Multi-dimensional monitoring and energy-saving analysis control method for coating system

Abstract

The invention relates to a multi-dimensional monitoring and energy-saving analysis control method for a coating system. Operation data of all functional units of the coating system are collected through a multi-dimensional monitoring sensor. Based on the collected data, a three-dimensional visual model is constructed, the position of a monitoring sensor is marked, an energy flow diagram is drawn, and visual display of energy flow is achieved. According to the method, an energy-saving efficiency evaluation index system is established, energy consumption indexes of all functional units are generated, and energy consumption comparative analysis is carried out by combining historical energy consumption data and utilizing a big data analysis model so as to identify energy consumption differences and formulate an energy-saving optimization scheme. The optimization scheme comprises the steps of selecting an optimal waste gas treatment path, adjusting the ratio of fresh air to circulating air, and dynamically adjusting spraying pressure, flow speed control parameters, spraying temperature and equipment starting and stopping time. After the optimization scheme is issued, the operation parameters are adjusted in real time, the system effect is continuously monitored, energy consumption statistics and trend prediction data are generated, follow-up optimization control is guided, and efficient and energy-saving operation of the coating system is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of control systems, and in particular to a multi-dimensional monitoring and energy-saving analysis control method for a coating system. Background Art

[0002] In the existing technology, the energy management and monitoring of the coating system usually rely on independent sensors and control devices to collect operating data, such as temperature, humidity, current consumption, gas consumption and steam consumption. The system generally adopts traditional energy consumption monitoring and simple energy optimization methods, which cannot fully and real-time display the energy usage of each functional unit, and lacks intuitive visual analysis of energy flow. Energy-saving measures are often based on the optimization of a single parameter, and fail to make full use of modern data analysis technology for global energy management.

[0003] Existing technologies have many limitations, such as the inability to conduct comprehensive multi-dimensional monitoring and optimization of the energy use of the entire coating system. Energy consumption analysis is usually limited to static or manual calculations, and fails to fully utilize real-time data for dynamic optimization. In addition, the lack of an accurate energy-saving efficiency evaluation system and an intelligent optimization solution based on big data analysis has led to low overall energy utilization and the energy-saving potential of system operations has not been effectively tapped.

[0004] In view of this, it is necessary to develop an improved coating system monitoring and energy-saving control method. Summary of the invention

[0005] The present application provides a coating system multi-dimensional monitoring and energy-saving analysis and control method to improve the energy management efficiency of the coating system.

[0006] The present application provides a coating system multi-dimensional monitoring and energy-saving analysis and control method, including: The operation data of each functional unit of the coating system is collected by using a multi-dimensional monitoring sensor, wherein the functional units include a fresh air energy recovery unit, a circulating air treatment unit, an exhaust gas treatment unit and a nitrogen spraying unit, and the operation data includes temperature, humidity, current consumption, gas consumption and steam consumption; Based on the collected operation data, a three-dimensional visualization model of the coating system is constructed; in the three-dimensional visualization model, the specific distribution position of each monitoring sensor is marked and an energy flow diagram is drawn, and the operation data is mapped to the model to achieve an intuitive visualization of the energy flow; based on the three-dimensional visualization model, an energy-saving efficiency evaluation index system corresponding to each functional unit is established; Generate energy consumption indicators of each functional unit using the operation data mapped in the three-dimensional visualization model, wherein the energy consumption indicators include steam recovery amount of the fresh air unit, energy utilization rate of the circulating air unit, VOC concentration of the exhaust gas treatment unit, and paint utilization rate of the nitrogen spray unit; Based on the generated energy consumption indicators and the stored historical energy consumption data, a big data analysis model is used to perform energy consumption comparison analysis; based on the analysis results of the energy consumption comparison analysis, the energy consumption difference between the current coating system and the traditional equipment is determined, and a specific energy-saving optimization plan is generated, wherein the energy-saving optimization plan includes selecting the best exhaust gas treatment path according to the real-time monitored exhaust gas VOC concentration, adjusting the ratio of fresh air to circulating air to optimize energy utilization, and dynamically adjusting key operating parameters to further reduce overall energy consumption, wherein the key operating parameters include spraying pressure, flow rate control parameters, spraying temperature, and equipment start and stop time; The energy-saving optimization plan is issued to each functional unit of the coating system, its operating parameters are adjusted in real time, and the operating effect of the optimized system is continuously monitored; the monitored operating effect is compared with the energy consumption data before optimization, the energy consumption changes are recorded and energy consumption statistics and trend forecast data are generated, wherein the trend forecast data is used to guide subsequent continuous optimization control, thereby realizing efficient and energy-saving operation of the coating system.

[0007] The beneficial effects of the technical solution provided by this application include: (1) By using multi-dimensional monitoring sensors to collect various operating data of the coating system, including temperature, humidity, current consumption, gas consumption and steam consumption, the present invention can comprehensively monitor the energy usage of each functional unit, providing a high-precision and real-time data basis for the overall energy consumption optimization of the system. (2) By constructing a three-dimensional visualization model and drawing an energy flow diagram, the present invention enables users to intuitively understand the energy flow and energy consumption distribution of each functional unit. This visualization method helps to quickly identify energy consumption hotspots and potential problems in energy use, thereby improving the energy management efficiency of the system. (3) Based on real-time data and big data analysis models, the present invention can generate specific energy-saving optimization plans, dynamically adjust the ratio of fresh air to circulating air, select the best exhaust gas treatment path, and adjust key operating parameters such as spraying pressure, flow rate, and temperature. This intelligent optimization method significantly improves the energy utilization rate of the coating system and reduces overall energy consumption. (4) By real-time monitoring the operating effect of the optimized system and comparing it with the energy consumption data before optimization, the present invention can generate energy consumption statistics and trend prediction data to guide subsequent continuous optimization control. This function ensures that the coating system is always in the best energy-saving state, significantly improving the long-term energy-saving effect and the stability of system operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 It is a flow chart of a multi-dimensional monitoring and energy-saving analysis and control method for a coating system provided in the first embodiment of the present application. DETAILED DESCRIPTION

[0009] Many specific details are described in the following description to facilitate a full understanding of the present application. However, the present application can be implemented in many other ways than those described herein, and those skilled in the art can make similar generalizations without violating the connotation of the present application, so the present application is not limited by the specific implementation disclosed below.

[0010] The first embodiment of the present application provides a method for multi-dimensional monitoring and energy-saving analysis and control of a coating system. Figure 1 , which is a schematic diagram of the first embodiment of the present application. Figure 1 A first embodiment of the present application provides a method for multi-dimensional monitoring and energy-saving analysis and control of a coating system, which is described in detail.

[0011] Step S101: Use multi-dimensional monitoring sensors to collect operating data of each functional unit of the coating system, where the functional units include a fresh air energy recovery unit, a circulating air treatment unit, an exhaust gas treatment unit and a nitrogen spray unit, and the operating data include temperature, humidity, current consumption, gas consumption and steam consumption.

[0012] In this embodiment, each functional unit includes but is not limited to a fresh air energy recovery unit, a circulating air treatment unit, an exhaust gas treatment unit, and a nitrogen spray unit. In order to achieve multi-dimensional monitoring, it is necessary to deploy multi-dimensional monitoring sensors at key locations of each functional unit, and to carry out detailed design of the installation and data collection of each sensor to meet the requirements of accurate acquisition and real-time transmission of data.

[0013] First, it is crucial to choose the right type of sensor. For example, the sensor used for temperature measurement should have a fast response time and high accuracy. Platinum resistance thermometers or thermocouple sensors are preferably used and installed at key points of each functional unit, such as the inlet and outlet of the fresh air energy recovery unit, the air circulation path of the circulating air treatment unit, the exhaust outlet of the exhaust gas treatment unit, and the spraying area of ​​the nitrogen spray unit. Humidity sensors are installed in the air circulation area to ensure that the dynamic changes in ambient humidity can be captured. Current consumption monitoring relies on current transformers or Rogowski coils, which are installed at the main electrical incoming line position of each unit to detect the power consumption of each unit in real time. Gas consumption monitoring equipment is preferably based on flow meters, such as turbine flow meters or ultrasonic flow meters, installed at key nodes of the gas supply pipeline to ensure accurate monitoring of gas flow and usage. For steam consumption monitoring, steam flow meters, especially differential pressure or vortex flow meters, can be used and placed at the steam supply and recovery pipelines.

[0014] Secondly, the selection of sensor installation locations should take into account the specific working characteristics of each functional unit. Taking the fresh air energy recovery unit as an example, in order to accurately evaluate the difference in inlet and outlet temperature and humidity, the sensors should be symmetrically arranged at the air inlet and exhaust port, and cooperate with the data acquisition system to ensure the real-time and synchronization of the data. In the circulating air treatment unit, the sensors should be evenly distributed at the starting and ending points of the air flow path to monitor the energy efficiency of the circulating air. For the exhaust gas treatment unit, the VOC (volatile organic compound) concentration sensor should be installed at the exhaust port to detect the pollutant content of the exhaust gas in real time, so as to optimize the selection of the exhaust gas treatment path later.

[0015] Next, all sensors are connected to the central data acquisition unit, using wired or wireless communication, which can be selected according to the actual environmental conditions of the factory. In the data acquisition unit, the operating data is initially processed, such as signal filtering, data denoising, etc., to improve the quality and reliability of the data. In addition, the data acquisition system should have a high-frequency data update capability to ensure real-time performance, especially in situations where environmental conditions change rapidly.

[0016] The entire implementation process not only focuses on the accuracy of the data, but also takes into account the stability and maintainability of the monitoring system. In order to ensure the long-term reliable operation of the sensor, each sensor should be calibrated and maintained regularly to avoid affecting the effect of the entire energy-saving control method due to data deviation.

[0017] Furthermore, the use of multi-dimensional monitoring sensors to collect operating data of each functional unit of the coating system includes: Determine the key monitoring locations in each functional unit and install corresponding multi-dimensional monitoring sensors; Regularly calibrate each monitoring sensor to ensure the accuracy and reliability of collected data; Collect monitoring data and perform preprocessing, including removing abnormal data and filling missing data, to provide high-quality data input for subsequent analysis.

[0018] First, it is necessary to determine the critical monitoring locations in each functional unit in order to reasonably deploy multi-dimensional monitoring sensors. Taking the fresh air energy recovery unit as an example, the key monitoring points include the various interfaces for fresh air entry and exhaust, which are critical for evaluating the energy recovery efficiency of the system. Temperature and humidity sensors can be installed on these interfaces to monitor the state changes of the fresh air. The circulating air treatment unit needs to install sensors in the air duct, especially at the key nodes of air circulation, to ensure that the air flow rate and temperature can be accurately recorded. The exhaust gas treatment unit should be equipped with a VOC (volatile organic compound) sensor installed at the exhaust gas outlet to monitor the pollutant concentration and support the optimization of the exhaust gas treatment path. The key monitoring points of the nitrogen spray unit are usually concentrated near the spray gun and the paint atomization area. These locations are suitable for installing flow rate sensors and pressure sensors to obtain real-time spraying data.

[0019] Once the sensor is installed, the system should be designed to calibrate the sensor regularly to ensure data accuracy and long-term reliability. The calibration process can include the use of standard reference sources, such as a test environment with known temperature or humidity conditions, to adjust and verify the accuracy of sensor readings. For VOC concentration sensors, standard gases can be used for calibration to ensure that the accuracy of the measured data is not affected by sensor drift. The calibration cycle can be set according to the characteristics of each sensor and the application environment. It is generally recommended to be performed at least once a quarter or adjusted according to the frequency of use.

[0020] The data collection process is the core of the system operation, and all collected data will be sent to the central data processing unit. During the data collection process, preprocessing is required to remove abnormal data that may affect the analysis results. For example, data preprocessing can include detecting and removing values ​​that are beyond a reasonable range (such as instantaneous and drastic fluctuations in temperature or pressure that may be caused by sensor failure), and using interpolation or averaging methods to fill in data gaps due to signal loss. This data cleaning and completion process is extremely critical because it can improve the reliability of subsequent analysis and optimization decisions.

[0021] The preprocessed data is stored in a database for further use. The data needs to be archived according to time series and functional units so that they can be quickly retrieved and analyzed later. For example, temperature and humidity data need to be associated with timestamps to enable trend analysis and historical comparison. In addition, a data redundant storage mechanism should be designed to prevent data loss and ensure the stability and efficiency of the entire system during high-load operation.

[0022] By implementing the above steps, the coating system can achieve efficient collection and processing of multi-dimensional monitoring data, thereby providing a reliable data basis for energy optimization and system performance improvement.

[0023] Step S102: Based on the collected operating data, a three-dimensional visualization model of the coating system is constructed; in the three-dimensional visualization model, the specific distribution position of each monitoring sensor is marked and an energy flow diagram is drawn, and the operating data is mapped to the model to achieve an intuitive visualization of the energy flow; based on the three-dimensional visualization model, an energy-saving efficiency evaluation index system corresponding to each functional unit is established.

[0024] First of all, the basis for building a 3D visualization model is accurate spatial modeling. Through detailed measurements of each functional unit in the coating system, the geometry, size and relative position of each unit are determined. This process can be completed with the help of 3D modeling software (such as AutoCAD, SolidWorks or other industrial design software) to ensure that all structural features are accurately represented. The fresh air energy recovery unit, circulating air treatment unit, exhaust gas treatment unit and nitrogen spray unit are all modeled in the 3D model and arranged in real proportion and spatial layout to reflect the actual system structure.

[0025] Next, map the locations of the multi-dimensional monitoring sensors to the 3D model. This requires converting the installation location of each sensor in the actual coating system into 3D spatial coordinates, and using a coordinate system to define the location of each sensor, such as specific coordinate points on the x, y, and z axes. The specific distribution location of each sensor is marked in the model, and a unique identifier is assigned to each sensor for clear display in the model. By associating the sensor's identification information with the collected data, users can click or select the sensor tag to view real-time or historical data.

[0026] The drawing of energy flow diagram is the core part of the 3D visualization model. This process clarifies the energy transmission method from one functional unit to another by defining the energy flow path. For example, the air flow direction in the fresh air unit and the exhaust emission path in the exhaust gas treatment unit need to be clearly marked, using different colors or graphic elements (such as arrows, lines) to indicate the direction and intensity of energy flow. The visualization of energy flow not only shows the flow trajectory of energy, but also provides dynamic effects, such as animation or changes in flow direction, to help users understand the energy transmission situation more intuitively.

[0027] In order to achieve real-time mapping of energy flows, the collected operating data is dynamically loaded into the 3D model. This requires establishing a data interface with the real-time data acquisition system to regularly or continuously update the values ​​in the model. For example, when temperature data is collected from the sensor of the fresh air unit, the temperature distribution in the model will be automatically updated to show the current temperature level. Other parameters such as humidity, current consumption, gas consumption and steam consumption are also mapped to the model through corresponding graphical representations to ensure that energy usage can be presented comprehensively and intuitively.

[0028] On the basis of the three-dimensional visualization model, an energy-saving efficiency evaluation index system corresponding to each functional unit is further established. Different evaluation indicators are set for each functional unit according to its specific energy consumption characteristics, such as the steam recovery efficiency of the fresh air unit, the air circulation utilization rate of the circulating air unit, the VOC concentration reduction effect of the exhaust gas treatment unit, and the paint utilization rate of the nitrogen spray unit. By displaying these indicators in the model in a visual way, users can clearly see the energy utilization of each unit and quickly identify which units have room for optimization.

[0029] The 3D visualization model can be further set up with interactive functions, allowing users to view the operation of different functional units from multiple angles. For example, users can rotate, zoom or click on a specific area to view detailed data, or even analyze the specific details of energy use through heat maps or data charts. This design ensures the ease of use and functionality of the model, providing a comprehensive data foundation and intuitive display tools for subsequent energy-saving analysis and optimization.

[0030] Furthermore, the energy-saving efficiency evaluation index system corresponding to each functional unit is established, including: Define the energy-saving efficiency evaluation index for each functional unit; Set a benchmark value for each evaluation indicator as a reference standard for measuring energy saving effects; Compare real-time operating data with benchmark values ​​and generate energy-saving efficiency reports to provide optimization suggestions.

[0031] First, it is crucial to define the energy-saving efficiency evaluation index of each functional unit. Specifically, the energy-saving efficiency index of the fresh air energy recovery unit can be set as the steam recovery rate, which measures the ratio of heat energy recovered from the air to the input energy, reflecting the efficiency of energy recovery. The index of the circulating air treatment unit can be defined as the energy utilization rate, that is, the percentage of energy effectively utilized in the air circulation process to the total input energy. For the exhaust gas treatment unit, the evaluation index can be the VOC (volatile organic compound) concentration reduction rate, which describes the effect of the exhaust gas purification system in reducing the concentration of pollutants. The energy-saving efficiency index of the nitrogen spray unit can be set as the paint utilization rate, that is, the ratio of the amount of paint actually attached to the target surface to the total amount of paint consumed. This indicator directly affects the degree of minimization of paint waste.

[0032] Once the energy efficiency indicators are defined, benchmark values ​​need to be set for each indicator. These benchmark values ​​are usually based on historical data, industry standards, or optimal performance parameters provided by manufacturers. For example, the steam recovery rate of the fresh air energy recovery unit can be set to 85% based on optimal operating conditions, while the energy utilization rate benchmark value of the circulating air unit may be 90%. These benchmark values ​​serve as a reference for measuring energy saving effects, allowing the system to evaluate in real time whether the current operating status meets the expected energy efficiency standards.

[0033] Comparing real-time operating data with benchmark values ​​is a core step in implementing the system. The system continuously monitors the actual performance of each functional unit and compares the collected data with the preset benchmark values. For example, if the VOC concentration of the exhaust gas treatment unit fails to decrease significantly and is higher than the set standard, the system will automatically mark this situation and analyze the potential causes, which may indicate the need to adjust the exhaust gas treatment path or strengthen the purification efforts. Similarly, if the paint utilization rate of the nitrogen spray unit is lower than the benchmark value, the system can identify this efficiency issue and prompt to adjust the spray pressure or flow rate to optimize the spraying effect.

[0034] Finally, an energy efficiency report is generated based on these real-time comparison results. This report not only summarizes the current performance of each functional unit, but also points out the deviation from the benchmark value and provides specific optimization suggestions. For example, if the steam recovery rate of the fresh air energy recovery unit does not reach the benchmark value, the report may recommend increasing the fan speed or optimizing the air flow path. The generation of the report can be automated, and the data analysis software will integrate multi-dimensional monitoring data and provide clear visual charts to help operators quickly understand system performance and take necessary optimization measures.

[0035] Furthermore, based on the collected operation data, a three-dimensional visualization model of the coating system is constructed; in the three-dimensional visualization model, the specific distribution position of each monitoring sensor is marked and an energy flow diagram is drawn, and the operation data is mapped to the model to achieve an intuitive visualization of the energy flow, including: Use coordinate transformation formula and data mapping algorithm to transform the position data of each monitoring sensor in the coating system Convert to coordinates in a 3D space model , The energy consumption correlation path of the monitoring sensor in three-dimensional space is determined by the following formula 1: in, To monitor the coordinates of the sensor in the three-dimensional space model; The actual position data of each monitoring sensor in the coating system; Radius adjustment parameters for the coating system; For the The polar angle of each monitoring sensor relative to a reference point; For the Temperature deviations associated with each monitoring sensor; is the reference temperature; The energy flow diagram is determined by the following flow calculation formula 2: in, For Monitoring sensors to The energy flow intensity of each monitoring sensor; is the stream intensity coefficient; is the energy consumption correction factor; is the energy loss coefficient between the two sensors; is the average energy consumption of the system.

[0036] First, the position data of each monitoring sensor in the coating system is expressed in three-dimensional coordinate form as , these coordinates represent the location of the sensor in the actual physical space. For example, sensors may be installed in different areas of a painting room to monitor key operating data such as temperature, humidity, and current consumption. In order to convert these actual location data into a 3D visualization model, a coordinate transformation formula is required, which facilitates the unified display and analysis of energy flows.

[0037] The following formula 1 is used to convert the actual position data of the sensor Convert to coordinates in a 3D space model In this formula, each term has a specific physical meaning and calculation method: : These are the converted coordinates that represent the exact location of the sensor in the 3D model. These coordinates are used to visually display the location of the sensor in the model.

[0038] : Represents the actual position data of the sensor, which is obtained by measuring and recording the installation position of the sensor.

[0039] : The radius adjustment parameter of the coating system is used to adjust and standardize the position distribution of each sensor in the model. Especially in systems with a large spatial range, this parameter can be set according to the physical size and layout of the system to ensure the accurate proportion of the 3D model.

[0040] : No. The polar angle of a sensor relative to a reference point is usually calculated by the relative azimuth angle between the sensor and the reference point. This angle is used to map the actual position of the sensor into the model and adjust their distribution in two-dimensional space according to different polar angles.

[0041] : With The temperature deviation associated with each sensor indicates the difference between the temperature currently monitored by the sensor and the reference temperature. This parameter is obtained through real-time temperature monitoring and can reflect the temperature changes in the local area of ​​the system.

[0042] : Reference temperature, used as a standard temperature to normalize the effects of temperature changes, is usually set to the ambient temperature or the ideal temperature at which the coating system operates.

[0043] Through this coordinate transformation formula, the system can adjust the actual location data into visual coordinates in the three-dimensional model, forming an intuitive sensor distribution map to facilitate subsequent analysis of energy flow.

[0044] Next, Equation 2 is used to draw the energy flow circle and determine the energy consumption correlation path between sensors: Each term also has a clear definition and calculation method in Formula 2: is the energy flow intensity, from Sensor to A sensor that indicates the strength and direction of energy flow. With this value, the system can visually show how energy flows between different functional units and which paths consume more or less energy.

[0045] The flow intensity coefficient is used to standardize and adjust the intensity of energy flow. This coefficient is set according to the specific characteristics of the coating system to ensure that the calculation result of energy flow intensity is consistent with the actual situation.

[0046] :This is the The sensor and The sum of the squares of the spatial distances between the sensors is used to calculate the Euclidean distance between two points. This value is used to quantify the distance between sensors and affects the calculation of energy flow intensity.

[0047] : Energy consumption correction factor, which corrects the attenuation caused by energy loss during energy flow. This factor is set according to the characteristics of energy transmission in the system. For example, in long-distance transmission, energy loss will be more significant, and the factor value can reflect this situation.

[0048] : No. The sensor and The energy loss coefficient between the sensors is used to represent the energy loss in the transmission path. This can be obtained through experiments or system characteristics analysis, such as the influence of factors such as air resistance or conduction loss.

[0049] : The average energy consumption value of the system, used as a reference for energy loss correction. This value is calculated from historical energy consumption data and provides a benchmark for standardizing and comparing the energy flow intensity of different paths.

[0050] Step S103: Generate energy consumption indicators for each functional unit using the operating data mapped in the three-dimensional visualization model, wherein the energy consumption indicators include the steam recovery amount of the fresh air unit, the energy utilization rate of the circulating air unit, the VOC concentration of the exhaust gas treatment unit, and the paint utilization rate of the nitrogen spray unit.

[0051] First, the collected operating data, such as temperature, humidity, current consumption, gas consumption, and steam consumption, are pre-processed and mapped into the 3D visualization model. Pre-processing includes data cleaning, filtering, and standardization to ensure the accuracy and consistency of the data. These processed data correspond to the location of each functional unit in the 3D model, so that the operating status and energy usage of each unit can be accurately reflected.

[0052] Next, to generate specific energy consumption indicators, different calculation methods are defined for each functional unit. For example, for the fresh air energy recovery unit, the steam recovery amount needs to be calculated based on the inlet and outlet temperature and humidity differences. By analyzing the temperature and humidity data of the fresh air unit, its steam recovery efficiency can be quantified, and then the steam recovery amount indicator of the unit can be generated. This process can be achieved through continuous sampling and real-time calculation, ensuring that the estimation of steam recovery amount is continuously updated during the operation of the coating system.

[0053] For the circulating air handling unit, the calculation of energy utilization rate needs to be combined with the velocity and flow data of the air circulation. By analyzing the real-time air flow characteristics of the unit, the efficiency of the air circulation system is determined, especially the balance point between energy use and air circulation. The energy utilization index generated can be used to identify the efficient or inefficient operation status of the circulating air handling unit and provide data support for subsequent optimization.

[0054] The VOC (volatile organic compound) concentration of the exhaust gas treatment unit is another key indicator. The generation process of this indicator is based on the VOC concentration data collected from the exhaust gas outlet. By continuously collecting and analyzing this data, the pollution level of exhaust gas emissions can be calculated and the effectiveness of the current exhaust gas treatment system can be judged according to the set environmental protection standards. This concentration indicator provides a scientific basis for optimizing the exhaust gas treatment path and selecting the best treatment method.

[0055] The paint utilization rate of the nitrogen spray unit is the last aspect to generate the energy consumption index. The calculation of this index requires comparing the amount of paint used during the spraying process with the amount of paint actually attached to the surface. The effective utilization rate of the paint is calculated by tracking the paint consumption data in real time through sensors and monitoring systems, combined with surface quality detection technology. A lower utilization rate may indicate waste or inefficient spraying, and further adjustments to the spraying parameters are needed to improve efficiency.

[0056] In order to integrate these energy consumption indicators, the system can display the calculation results of each functional unit in a three-dimensional visualization model in the form of charts or numbers. In this way, users can understand the energy usage of each functional unit at a glance, and can make decisions quickly based on actual operation data to optimize system performance.

[0057] Furthermore, generating the energy consumption index of each functional unit includes: Extract real-time operation data from the 3D visualization model and calculate the instantaneous energy consumption of each functional unit; Smooth the energy consumption data to eliminate the impact of instantaneous fluctuations on the overall energy consumption index; Generate an energy consumption trend curve for each functional unit and display it on the user interface so that users can intuitively understand the energy usage of each unit.

[0058] First of all, extracting real-time operating data from the three-dimensional visualization model is the basis for generating energy consumption indicators. The real-time data of each functional unit, including temperature, humidity, current consumption, gas consumption and steam consumption, are read directly from the model and transmitted to the central data processing unit. This data extraction process can be automated through the data interface to ensure efficient data collection and transmission. For the fresh air energy recovery unit, this may involve collecting real-time parameters such as air flow rate and temperature difference in order to calculate its immediate energy consumption. For the circulating air treatment unit, the system will extract data such as air circulation speed and temperature difference to determine energy consumption. The energy consumption data of the exhaust gas treatment unit and the nitrogen spray unit are calculated based on the changes in VOC concentration and parameters such as spraying pressure and flow rate.

[0059] Next, the energy consumption data is smoothed to eliminate the impact of instantaneous fluctuations on the overall energy consumption index. This step is essential because the system may experience instantaneous data fluctuations during operation, which may be caused by external interference, equipment startup and shutdown, or data collection errors. To solve this problem, data smoothing algorithms, such as moving averages or weighted filters, can be used to smooth short-term fluctuations, making energy consumption data more stable and accurately reflecting long-term trends. This data processing technology is often used in industrial environments to ensure the reliability of energy consumption data and reduce misjudgments caused by short-term anomalies.

[0060] Finally, generate the energy consumption trend curve of each functional unit and display it on the user interface. This part is crucial. The energy consumption trend curve directly displays the energy usage of each functional unit through a visual chart, allowing operators to identify energy consumption changes at a glance. The user interface design should include a dynamic update function to ensure that the trend curve can reflect the system operation status in real time. For example, when the energy consumption of the fresh air energy recovery unit fluctuates regularly with temperature changes, users can clearly observe these changing trends through the curve. If the energy consumption of the circulating air processing unit gradually increases, the system can highlight this trend to prompt the user to take optimization measures. In addition, the user interface can be designed to support interactive functions, and users can select a specific time range to zoom in to view energy consumption data, or compare the energy consumption performance between different functional units.

[0061] This real-time generation and presentation of energy consumption metrics enables operators to better monitor system performance, identify energy-saving opportunities, and make adjustments quickly.

[0062] Furthermore, the use of the operating data mapped in the three-dimensional visualization model to generate energy consumption indicators of each functional unit includes: Obtain and use temperature, humidity, current consumption, gas consumption, and steam consumption data mapped to a 3D visualization model as input parameters to calculate steam recovery for the fresh air unit , Energy utilization rate of circulating air unit , VOC concentration of exhaust gas treatment unit , and paint utilization of nitrogen spray units ; The steam recovery amount Calculated by the following formula 3: in, and are the inlet and outlet temperatures of the fresh air unit, respectively; is the steam flow rate; is the relative humidity, and is an empirical constant; Energy utilization rate Calculated by the following formula 4: in, is the output power of the circulating air unit; is the input power; is the temperature difference; is the ambient temperature; is the temperature difference correction coefficient; The VOC concentration Calculated according to the following formula 5: in, is the exhaust gas flow rate; is the exhaust gas treatment area; and is the optimization coefficient; The paint utilization rate Calculate according to the following formula 6: in, is the actual coating quality; is the total paint usage; is the overspray distance; is the total spraying distance; is the adjustment factor.

[0063] First, the system acquires and uses the temperature, humidity, current consumption, gas consumption, and steam consumption data mapped to the 3D visualization model as input parameters. These parameters are collected in real time by multi-dimensional monitoring sensors installed in the coating system and displayed intuitively in the 3D model. For example, the data collected by the temperature and humidity sensors are mapped to the inlet and outlet locations of the fresh air unit to help calculate the amount of steam recovery. ; Current and energy consumption data are used to evaluate the energy efficiency of the circulating air unit ; The relevant data of the exhaust gas treatment unit and the nitrogen spraying unit are also calculated to obtain specific indicators.

[0064] Steam recovery capacity The calculation formula is: The detailed description of the formula is as follows: and :These two parameters represent the inlet and outlet temperatures of the fresh air unit, respectively, and are obtained through the temperature sensor. For example, if the temperature of the fresh air entering is , when discharged, it drops to ,but for , which indicates the amount of heat energy recovered.

[0065] : Steam flow rate, measured by a flow meter, represents the volume or mass flow rate of steam per unit time. This parameter is crucial for calculating the amount of steam to be recovered.

[0066] RH: Relative humidity, measured by a humidity sensor, indicates how saturated the air is with water vapor. The higher the relative humidity, the less efficient the vapor recovery will be, so an adjustment factor is added to the formula , used to correct the effect of humidity.

[0067] and : These are empirical constants obtained through experiments and historical data and used to adjust and standardize calculation results. For example, may be used to convert the temperature difference into units of actual thermal energy recovered, while Used to correct the effect of humidity on energy recovery.

[0068] Energy efficiency The calculation formula is as follows: and , respectively represent the output power and input power of the circulating air unit. The input power is measured by the energy consumption monitoring device, and the output power can be obtained by calculating the kinetic energy or mechanical power of the circulating air. These power data help evaluate the energy efficiency of the system and are usually provided by current sensors and power calculation modules.

[0069] : Temperature difference, which indicates the change in temperature of the air in the circulating air unit. If the temperature drops too much, it may mean energy is wasted, so correction is needed.

[0070] : Ambient temperature, measured by an external temperature sensor, used to normalize the effect of temperature differences.

[0071] : Temperature difference correction coefficient, used to adjust the impact of temperature changes on energy efficiency and ensure the consistency of calculation results under different environmental conditions. For example, May be determined experimentally to correct for deviations under extreme temperature conditions.

[0072] VOC concentration The calculation formula is as follows: In the formula: : Exhaust flow rate, measured by the flow rate sensor, indicates the flow rate of the exhaust gas in the pipeline. If the flow rate is high, the VOC purification efficiency may be reduced, thus affecting the treatment effect.

[0073] : Waste gas treatment area refers to the purification area of ​​the waste gas treatment unit, which is determined by equipment parameters or physical measurements and is used to calculate the effect of waste gas purification.

[0074] and : Optimization coefficient, set based on experimental data, used to adjust the concentration calculation results. For example, can be used as the calibration value of the initial concentration, and Adjust for changes in purge rate.

[0075] Paint utilization The calculation formula is as follows: In the formula: and They are the actual coating mass and the total paint usage. The actual coating mass can be obtained by measuring the coating weight after painting, while the total paint usage is recorded in the material management system of the spraying process.

[0076] and : Overspray distance and total spray distance, measured by the positioning system of the spraying equipment. Overspray distance indicates the distance wasted by the paint failing to adhere to the target surface, while total spray distance is the overall range of paint spraying.

[0077] : Adjustment factor to correct the effect of overspray on paint utilization. This factor is usually set based on actual operating conditions to ensure accurate calculation results in different spraying environments.

[0078] Step S104: Based on the generated energy consumption index and the stored historical energy consumption data, use the big data analysis model to perform energy consumption comparison analysis; according to the analysis results of the energy consumption comparison analysis, determine the energy consumption difference between the current coating system and the traditional equipment, and generate a specific energy-saving optimization plan, wherein the energy-saving optimization plan includes selecting the best exhaust gas treatment path according to the real-time monitored exhaust gas VOC concentration, adjusting the ratio of fresh air to circulating air to optimize energy utilization, and dynamically adjusting key operating parameters to further reduce overall energy consumption, wherein the key operating parameters include spraying pressure, flow rate control parameters, spraying temperature, and equipment start and stop time.

[0079] First, the generation of energy consumption indicators is the basis of the entire analysis process. These indicators include the steam recovery volume of the fresh air unit, the energy utilization rate of the circulating air unit, the VOC concentration of the exhaust gas treatment unit, and the paint utilization rate of the nitrogen spray unit. These data will be stored in the central database and combined with the historical energy consumption data to provide a comprehensive reference for subsequent big data analysis.

[0080] The big data analysis model uses a variety of algorithms and data processing methods to explore potential patterns and trends in the data. First, the historical energy consumption data is preprocessed, including data cleaning, denoising and normalization, to ensure that all data points are consistent and comparable. Next, the real-time energy consumption indicators are integrated with historical data to create a multidimensional data set covering multiple aspects such as time dimension, energy use type, and operating parameters of each functional unit.

[0081] In order to conduct comparative analysis of energy consumption, regression analysis and machine learning algorithms can be used to predict the difference between the energy consumption of the current system and that of traditional equipment. Specifically, by training on large-scale data sets, the model can learn the impact of the operating parameters of each functional unit on energy consumption and accurately predict energy consumption under different conditions. Then, by comparing the real-time energy consumption data with the model prediction results, the specific factors that cause high energy consumption in the current system can be identified. For example, if the VOC concentration of the exhaust gas treatment unit is high, the model can identify this problem and prompt the need to adjust the exhaust gas treatment path.

[0082] Based on the results of the energy consumption comparison analysis, the system generates specific energy-saving optimization plans. These plans are designed to respond to energy consumption fluctuations in real time, giving priority to adjustment measures that can minimize energy consumption. For example, if the analysis shows that the current ratio of fresh air to circulating air is not good, the system will recommend adjusting the ratio to improve energy utilization. Similarly, when it is detected that the spraying pressure or flow rate is not within the optimal range, the system will recommend adjusting these parameters to optimize the spraying effect and reduce waste. In addition, for the selection of exhaust gas treatment paths, the best treatment path can be automatically recommended based on the real-time monitored VOC concentration to ensure that both energy consumption can be reduced and environmental protection standards can be met.

[0083] Dynamic adjustment of key operating parameters is an important part of achieving energy saving, including spray pressure, flow rate control parameters, spray temperature and equipment start and stop time. The optimization scheme will adjust parameters without affecting production quality as much as possible. For example, spray pressure can be fine-tuned based on real-time feedback of paint utilization and spray effect, while flow rate control can be combined with temperature changes to ensure the stability of the spray environment.

[0084] In order to achieve this automatic control, the system needs to be connected to the controller of each functional unit, and can transmit the optimization parameters and perform adjustments in real time. At the same time, to ensure the effectiveness of the adjustment measures, the system continuously monitors the operation effect after optimization, compares and analyzes the energy consumption data with the baseline data before optimization, records the change trend and makes statistics. Through the generated energy consumption statistical report and trend forecast data, users can intuitively understand the effect of energy-saving measures and adjust future energy-saving strategies according to long-term trends, so as to achieve the goal of efficient energy saving.

[0085] Furthermore, the energy consumption comparison analysis using the big data analysis model includes: Select appropriate big data analysis algorithms, including regression analysis and cluster analysis, to process energy consumption data; Compare the generated energy consumption indicators with historical energy consumption data to identify the main reasons for changes in energy consumption; Generate a report based on the analysis results, highlight key points for energy consumption reduction and propose possible directions for further optimization.

[0086] First of all, choosing the right big data analysis algorithm is the basis for the entire energy consumption comparative analysis. According to the different characteristics of energy consumption data, two main algorithms can be used: regression analysis and cluster analysis. Regression analysis is used to establish a relationship model between energy consumption and influencing factors. For example, by analyzing historical data, the linear or nonlinear relationship between temperature, humidity, or equipment operating status and energy consumption can be determined to predict future energy usage. Cluster analysis is used to group data with similar energy consumption patterns to identify different energy consumption behaviors. For example, data during high energy consumption periods can be clustered with data during low energy consumption periods to find differences in system energy consumption performance under specific conditions. This combination of regression analysis and cluster analysis helps to fully understand and optimize the energy use of the coating system.

[0087] Next, the generated energy consumption indicators are compared with the stored historical energy consumption data to identify the main reasons for the change in energy consumption. In this step, the system compares the current real-time data with the historical average or baseline value to determine the specific source of abnormal fluctuations or increases or decreases in energy consumption. For example, if a sudden increase in energy consumption is found in the fresh air energy recovery unit, the system can trace it back to potential causes such as changes in the external environment or decreased equipment efficiency by analyzing temperature and humidity data. Through this comparison, it is possible to quickly identify whether the increase in energy consumption is caused by external uncontrollable factors or improper equipment or parameter settings within the system. This automated data analysis process enables the system to quickly identify problems and provide targeted feedback.

[0088] Based on the above analysis results, the system generates a detailed energy consumption analysis report. The report includes a trend chart of energy consumption data, a detailed description of key energy consumption influencing factors, and key points for energy consumption reduction. This report not only shows the difference between current energy consumption and historical data, but also highlights where energy saving potential lies. For example, if the cluster analysis results show that energy consumption is abnormally high during certain time periods, the report will point out the common characteristics of these high energy consumption periods and recommend specific improvement measures. In addition, the report can also propose possible directions for further optimization based on the regression model, such as adjusting the operating parameters of specific equipment or optimizing resource allocation strategies to reduce overall energy consumption.

[0089] The report ultimately provides operators with clear optimization recommendations to help develop precise energy-saving strategies. For example, if the analysis shows that adjusting the spray temperature can significantly reduce energy consumption, the report will provide a feasible temperature setting range or recommend adjusting the equipment start and stop times to better match energy consumption needs.

[0090] Furthermore, based on the generated energy consumption index and the stored historical energy consumption data, a big data analysis model is used to perform energy consumption comparison analysis; according to the analysis results of the energy consumption comparison analysis, the energy consumption difference between the current coating system and the traditional equipment is determined, and a specific energy-saving optimization plan is generated, including: Taking the energy consumption index and the historical energy consumption data of traditional equipment as input, the following steps are performed using the multivariate big data analysis model: The energy consumption difference is calculated using the energy consumption difference model provided by the following formula 7: in, The difference in energy consumption between the current coating system and traditional equipment; and is the adjustment coefficient; For the coating system Energy consumption indicators; For traditional equipment Items of historical energy consumption data; is the total number of categories of energy consumption indicators in the coating system, including the steam recovery amount of the fresh air unit , Energy utilization rate of circulating air unit , VOC concentration of exhaust gas treatment unit , and paint utilization of nitrogen spray units ; A genetic algorithm is used to solve a multi-objective optimization problem and generate optimization parameters. The objective function of the multi-objective optimization problem adopts the following formula 8: in, The optimization parameter vector is the result of the optimization algorithm output, including the ratio of fresh air to circulating air, exhaust gas treatment path selection, spraying pressure, flow rate control parameters, spraying temperature, and equipment start and stop time; is a set of all adjustable parameters used in the optimization process, including fresh air and circulating air ratio parameters, exhaust gas treatment path selection parameters, spray pressure setting parameters, flow rate control parameters, spray temperature setting parameters, and equipment start and stop time setting parameters; is the number of target energy consumption values, indicating the total number of all optimization objectives; According to the parameter set The predicted Energy consumption value; For the Target energy consumption value; is the regularization parameter; is the weight adjustment factor; For parameter collection The number of parameters in ; is the first parameters; Using the energy consumption difference Evaluate the energy saving potential of the current painting system and optimize the parameters Dynamically adjust the ratio of fresh air to circulating air, select the optimal exhaust gas treatment path, and accurately adjust the spraying pressure, flow rate control parameters, spraying temperature, and equipment start and stop time to maximize the overall energy saving effect.

[0091] First, the generated energy consumption index and the historical energy consumption data of traditional equipment are used as input, and the multivariate big data analysis model is used to quantify the energy consumption difference. This analysis is completed through the following energy consumption difference model formula 7: Each term of Formula 7 is described in detail below: : Represents the difference in energy consumption between the current coating system and traditional equipment. The higher this value is, the greater the deviation in energy consumption between the coating system and traditional equipment is, indicating that optimization is needed.

[0092] : These are adjustment coefficients that adjust the sensitivity and nonlinearity of the energy consumption difference calculation. Specifically, Amplify or reduce the effect of linear differences, and Used to introduce and adjust the nonlinear effects of the sine function. Through experiments and historical data analysis, these coefficients can be optimized to improve the model's sensitivity to different types of energy consumption fluctuations. For example, if a certain type of energy consumption indicator fluctuates greatly, it can be adjusted by increasing to more accurately reflect these changes.

[0093] : This is the first Energy consumption indicators, such as steam recovery from fresh air units , Energy utilization rate of circulating air unit , VOC concentration of exhaust gas treatment unit , and paint utilization of nitrogen spray units These indicators are obtained through real-time monitoring equipment. For example, if the measured steam recovery volume of the fresh air unit is ,but That's 200.

[0094] :The first Assume that the historical average steam recovery capacity of traditional equipment is , then the corresponding is 150. These historical data are usually obtained through long-term monitoring or standard values.

[0095] : The total number of categories of energy consumption indicators. In the specific application of the present invention, all major energy consumption indicators are included, such as etc., together constitute a comprehensive energy consumption assessment framework.

[0096] Next, the system uses a genetic algorithm to solve the multi-objective optimization problem, and the objective function is defined by Formula 8 as follows: Each term of Formula 8 is described in detail below: : The optimization parameter vector is the output of the genetic algorithm, which contains specific optimization settings, such as the ratio of fresh air to circulating air, exhaust gas treatment path selection, spraying pressure, flow rate control parameters, spraying temperature, and equipment start and stop time. These parameters will be automatically adjusted to achieve maximum energy saving. For example, if the optimization result recommends setting the ratio of fresh air to circulating air to 70:30, this setting will be applied to the system in real time.

[0097] : A set of adjustable parameters, including all system settings that can be optimized. Specifically, it includes fresh air and circulating air ratio parameters, exhaust gas treatment path selection parameters, spray pressure setting parameters, flow rate control parameters, spray temperature setting parameters, and equipment start and stop time setting parameters. For example, if the flow rate control parameter Indicates that the air flow velocity range is ,The optimization algorithm may recommend specific values ​​to achieve energy saving goals.

[0098] : The number of target energy consumption values, which represents the multiple energy-saving goals that the system needs to achieve in multi-objective optimization, such as reducing electricity consumption and gas usage at the same time.

[0099] : According to the parameter set The predicted An energy consumption value, calculated using a simulation or machine learning model, represents the expected energy consumption under the current settings.

[0100] : No. A target energy consumption value is the optimal energy consumption level that you want to achieve, usually determined by energy-saving standards or industry best practices.

[0101] : Regularization parameter, used to control the balance during the optimization process, prevent over-adjustment of parameters and maintain system stability. Larger The value can prevent overfitting.

[0102] : Weight adjustment factor, which is used to adjust the relative importance of each parameter in the optimization process. For example, if a parameter has a greater impact on energy consumption, it can be assigned a higher weight. .

[0103] : parameter collection The number of parameters in indicates the total number of all parameters that can be optimized.

[0104] : The first parameter in the parameter set A parameter defines each adjustable setting, such as spray pressure or temperature setting.

[0105] Through the above analysis and optimization process, the system can evaluate the energy consumption difference in real time. And use the optimization parameters Make dynamic adjustments. For example, if the analysis results show that current energy consumption is significantly higher than historical levels, the optimization plan may recommend reducing the fresh air volume and increasing the circulating air volume to better recover heat energy. In addition, the system can adjust the exhaust gas treatment path according to the exhaust gas concentration to optimize purification efficiency and energy consumption.

[0106] First of all, genetic algorithm is a bionic optimization technology that simulates the process of natural selection and genetic evolution to find the optimal solution. Its main steps include population initialization, fitness evaluation, selection, crossover, mutation and iterative update, and finally generate the optimization parameter vector In this method, the goal is to minimize the objective function in Formula 8, thereby optimizing the energy utilization parameters, including the ratio of fresh air to circulating air, exhaust gas treatment path, spraying pressure, flow rate control parameters, spraying temperature, and equipment start and stop time.

[0107] Step 1: Population initialization At the beginning, a random set of parameters is generated , where each individual (i.e. candidate solution) consists of a set of adjustable parameters. These parameters include the ratio of fresh air to circulating air, exhaust gas treatment path selection, spray pressure setting, flow rate control, spray temperature setting, and equipment start and stop time. The entire population consists of several individuals, usually between dozens and hundreds of individuals, to ensure the diversity of solutions. The parameter values ​​of each individual are randomly generated within a preset range.

[0108] Step 2: Fitness Evaluation For each individual in the population, calculate its fitness, that is, the value of the objective function. The objective function consists of two parts: energy consumption prediction error and regularization term. Specifically, for each individual parameter set ,according to and Calculate the normalized error of all target energy values ​​and use the first part of the formula to evaluate the energy matching degree. The regularization term is obtained by and weight adjustment factor Control to prevent over-adjustment of parameters. The total fitness value is used to measure the quality of each individual.

[0109] Step 3: Select According to the fitness value, excellent individuals are selected to enter the next generation. Common methods are roulette selection or tournament selection, which prioritizes individuals with lower fitness values ​​(i.e., individuals with smaller objective function values). These selection strategies ensure that better individuals have a higher probability of being passed to the next generation, but also retain a certain degree of randomness to prevent falling into local optimality.

[0110] Step 4: Crossover (recombination) The selected individuals are paired in pairs and crossover is performed. Crossover refers to generating new offspring individuals by exchanging some parameter values. For example, assuming that two individuals have different fresh air and circulating air ratios and spray pressure settings, a parameter point can be randomly selected for exchange to generate a new individual with a combination of parent characteristics. The crossover operation promotes the diversity of understanding and helps to explore better parameter combinations.

[0111] Step 5: Mutation The offspring individuals generated by the crossover are mutated, that is, some parameter values ​​are randomly changed. The purpose of mutation is to increase the diversity of the population and prevent the algorithm from converging to the local optimal solution too early. For example, if the flow rate control parameter of an individual is originally , can randomly increase or decrease the value within a certain range. The probability of mutation operation is usually low, generally in arrive between , to ensure that optimization relies primarily on selection and crossover, but still maintains appropriate exploration capabilities.

[0112] Step 6: Select the update Repeat the above fitness evaluation, selection, crossover and mutation steps to generate a new generation of population. Each generation of population is closer to the optimal solution than the previous generation because individuals with lower fitness are gradually eliminated. After several generations (for example, 50 to 200 generations), the genetic algorithm will gradually converge to an optimized parameter set. , this set of parameters can significantly improve the energy efficiency of the coating system.

[0113] When the genetic algorithm reaches the preset number of iterations or the fitness value no longer improves significantly, the optimized parameter vector is output. This parameter vector includes all adjustable operating settings, such as the ratio of fresh air to recirculated air, exhaust gas treatment path, spraying pressure, flow rate control, spraying temperature, and equipment start and stop time. By applying these optimized parameters, the system can maximize the overall energy saving effect and significantly improve operating efficiency.

[0114] Step S105: Issue the energy-saving optimization plan to each functional unit of the coating system, adjust its operating parameters in real time, and continuously monitor the operating effect of the optimized system; compare the monitored operating effect with the energy consumption data before optimization, record the energy consumption changes and generate energy consumption statistics and trend forecast data, wherein the trend forecast data is used to guide subsequent continuous optimization control, thereby achieving efficient and energy-saving operation of the coating system.

[0115] First, the issuance and execution of energy-saving optimization plans rely on an integrated control system that is connected to the control modules of each functional unit. Through the central control unit, optimization instructions can be quickly distributed to the fresh air energy recovery unit, circulating air treatment unit, exhaust gas treatment unit, and nitrogen spray unit. These control modules need to have real-time response capabilities and be able to flexibly adjust their respective operating parameters. For example, after receiving the optimization plan, the central control unit will immediately send specific parameter adjustment instructions, such as changing the ratio of fresh air to circulating air, adjusting the spraying pressure, or modifying the start and stop time of the equipment.

[0116] In the fresh air energy recovery unit, the main parameters adjusted by the control system may include the flow ratio of fresh air and circulating air to maximize energy utilization. This adjustment is achieved by adjusting the air valve or fan speed to ensure that the ratio of fresh air and circulating air is always optimized. In the circulating air treatment unit, the control system can accurately adjust the air flow rate and temperature to ensure that the air maintains suitable process conditions with the lowest energy consumption during the circulation process. This adjustment is achieved through intelligent adjustment of the flow rate control valve and heater.

[0117] Operation optimization of the exhaust gas treatment unit involves real-time monitoring and adjustment of the exhaust gas treatment path. Depending on the current VOC concentration in the exhaust gas, the system will select the most appropriate treatment path. For example, if the VOC concentration is high, the system can choose a more stringent treatment path, such as purification through a high-efficiency treatment device, while at lower concentrations, a less energy-intensive treatment method can be used. This path selection is achieved by controlling the switch state and flow direction of the exhaust gas treatment valve to ensure that both environmental protection requirements and energy saving are met.

[0118] In the nitrogen spray unit, the system dynamically adjusts the spray pressure and flow rate control parameters to optimize the utilization of the paint. Based on real-time feedback, the control system fine-tunes the spray pressure to ensure coating quality while avoiding paint waste. The flow rate controller can be adjusted according to process requirements to ensure a smooth spray process and uniform paint adhesion. In addition, the spray temperature can be automatically adjusted according to environmental conditions to ensure spraying results while saving energy.

[0119] The implementation plan also requires continuous monitoring of the operating effect of the optimized system. To this end, sensors collect operating data from each functional unit in real time and send this data to the central control unit for analysis. The system compares the optimized energy consumption data with the baseline data before optimization to evaluate the actual effect of the energy-saving measures. For example, if the steam recovery efficiency of the fresh air energy recovery unit is improved, the energy consumption data will show a corresponding downward trend, which provides a direct quantitative basis for the energy-saving effect.

[0120] In order to better guide the subsequent continuous optimization control, the central control unit automatically records all energy consumption changes and integrates these data into energy consumption statistics and trend forecast reports. These reports can identify long-term trends and predict possible future energy consumption peaks or fluctuations, thereby providing decision support for system operators. For example, trend forecast data can indicate when energy consumption peaks may occur, so that operating parameters can be adjusted in advance to balance energy loads and further optimize system performance.

[0121] Furthermore, the generating of energy consumption statistics and trend forecasting data includes: Collect optimized energy consumption data and integrate it with historical data to generate long-term energy consumption trend charts; Use trend prediction algorithms to predict energy consumption over a period of time in the future and identify potential energy consumption peaks in advance; Provide users with trend forecast reports and recommend necessary preventive adjustments before peak energy consumption periods to further improve energy efficiency.

[0122] First, the system needs to collect optimized energy consumption data. The optimized data includes real-time energy consumption information of each functional unit of the coating system, such as the steam recovery efficiency of the fresh air energy recovery unit, the energy utilization rate of the circulating air treatment unit, the VOC concentration treatment effect of the exhaust gas treatment unit, and the paint utilization rate of the nitrogen spray unit. These data will be recorded and stored in the central database regularly or continuously. To ensure the comprehensiveness of the analysis, these real-time data are integrated with the existing historical energy consumption data to form a complete data set. Through the integration process, the system can obtain detailed energy consumption change information and generate a long-term energy consumption trend chart. This trend chart visualizes the change pattern of energy consumption over time, allowing users to clearly see the improvement of the system energy consumption performance after implementing the energy-saving optimization plan.

[0123] Next, a trend prediction algorithm is used to predict energy consumption for a period of time in the future. The prediction algorithm used can be a time series-based model, such as ARIMA (autoregressive integrated moving average model) or LSTM (long short-term memory neural network), depending on the complexity of the data and the accuracy requirements of the prediction. Through these algorithms, the system can analyze the changing trends of historical and current energy consumption data, identify potential cyclical or seasonal patterns, and accurately predict future energy consumption performance. For example, if historical data shows that energy consumption will peak every afternoon, the algorithm can identify possible future energy consumption peaks in advance based on these patterns, and then issue an early warning.

[0124] Trend forecasting is not limited to identifying peak energy consumption, but can also provide users with specific energy-saving suggestions in advance. The system automatically generates a trend forecast report that includes upcoming energy consumption changes, possible peak time periods, and their causes. This information can help users understand the distribution of future energy loads and prepare for peak energy consumption. In order to further improve energy efficiency, the report will also give preventive adjustment suggestions. For example, if it is predicted that high temperatures in the afternoon will cause an increase in energy consumption in the fresh air energy recovery unit, the system may recommend adjusting the ratio of fresh air to circulating air before the peak period, or optimizing the start and stop time of the equipment in a timely manner to balance the energy load.

[0125] This prediction and adjustment mechanism provides users with ample time to react, thus avoiding unnecessary energy waste and cost increases.

[0126] Furthermore, the energy-saving optimization scheme is issued to each functional unit of the coating system and the operating parameters are adjusted in real time, including: Transmit the optimization scheme to the control module of each functional unit and perform automatic parameter adjustment; Monitor the adjusted operating effect in real time and make further fine-tuning based on feedback data to ensure the expected energy-saving effect is achieved; Record all adjustment operations and effects to provide reference and improvement basis for future energy-saving optimization.

[0127] First, the optimization plan is automatically transmitted to the control module of each functional unit through the central control system. The central control system can communicate with each functional unit in real time to quickly issue optimization instructions. These instructions are formulated based on the energy-saving analysis results generated previously, including adjusting the air volume ratio of the fresh air energy recovery unit, optimizing the wind speed setting of the circulating air treatment unit, selecting the optimal treatment path of the exhaust gas treatment unit, or accurately adjusting the spraying pressure and flow rate of the nitrogen spray unit. The transmission process ensures that the control module of each unit can immediately receive and execute these adjustment instructions. For example, when the system determines that the spraying pressure needs to be reduced to reduce energy consumption, the control module will automatically adjust the corresponding actuator to ensure that the spraying equipment operates under the new set parameters.

[0128] Real-time monitoring is the core of the solution to ensure that energy-saving adjustments can achieve the expected results. Each functional unit is equipped with a monitoring sensor to continuously collect adjusted operating data and send it back to the central control system. The system evaluates the effectiveness of energy-saving measures by analyzing feedback data. For example, if the fresh air energy recovery unit fails to significantly improve the steam recovery rate after adjustment, the system will analyze the possible reasons and make further fine-tuning. This fine-tuning process can be dynamic and automatic. The system optimizes parameter settings in real time based on preset algorithms to finely control energy use and maximize energy saving effects.

[0129] To ensure the traceability and continuous improvement of the entire process, the system records all parameter adjustment operations and their effects. This includes the time of each parameter adjustment, the adjustment content, the operating effect data, and the comparative analysis of the energy-saving results and expectations. Such a recording mechanism not only provides transparency for current energy-saving management, but also provides a valuable reference for future optimization strategies. By analyzing historical adjustment data, the system can gradually improve the energy-saving optimization model, learn which adjustments are most effective under specific conditions, and recommend the same or improved strategies in similar situations.

[0130] This implementation scheme ensures that the functional units of the coating system can quickly respond and adapt to changing operating conditions, achieving dynamic optimization of energy consumption.

[0131] A second embodiment of the present application provides an electronic device, the electronic device comprising: processor; The memory is used to store a program, and when the program is read and executed by the processor, it executes a multi-dimensional monitoring and energy-saving analysis control method for a coating system provided in the first embodiment of the present application.

[0132] The third embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, a multi-dimensional monitoring and energy-saving analysis and control method for a coating system provided in the first embodiment of the present application is executed.

[0133] Although the present application is disclosed as above in the form of a preferred embodiment, it is not intended to limit the present application. Any technical personnel in this field may make possible changes and modifications without departing from the spirit and scope of the present application. Therefore, the scope of protection of the present application shall be based on the scope defined by the claims of the present application.

Claims

1. A coating system multi-dimensional monitoring and energy-saving analysis and control method, characterized in that: include: The operation data of each functional unit of the coating system is collected by using a multi-dimensional monitoring sensor, wherein the functional units include a fresh air energy recovery unit, a circulating air treatment unit, an exhaust gas treatment unit and a nitrogen spraying unit, and the operation data includes temperature, humidity, current consumption, gas consumption and steam consumption; Based on the collected operation data, a three-dimensional visualization model of the coating system is constructed; in the three-dimensional visualization model, the specific distribution position of each monitoring sensor is marked and an energy flow diagram is drawn, and the operation data is mapped to the model to achieve an intuitive visualization of the energy flow; based on the three-dimensional visualization model, an energy-saving efficiency evaluation index system corresponding to each functional unit is established; Generate energy consumption indicators of each functional unit using the operation data mapped in the three-dimensional visualization model, wherein the energy consumption indicators include steam recovery amount of the fresh air unit, energy utilization rate of the circulating air unit, VOC concentration of the exhaust gas treatment unit, and paint utilization rate of the nitrogen spray unit; Based on the generated energy consumption indicators and the stored historical energy consumption data, a big data analysis model is used to perform energy consumption comparison analysis; based on the analysis results of the energy consumption comparison analysis, the energy consumption difference between the current coating system and the traditional equipment is determined, and a specific energy-saving optimization plan is generated, wherein the energy-saving optimization plan includes selecting the best exhaust gas treatment path according to the real-time monitored exhaust gas VOC concentration, adjusting the ratio of fresh air to circulating air to optimize energy utilization, and dynamically adjusting key operating parameters to further reduce overall energy consumption, wherein the key operating parameters include spraying pressure, flow rate control parameters, spraying temperature, and equipment start and stop time; The energy-saving optimization plan is issued to each functional unit of the coating system, its operating parameters are adjusted in real time, and the operating effect of the optimized system is continuously monitored; the monitored operating effect is compared with the energy consumption data before optimization, the energy consumption changes are recorded and energy consumption statistics and trend forecast data are generated, wherein the trend forecast data is used to guide subsequent continuous optimization control, thereby realizing efficient and energy-saving operation of the coating system.

2. The multi-dimensional monitoring and energy-saving analysis and control method of the coating system according to claim 1 is characterized in that: The multi-dimensional monitoring sensor is used to collect the operation data of each functional unit of the coating system, including: Determine the key monitoring locations in each functional unit and install corresponding multi-dimensional monitoring sensors; Regularly calibrate each monitoring sensor to ensure the accuracy and reliability of collected data; Collect monitoring data and perform preprocessing, including removing abnormal data and filling missing data, to provide high-quality data input for subsequent analysis.

3. The multi-dimensional monitoring and energy-saving analysis and control method of a coating system according to claim 1 is characterized in that: The energy-saving efficiency evaluation index system corresponding to each functional unit is established, including: Define the energy-saving efficiency evaluation index for each functional unit; Set a benchmark value for each evaluation indicator as a reference standard for measuring energy saving effects; Compare real-time operating data with benchmark values ​​and generate energy-saving efficiency reports to provide optimization suggestions.

4. The multi-dimensional monitoring and energy-saving analysis and control method of a coating system according to claim 1 is characterized in that: The generating of energy consumption indicators of each functional unit includes: Extract real-time operation data from the 3D visualization model and calculate the instantaneous energy consumption of each functional unit; Smooth the energy consumption data to eliminate the impact of instantaneous fluctuations on the overall energy consumption index; Generate an energy consumption trend curve for each functional unit and display it on the user interface so that users can intuitively understand the energy usage of each unit.

5. The coating system multi-dimensional monitoring and energy-saving analysis and control method according to claim 1 is characterized in that: The energy consumption comparison analysis using the big data analysis model includes: Select appropriate big data analysis algorithms, including regression analysis and cluster analysis, to process energy consumption data; Compare the generated energy consumption indicators with historical energy consumption data to identify the main reasons for changes in energy consumption; Generate a report based on the analysis results, highlight key points for energy consumption reduction and propose possible directions for further optimization.

6. The multi-dimensional monitoring and energy-saving analysis and control method of a coating system according to claim 1 is characterized in that: The generating of energy consumption statistics and trend forecast data includes: Collect optimized energy consumption data and integrate it with historical data to generate long-term energy consumption trend charts; Use trend prediction algorithms to predict energy consumption over a period of time in the future and identify potential energy consumption peaks in advance; Provide users with trend forecast reports and recommend necessary preventive adjustments before peak energy consumption periods to further improve energy efficiency.

7. The coating system multi-dimensional monitoring and energy-saving analysis and control method according to claim 1 is characterized in that: The energy-saving optimization plan is issued to each functional unit of the coating system to adjust the operating parameters in real time, including: Transmit the optimization scheme to the control module of each functional unit and perform automatic parameter adjustment; Monitor the adjusted operating effect in real time and make further fine-tuning based on feedback data to ensure the expected energy-saving effect is achieved; Record all adjustment operations and effects to provide reference and improvement basis for future energy-saving optimization.

8. The coating system multi-dimensional monitoring and energy-saving analysis and control method according to claim 1 is characterized in that: Based on the collected operation data, a three-dimensional visualization model of the coating system is constructed; in the three-dimensional visualization model, the specific distribution position of each monitoring sensor is marked and an energy flow diagram is drawn, and the operation data is mapped to the model to achieve an intuitive visualization of the energy flow, including: Use coordinate transformation formula and data mapping algorithm to transform the position data of each monitoring sensor in the coating system Convert to coordinates in a three-dimensional space model , The energy consumption correlation path of the monitoring sensor in three-dimensional space is determined by the following formula 1: in, To monitor the coordinates of the sensor in the three-dimensional space model; The actual position data of each monitoring sensor in the coating system; Radius adjustment parameters for the coating system; For the The polar angle of each monitoring sensor relative to a reference point; For the Temperature deviations associated with each monitoring sensor; is the reference temperature; The energy flow diagram is determined by the following flow calculation formula 2: in, For Monitoring sensors to The energy flow intensity of each monitoring sensor; is the stream intensity coefficient; is the energy consumption correction factor; is the energy loss coefficient between the two sensors; is the average energy consumption of the system.

9. The coating system multi-dimensional monitoring and energy-saving analysis and control method according to claim 1 is characterized in that: The step of generating energy consumption indicators of each functional unit by using the operation data mapped in the three-dimensional visualization model includes: Obtain and use temperature, humidity, current consumption, gas consumption, and steam consumption data mapped to a 3D visualization model as input parameters to calculate steam recovery for the fresh air unit , Energy utilization rate of circulating air unit , VOC concentration of exhaust gas treatment unit , and paint utilization of nitrogen spray units ; The steam recovery amount Calculated by the following formula 3: in, and are the inlet and outlet temperatures of the fresh air unit, respectively; is the steam flow rate; is the relative humidity, and is an empirical constant; Energy utilization rate Calculated by the following formula 4: in, is the output power of the circulating air unit; is the input power; is the temperature difference; is the ambient temperature; is the temperature difference correction coefficient; The VOC concentration Calculated according to the following formula 5: in, is the exhaust gas flow rate; is the exhaust gas treatment area; and is the optimization coefficient; The paint utilization rate Calculate according to the following formula 6: in, is the actual coating quality; is the total paint usage; is the overspray distance; is the total spraying distance; is the adjustment factor.

10. The coating system multi-dimensional monitoring and energy-saving analysis and control method according to claim 1, characterized in that: Based on the generated energy consumption index and the stored historical energy consumption data, a big data analysis model is used to perform energy consumption comparison analysis; according to the analysis results of the energy consumption comparison analysis, the energy consumption difference between the current coating system and the traditional equipment is determined, and a specific energy-saving optimization plan is generated, including: Taking the energy consumption index and the historical energy consumption data of traditional equipment as input, the following steps are performed using the multivariate big data analysis model: The energy consumption difference is calculated using the energy consumption difference model provided by the following formula 7: in, The difference in energy consumption between the current coating system and traditional equipment; and is the adjustment coefficient; For the coating system Energy consumption indicators; For traditional equipment Items of historical energy consumption data; is the total number of categories of energy consumption indicators in the coating system, including the steam recovery amount of the fresh air unit , Energy utilization rate of circulating air unit , VOC concentration of exhaust gas treatment unit , and paint utilization of nitrogen spray units ; A genetic algorithm is used to solve a multi-objective optimization problem and generate optimization parameters. The objective function of the multi-objective optimization problem adopts the following formula 8: in, The optimization parameter vector is the result of the optimization algorithm output, including the ratio of fresh air to circulating air, exhaust gas treatment path selection, spraying pressure, flow rate control parameters, spraying temperature, and equipment start and stop time; is a set of all adjustable parameters used in the optimization process, including fresh air and circulating air ratio parameters, exhaust gas treatment path selection parameters, spray pressure setting parameters, flow rate control parameters, spray temperature setting parameters, and equipment start and stop time setting parameters; is the number of target energy consumption values, indicating the total number of all optimization objectives; According to the parameter set The predicted Energy consumption value; For the Target energy consumption value; is the regularization parameter; is the weight adjustment factor; For parameter collection The number of parameters in ; is the first parameters; Using the energy consumption difference Evaluate the energy saving potential of the current coating system and optimize the parameters Dynamically adjust the ratio of fresh air to circulating air, select the optimal exhaust gas treatment path, and accurately adjust the spraying pressure, flow rate control parameters, spraying temperature, and equipment start and stop time to maximize the overall energy saving effect.

Images