Wooden door production line energy efficiency multi-objective optimization method and system

Abstract

The invention provides a wooden door production line energy efficiency multi-objective optimization method and system. According to the method, the temperature and humidity data of the pressing section and the deformation displacement data of the wooden door material are collected in real time, the dynamic fluctuation characteristic and the displacement distribution characteristic are extracted, the collaborative correlation coefficient between the environment change and the material deformation is established, and the collaborative mapping rule is formulated in combination with the intermittent action characteristic of the pressing equipment. Based on the rule, a high energy efficiency loss scene is identified, environment control sensitive sections are divided, and a process compensation strategy for a high deformation risk stage is matched. And then, according to a strategy, self-adaptively adjusting local environment parameters, generating a global optimization instruction in combination with yield constraint, and finally executing the instruction in a pressing section to realize dynamic balance of energy consumption control and deformation control. Intelligent collaborative optimization of energy consumption and material deformation in the wooden door pressing production process is achieved, and the energy efficiency loss and the process quality are dynamically balanced on the premise that the yield is guaranteed.

Description

Technical Field

[0001] This application relates to the fields of intelligent manufacturing and industrial automation technology, and in particular to a multi-objective optimization method and system for energy efficiency of a wooden door production line. Background Technology

[0002] In the wood door lamination process, fluctuations in ambient temperature and humidity, as well as material deformation, significantly affect lamination quality and energy efficiency. Because wood door materials are sensitive to temperature and humidity, intermittent start-up and shutdown of the lamination equipment can easily lead to sudden changes in local environmental parameters, causing problems such as material deformation and poor bonding, while also resulting in energy waste. Therefore, there is an urgent need for a multi-objective collaborative control method that can sense the environmental and material conditions in real time and dynamically optimize process parameters to reduce energy consumption while ensuring a high yield rate.

[0003] Currently, some advanced production lines employ closed-loop environmental control systems based on fixed thresholds. These systems monitor the workshop environment by deploying temperature and humidity sensors and trigger air conditioning or humidification equipment to adjust based on preset temperature and humidity thresholds. The system maintains stable environmental parameters through a PID algorithm and employs a timing control strategy during the start-up and shutdown phases of the pressing equipment to briefly increase the intensity of environmental regulation to counteract interference caused by equipment operation.

[0004] While existing solutions can achieve basic environmental control, they have significant limitations: their static thresholds cannot adapt to dynamic process requirements, and the sensitivity differences of wooden door materials to environmental fluctuations at different pressing stages have not been quantitatively considered; at the same time, the coupling relationship between the environment and material deformation has not been modeled, resulting in control strategies targeting only environmental parameters rather than actual deformation risks, which can easily lead to excessive energy consumption or insufficient local control; in addition, energy efficiency losses in intermittent production have not been optimized, and high-energy-consuming operations during equipment start-up and shutdown lack a collaborative decision-making mechanism with material deformation control. Summary of the Invention

[0005] This application provides a multi-objective optimization method and system for energy efficiency of wooden door production lines, which solves the problems in the prior art where static environmental control strategies cannot dynamically adapt to the pressing process requirements, environmental parameters and material deformation lack a synergistic optimization mechanism, and energy efficiency loss and deformation control are difficult to balance during intermittent production.

[0006] Firstly, this application provides a multi-objective optimization method for energy efficiency of a wooden door production line, including: In the workshop environment of the pressing section of the wooden door production line, the temperature and humidity data of the environment are collected in real time, the dynamic fluctuation characteristics of the temperature and humidity data are extracted, and the deformation and displacement data of the continuously moving wooden door material are collected to identify the displacement distribution characteristics of the deformation and displacement data. The dynamic fluctuation characteristics and the displacement distribution characteristics are correlated with multi-source data to generate a collaborative correlation coefficient that characterizes the energy loss of material deformation triggered by environmental changes, and a collaborative mapping rule is established between the collaborative correlation coefficient and the intermittent characteristics of the pressing equipment. Based on the collaborative mapping rules, identify high-energy-efficiency loss operation scenarios in the equipment start-up and pressure holding stages of the pressing process in the pressing section, divide the corresponding environmental control sensitive sections, and simultaneously match the segmented process compensation strategies for the high deformation risk stages. The process compensation strategy drives the adaptive adjustment of various local environmental parameters, and at the same time, combined with the production yield constraint of the pressing process, a coordinated optimization instruction for the global environmental setpoint is generated. The coordination and optimization instructions are executed in the pressing section to achieve a dynamic balance between the energy consumption control target of the wooden door production line and the deformation control target of the wooden door material during the intermittent operation of the pressing section.

[0007] Optionally, the dynamic fluctuation characteristics and the displacement distribution characteristics are subjected to multi-source data correlation processing to generate a collaborative correlation coefficient characterizing the energy efficiency loss of material deformation triggered by environmental changes, and a collaborative mapping rule is established between the collaborative correlation coefficient and the intermittent characteristics of the pressing equipment, including: Based on the time-point sequence of the dynamic fluctuation characteristics and the time-point sequence of the displacement distribution characteristics, environmental change data values ​​and material deformation data values ​​at the same time point are selected. For each identical time point, the ratio between the magnitude of the change in the environmental change data value and the magnitude of the change in the material deformation data value is calculated, and used as the single influencing factor for the corresponding time point; The single influencing factors at all time points are summarized, and the corresponding synergistic correlation coefficients are calculated using preset calculation rules. Obtain the equipment operation records of the intermittent characteristics of the pressing equipment, and construct a direct relationship model between the collaborative correlation coefficient and the equipment pressure holding duration segment in the equipment operation records to form a collaborative mapping rule.

[0008] Optionally, based on the collaborative mapping rules, high-energy-efficiency-loss operation scenarios during the equipment start-up, shutdown, and pressure-holding stages of the pressing process in the pressing section are identified, corresponding environmentally sensitive sections are divided, and segmented process compensation strategies for high-deformation-risk stages are matched simultaneously, including: The equipment operation phase with the cooperative correlation coefficient higher than the set value is extracted from the cooperative mapping rule and used as the high energy efficiency loss operation scenario. The entire pressing process is divided into multiple consecutive time periods, each time period corresponding to a equipment operation stage, including equipment start-up time period, equipment stop time period, and equipment pressure holding time period; The stage in which the deformation value in the displacement distribution characteristics exceeds the risk threshold is identified as the high deformation risk stage. Each environmentally sensitive section is assigned a segment-specific process compensation strategy that matches the high deformation risk stage, including increasing the intensity of environmental temperature control during the equipment startup period and reducing the intensity of environmental humidity control during the equipment pressure holding period.

[0009] Optionally, a segment-specific process compensation strategy matching the high-deformation-risk stage is assigned to each environmentally sensitive segment, including: For each environmentally sensitive section, determine whether the time range of the corresponding section overlaps with the time range of the high deformation risk stage; When there is overlap, a preset process compensation strategy is selected according to the type of the corresponding environmental control sensitive section; The types of environmentally sensitive sections include equipment start-up time period and equipment pressure holding time period. The preset process compensation strategy includes: increasing the intensity of environmental temperature control during the equipment start-up time period and decreasing the intensity of environmental humidity control during the equipment pressure holding time period. The selected process compensation strategy is assigned to the corresponding environmental control sensitive area.

[0010] Optionally, the process compensation strategy drives adaptive adjustment of various local environmental parameters, while simultaneously generating a coordinated optimization instruction for the global environmental setpoint based on the production yield constraints of the pressing process, including: Within the environmentally sensitive area, various local environmental parameters, including ambient temperature setpoints or ambient humidity setpoints, are modified in real time according to the process compensation strategy. Obtain real-time production yield data for the pressing process and compare it with a preset yield lower limit. When the modified environmental parameters cause the real-time production yield data to be lower than the preset yield lower limit, the environmental parameters are adjusted in reverse to meet the yield requirements. By combining the adjustment results of all environmentally sensitive areas and yield rate constraints, unified global setpoints for ambient temperature and ambient humidity are generated as the coordination and optimization instructions.

[0011] Optionally, the coordination optimization command is executed in the pressing section to achieve a dynamic balance between the energy consumption control target of the wooden door production line and the deformation control target of the wooden door material during the intermittent operation of the pressing section, including: The global setpoints for ambient temperature and ambient humidity in the coordination and optimization command are input into the environmental control system of the pressing section. During the intermittent operation of the equipment in the pressing section, the actual energy consumption data and actual deformation data of the wooden door production line are monitored simultaneously. Compare the actual energy consumption data with the preset energy consumption upper limit value, and compare the actual deformation data with the preset deformation upper limit value; When the actual energy consumption data exceeds the preset energy consumption upper limit or the actual deformation data exceeds the preset deformation upper limit, the global setting value of ambient temperature or the global setting value of ambient humidity is dynamically fine-tuned to ensure that the energy consumption control target and the deformation control target are met simultaneously.

[0012] Optionally, a direct relationship model is constructed between the collaborative correlation coefficient and the equipment pressure holding duration segment in the equipment operation record to form a collaborative mapping rule, including: Read the value of the pressure holding duration from the equipment operation record; Obtain a pre-stored scaling factor value, which represents the change in the equipment pressure holding duration when the collaborative correlation coefficient changes by a unit; Based on the current value of the collaborative correlation coefficient and the proportional factor value, calculate the adjustment value of the equipment pressure holding duration period; The adjustment value is associated with the pressure holding duration of the device to form a collaborative mapping rule.

[0013] Secondly, this application provides a multi-objective energy efficiency optimization system for a wooden door production line, including: The data acquisition module is used to collect temperature and humidity data in real time in the workshop environment of the pressing section of the wooden door production line, extract the dynamic fluctuation characteristics of the temperature and humidity data, and simultaneously collect deformation and displacement data of the continuously moving wooden door material to identify the displacement distribution characteristics of the deformation and displacement data. The generation module is used to perform multi-source data association processing on the dynamic fluctuation characteristics and the displacement distribution characteristics to generate a collaborative correlation coefficient characterizing the energy efficiency loss of material deformation triggered by environmental changes, and to establish a collaborative mapping rule between the collaborative correlation coefficient and the intermittent characteristics of the pressing equipment. The matching module is used to identify high energy-efficiency loss operation scenarios in the equipment start-up and pressure holding stages of the pressing process in the pressing section based on the collaborative mapping rules, divide the corresponding environmental control sensitive sections, and simultaneously match the segmented process compensation strategies for the high deformation risk stages. The adjustment module is used to drive the adaptive adjustment of various local environmental parameters according to the process compensation strategy, and at the same time generate a coordinated optimization instruction for the global environmental setpoint in combination with the production yield constraint of the pressing process. The control module is used to execute the coordination and optimization instructions in the pressing section, so that the energy consumption control target of the wooden door production line and the deformation control target of the wooden door material can be dynamically balanced during the intermittent operation of the pressing section.

[0014] Thirdly, this application provides a computing device, including a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are to be invoked and executed by the processing component to implement a multi-objective optimization method for energy efficiency of a wooden door production line as described in the first aspect above.

[0015] Fourthly, this application provides a computer storage medium storing a computer program, which, when executed by a computer, implements a multi-objective optimization method for energy efficiency of a wooden door production line as described in the first aspect.

[0016] This application generates a collaborative correlation coefficient and establishes a mapping rule between the dynamic fluctuation characteristics of temperature and humidity and the distribution characteristics of deformation displacement through multi-source data correlation processing. This reveals the inherent correlation between environmental changes, material deformation, and equipment operation, providing a theoretical basis for precise optimization. By identifying high-energy-efficiency-loss operation scenarios based on collaborative mapping rules, dividing environmental control sensitive sections, and matching segmented process compensation strategies, differentiated control can be implemented for the characteristics of different production stages, improving the accuracy and effectiveness of optimization. By driving local environmental parameters to adaptively adjust according to process compensation strategies and generating global optimization instructions in combination with yield constraints, the unity of precise local control and global coordinated optimization can be achieved, ensuring improved energy efficiency while guaranteeing quality. By executing coordinated optimization instructions in the pressing section, a dynamic balance between energy consumption control and deformation control objectives can be achieved, ultimately achieving multi-objective collaborative optimization of wooden door pressing production.

[0017] Furthermore, by establishing a collaborative mapping relationship between dynamic characteristics of temperature and humidity, material deformation properties, and intermittent operation of equipment, a dynamic optimization model linking the environment, materials, and equipment was constructed. This model can accurately quantify the impact of environmental changes on material deformation and energy loss, providing a scientific basis for implementing precise process parameter optimization. It is the core technical support for the entire method to achieve multi-objective collaborative optimization.

[0018] These or other aspects of this application will become more apparent in the following description of the embodiments. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 The flowchart of a multi-objective optimization method for energy efficiency of a wooden door production line provided in this application is shown; Figure 2 This paper shows a schematic diagram of the multi-objective optimization system for energy efficiency of a wood door production line provided in this application; Figure 3 A schematic diagram of the structure of a computing device provided in this application is shown. Detailed Implementation

[0021] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0022] In some of the processes described in the specification, claims, and accompanying drawings of this application, multiple operations appearing in a specific order are included. However, it should be clearly understood that these operations may not be executed in the order they appear herein, or may be executed in parallel. The operation numbers, such as 101, 102, etc., are merely used to distinguish different operations and do not themselves represent any execution order. Furthermore, these processes may include more or fewer operations, and these operations may be executed sequentially or in parallel. It should be noted that the descriptions such as "first," "second," etc., in this document are used to distinguish different messages, devices, modules, etc., and do not represent a chronological order, nor do they limit "first" and "second" to different types.

[0023] In traditional mortise and tenon joinery for wooden doors, the uneven density and complex fiber direction of wood easily lead to inaccurate fits between the tenon and mortise. Existing calibration methods mostly rely on manual experience or mechanical adjustments of fixed parameters, failing to adapt to the natural differences in wood in real time. For example, when the cutting tool encounters harder areas of wood, increased resistance may cause deviations, resulting in excessively large or small gaps between the tenon and mortise; conversely, encountering looser areas may lead to over-cutting, affecting structural strength. This "one-size-fits-all" processing method not only reduces the assembly precision of wooden doors but also increases the risk of rework and material waste.

[0024] To address the aforementioned problems, this invention proposes a real-time laser calibration method that dynamically adjusts processing parameters by scanning the density distribution and fiber orientation of the wood. Specifically, this method first uses a laser to non-contactly measure the hardness distribution of the wood, then, combined with the design shape of the mortise and tenon joints, intelligently calculates the optimal cutting path. During processing, the system monitors the diffraction pattern of the laser as it penetrates the wood in real time, analyzes deformation trends, and automatically corrects the tool's trajectory. This method is like equipping the processing equipment with "intelligent eyes" and an "adaptive brain," enabling flexible adjustments based on the actual conditions of the wood to ensure a perfect fit between the mortise and tenon joints. Compared to traditional processes, it not only significantly improves processing accuracy but also reduces the scrap rate caused by fluctuations in wood properties, truly achieving high-quality, high-efficiency, and intelligent production.

[0025] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0026] Figure 1 This application provides a flowchart of a multi-objective optimization method for energy efficiency in a wooden door production line, as shown in the embodiments below. Figure 1 As shown, the method includes: 101. In the workshop environment of the pressing section of the wooden door production line, collect temperature and humidity data in real time, extract the dynamic fluctuation characteristics of the temperature and humidity data, and at the same time collect deformation and displacement data of the continuously moving wooden door material, and identify the displacement distribution characteristics of the deformation and displacement data. In the above scheme, temperature and humidity data refer to the real-time air temperature and humidity values ​​detected by sensors installed in the lamination workshop of the wooden door production line, which are used to reflect the actual state of the production environment; dynamic fluctuation characteristics refer to the regular information of the frequency of change, the magnitude of rise and fall, and the trend of sudden jumps in temperature and humidity data; deformation and displacement data refer to the real-time recording of the positional offset of key points on the surface of the moving wooden door using a laser measuring device, which reflects the degree of bending or expansion of the material caused by environmental changes; displacement distribution characteristics refer to the difference in displacement changes in different areas of the wooden door.

[0027] In this embodiment, firstly, temperature and humidity sensor arrays are installed at the entrance and exit of the pressing section of the wooden door production line, collecting data every 0.5 seconds. After smoothing the noise data using a moving average algorithm, peak detection technology is used to extract the time periods of drastic temperature and humidity changes and mark them as high fluctuation intervals. Secondly, a laser rangefinder group is deployed above the conveyor track to scan the three-dimensional coordinates of 50 marked points on the wooden door surface every 0.1 seconds, and image recognition technology is used to track the offset of the door panel outline. Next, the two types of data are unified with a timestamp, and the displacement data at the corresponding time is located according to the high fluctuation interval. The standard deviation of the displacement change difference between the edge and center areas of the wooden door is calculated and marked as the displacement distribution dispersion coefficient. Finally, the joint features of the temperature and humidity fluctuation amplitude and the displacement dispersion coefficient are extracted. For example, when the humidity fluctuation amplitude reaches a set threshold, the displacement dispersion coefficient of the wooden door edge increases significantly.

[0028] In practical application, a sensing system was deployed in the pressing section of the wooden door manufacturer A's workshop. During continuous production, the system detected that the humidity in area B fluctuated frequently beyond the set range within 3 minutes. Simultaneously, laser scanning revealed that when wooden door material numbered C passed through this area during the same time period, the displacement difference between its edge point and center point increased from the normal value of 0.5 mm to 1.2 mm. The system correlated the periods of humidity fluctuation with the periods of abnormal displacement changes through timestamp alignment, identifying that when the humidity fluctuation amplitude reached a critical value, the displacement dispersion coefficient synchronously increased to more than twice the normal value.

[0029] This solution transforms the impact of transient temperature and humidity changes on material deformation, which was previously difficult to quantify, into a quantifiable characteristic. By precisely matching periods of environmental fluctuations with corresponding areas of material displacement anomalies, the system can predict moments of high deformation risk, providing targeted objectives for subsequent process compensation. Ultimately, it establishes a real-time mapping capability between environmental data and material dynamic response, solving the problem that manual sampling cannot capture transient anomalies.

[0030] 102. Perform multi-source data association processing on the dynamic fluctuation characteristics and the displacement distribution characteristics to generate a collaborative correlation coefficient characterizing the energy loss of material deformation triggered by environmental changes, and establish a collaborative mapping rule between the collaborative correlation coefficient and the intermittent characteristics of the pressing equipment. Optionally, step 102 may specifically include the following steps: 1021. Based on the time-point sequence of the dynamic fluctuation characteristics and the time-point sequence of the displacement distribution characteristics, select environmental change data values ​​and material deformation data values ​​at the same time point; 1022. For each identical time point, calculate the ratio between the magnitude of the change in the environmental change data value and the magnitude of the change in the material deformation data value, and use it as the single influencing factor for the corresponding time point; 1023. Summarize the single influencing factors at all time points and calculate the corresponding synergistic correlation coefficients using preset calculation rules; 1024. Obtain the equipment operation record of the intermittent characteristics of the pressing equipment, and construct a direct relationship model between the collaborative correlation coefficient and the equipment pressure holding duration segment in the equipment operation record to form a collaborative mapping rule.

[0031] Specifically, step 1024 may include the following processes: reading the value of the device holding pressure duration from the device operation record; obtaining a pre-stored scaling factor value, wherein the scaling factor value represents the amount of change in the device holding pressure duration when the collaborative correlation coefficient changes by a unit; calculating the adjustment value of the device holding pressure duration based on the current value of the collaborative correlation coefficient and the scaling factor value; and associating the adjustment value with the device holding pressure duration to form a collaborative mapping rule.

[0032] In the above scheme, dynamic fluctuation characteristics refer to the regularity information of rapid rise and fall amplitude and jump frequency extracted from temperature and humidity data; displacement distribution characteristics refer to the deformation difference pattern of different areas on the surface of the wooden door; multi-source data association processing refers to time matching analysis of environmental change and material deformation data; the cooperative correlation coefficient is a value that quantifies the intensity of the influence of environmental changes on material deformation, and the larger the value, the more significant the environmental influence; the intermittent characteristics of pressing equipment operation refer to the working time pattern of pressing machinery in the "start-hold pressure-stop" cycle; the cooperative mapping rule is the calculation logic that describes the correspondence between the cooperative correlation coefficient and the adjustment amount of equipment holding pressure time; the proportional factor value is the increase or decrease in equipment holding pressure time corresponding to the unit correlation coefficient change determined in advance through experiments.

[0033] In this embodiment, the system first executes step 1021, which, based on the time sequence of the dynamic fluctuation characteristics and the time sequence of the displacement distribution characteristics, synchronously extracts environmental change data and material deformation data at the same time point from the temperature and humidity sensors and the displacement sensor. For example, the humidity fluctuation value at 10:00 AM is collected and recorded as a change of 15 units, and the deformation displacement value of the wooden door edge at the same time point is recorded as a change of 1.2 mm. Next, step 1022 is executed, which, for each same time point, calculates the ratio between the change in the environmental change data value and the change in the material deformation data value, as the single influence factor for the corresponding time point; the formula for calculating the single influence factor for the time point is: influence factor = environmental change ÷ displacement change, and the value is calculated as 15 ÷ 1.2 = 12.5. Then, in step 1023, the system processes the single influence factor at three consecutive time points: summarizing the influence factor values ​​of 12.5 at 10:00, 11.8 at 10:01, and 13.2 at 10:02. Using the formula: Coefficient of Coordination = (Factor 1 + Factor 2 + Factor 3) ÷ Number of Time Points, the average value is calculated to obtain the coefficient of coordination for the corresponding time point: (12.5 + 11.8 + 13.2) ÷ 3 = 37.5 ÷ 3 = 12.5. Finally, step 1024 is executed, reading the current holding time of 90 seconds from the equipment operation record, calling the preset proportional factor of 0.8 seconds / unit correlation coefficient, and using the formula: Holding Adjustment Value = Coefficient of Coordination × Proportional Factor, for example, calculating 12.5 × 0.8 = 10 seconds. This adjustment value is then correlated with the equipment to form a rule: when the correlation coefficient is 12.5, the holding time is automatically extended to 100 seconds. The collaborative mapping rules are transmitted to the pressing equipment control system in real time, and adjustments are immediately triggered when similar environmental fluctuations are detected in the workshop in the afternoon.

[0034] In practical application, in area B of the pressing workshop of wooden door manufacturing company A, the system detected a sudden increase of 20 units in humidity within one minute at 14:00. Simultaneously, laser scanning detected a 1.5 mm increase in the displacement of the edge of wooden door C passing by. The system immediately extracted the environmental change of 20 units and the displacement change of 1.5 mm, and calculated the current value using the influence factor formula: environmental change ÷ displacement change ≈ 13.3. Subsequently, at 14:01, a further increase of 18 units in humidity and a corresponding displacement increase of 1.3 mm were detected, and the factor was calculated as 18 ÷ 1.3 ≈ 13.8. At 14:02, a humidity fluctuation of 22 units and a displacement change of 1.7 mm were recorded, with a factor value of 22 ÷ 1.7 ≈ 12.9. The system summarized the data over three consecutive minutes, averaged the values, and generated a co-correlation coefficient: (13.3 + 13.8 + 12.9) ÷ 3 = 13.3, then call the preset scaling factor 0.6 (that is, the pressure holding time needs to be adjusted by 0.6 seconds per unit of the correlation coefficient), calculate the adjustment value 13.3 × 0.6 = 8 seconds, combine it with the current pressure holding time of 95 seconds of device D to generate a new instruction 95 seconds + 8 seconds = 103 seconds, and send the instruction to device D for execution before 14:03, so that the subsequent scan shows that the C-shaped variable of the wooden door returns to the normal threshold.

[0035] This solution establishes a seamless decision-making chain between environmental data, material response, and equipment control, enabling previously isolated environmental fluctuation information to directly drive equipment parameter adjustments. When drastic environmental changes cause abnormal material deformation, the system automatically generates an adaptation plan for the equipment's pressure holding time. For example, in the event of a sudden increase in humidity, the pressure holding time is immediately extended to counteract the material expansion effect. This avoids the lag of manual adjustments and prevents material loss and energy waste caused by slow equipment response, achieving closed-loop control of environmental disturbances.

[0036] 103. Based on the collaborative mapping rules, identify high energy efficiency loss operation scenarios in the equipment start-up and pressure holding stages of the pressing process in the pressing section, divide the corresponding environmental control sensitive sections, and simultaneously match the segmented process compensation strategy for the high deformation risk stage. Optionally, step 103 may specifically include the following steps: 1031. Extract the equipment operation phase from the cooperative mapping rule where the cooperative correlation coefficient is higher than a set value, and use it as the high energy efficiency loss operation scenario; 1032. The complete process of the pressing process is divided into multiple consecutive time periods, each time period corresponding to a equipment operation stage, including equipment start-up time period, equipment stop time period and equipment pressure holding time period; 1033. The stage in which the deformation value in the displacement distribution characteristics exceeds the risk threshold is identified as the high deformation risk stage; 1034. Assign a segment-specific process compensation strategy to each environmentally sensitive segment that matches the high deformation risk stage, including increasing the intensity of environmental temperature control during the equipment start-up period and reducing the intensity of environmental humidity control during the equipment pressure holding period.

[0037] Step 1034 may specifically include the following process: for each environmentally sensitive control segment, determine whether the time range of the corresponding segment overlaps with the time range of the high deformation risk stage; when there is an overlap, select a preset process compensation strategy according to the type of the corresponding environmentally sensitive control segment; wherein, the type of the environmentally sensitive control segment includes the equipment start-up time period and the equipment pressure holding time period, and the preset process compensation strategy includes: increasing the environmental temperature control intensity during the equipment start-up time period and decreasing the environmental humidity control intensity during the equipment pressure holding time period; and assign the selected process compensation strategy to the corresponding environmentally sensitive control segment.

[0038] In the above scheme, the collaborative mapping rule refers to the pre-established relationship table between the correlation coefficient and the equipment operating parameters; the high energy efficiency loss operation scenario refers to the period of additional energy consumption generated during the equipment pressure holding or start-up and shutdown phases when the correlation coefficient exceeds the critical value; the environmental control sensitive section is the key period of drastic fluctuations in workshop temperature and humidity; the high deformation risk stage refers to the processing period when the displacement of wooden doors exceeds the safe range; the segmented process compensation strategy is a control scheme matched according to different equipment states, such as reducing the dehumidification intensity during pressure holding.

[0039] In this embodiment, the system first scans the collaborative mapping rules in step 1031, filtering out operation stages with correlation coefficients exceeding a set risk value of 10 as high-energy-efficiency loss scenarios. For example, the record of equipment D in the pressure holding stage shows that the current correlation coefficient of 12.5 is greater than the critical value and is marked as a high-energy-consumption period. Then, step 1032 is executed to divide the complete pressing process into continuous operation segments, each time segment corresponding to a equipment operation stage, including the equipment start-up time segment, the equipment stop time segment, and the equipment pressure holding time segment. For example, it is identified that 10:05 AM to 10:07 AM is the start-up stage, taking 20 seconds; 10:07 AM to 10:10 AM is the pressure holding stage, lasting 180 seconds; and 10:10 AM to 10:11 AM is the stop stage, taking 10 seconds. At this time, in step 1033, the deformation value in the displacement distribution characteristics is detected. When the value of number C is found... If the door's edge displacement reaches 2.1 mm at 10:08, exceeding the safety threshold of 1.8 mm, the period from 10:08 to 10:10 is immediately marked as a high-risk phase. Finally, in step 1034, for each environmentally sensitive control segment, it is determined whether the time range of the corresponding segment overlaps with the time range of the high deformation risk phase. After comparing the overlap of time periods and determining that the pressure holding phase from 10:07 to 10:10 completely covers the high-risk period, strategy matching is automatically triggered: during the start-up phase from 10:05 to 10:07, the heater power is increased from the original 4000 watts to 4800 watts to enhance the wood preheating effect. The preset process compensation strategy includes: increasing the intensity of environmental temperature control during the equipment start-up period and reducing the intensity of environmental humidity control during the equipment pressure holding period. The selected process compensation strategy is assigned to the corresponding environmentally sensitive control segment. The calculation method is to multiply the original power by an adjustment coefficient of 1.2, i.e., 4000 multiplied by 1.2 equals 4800 watts. At the same time, during the pressure holding stage from 10:07 to 10:10, the dehumidification power is reduced from 3000 watts to 2400 watts to reduce energy waste. The calculation process is to multiply 3000 by a coefficient of 0.8, which equals 2400 watts. The entire process takes less than two seconds to complete the control command issuance.

[0040] In practical application, during production in area B of workshop A, when the system detected that the synergistic correlation coefficient of equipment D reached 12 during the pressure holding phase at 10:30 AM, exceeding the threshold of 10, it was marked as a high-energy-efficiency loss scenario. At the same time, it was detected that the displacement of wooden door C reached 2.0 mm between 10:31 and 10:33 AM, exceeding the risk threshold of 1.8 mm, thus identifying this period as a high-risk phase. After dividing the pressing process into intervals of 20 seconds of start-up, 110 seconds of pressure holding, and 10 seconds of stop, it was determined that the pressure holding phase of 10:30-10:33 AM overlapped with the high-risk period. The system immediately increased the power of the hot air unit to 1.3 times its original value during the start-up phase to reduce the moisture absorption of the wood, and reduced the power of the dehumidifier to 0.7 times its original value during the pressure holding phase to avoid over-drying, so that the subsequent deformation of wooden door C returned to the safe range of 1.2 mm.

[0041] This solution achieves precise coordination between environmental control and equipment operation. When abnormal temperature and humidity are detected, increasing the risk of wooden door deformation, the system can automatically increase the heating intensity during startup to prevent the material from absorbing moisture and expanding, while reducing the dehumidification intensity during the pressure holding stage to avoid energy waste. For example, short-term high-temperature treatment during equipment startup improves the stability of the wood, while appropriately weakening environmental control during pressure holding ensures deformation repair while reducing redundant energy consumption, ultimately achieving dynamic optimization between energy consumption control and material qualification rate.

[0042] 104. Based on the process compensation strategy, drive the local environmental parameters to adaptively adjust, and at the same time, generate a coordinated optimization instruction for the global environmental setpoint by combining the production yield constraints of the pressing process. Optionally, step 104 may specifically include the following steps: 1041. Within the environmentally sensitive control zone, various local environmental parameters, including ambient temperature setpoints or ambient humidity setpoints, are modified in real time according to the process compensation strategy. 1042. Obtain the real-time production yield data of the pressing process and compare it with the preset yield lower limit value; 1043. When the modified environmental parameters cause the real-time production yield data to be lower than the preset yield lower limit, the environmental parameters are adjusted in reverse to meet the yield requirements. 1044. Based on the adjustment results of all environmentally sensitive sections and the yield rate constraints, generate unified global setpoints for ambient temperature and ambient humidity as the coordination optimization instructions.

[0043] In the above scheme, the process compensation strategy refers to the environmental adjustment scheme formulated for different equipment stages; various environmental parameters refer to the temperature and humidity setpoints adjusted in real time; the production yield rate is the proportion of qualified wooden doors produced; the yield rate lower limit is the minimum qualified standard; and the coordination and optimization instruction is the final determined unified temperature and humidity control value for the entire workshop.

[0044] In this embodiment, firstly, during the sensitive startup period of device D (10:05-10:07), the local environmental parameters are modified according to a preset compensation strategy via 1041. Specifically, this involves calling the temperature control system interface of area B to increase the temperature setpoint from 24 degrees to 26 degrees in real time. Based on the "1.2 times temperature increase during startup" rule in the compensation strategy, the result is calculated as: 24 × 1.2 = 28.8 ≈ 26 degrees, rounded down. Next, at 1042, the latest real-time yield data of the pressing process is obtained at 10:30. The yield rate reported by the visual inspection system is 89%. For example, if two out of 20 wooden doors C are found to have excessive edge warping, an abnormal alarm is triggered when compared with the preset lower limit of 90%. Subsequently, 1043, when the modified... When environmental parameters cause the real-time production yield rate to fall below the preset yield rate lower limit, the parameters are automatically adjusted in reverse using a gradient callback algorithm: the temperature is reduced from 26 degrees Celsius in steps of 0.5 degrees Celsius per minute, for example, from 10:35 to 25.5 degrees Celsius, from 10:40 to 25 degrees Celsius, until the yield rate recovers to 92% at 11:00 and the callback stops; finally, by combining the adjusted temperature of 25 degrees Celsius in region B, the temperature of 26 degrees Celsius in region C, and the yield rate constraint, the weighted average formula is used for the calculation: global temperature value = weight of region B 0.6 × 25 + weight of region C 0.4 × 26 = 25.4 degrees Celsius, combined with the yield rate constraint, the final instruction "overall workshop temperature 25.4 degrees Celsius / humidity 49%" is generated.

[0045] In practical applications, in the pressing workshop of Company A, the system implements a compensation strategy in area B for the pressure holding stage of equipment D, reducing the humidity from 55% to 50%. Two hours later, quality inspection shows that the yield rate in this area has dropped from 92% to 89%, which is lower than the set lower limit of 90%. The system immediately adjusts the humidity back to 52%, causing the yield rate of the next batch to rise to 91%. At the same time, the system integrates the temperature adjustment data of 28 degrees in area E with the overall requirements to finally generate a unified instruction for the entire workshop: maintain the temperature at 25 degrees and the humidity at 51% to ensure that the yield rate of all areas meets the standard.

[0046] This solution uses real-time feedback of yield rate to constrain environmental parameter adjustments and avoid overcompensation. For example, if excessive humidity drop causes wood to crack, parameters are immediately reverted to ensure the bottom line of quality. At the same time, the global coordination function prevents parameter conflicts between regions and achieves balanced control of temperature and humidity throughout the workshop, maintaining material processing stability and ensuring product quality pass rate.

[0047] 105. Execute the coordination and optimization command in the pressing section to achieve a dynamic balance between the energy consumption control target of the wooden door production line and the deformation control target of the wooden door material during the intermittent operation of the pressing section.

[0048] Optionally, step 105 may specifically include the following steps: 1051. Input the global setpoints for ambient temperature and ambient humidity in the coordination and optimization command to the environmental control system of the pressing section; 1052. During the intermittent operation of the equipment in the pressing section, the actual energy consumption data and actual deformation data of the wooden door production line are monitored simultaneously. 1053. Compare the actual energy consumption data with the preset energy consumption upper limit value, and the actual deformation data with the preset deformation upper limit value; 1054. When the actual energy consumption data exceeds the preset energy consumption upper limit or the actual deformation data exceeds the preset deformation upper limit, the global setting value of ambient temperature or the global setting value of ambient humidity is dynamically fine-tuned to ensure that the energy consumption control target and the deformation control target are met simultaneously.

[0049] In the above scheme, the coordinated optimization instruction refers to the temperature and humidity control values ​​that need to be uniformly executed throughout the entire pressing section; the environmental control system refers to the automatic control network composed of equipment such as air conditioners and dehumidifiers that regulate temperature and humidity in the workshop; the actual energy consumption data refers to the real-time energy consumption of the pressing equipment, such as electrical energy; the actual deformation data refers to the bending expansion displacement measured in real time on the surface of the wooden door; the preset energy consumption upper limit refers to the maximum energy consumption allowed by the production process; the preset deformation upper limit refers to the maximum safe deformation allowed by the wooden door material; and the dynamic fine-tuning refers to the small-scale correction of the control parameters when the monitoring data exceeds the standard.

[0050] In this embodiment, firstly, in stage 1051, the set values ​​in the coordination optimization command are sent to the environmental control system. These set values ​​include global set values ​​for ambient temperature and ambient humidity. For example, a temperature of 25.5 degrees Celsius and a humidity of 49% are sent to the air conditioning and dehumidifier units in area B for automatic environmental adjustment. Secondly, in stage 1052, actual data is synchronously collected during the equipment operation intervals in the pressing section. For example, the equipment's power meter displays 350 kWh per hour as actual energy consumption data, while laser scanning shows a 1.8 mm displacement at the edge of the wooden door as actual deformation data. Then, in stage 1053... The process compares the actual energy consumption data and the actual deformation data with preset upper limits, such as an energy consumption upper limit of 300 kWh and a deformation upper limit of 1.5 mm. If the value of 350 exceeds 300, an alarm is triggered. Finally, in stage 1054, when the actual energy consumption data exceeds the preset energy consumption upper limit or the actual deformation data exceeds the preset deformation upper limit, an automatic fine-tuning algorithm is activated. When the energy consumption exceeds the limit, the temperature setting is increased by 0.3 degrees to 25.8 degrees to reduce the energy consumption to 320 kWh while keeping the deformation within an acceptable range of 1.6 mm, thereby ensuring that both the energy consumption control target and the deformation control target are met simultaneously.

[0051] In practical applications, in the pressing workshop of wooden door company A, the system executes global commands to set the temperature to 26 degrees Celsius and the humidity to 50%. During the equipment's pressure holding interval, the actual energy consumption is monitored to reach 420 kWh, exceeding the preset upper limit of 380 kWh. At the same time, the deformation displacement of wooden door G reaches 1.9 mm, exceeding the upper limit of 1.6 mm. The system immediately activates the fine-tuning mechanism to raise the temperature by 0.5 degrees Celsius to 26.5 degrees Celsius and maintain the humidity at 50%. This reduces the energy consumption of the next work cycle to 390 kWh and the deformation to 1.7 mm, thus controlling material deformation and avoiding energy waste.

[0052] This solution uses a real-time monitoring and automatic correction mechanism to quickly respond to data changes during equipment operation intervals. When energy consumption or material deformation approaches dangerous values, it automatically fine-tunes environmental parameters to avoid imbalances caused by single-objective optimization. For example, a slight increase in temperature reduces equipment load and energy consumption while alleviating stress concentration in the wood, ultimately maintaining a stable balance between the quality and energy efficiency of wooden doors during continuous production.

[0053] This plan Figure 2 This application provides a schematic diagram of the structure of a multi-objective energy efficiency optimization system for a wooden door production line, as shown in the embodiment of the present application. Figure 2 As shown, the system includes: The data acquisition module 21 is used to collect temperature and humidity data in real time in the workshop environment of the pressing section of the wooden door production line, extract the dynamic fluctuation characteristics of the temperature and humidity data, and simultaneously collect deformation and displacement data of the continuously moving wooden door material to identify the displacement distribution characteristics of the deformation and displacement data. The generation module 22 is used to perform multi-source data association processing on the dynamic fluctuation characteristics and the displacement distribution characteristics to generate a collaborative correlation coefficient characterizing the energy efficiency loss of material deformation triggered by environmental changes, and to establish a collaborative mapping rule between the collaborative correlation coefficient and the intermittent characteristics of the pressing equipment. Matching module 23 is used to identify high energy efficiency loss operation scenarios in the equipment start-up and pressure holding stages of the pressing process in the pressing section based on the collaborative mapping rules, divide the corresponding environmental control sensitive sections, and simultaneously match the segmented process compensation strategy for the high deformation risk stage. Adjustment module 24 is used to drive local environmental parameters to adaptively adjust according to the process compensation strategy, and at the same time generate a coordinated optimization instruction for the global environment setpoint in combination with the production yield constraint of the pressing process. The control module 25 is used to execute the coordination and optimization instructions in the pressing section, so that the energy consumption control target of the wooden door production line and the deformation control target of the wooden door material can be dynamically balanced during the intermittent operation of the pressing section.

[0054] Figure 2 The aforementioned multi-objective energy efficiency optimization system for a wooden door production line can perform... Figure 1The implementation principle and technical effects of the multi-objective energy efficiency optimization method for a wooden door production line described in the illustrated embodiment will not be repeated here. The specific methods by which each module and unit of the multi-objective energy efficiency optimization system for a wooden door production line in the above embodiments are performed have been described in detail in the embodiments related to this method, and will not be elaborated upon here.

[0055] In one possible design, Figure 2 The multi-objective energy efficiency optimization system for a wooden door production line shown in the embodiment can be implemented as a computing device, such as... Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32; The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are invoked and executed by the processing component 32.

[0056] The processing component 32 is used for the above Figure 1 The embodiment describes a multi-objective optimization method for energy efficiency of a wooden door production line.

[0057] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above-described method. Alternatively, the processing component may be implemented as one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above-described method.

[0058] Storage component 31 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0059] Of course, computing devices may also include other components, such as input / output interfaces, display components, communication components, etc.

[0060] Input / output interfaces provide interfaces between processing components and peripheral interface modules, which can be output devices, input devices, etc.

[0061] The communication components are configured to facilitate wired or wireless communication between computing devices and other devices.

[0062] The computing device can be a physical device or an elastic computing host provided by a cloud computing platform. In this case, the computing device can refer to a cloud server, and the aforementioned processing components, storage components, etc., can be basic server resources rented or purchased from the cloud computing platform.

[0063] This application also provides a computer storage medium storing a computer program, which, when executed by a computer, can perform the above-described functions. Figure 1 The embodiment shown illustrates a multi-objective optimization method for energy efficiency of a wooden door production line.

[0064] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0065] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0066] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0067] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A multi-objective optimization method for energy efficiency of a wooden door production line, characterized in that, include: In the workshop environment of the pressing section of the wooden door production line, the temperature and humidity data of the environment are collected in real time, the dynamic fluctuation characteristics of the temperature and humidity data are extracted, and the deformation and displacement data of the continuously moving wooden door material are collected to identify the displacement distribution characteristics of the deformation and displacement data. The dynamic fluctuation characteristics and the displacement distribution characteristics are correlated with multi-source data to generate a collaborative correlation coefficient that characterizes the energy loss of material deformation triggered by environmental changes, and a collaborative mapping rule is established between the collaborative correlation coefficient and the intermittent characteristics of the pressing equipment. Based on the collaborative mapping rules, identify high-energy-efficiency loss operation scenarios in the equipment start-up and pressure holding stages of the pressing process in the pressing section, divide the corresponding environmental control sensitive sections, and simultaneously match the segmented process compensation strategies for the high deformation risk stages. The process compensation strategy drives the adaptive adjustment of various local environmental parameters, and at the same time, combined with the production yield constraint of the pressing process, a coordinated optimization instruction for the global environmental setpoint is generated. The coordination and optimization instructions are executed in the pressing section to achieve a dynamic balance between the energy consumption control target of the wooden door production line and the deformation control target of the wooden door material during the intermittent operation of the pressing section.

2. The method according to claim 1, characterized in that, The dynamic fluctuation characteristics and displacement distribution characteristics are correlated using multi-source data to generate a collaborative correlation coefficient characterizing the energy efficiency loss of material deformation triggered by environmental changes. A collaborative mapping rule is then established between this collaborative correlation coefficient and the intermittent characteristics of the pressing equipment, including: Based on the time-point sequence of the dynamic fluctuation characteristics and the time-point sequence of the displacement distribution characteristics, environmental change data values ​​and material deformation data values ​​at the same time point are selected. For each identical time point, the ratio between the magnitude of the change in the environmental change data value and the magnitude of the change in the material deformation data value is calculated, and used as the single influencing factor for the corresponding time point; The single influencing factors at all time points are summarized, and the corresponding synergistic correlation coefficients are calculated using preset calculation rules. Obtain the equipment operation records of the intermittent characteristics of the pressing equipment, and construct a direct relationship model between the collaborative correlation coefficient and the equipment pressure holding duration segment in the equipment operation records to form a collaborative mapping rule.

3. The method according to claim 1, characterized in that, Based on the collaborative mapping rules, high-energy-efficiency loss operation scenarios during equipment start-up, shutdown, and pressure holding stages in the pressing process of the pressing section are identified. Corresponding environmentally sensitive sections are then divided, and segmented process compensation strategies for high-deformation-risk stages are matched, including: The equipment operation phase with the cooperative correlation coefficient higher than the set value is extracted from the cooperative mapping rule and used as the high energy efficiency loss operation scenario. The entire pressing process is divided into multiple consecutive time periods, each time period corresponding to a equipment operation stage, including equipment start-up time period, equipment stop time period, and equipment pressure holding time period; The stage in which the deformation value in the displacement distribution characteristics exceeds the risk threshold is identified as the high deformation risk stage. Each environmentally sensitive section is assigned a segment-specific process compensation strategy that matches the high deformation risk stage, including increasing the intensity of environmental temperature control during the equipment startup period and reducing the intensity of environmental humidity control during the equipment pressure holding period.

4. The method according to claim 3, characterized in that, Assigning segment-specific process compensation strategies to each environmentally sensitive segment to match the high-deformation-risk stage, including: For each environmentally sensitive section, determine whether the time range of the corresponding section overlaps with the time range of the high deformation risk stage; When there is overlap, a preset process compensation strategy is selected according to the type of the corresponding environmental control sensitive section; The types of environmentally sensitive sections include equipment start-up time period and equipment pressure holding time period. The preset process compensation strategy includes: increasing the intensity of environmental temperature control during the equipment start-up time period and decreasing the intensity of environmental humidity control during the equipment pressure holding time period. The selected process compensation strategy is assigned to the corresponding environmental control sensitive area.

5. The method according to claim 1, characterized in that, Based on the process compensation strategy, various local environmental parameters are adaptively adjusted. Simultaneously, combined with the production yield constraints of the pressing process, a coordinated optimization instruction for the global environmental setpoint is generated, including: Within the environmentally sensitive control zone, various local environmental parameters, including ambient temperature setpoints or ambient humidity setpoints, are modified in real time according to the process compensation strategy. Obtain real-time production yield data for the pressing process and compare it with a preset yield lower limit. When the modified environmental parameters cause the real-time production yield data to be lower than the preset yield lower limit, the environmental parameters are adjusted in reverse to meet the yield requirements. By combining the adjustment results of all environmentally sensitive areas and yield rate constraints, unified global setpoints for ambient temperature and ambient humidity are generated as the coordination and optimization instructions.

6. The method according to claim 1, characterized in that, The coordination and optimization instructions are executed in the pressing section to achieve a dynamic balance between the energy consumption control target of the wooden door production line and the deformation control target of the wooden door material during the intermittent operation of the pressing section, including: The global setpoints for ambient temperature and ambient humidity in the coordination and optimization command are input into the environmental control system of the pressing section. During the intermittent operation of the equipment in the pressing section, the actual energy consumption data and actual deformation data of the wooden door production line are monitored simultaneously. Compare the actual energy consumption data with the preset energy consumption upper limit value, and compare the actual deformation data with the preset deformation upper limit value; When the actual energy consumption data exceeds the preset energy consumption upper limit or the actual deformation data exceeds the preset deformation upper limit, the global setting value of ambient temperature or the global setting value of ambient humidity is dynamically fine-tuned to ensure that the energy consumption control target and the deformation control target are met simultaneously.

7. The method according to claim 2, characterized in that, A direct relationship model is constructed between the collaborative correlation coefficient and the equipment pressure holding duration segment in the equipment operation record to form a collaborative mapping rule, including: Read the value of the pressure holding duration from the equipment operation record; Obtain a pre-stored scaling factor value, which represents the change in the equipment pressure holding duration when the collaborative correlation coefficient changes by a unit; Based on the current value of the collaborative correlation coefficient and the proportional factor value, calculate the adjustment value of the equipment pressure holding duration period; The adjustment value is associated with the pressure holding duration of the device to form a collaborative mapping rule.

8. A multi-objective energy efficiency optimization system for a wooden door production line, characterized in that, include: In the workshop environment of the pressing section of the wooden door production line, the temperature and humidity data of the environment are collected in real time, the dynamic fluctuation characteristics of the temperature and humidity data are extracted, and the deformation and displacement data of the continuously moving wooden door material are collected to identify the displacement distribution characteristics of the deformation and displacement data. The dynamic fluctuation characteristics and the displacement distribution characteristics are correlated with multi-source data to generate a collaborative correlation coefficient that characterizes the energy loss of material deformation triggered by environmental changes, and a collaborative mapping rule is established between the collaborative correlation coefficient and the intermittent characteristics of the pressing equipment. Based on the collaborative mapping rules, identify high-energy-efficiency loss operation scenarios in the equipment start-up and pressure holding stages of the pressing process in the pressing section, divide the corresponding environmental control sensitive sections, and simultaneously match the segmented process compensation strategies for the high deformation risk stages. The process compensation strategy drives the adaptive adjustment of various local environmental parameters, and at the same time, combined with the production yield constraint of the pressing process, a coordinated optimization instruction for the global environmental setpoint is generated. The coordination and optimization instructions are executed in the pressing section to achieve a dynamic balance between the energy consumption control target of the wooden door production line and the deformation control target of the wooden door material during the intermittent operation of the pressing section.

9. A computing device, characterized in that, It includes a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are invoked and executed by the processing component to implement a multi-objective energy efficiency optimization method for a wooden door production line as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that, The device contains a computer program that, when executed by a computer, implements a multi-objective optimization method for energy efficiency of a wooden door production line as described in any one of claims 1 to 7.

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