Production line energy efficiency management method and system for intelligent manufacturing

Abstract

The invention is suitable for the technical field of intelligent manufacturing and electrical equipment processing, and provides a production line energy efficiency management method and system oriented to intelligent manufacturing. And step-by-step adjustment is carried out on the heating energy application parameters of the current heating process, and the process quality evaluation value of the corresponding busbar is monitored after each adjustment until the heating energy application parameters which can enable the process quality evaluation value to reach the quality standard under the current fluctuation intensity are identified. According to the method, a fluctuation intensity identification mechanism based on energy consumption high-frequency perturbation characteristics is introduced, so that early capture of hidden working condition deviation in a busbar thermal shrinkage heating process is realized, and hidden quality risks caused by previous process differences or material characteristics can be identified when the fluctuation of heating power is still in an allowable range.

Description

Technical Field

[0001] This invention relates to the field of intelligent manufacturing and electrical equipment processing technology, and in particular to a method and system for energy efficiency management of production lines for intelligent manufacturing. Background Technology

[0002] The widespread adoption of intelligent manufacturing in the electrical cabinet production field has continuously enhanced the automation and precision management of busbar processing. As a crucial conductive component in electrical cabinets responsible for high-current transmission, the busbar's surface requires a heat-shrink tubing layer to form an insulating protective layer. Therefore, the heat-shrink heating process is an indispensable and critical step in busbar production, as its heating quality directly affects insulation integrity, heat shrink tightness, and overall electrical safety performance. To ensure heating effectiveness, current technologies generally employ power regulation, electric furnace temperature control, and conveyor heating methods, resulting in high energy consumption for this process across the entire production line, making it a key focus of energy efficiency management.

[0003] To improve production quality and energy efficiency, existing technologies typically monitor macroscopic parameters such as furnace temperature, power exceeding limits, equipment malfunctions, and conveyor cycle time, triggering alarms when temperatures exceed limits or power is abnormal. However, in actual production, busbars exhibit varying thermal response characteristics upon entering the furnace due to complex pre-processing steps, different material batches, and variations in surface conditions. This results in small, high-frequency fluctuations in the heating power curve, often within the permissible range. Existing technologies usually treat these fluctuations as normal manifestations of equipment control or furnace thermal inertia, failing to further analyze their potential correlation with busbar condition or subsequent process quality, thus ignoring the impact of hidden operating condition deviations on the final heat-shrink quality.

[0004] Without in-depth correlation analysis, existing energy efficiency management mechanisms cannot identify hidden quality risks caused by deviations in preceding processes, material differences, or abnormal thermal responses of materials before energy consumption fluctuations exceed limits. Furthermore, existing technologies often fail to adjust heating energy application parameters based on the differences in energy consumption characteristics between different batches of busbars, typically relying on fixed process parameters or manual experience for adjustments. This can lead to unnecessary energy waste and fluctuations in heat shrink quality. Therefore, establishing a quantitative relationship between energy consumption characteristics and process quality based on permissible energy consumption perturbations, and achieving adaptive energy efficiency compensation accordingly, is a critical technical problem that urgently needs to be solved. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for energy efficiency management of production lines for intelligent manufacturing, aiming to solve the problems mentioned in the background art.

[0006] This invention is implemented as follows: a production line energy efficiency management method for intelligent manufacturing, the method comprising: For the heat shrinking heating process of the current batch of busbars, when it is detected that the power curve of the heating furnace of the previous preset number of busbars shows the energy consumption high-frequency perturbation characteristics within the allowable range during the heating process, the historical heating database is retrieved. Samples with high-frequency perturbation characteristics of energy consumption that are consistent with the current busbar type, have the same heating energy application parameters, and correspond to different levels of fluctuation intensity are selected from the database, and the process quality evaluation value corresponding to each sample is determined. Based on several samples, the threshold fluctuation intensity at which the corresponding process quality evaluation value first falls below the quality benchmark and shows a downward trend when the fluctuation intensity increases step by step is identified, and this fluctuation intensity is defined as the quality sensitive fluctuation threshold. When it is determined that the fluctuation intensity of the high-frequency perturbation characteristic of the energy consumption of the current batch of busbars exceeds the quality-sensitive fluctuation threshold, the heating energy application parameters of the current heating process are adjusted step by step, and the process quality evaluation value of the corresponding busbar is monitored after each adjustment until the heating energy application parameters that can make the process quality evaluation value reach the quality benchmark under the current fluctuation intensity are identified. The identified heating energy application parameters are applied to the current batch of busbar heat shrink heating process.

[0007] As a further limitation of the technical solution of the present invention, the high-frequency perturbation feature of energy consumption refers to: based on the time series data of the power curve of the heating furnace, under the premise that the power amplitude is kept within the allowable range, the local high-frequency fluctuation feature in the power curve that has a change frequency higher than the normal operation benchmark and lower than the preset upper limit.

[0008] As a further limitation of the technical solution of the present invention, the fluctuation intensity of the energy consumption high-frequency perturbation feature is determined based on the magnitude of the change frequency of the local high-frequency fluctuation feature compared with the normal operation reference.

[0009] As a further limitation of the technical solution of the present invention, the heating energy application parameter refers to at least one of the following: furnace temperature setpoint, heating power output limit, and material conveying speed.

[0010] As a further limitation of the technical solution of the present invention, the acquisition of the process quality evaluation value includes: performing at least one of the following tests on the appearance forming degree, heat shrink tightness and insulation coverage integrity based on the heated busbar, and quantifying the test results into a process quality evaluation value.

[0011] As a further limitation of the technical solution of this embodiment of the invention, based on several samples, the step of identifying the threshold fluctuation intensity at which the corresponding process quality evaluation value first falls below the quality benchmark and shows a downward trend when the fluctuation intensity increases step by step, and defining this fluctuation intensity as the quality-sensitive fluctuation threshold, includes: Several samples were sorted from low to high according to the fluctuation intensity to obtain the corresponding process quality evaluation value change sequence; The change sequence was analyzed to identify the threshold fluctuation intensity where the process quality evaluation value first appeared to be lower than the quality benchmark and then showed a continuous downward trend. The intensity of this fluctuation is determined as the quality-sensitive fluctuation threshold.

[0012] An energy efficiency management system for production lines in intelligent manufacturing, the system comprising: The power characteristic detection module is used to retrieve the historical heating database when the heating furnace power curve of the previous preset number of busbars shows energy consumption high-frequency perturbation characteristics within the allowable range during the heat shrink heating process of the current batch of busbars. The sample screening module is used to select samples from the database that are consistent with the current busbar type, have the same heating energy application parameters, and correspond to different levels of fluctuation intensity of energy consumption high-frequency perturbation characteristics, and determine the process quality evaluation value corresponding to each sample. The threshold identification module is used to identify, based on several samples, the threshold fluctuation intensity at which the corresponding process quality evaluation value first falls below the quality benchmark and shows a downward trend when the fluctuation intensity increases step by step, and defines the fluctuation intensity as the quality sensitive fluctuation threshold. The parameter adjustment module is used to adjust the heating energy application parameters of the current heating process step by step when the fluctuation intensity of the high-frequency perturbation characteristics of the current batch of busbars exceeds the quality sensitive fluctuation threshold. After each adjustment, the process quality evaluation value of the corresponding busbar is monitored until the heating energy application parameters that can make the process quality evaluation value reach the quality benchmark under the current fluctuation intensity are identified. The parameter application module is used to apply the identified heating energy application parameters to the current batch of busbar heat shrinking heating process.

[0013] As a further limitation of the technical solution of the present invention, the high-frequency perturbation feature of energy consumption refers to: based on the time series data of the power curve of the heating furnace, under the premise that the power amplitude is kept within the allowable range, the local high-frequency fluctuation feature in the power curve that has a change frequency higher than the normal operation benchmark and lower than the preset upper limit.

[0014] As a further limitation of the technical solution of the present invention, the fluctuation intensity of the energy consumption high-frequency perturbation feature is determined based on the magnitude of the change frequency of the local high-frequency fluctuation feature compared with the normal operation reference.

[0015] As a further limitation of the technical solution of the present invention, the heating energy application parameter refers to at least one of the following: furnace temperature setpoint, heating power output limit, and material conveying speed.

[0016] Compared with the prior art, the present invention has the following beneficial effects: This invention introduces a fluctuation intensity identification mechanism based on high-frequency perturbation characteristics of energy consumption, enabling early detection of hidden operating condition deviations in the busbar heat shrinking heating process. It can identify latent quality risks caused by differences in preceding processes or material properties even when heating power fluctuations are still within acceptable limits. Compared to traditional methods that rely solely on over-limit alarms or manual experience, this invention constructs a correlation between fluctuation intensity and process quality by screening historical samples and extracts quality-sensitive fluctuation thresholds, thus quantitatively characterizing the changing trends of process quality.

[0017] Building upon this foundation, the present invention further achieves adaptive energy efficiency compensation for the actual fluctuation intensity of the current batch by adjusting the heating energy application parameters step by step, enabling the process quality to recover to the quality benchmark with minimal necessary energy consumption. This not only avoids unnecessary excessive energy input but also improves the stability and consistency of heat-shrinkable quality, creating a closed-loop linkage between energy efficiency optimization and process quality control. This significantly improves the intelligence level and energy efficiency management accuracy of the electrical cabinet busbar production process, demonstrating promising prospects for industrial applications. Attached Figure Description

[0018] Figure 1 A flowchart of the method provided in the embodiments of the present invention; Figure 2 This is a flowchart illustrating the method for determining the quality-sensitive fluctuation threshold provided in the embodiments of the present invention; Figure 3 The application architecture diagram of the system provided in the embodiments of the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0020] Figure 1 A flowchart of the method provided by an embodiment of the present invention is shown.

[0021] Specifically, an energy efficiency management method for production lines in intelligent manufacturing includes the following steps: Step S100: For the heat shrinking heating process of the current batch of busbars, when it is detected that the power curve of the heating furnace of the previous preset number of busbars shows the energy consumption high-frequency perturbation characteristics within the allowable range during the heating process, the historical heating database is retrieved.

[0022] The high-frequency perturbation feature of energy consumption refers to the local high-frequency fluctuation feature in the power curve that appears based on the time-series data of the power curve of the heating furnace, provided that the power amplitude is kept within the allowable range, and the frequency of change is higher than the normal operation benchmark but lower than the preset upper limit.

[0023] In this embodiment of the invention, the busbar is used in electrical cabinets (such as distribution cabinets, control cabinets, etc.) as a core conductive component for high-current transmission. It is typically made of copper or aluminum and features large cross-sectional dimensions, strong current-carrying capacity, and stable conductivity. During the assembly of the electrical cabinet, the busbar not only performs electrical connection functions but also directly relates to the electrical safety and operational reliability of the entire cabinet. Therefore, strict requirements are placed on its dimensional accuracy, installation stability, and insulation quality during processing and assembly.

[0024] In the insulation process of busbars, a heat-shrinkable heating process is typically required to fully shrink the outer heat-shrinkable tubing and tightly adhere it to the busbar surface, forming a uniform, continuous, and defect-free insulation coating. This prevents problems such as partial discharge, insulation breakdown, or structural loosening due to vibration from occurring in the electrical cabinet during subsequent operation. This heat-shrinkable heating process is highly sensitive to temperature uniformity, heating duration, and the amount of energy applied. Since the heating furnace is a continuously high-power operating device, its overall energy consumption accounts for a significant portion of the electrical cabinet production line, making it a primary energy efficiency target that must be monitored and controlled.

[0025] The power curve of the heating furnace can be obtained through the real-time power acquisition module of the heating system. This module periodically samples the power output of the heating furnace and forms time-series power data. Under normal heat shrink heating conditions, even if the equipment operates stably, the power curve will usually show high-frequency fluctuations. This is an inherent characteristic of existing heating control systems, and its sources include PID regulation of the temperature control system, on / off control of heating elements, temperature compensation caused by the thermal inertia of the furnace body, and differences in the heat absorption characteristics of batches of loaded materials. Therefore, high-frequency fluctuations in energy consumption are a common and normal phenomenon in existing technology and can be directly identified by existing monitoring systems. They will not trigger any alarms or be considered as process abnormalities.

[0026] Based on this, the "high-frequency perturbation characteristics of energy consumption" described in this invention are not a new signal form, but rather a generalized description of high-frequency fluctuations that can normally occur within the allowable power range. Preferably, the so-called high-frequency perturbation characteristics of energy consumption refer to localized high-frequency fluctuations in the power curve when the power amplitude of the heating furnace remains within the allowable range. The frequency of these fluctuations is higher than the reference level of normal operating conditions, but still does not reach the alarm threshold of the existing control system. In other words, in the prior art, these fluctuations are considered "normal but slightly high fluctuations," and because they do not exceed the allowable range, they do not attract attention. However, they may imply factors such as material state deviations, fluctuations in the quality of previous processing, or slight changes in the furnace thermal field. This invention analyzes these perturbation characteristics that are observable but not utilized in the prior art. By further identifying changes in their fluctuation intensity and combining them with historical data modeling, it infers potential operating condition deviations that may exist in the current batch of busbars and implements subsequent energy efficiency control strategies accordingly.

[0027] Furthermore, the energy efficiency management method for intelligent manufacturing production lines also includes the following steps: Step S200: Select samples from the database that are consistent with the current busbar type, have the same heating energy application parameters, and correspond to different levels of fluctuation intensity, and determine the process quality evaluation value corresponding to each sample.

[0028] The fluctuation intensity of the energy consumption high-frequency perturbation feature is determined based on the magnitude of the change frequency of the local high-frequency fluctuation feature compared to the normal operating reference.

[0029] The heating energy application parameters refer to at least one of the following: furnace temperature setpoint, upper limit of heating power output, and material conveying speed.

[0030] The process quality evaluation value is obtained by: testing at least one of the following based on the heated busbar: appearance forming degree, heat shrinking tightness, and insulation coverage integrity, and quantifying the test results into a process quality evaluation value.

[0031] In the embodiments of this invention, those skilled in the art have observed through long-term production practice that different batches of busbars are affected by a combination of factors, including pre-processing steps, material batch differences, and processing cycle fluctuations, before entering the heat shrinking heating process. For example, the preceding bending, punching, cleaning, and assembly processes may lead to differences in the surface condition, heat capacity, local stress, and heat shrink tubing adhesion of the busbars; the thermal conductivity of different batches of copper or aluminum materials may also have slight deviations. These factors may manifest as different energy absorption rates or thermal response characteristics after the busbars enter the heating furnace, thus exhibiting different levels of high-frequency perturbation characteristics in energy consumption on the power curve of the heating furnace.

[0032] Typically, when the deviation caused by the preceding process is significant, the uneven energy absorption or sluggish thermal response of the busbar in the initial heating stage is more pronounced. This is reflected in the power curve as an increase in the amplitude of the high-frequency perturbation characteristics, or a higher degree of sudden density of its characteristics than the normal operating baseline.

[0033] In other words, deviations in operating conditions generated in the preceding processes are often amplified and manifested as abnormal changes in energy absorption characteristics after the busbar enters the heat shrinking heating process: the larger the preceding deviation, the more unstable the thermal response of the busbar during the heating process, and the more obvious the high-frequency perturbation characteristics of energy consumption shown on its power curve, with higher fluctuation intensity. This increase in fluctuation intensity essentially reflects a more "intense" subtle adjustment in the busbar's demand for heating energy within a short timescale, and is an outward manifestation of the impact of latent defects in the preceding processes on subsequent heat shrinking behavior. Since a larger preceding deviation usually means a more severe inconsistency in the busbar surface, heat capacity, or sleeve bonding state, the subsequent process quality risks are also higher, providing identifiable characteristic basis for the subsequent energy efficiency compensation and process quality restoration of this invention.

[0034] However, it should be noted that such differences do not necessarily lead to substandard final heat-shrink quality. When the busbar can still achieve the required forming quality despite the presence of high-frequency perturbation characteristics, differences in its preceding processes or inherent characteristics remain within acceptable limits. However, if these differences cause a decrease in the process quality evaluation value, targeted compensation is needed through heating energy application parameters to restore the heat-shrink quality. Simultaneously, to avoid unnecessary energy waste, it is necessary to achieve the lowest possible energy consumption while ensuring quality; this is precisely the core challenge that this invention aims to address.

[0035] Based on this, the purpose of step S200 is to select batch samples from the historical heating database that are consistent with the current busbar type and heating energy application parameters, but differ in the intensity of high-frequency perturbation characteristics in energy consumption, in order to construct a comparable reference sample set. This ensures that differences in preceding processes and the inherent characteristics of the busbar itself are fully reflected among the samples. By selecting these samples and their corresponding process quality evaluation values, the heat shrink quality performance under different perturbation intensities can be accurately described, which is used for subsequent identification of quality-sensitive perturbation thresholds. The selection conditions in this step are set quite strictly to ensure that the only significant difference between samples comes from the intensity of perturbation characteristics in energy consumption, avoiding interference from other factors such as heating energy application parameters, thereby improving the reliability of quality threshold identification.

[0036] Regarding the determination of fluctuation intensity, the "frequency of change exceeding the normal operating baseline" is quantified based on mature signal analysis methods in existing technologies. This can be achieved through frequency analysis based on the number of statistical fluctuations, extraction of the energy proportion of high-frequency components based on frequency domain energy, or calculation of local peak density based on peak identification. These methods are commonly used data analysis techniques in existing industrial process monitoring systems; this invention only combines and applies them without any improvement to the basic algorithms. Furthermore, in practical implementation, the exceedance amplitude can also be comprehensively determined by combining the duration of power fluctuations or changes in fluctuation patterns. The specific method can be selected by the equipment manufacturer or process engineer based on the capabilities of the acquisition system.

[0037] Regarding the parameters for applying heating energy, this invention uses furnace temperature setpoint, upper limit of heating power output, and material conveying speed as typical examples. These three parameters directly determine the heat energy absorbed by a unit workpiece and are the most commonly used process adjustment variables on the production line. Furthermore, it should be noted that those skilled in the art can include other common process control parameters, such as zoned temperature compensation, heating section power allocation, or heating mode switching, within the scope of heating energy application parameters based on the structure and control method of the heating furnace. Such equivalent alternatives should be considered within the protection scope of this invention.

[0038] Regarding process quality evaluation values, this invention uses appearance forming degree, heat shrink tightness, and insulation coverage integrity as quantitative criteria. These indicators are commonly used evaluation items in the existing technology for busbar heat shrink quality inspection. Visual inspection, contact measurement, and thermal imaging detection are widely used in the field to structurally quantify these indicators. Therefore, the extraction of process quality evaluation values ​​in this invention is a direct use of existing quality inspection methods and does not involve any new detection technologies. By statistically analyzing and comparing the process quality evaluation values ​​of samples, the actual quality performance corresponding to the fluctuation intensity of high-frequency perturbation characteristics at different energy consumption levels can be clearly identified, thus providing a data basis for subsequent threshold determination and optimization of heating energy application parameters.

[0039] Furthermore, the energy efficiency management method for intelligent manufacturing production lines also includes the following steps: Step S300: Based on several samples, identify the threshold fluctuation intensity at which the corresponding process quality evaluation value first falls below the quality benchmark and shows a downward trend when the fluctuation intensity increases step by step, and define this fluctuation intensity as the quality sensitive fluctuation threshold.

[0040] Specifically, Figure 2 A flowchart for determining the quality-sensitive fluctuation threshold is shown.

[0041] Based on several samples, the threshold fluctuation intensity at which the corresponding process quality evaluation value first falls below the quality benchmark and shows a downward trend when the fluctuation intensity increases step by step is identified, and this fluctuation intensity is defined as the quality-sensitive fluctuation threshold. The specific steps include: Step S301: Sort several samples according to the fluctuation intensity from low to high to obtain the corresponding process quality evaluation value change sequence; Step S302: Analyze the change sequence to identify the threshold fluctuation intensity where the process quality evaluation value first appears to be lower than the quality benchmark and then shows a continuous downward trend. Step S303: Determine the fluctuation intensity as the quality-sensitive fluctuation threshold.

[0042] In this embodiment of the invention, step S300 echoes step S200, together forming the core analytical logic chain of the invention. Step S200 serves to obtain a data foundation reflecting the changes in high-frequency energy consumption perturbations caused by differences in preceding processes or the busbar's own thermal response, by screening several samples with identical heating energy application parameters but different high-frequency perturbation intensity characteristics. In other words, step S200 provides a comparison sample between different perturbation intensities and process quality evaluation values ​​for subsequent threshold identification, enabling changes in process quality to correspond to specific perturbation intensity levels.

[0043] Based on this, step S300 aims to utilize the differences between the aforementioned samples to identify the starting point where the fluctuation intensity of the high-frequency perturbation characteristics in energy consumption adversely affects process quality. By ranking and trend analyzing the process quality evaluation values ​​of samples with different fluctuation intensities, the critical point at which quality performance begins to deteriorate can be clearly identified, i.e., the fluctuation intensity corresponding to the first occurrence of a process quality evaluation value falling below the quality benchmark and showing a downward trend. This fluctuation intensity is determined as the quality-sensitive fluctuation threshold, used to characterize the boundary at which the high-frequency perturbation characteristics in energy consumption change from "acceptable normal deviation" to "deviation that may lead to quality risk".

[0044] Determining the quality-sensitive fluctuation threshold is crucial to this invention. On one hand, this threshold precisely defines the trigger point for process risks, enabling the system to promptly trigger compensation strategies when the perceived fluctuation intensity exceeds the threshold, without waiting for actual quality degradation before correction. On the other hand, determining this threshold provides a target benchmark for the step-by-step adjustment of heating energy application parameters in subsequent steps. This allows process parameter adjustments to move beyond empirical judgment and instead rely on targeted compensation based on quantitative data, improving the accuracy and effectiveness of energy consumption control. Overall, the quality-sensitive fluctuation threshold constitutes the core node of the energy efficiency control logic in this invention. Its identification results not only demonstrate the correlation between high-frequency perturbation characteristics of energy consumption and process quality but also provide a clear basis for subsequent energy application strategy optimization.

[0045] Furthermore, the energy efficiency management method for intelligent manufacturing production lines also includes the following steps: Step S400: When it is determined that the fluctuation intensity of the high-frequency perturbation characteristic of the energy consumption of the current batch of busbars exceeds the quality sensitive fluctuation threshold, the heating energy application parameters of the current heating process are adjusted step by step, and the process quality evaluation value of the corresponding busbar is monitored after each adjustment until the heating energy application parameters that can make the process quality evaluation value reach the quality benchmark under the current fluctuation intensity are identified. Step S500: Apply the identified heating energy application parameters to the current batch of busbar heat shrinking heating process.

[0046] In this embodiment of the invention, when the fluctuation intensity of the high-frequency perturbation characteristic of the energy consumption of the current batch of busbars exceeds the quality-sensitive fluctuation threshold, it indicates that the batch of busbars has accumulated significant deviations in previous processes or differences in material characteristics before entering the heat shrinking heating process, and its thermal response behavior in the heating stage deviates considerably from the standard state. If the original heating energy application parameters are still used under these circumstances, it is easy to cause quality problems such as insufficient heat shrinking, uneven heat shrinking, or abnormal insulation coverage. Therefore, in this embodiment of the invention, when the judgment condition is triggered, the heating energy application parameters of the current heating process are adjusted step by step. The step-by-step adjustment method can be to sequentially increase the furnace temperature setpoint, the upper limit of heating power output, or decrease the material conveying speed according to a predetermined adjustment step, so that the busbars can obtain more sufficient or more uniform energy application in the heating furnace; after each adjustment step is completed, the process quality evaluation value of the corresponding busbar is monitored to determine whether it has returned to the quality benchmark. Once the process quality evaluation value corresponding to a certain adjustment step is detected to reach or exceed the quality benchmark, the heating energy application parameters corresponding to that stage can be determined as the target adjustment parameters suitable for the current fluctuation intensity. This step-by-step adjustment process can minimize excessive energy compensation while ensuring quality recovery, resulting in better energy efficiency of the final selected heating energy application parameters.

[0047] In step S500, the identified heating energy application parameters are applied to the heat shrinking heating process of the current batch of busbars. Specifically, after determining the target heating energy application parameters, the control system of the heating furnace is updated so that subsequent busbars entering the furnace in the same batch undergo heat shrinking heating according to these parameters. The control system will control the furnace temperature, adjust the power output, and coordinate the conveyor cycle according to the new parameters throughout the heating process to ensure that the same batch of busbars achieves heating effects that meet quality standards under the current fluctuation intensity conditions. This application step can be completed through a parameter configuration interface in an automated system or through an operation panel in manual management mode; the process is simple and takes effect in real time.

[0048] The overall technical solution of this invention has significant beneficial effects. In production practice, the high-frequency perturbation characteristics of energy consumption caused by deviations in previous processes or material differences in busbars often do not exceed the allowable range, thus failing to attract the attention of traditional monitoring systems. However, the potential quality risks reflected by these perturbations gradually accumulate during the heat shrinking process. This invention, by identifying and quantifying the high-frequency perturbation characteristics of energy consumption and combining them with historical data screening, can establish a link between deviations in previous processes and the quality results during the heating stage. By determining the quality-sensitive fluctuation threshold, potential anomalies can be detected early in production. By adjusting the heating energy application parameters step by step, quality recovery can be achieved while maintaining optimal energy efficiency. This invention solves the core research problem of identifying hidden deviations in operating conditions and performing minimum energy adjustment to restore quality within the allowable energy consumption fluctuation range, achieving integrated control of energy efficiency and quality.

[0049] This invention has promising application prospects. In electrical cabinet busbar processing production lines, due to batch fluctuations and process complexity, the heat shrinking heating process is often the critical link with the most significant quality fluctuations and the highest energy consumption. The energy efficiency management method based on high-frequency perturbation characteristics of energy consumption provided by this invention can be embedded into existing intelligent manufacturing systems without adding extra hardware. It can be implemented solely based on power curves, quality inspection data, and adjustment strategies, and is applicable to different types of heating furnaces, different process scales, and multiple busbar production scenarios. Furthermore, this method can also be extended to other critical processes requiring heat input, such as copper busbar drying and insulating varnish curing, achieving broader energy efficiency optimization value.

[0050] Furthermore, Figure 3 An application architecture diagram of the system provided in an embodiment of the present invention is shown.

[0051] In another preferred embodiment of the present invention, a production line energy efficiency management system for intelligent manufacturing includes: The power characteristic detection module 100 is used to retrieve the historical heating database when the heating furnace power curve of the previous preset number of busbars shows energy consumption high-frequency perturbation characteristics within the allowable range during the heat shrink heating process of the current batch of busbars.

[0052] The high-frequency perturbation feature of energy consumption refers to the local high-frequency fluctuation feature in the power curve that appears based on the time-series data of the power curve of the heating furnace, provided that the power amplitude is kept within the allowable range, and the frequency of change is higher than the normal operation benchmark but lower than the preset upper limit.

[0053] Furthermore, the energy efficiency management system for intelligent manufacturing production lines also includes: The sample screening module 200 is used to screen samples from the database that are consistent with the current busbar type, have the same heating energy application parameters, and correspond to different levels of fluctuation intensity of energy consumption high-frequency perturbation characteristics, and determine the process quality evaluation value corresponding to each sample.

[0054] The fluctuation intensity of the energy consumption high-frequency perturbation feature is determined based on the magnitude of the change frequency of the local high-frequency fluctuation feature compared to the normal operating reference.

[0055] The heating energy application parameters refer to at least one of the following: furnace temperature setpoint, upper limit of heating power output, and material conveying speed.

[0056] Furthermore, the energy efficiency management system for intelligent manufacturing production lines also includes: The threshold identification module 300 is used to identify, based on several samples, the threshold fluctuation intensity at which the corresponding process quality evaluation value first falls below the quality benchmark and shows a downward trend when the fluctuation intensity increases step by step, and defines the fluctuation intensity as the quality sensitive fluctuation threshold.

[0057] Furthermore, the energy efficiency management system for intelligent manufacturing production lines also includes: The parameter adjustment module 400 is used to adjust the heating energy application parameters of the current heating process step by step when the fluctuation intensity of the high-frequency perturbation characteristics of the current batch of busbars exceeds the quality sensitive fluctuation threshold. After each adjustment, the process quality evaluation value of the corresponding busbar is monitored until the heating energy application parameters that can make the process quality evaluation value reach the quality benchmark under the current fluctuation intensity are identified.

[0058] Furthermore, the energy efficiency management system for intelligent manufacturing production lines also includes: The parameter application module 500 is used to apply the identified heating energy application parameters to the current batch of busbar heat shrink heating process.

[0059] It should be understood that although the steps in the flowcharts of the various embodiments of the present invention are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the various embodiments may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least a portion of the sub-steps or stages of other steps.

[0060] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0061] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0062] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

[0063] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A production line energy efficiency management method for intelligent manufacturing, characterized in that, The method includes: For the heat shrinking heating process of the current batch of busbars, when it is detected that the power curve of the heating furnace of the previous preset number of busbars shows the energy consumption high-frequency perturbation characteristics within the allowable range during the heating process, the historical heating database is retrieved. Samples with high-frequency perturbation characteristics of energy consumption that are consistent with the current busbar type, have the same heating energy application parameters, and correspond to different levels of fluctuation intensity are selected from the database, and the process quality evaluation value corresponding to each sample is determined. Based on several samples, the threshold fluctuation intensity at which the corresponding process quality evaluation value first falls below the quality benchmark and shows a downward trend when the fluctuation intensity increases step by step is identified, and this fluctuation intensity is defined as the quality sensitive fluctuation threshold. When it is determined that the fluctuation intensity of the high-frequency perturbation characteristic of the energy consumption of the current batch of busbars exceeds the quality-sensitive fluctuation threshold, the heating energy application parameters of the current heating process are adjusted step by step, and the process quality evaluation value of the corresponding busbar is monitored after each adjustment until the heating energy application parameters that can make the process quality evaluation value reach the quality benchmark under the current fluctuation intensity are identified. The identified heating energy application parameters are applied to the current batch of busbar heat shrink heating process.

2. The energy efficiency management method for production lines in intelligent manufacturing according to claim 1, characterized in that, The high-frequency perturbation feature of energy consumption refers to the local high-frequency fluctuation feature in the power curve that appears based on the time-series data of the power curve of the heating furnace, provided that the power amplitude is kept within the allowable range, and the frequency of change is higher than the normal operation benchmark but lower than the preset upper limit.

3. The energy efficiency management method for production lines in intelligent manufacturing according to claim 2, characterized in that, The fluctuation intensity of the energy consumption high-frequency perturbation feature is determined based on the magnitude of the change frequency of the local high-frequency fluctuation feature compared to the normal operating reference.

4. The energy efficiency management method for production lines oriented towards intelligent manufacturing according to claim 1, characterized in that, The heating energy application parameters refer to at least one of the following: furnace temperature setpoint, upper limit of heating power output, and material conveying speed.

5. The energy efficiency management method for production lines in intelligent manufacturing according to claim 1, characterized in that, The process quality evaluation value is obtained by: testing at least one of the following based on the heated busbar: appearance forming degree, heat shrinking tightness, and insulation coverage integrity, and quantifying the test results into a process quality evaluation value.

6. The energy efficiency management method for production lines oriented towards intelligent manufacturing according to claim 1, characterized in that, Based on several samples, the steps to identify the threshold fluctuation intensity at which the corresponding process quality evaluation value first falls below the quality benchmark and shows a downward trend when the fluctuation intensity increases step by step, and to define this fluctuation intensity as the quality-sensitive fluctuation threshold, include: Several samples were sorted from low to high according to the fluctuation intensity to obtain the corresponding process quality evaluation value change sequence; The change sequence was analyzed to identify the threshold fluctuation intensity where the process quality evaluation value first appeared to be lower than the quality benchmark and then showed a continuous downward trend. The intensity of this fluctuation is determined as the quality-sensitive fluctuation threshold.

7. An energy efficiency management system for production lines in intelligent manufacturing, characterized in that, The system includes: The power characteristic detection module is used to retrieve the historical heating database when the heating furnace power curve of the previous preset number of busbars shows energy consumption high-frequency perturbation characteristics within the allowable range during the heat shrink heating process of the current batch of busbars. The sample screening module is used to select samples from the database that are consistent with the current busbar type, have the same heating energy application parameters, and correspond to different levels of fluctuation intensity of energy consumption high-frequency perturbation characteristics, and determine the process quality evaluation value corresponding to each sample. The threshold identification module is used to identify, based on several samples, the threshold fluctuation intensity at which the corresponding process quality evaluation value first falls below the quality benchmark and shows a downward trend when the fluctuation intensity increases step by step, and defines the fluctuation intensity as the quality sensitive fluctuation threshold. The parameter adjustment module is used to adjust the heating energy application parameters of the current heating process step by step when the fluctuation intensity of the high-frequency perturbation characteristics of the current batch of busbars exceeds the quality sensitive fluctuation threshold. After each adjustment, the process quality evaluation value of the corresponding busbar is monitored until the heating energy application parameters that can make the process quality evaluation value reach the quality benchmark under the current fluctuation intensity are identified. The parameter application module is used to apply the identified heating energy application parameters to the current batch of busbar heat shrinking heating process.

8. The production line energy efficiency management system for intelligent manufacturing according to claim 7, characterized in that, The high-frequency perturbation feature of energy consumption refers to the local high-frequency fluctuation feature in the power curve that appears based on the time-series data of the power curve of the heating furnace, provided that the power amplitude is kept within the allowable range, and the frequency of change is higher than the normal operation benchmark but lower than the preset upper limit.

9. The production line energy efficiency management system for intelligent manufacturing according to claim 8, characterized in that, The fluctuation intensity of the energy consumption high-frequency perturbation feature is determined based on the magnitude of the change frequency of the local high-frequency fluctuation feature compared to the normal operating reference.

10. The production line energy efficiency management system for intelligent manufacturing according to claim 9, characterized in that, The heating energy application parameters refer to at least one of the following: furnace temperature setpoint, upper limit of heating power output, and material conveying speed.

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