the energy consumption data stream acquired by the wire production line to obtain a plurality of energy consumption data segments. Any energy consumption data segment includes a start and end time marker.

[0108] The encapsulation module 203 is configured to determine semantic information according to any energy consumption data segment, and perform encapsulation and storage of the energy consumption data segment and the corresponding semantic information. The semantic information includes at least one of a PLC anchor signal name corresponding to a trigger of a start time range and an end time range of the energy consumption data segment, a process, an MES work order number, a product code, and a process parameter setting value.

[0109] The acquisition module 201 is a unit for acquiring a programmable logic controller (PLC) anchor signal. The PLC anchor signal is a key signal in a PLC for identifying a state or an event of a production device. The PLC anchor signal can be implemented by using a relay state, an input point state, a data register value, or a logic flag bit. The acquisition and segmentation module 202 is a unit for acquiring an energy consumption data stream and performing segmentation processing based on the PLC anchor signal. The energy consumption data stream is continuous acquisition of electrical parameter data of each energy consumption device of an electric wire production line, such as current, voltage, or power. The energy consumption data segment is a part of the energy consumption data stream that is divided according to a specific event and has a start time mark and an end time mark. The encapsulation module 203 is a unit for associating and storing the energy consumption data segment and semantic information. The semantic information is production context information related to the energy consumption data segment, which can include at least one of a PLC anchor signal name corresponding to a trigger of a start time range and an end time range of the energy consumption data segment, a process, an MES work order number, a product code, or a process parameter setting value.

[0110] The scheme of the present application monitors the PLC anchor signals inside the programmable logic controller (PLC) in real time through an acquisition module, which are accurate identifiers of the production equipment state or key events. The acquisition and segmentation module responds to the state changes of these PLC anchor signals, cutting the continuously acquired energy consumption data stream of the wire production line at the moment when the signal changes occur, forming energy consumption data segments with clear start and end time markers. It is precisely because the segmentation of the energy consumption data stream is directly triggered by the PLC anchor signals that the time boundaries of the energy consumption data segments can be strictly aligned with actual production events (such as equipment start / stop, process parameter adjustment) on a millisecond time scale. Subsequently, the encapsulation module determines the semantic information corresponding to each energy consumption data segment according to the data segment, which includes the PLC anchor signal name that triggered the data acquisition of the segment, the related process, and the work order number, product code, process parameter setting value, etc. production context information obtained from the manufacturing execution system (MES). By encapsulating and storing the energy consumption data segment and its corresponding semantic information, the system closely associates the originally isolated energy consumption data with specific production processes, product information, and process states. This system-level implementation materializes the data acquisition, segmentation, and information association steps in the method into the collaborative work of functional modules, overcoming the problem of traditional data acquisition methods based on simple time intervals or manual marking that cannot accurately synchronize and attribute data with production events in small-batch, multi-variety production environments, and providing a reliable data foundation for subsequent fine energy efficiency analysis based on work orders, products, or process stages.

[0111] Through the above technical solution, the system can accurately respond to key production events of the wire production line in real time, segment the continuous energy consumption data stream according to these events, and associate each data segment with rich production context information. This solves the problem of time synchronization and inaccurate association of energy consumption data and production process information in the prior art, enabling the collected energy consumption data to accurately reflect the actual energy consumption of a specific work order, product specification, or process stage, and providing reliable data support for fine energy efficiency analysis, production cost accounting, and process optimization.

[0112] The above only describes the embodiments of the present application and is not intended to limit the protection scope of the present application. For those skilled in the art, the present application can have various modifications and changes. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A data acquisition and control method for a wire production line, wherein the wire production line includes a programmable logic controller (PLC) and a manufacturing execution system (MES), characterized in that: The method comprises: Obtaining a PLC anchor signal within the programmable logic controller (PLC); the PLC anchor signal being at least one of a relay state, an input point state, a data register value, and a logic flag; the PLC anchor signal being used to identify a production equipment start, stop, process parameter adjustment, or material switching event; In response to the state change of the PLC anchor signal, the energy consumption data stream collected by the wire production line is segmented to obtain a plurality of energy consumption data segments; wherein each of the energy consumption data segments includes a start and end time stamp; Determine a semantic information based on any of the energy consumption data segments, and encapsulate and store the energy consumption data segment and its corresponding semantic information report; the semantic information includes the PLC anchor signal name corresponding to the start and end time range of the energy consumption data segment, and at least one of its process, MES work order number, product code, and process parameter setting value.

2. A wire production line data acquisition and control method according to claim 1, characterized in that: The method further includes pre-establishing a mapping rule between MES work order information and PLC anchor signals, including: The work order information issued by the manufacturing execution system MES is associated with the PLC anchor signal to obtain the corresponding changed MES work order information in response to the state change of the PLC anchor signal; the MES work order information includes product specification information and / or process flow information.

3. The data acquisition and control method for a wire production line according to claim 2, characterized in that: Determining semantic information according to any of the energy consumption data segments, and encapsulating and storing the energy consumption data segments and their corresponding semantic information, including: monitoring the energy consumption characteristics within the energy consumption data segment; if the energy consumption characteristics show a preset characteristic change, obtaining a non-anchored operating parameter within the PLC; the non-anchored operating parameter is related to the operating status of the wire production line; Determining, based on the characteristic change of the energy consumption characteristics and the non-anchored operating parameters, the meaning of the adjustment process from a preset operating condition rule library to supplement or correct the meaning of the process represented by the PLC anchor signal; The semantic information is determined according to the meaning of the adjustment process and the MES work order information, and the energy consumption data segment is encapsulated.

4. A data acquisition and control method for a wire production line according to claim 3, characterized in that: According to the characteristic change of the energy consumption characteristics and the non-anchored operating parameters, the meaning of the adjustment process is determined from the preset operating condition rule library, including: By utilizing the characteristic change of the energy consumption characteristics and the non-anchored operating parameters, a matching search is performed in the preset operating condition rule library to obtain the meaning of the adjustment process.

5. The data acquisition and control method for a wire production line according to claim 3, characterized in that: Determining the meaning of the adjustment process from a preset operating condition rule library based on the characteristic change of the energy consumption characteristics and the non-anchored operating parameters, further comprising: If the adjustment process meaning is not found in the preset working condition rule library, the characteristic changes of the energy consumption characteristics, the non-anchored operating parameters, the product specification information and / or process flow information in the MES work order information are compared with the historical working condition data to determine the historical process meaning whose similarity meets the preset conditions as the adjustment process meaning; wherein, the historical working condition data includes historical energy consumption characteristics, historical non-anchored operating parameters and corresponding historical process meanings.

6. A wire production line data acquisition and control method according to claim 5, characterized in that: According to the characteristic change of the energy consumption characteristics, the non-anchored operating parameters, the product specification information and / or process flow information in the MES work order information, and the historical working condition data are compared to determine the historical process meaning whose similarity meets the preset conditions as the adjustment process meaning, including: According to the MES work order information, matching and obtaining contribution parameters corresponding to the characteristic changes of the energy consumption characteristics, the non-anchored operating parameters, the product specification information, and the process flow information from a contribution rule set; According to the contribution parameter, the characteristic change of the energy consumption characteristics, the non-anchored operating parameters, the product specification information, the process flow information and the matching of historical corresponding items are integrated to obtain a comprehensive similarity; According to the comprehensive similarity, the corresponding historical process meaning is selected from the historical working condition data as the adjustment process meaning.

7. The data acquisition and control method for a wire production line according to claim 1, characterized in that: The energy consumption data stream is the electrical parameters of each energy-consuming device in the wire production line, including: current, voltage and / or power; In response to the state change of the PLC anchor signal, the energy consumption data stream collected by the wire production line is segmented to obtain multiple energy consumption data segments, including: in response to the state change of the PLC anchor signal, the current moment is determined as an energy consumption data segmentation point, thereby cutting the continuous energy consumption data stream into multiple energy consumption data segments with start and end time marks.

8. The data acquisition and control method for a wire production line according to claim 6, characterized in that: According to the MES work order information, matching and obtaining contribution parameters corresponding to the characteristic change of the energy consumption characteristics, the non-anchored operating parameters, the product specification information, and the process flow information from a contribution rule set, including: Evaluate the similarity between the MES work order information and each rule condition in the contribution rule set to obtain a similarity evaluation result; Based on the similarity evaluation result, at least one rule whose similarity meets a preset similarity standard is selected, and a historical contribution parameter corresponding to the at least one rule is obtained; the historical contribution parameter includes the historical contribution of each of the characteristic change of the energy consumption characteristics, the non-anchored operating parameters, the product specification information, and the process flow information in the similarity calculation; Based on the similarity evaluation result and the historical contribution parameters obtained, contribution parameters applicable to the current MES work order information are generated through parameter adjustment logic; the contribution parameters include the characteristic changes of the energy consumption characteristics, the non-anchored operating parameters, the product specification information, and the process flow information, and their respective contributions in the similarity calculation.

9. The data acquisition and control method for a wire production line according to claim 8, characterized in that: Based on the approximation evaluation result and the acquired historical contribution parameters, a contribution parameter applicable to the current MES work order information is generated through parameter adjustment logic, including: According to the approximation evaluation result, a weight value is set for the historical contribution parameter corresponding to each approximation rule; the weight value is determined according to the approximation degree between the approximation rule and the MES work order information corresponding to the current working condition; A preset aggregation calculation method is adopted to aggregate each of the historical contribution parameters based on its corresponding weight value to generate a contribution parameter applicable to the current MES work order information.

10. A data acquisition and control system for a wire production line, comprising a programmable logic controller (PLC) and a manufacturing execution system (MES), characterized in that: The system comprises: an acquisition module, configured to acquire a PLC anchor signal within the programmable logic controller (PLC); the PLC anchor signal being at least one of a relay state, an input point state, a data register value, and a logic flag; the PLC anchor signal being used to identify a production equipment start, stop, process parameter adjustment, or material switching event; A collection and segmentation module, configured to segment the energy consumption data stream collected by the wire production line in response to a state change of the PLC anchor signal to obtain a plurality of energy consumption data segments; wherein each of the energy consumption data segments includes a start and end time stamp; The encapsulation module is used to determine a semantic information based on any of the energy consumption data segments, and to encapsulate and store the energy consumption data segment and its corresponding semantic information report; the semantic information includes the PLC anchor signal name corresponding to the start and end time range of the energy consumption data segment, and at least one of its process, MES work order number, product code, and process parameter setting value.

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