Intelligent management system for production data of color steel composite board

Abstract

The present application relates to the technical field of intelligent management of color steel composite board, and particularly relates to a color steel composite board production data intelligent management system; the system comprises a data acquisition module, a plate blank tracking module, an energy consumption collection module, an energy consumption analysis module and a feedback optimization module. The present application realizes accurate identification of the time node of the plate blank entering the foaming furnace, guarantees the accuracy of energy consumption statistics, and realizes fine energy management; each plate blank is tracked in real time by using the fusion of vision and RFID, and its "energy consumption responsibility period" in the foaming furnace is accurately defined, and then the total energy consumption is accurately collected to a single plate blank by using integral calculation and proportional allocation, when an energy consumption anomaly is detected, the accuracy of the tracking time sequence is first verified to realize system self-calibration; if the time sequence is correct, further superimposed analysis of key parameter curves such as gas flow, temperature, speed and raw material pressure is performed, the abnormal reason is intelligently judged, and the parameters are automatically adjusted or the precise alarm is triggered.

Description

Technical Field

[0001] This invention relates to the field of intelligent management technology for color steel composite panels, and in particular to an intelligent management system for color steel composite panel production data. Background Technology

[0002] Color-coated steel composite panels, as an important building envelope material, are widely used due to their advantages such as lightweight, high strength, thermal insulation, and convenient installation. Their production process mainly includes feeding and uncoiling, panel forming, core material injection, composite curing, cooling and shaping, and cutting and packaging. Among these, "composite curing" is the core process. After the slab enters the foaming furnace, the polyurethane and other core material raw materials undergo a foaming and curing reaction under high temperature, bonding with the upper and lower color-coated steel panels under the pressure of a "double-belt machine" to form a whole. The heating process in the foaming furnace is the focus of energy consumption for the entire production line, and its energy consumption control level directly affects the company's production costs and green manufacturing level. Currently, energy management in the color-coated steel composite panel production field generally suffers from extensive problems. Most companies can only conduct monthly or annual statistics on total energy consumption at the workshop or production line level, failing to accurately link energy consumption to the smallest production unit—each slab. This is mainly due to the lack of precise tracking of production objects and corresponding refined energy consumption metering methods. Existing technologies suffer from drawbacks such as insufficient slab tracking accuracy, crude energy consumption collection methods, delayed and inefficient anomaly diagnosis, and isolated information between systems. Insufficient slab tracking accuracy stems from the difficulty of uniquely identifying and tracking the real-time position of each slab on a continuous production line using traditional methods. Because multiple slabs exist simultaneously in the foaming furnace and there is thermal inertia, it is impossible to accurately define the "energy consumption responsibility period" for each slab, leading to ambiguity in energy consumption attribution. Crude energy consumption collection methods are often based on simple time-segmentation or average allocation, failing to consider factors such as the actual position of the slab in the furnace, production line speed fluctuations, and equipment idle energy consumption, resulting in severely distorted data that fails to reflect true production energy consumption. Delayed and inefficient anomaly diagnosis arises because traditional methods rely on manual experience for post-event investigation when energy consumption anomalies occur, failing to provide real-time warnings. The lack of data support in the analysis process makes it difficult to quickly pinpoint the root cause (such as decreased equipment efficiency, process parameter misalignment, raw material abnormalities, etc.), leading to missed opportunities for optimal adjustments and resulting in continuous energy waste and quality risks. Information silos between systems arise because the production execution system, equipment control system, and energy management system form information islands, making it difficult to effectively correlate energy consumption data, equipment operating parameters, and production tracking information, thus hindering data-driven closed-loop optimization. Therefore, developing an intelligent system capable of achieving refined energy management at the slab level and possessing anomaly self-diagnosis and optimization functions is of great significance for promoting the digital transformation and energy conservation and emission reduction of the color steel composite panel industry.

[0003] Chinese Patent Publication No. CN118681766A discloses a color steel plate processing and manufacturing system, including a data acquisition module, a data classification module, and an intelligent control module. The data acquisition module determines the degree of change in drying thickness and the degree of difference in dried surface, and determines primer drying characterization parameters based on the degree of difference in dried surface and the corresponding degree of change in drying thickness for each color steel plate, and determines the drying state category based on the primer drying characterization parameters. The intelligent control module determines the bubble uniformity and crack uniformity of the dried primer based on the dried primer surface image, determines whether to correct the topcoat drying parameters based on the drying state category, and determines the correction strategy for the topcoat drying parameters based on the bubble uniformity and crack uniformity of the dried primer. Therefore, existing color steel composite plate production data management technologies lack precise tracking of production objects and matching refined energy consumption metering methods, resulting in low energy management accuracy. Summary of the Invention

[0004] To address this issue, the present invention provides an intelligent management system for color steel composite panel production data, which overcomes the problem of low accuracy in energy consumption measurement at the slab level in the prior art due to the lack of precise tracking of production objects and matching refined energy consumption measurement methods.

[0005] To achieve the above objectives, the present invention provides an intelligent management system for color steel composite panel production data, comprising:

[0006] The data acquisition module is used to collect energy data and operating parameters of key equipment on the production line;

[0007] A slab tracking module, which is connected to the data acquisition module, is used to identify and track each target slab entering the heating zone of the foaming furnace, and output the tracking event stream of any target slab;

[0008] An energy consumption collection module, which is connected to the slab tracking module, is used to determine the energy consumption responsibility period and slab production energy consumption of the target slab at the energy consumption collection point based on the tracking event flow.

[0009] An energy consumption analysis module, which is connected to the energy consumption collection module and the data acquisition module, is used to determine whether to mark the energy consumption collection point as an abnormal energy consumption event based on the comparison results between the slab production energy consumption and the standard energy consumption baseline, and to calculate the corresponding actual time deviation based on the energy data and the energy consumption responsibility period.

[0010] The feedback optimization module, which is connected to the energy consumption analysis module and the data acquisition module, is used to correct the energy consumption responsibility period or issue an alarm based on the comparison result of the actual time deviation and the standard time deviation.

[0011] Furthermore, the slab tracking module includes a slab identification unit, a position synchronization unit, and an event output unit;

[0012] The slab identification unit is used to capture the identification code of each slab in real time and generate the corresponding slab ID.

[0013] The position synchronization unit is used to synchronously record the encoder count value at the moment when the slab ID is identified, so as to bind the slab ID with the production line position.

[0014] The event output unit is used to generate a tracking event stream based on the slab ID, timestamp, and encoder count value.

[0015] Furthermore, the energy data includes gas flow rate; the energy consumption collection module includes a time period determination unit, an energy consumption extraction unit, and an energy consumption calculation unit;

[0016] The time period determination unit is used to determine the entry time and exit time of the target slab in the foaming furnace based on the tracking event flow of the target slab, as the energy consumption responsibility period.

[0017] The energy consumption extraction unit is used to extract the total gas flow curve of the foaming furnace during the energy consumption responsibility period from the time-series database.

[0018] The energy consumption calculation unit is used to calculate the energy consumption of slab production based on the total gas flow curve and the slab length.

[0019] Furthermore, the energy consumption calculation unit includes a first calculation subunit and a second calculation subunit;

[0020] The first calculation subunit integrates the total gas flow curve over the energy consumption responsibility period to obtain the total gas consumption during the energy consumption responsibility period, which is taken as the total energy consumption.

[0021] The second calculation subunit is used to calculate the product of the total energy consumption and the proportional coefficient to obtain the energy consumption for slab production;

[0022] The proportionality coefficient is the ratio of the slab length to the effective length of the furnace.

[0023] Furthermore, the energy data also includes power curves;

[0024] The energy consumption analysis module includes an energy consumption comparison unit, a calibration unit, and a time series analysis unit.

[0025] The energy consumption comparison unit is used to compare the energy consumption of slab production with the standard energy consumption baseline range to obtain a first comparison result and a second comparison result.

[0026] The calibration unit is used to mark the energy consumption aggregation point as an abnormal energy consumption event when the first comparison result is obtained;

[0027] The time series analysis unit is used to determine the energy consumption decrease node based on the power curve, and to calculate the actual time deviation based on the energy consumption decrease node and the departure time.

[0028] The first comparison result shows that the energy consumption of slab production is not within the standard energy consumption baseline range;

[0029] The second comparison result shows that the energy consumption for slab production is within the standard energy consumption baseline range.

[0030] Furthermore, the timing verification unit is used to correct the energy consumption responsibility period of the next target slab when the actual time deviation is greater than the standard time deviation;

[0031] The root cause diagnosis unit is used to perform root cause diagnosis on the operating status of the composite process based on the operating equipment parameter curve when the actual time deviation is less than or equal to the standard time deviation, so as to output a fault warning prompt.

[0032] Furthermore, the timing verification unit includes a first correction subunit and a second correction subunit;

[0033] The first correction subunit is used to increase the initial time compensation parameter to the correction time compensation parameter when the actual time deviation is greater than the standard time deviation.

[0034] The second correction subunit is used to calculate the sum of the correction time compensation parameter and the departure time to obtain the correction departure time, and to determine the energy consumption responsibility period of the next slab based on the correction departure time.

[0035] Furthermore, the feedback optimization module includes a timing verification unit and a root cause diagnosis unit;

[0036] The root cause diagnosis unit includes a superimposed curve acquisition unit and an anomaly analysis unit;

[0037] The superimposed curve acquisition unit is used to acquire the key parameter curves corresponding to the curves calibrated as abnormal energy consumption events in the time series database;

[0038] The anomaly analysis unit is used to adjust the corresponding operating parameters or output alarm prompts based on the key parameter curves.

[0039] Furthermore, the anomaly analysis unit includes a gas flow analysis subunit, a temperature analysis subunit, a velocity analysis subunit, and a raw material pressure subunit;

[0040] The gas flow analysis subunit is used to determine whether the gas flow exceeds the limit based on the comparison between the actual gas flow and the upper limit of gas flow.

[0041] The temperature analysis subunit is used to obtain the difference between the actual temperature value and the set temperature value when the gas flow exceeds the standard, as the real-time temperature difference value, and to determine whether to lower the heating furnace temperature set value based on the comparison result between the real-time temperature difference value and the allowable temperature difference value.

[0042] The speed analysis subunit is used to compare the actual operating speed of the production line with the historical average speed when the real-time temperature difference is less than or equal to the allowable temperature difference, and to determine whether the gas flow exceeding the standard is a reasonable fluctuation based on the comparison results.

[0043] The raw material pressure subunit is used to analyze the fluctuation of raw material injection pressure and to issue the highest priority alarm when it is determined that the raw material injection pressure fluctuates drastically.

[0044] Furthermore, the feedback optimization module also includes a verification unit;

[0045] The verification unit is used to verify whether the slab tracking is normal based on the actual offset distance and actual dwell time of the target slab.

[0046] Compared with existing technologies, the advantages of this invention are that by accurately identifying the time node when the slab enters the foaming furnace, the accuracy of energy consumption statistics is ensured, and refined energy management is achieved. By using vision and RFID fusion technology to assign a unique identity to each slab and track it in real time, the "energy consumption responsibility period" in the foaming furnace is accurately defined. Then, by using integral calculation and proportional allocation, the total energy consumption is accurately collected to a single slab. When the system detects an energy consumption anomaly, it can automatically perform root cause diagnosis: first, by analyzing the power curve to verify the accuracy of the tracking time sequence, the system can achieve self-calibration; if the time sequence is correct, the system further analyzes the curves of key parameters such as gas flow, temperature, speed, and raw material pressure to intelligently determine whether the root cause of the anomaly is a decrease in equipment efficiency, process parameter imbalance, or raw material system failure, and automatically adjusts the parameters or triggers a precise alarm. Attached Figure Description

[0047] Figure 1 This is a schematic diagram of the intelligent management system for color steel composite panel production data according to an embodiment of the present invention;

[0048] Figure 2 This is a schematic diagram of the slab tracking module according to an embodiment of the present invention;

[0049] Figure 3 This is a schematic diagram of the energy consumption collection module according to an embodiment of the present invention;

[0050] Figure 4 This is a schematic diagram of the energy consumption analysis module in an embodiment of the present invention. Detailed Implementation

[0051] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0052] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0053] It should be noted that in the description of this invention, the terms "upper", "lower", "left", "right", "inner", "outer", etc., which indicate directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0054] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0055] Please see Figure 1 The diagram shown is a structural schematic of the intelligent management system for color steel composite panel production data according to an embodiment of the present invention. The present invention provides an intelligent management system for color steel composite panel production data, comprising:

[0056] The data acquisition module is used to collect energy data and operating parameters of key equipment in the production line. The operating parameters include actual temperature, raw material injection pressure and actual operating speed.

[0057] A slab tracking module, which is connected to the data acquisition module, is used to identify and track each target slab entering the heating zone of the foaming furnace, and output the tracking event stream of any target slab;

[0058] An energy consumption collection module, which is connected to the slab tracking module, is used to determine the energy consumption responsibility period and slab production energy consumption of the target slab at the energy consumption collection point based on the tracking event flow.

[0059] An energy consumption analysis module, which is connected to the energy consumption collection module and the data acquisition module, is used to determine whether to mark the energy consumption collection point as an abnormal energy consumption event based on the comparison results between the slab production energy consumption and the standard energy consumption baseline, and to calculate the corresponding actual time deviation based on the energy data and the energy consumption responsibility period.

[0060] The feedback optimization module, which is connected to the energy consumption analysis module and the data acquisition module, is used to correct the energy consumption responsibility period or issue an alarm based on the comparison result of the actual time deviation and the standard time deviation.

[0061] In this embodiment, the production process of color steel composite panels includes feeding and uncoiling, panel pretreatment and forming, core material injection, lamination and curing, cooling and shaping, cutting and packaging. Among these, the most crucial process is "lamination and curing." The entry of the slab into the foaming furnace is the starting step of this critical process. Heating the foaming furnace using gas, steam, or electricity is the most energy-intensive part of the entire production line. Therefore, precise identification of the time the slab enters the foaming furnace ensures accurate energy consumption statistics and achieves refined energy management. The use of visual and RFID fusion technology further enhances this process. Each slab is assigned a unique identity and tracked in real time, accurately defining its "energy consumption responsibility period" within the foaming furnace. Then, using integral calculation and proportional allocation, the total energy consumption is precisely attributed to each slab. When the system detects an energy consumption anomaly, it can automatically perform root cause diagnosis: first, it verifies the accuracy of the tracking timing by analyzing the power curve, achieving system self-calibration; if the timing is correct, it further overlays and analyzes key parameter curves such as gas flow rate, temperature, speed, and raw material pressure to intelligently determine whether the root cause of the anomaly is a decrease in equipment efficiency, process parameter imbalance, or raw material system failure, and automatically adjusts parameters or triggers a precise alarm.

[0062] In this embodiment, the upper and lower layers of color-coated steel sheets have been prepared through feeding and uncoiling, and the steel sheets have been cleaned and rolled into the required waveform. During the composite process, the upper and lower steel sheets are joined together at the entrance of the foaming furnace to form a "sandwich" structure, encasing the core material. The foaming furnace provides a precisely controlled high-temperature environment (usually 40-60°C), where the polyurethane raw material undergoes a rapid chemical reaction, including foaming and curing. The foaming process involves the liquid raw materials being mixed and then expanding into a foamy state, filling the entire cavity. The curing process involves the foam quickly hardening and solidifying to form a solid core layer. Then, the composite is completed through strong bonding and shaping. During the strong bonding process, the foamed polyurethane and the back coating of the upper and lower color-coated steel sheets generate extremely strong chemical adhesion, firmly combining the three into a complete whole. Inside the foaming furnace, the sheet is passed through a device called a "double belt machine," where two huge steel belts or blanket belts apply pressure to the sheet to ensure uniform thickness, a flat surface, and final shaping.

[0063] See Figure 2 As shown, it is a structural schematic diagram of the slab tracking module in an embodiment of the present invention;

[0064] Specifically, the slab tracking module includes a slab identification unit, a position synchronization unit, and an event output unit;

[0065] The slab identification unit is used to capture the identification code of each slab in real time and generate the corresponding slab ID.

[0066] The position synchronization unit is used to synchronously record the encoder count value at the moment when the slab ID is identified, so as to bind the slab ID with the production line position.

[0067] The event output unit is used to generate a tracking event stream based on the slab ID, timestamp, and encoder count value.

[0068] In this embodiment, a high-definition industrial camera and its supporting light source are deployed at the foaming inlet. Since each slab substrate is coated or affixed with a high-contrast QR code / Data Matrix code, RFID reading is precisely triggered at the foaming inlet and outlet gratings to record the slab ID and the time of passage. That is, the vision system can capture and decode in real time, assigning a unique ID to the slab. The vision system is linked with the production line encoder. When the slab ID is identified, the encoder count value at that moment is recorded simultaneously, thereby accurately binding the slab ID with the absolute position of the production line to generate a tracking event stream with a timestamp, for example: Time: 10:00:00, Slab ID: PF001, Event: Entering the foaming furnace inlet, Encoder value: 15000.

[0069] See Figure 3 As shown, it is a structural schematic diagram of the energy consumption collection module in an embodiment of the present invention;

[0070] Specifically, the energy data includes gas flow rate; the energy consumption collection module includes a time period determination unit, an energy consumption extraction unit, and an energy consumption calculation unit.

[0071] The time period determination unit is used to determine the entry time and exit time of the target slab in the foaming furnace based on the tracking event flow of the target slab, as the energy consumption responsibility period.

[0072] The energy consumption extraction unit is used to extract the total gas flow curve of the foaming furnace during the energy consumption responsibility period from the time-series database.

[0073] The energy consumption calculation unit is used to calculate the energy consumption of slab production based on the total gas flow curve and the slab length.

[0074] In this embodiment, the data acquisition module collects energy data from the gas main of the foaming furnace at a preset acquisition cycle and stores it in a time-series database. The energy data includes gas flow rate and power curve.

[0075] Specifically, the energy consumption calculation unit includes a first calculation subunit and a second calculation subunit;

[0076] The first calculation subunit integrates the total gas flow curve over the energy consumption responsibility period to obtain the total gas consumption during the energy consumption responsibility period, which is taken as the total energy consumption.

[0077] The second calculation subunit is used to calculate the product of the total energy consumption and the proportional coefficient to obtain the energy consumption for slab production;

[0078] The proportionality coefficient is the ratio of the slab length to the effective length of the furnace.

[0079] In this embodiment, the energy consumption collection point corresponds to the heating zone of the foaming furnace. By acquiring the virtual boundaries of the foaming furnace heating zone where the head and tail of the target slab cross, i.e. the inlet and outlet gratings, the energy consumption responsibility period of the target slab in the foaming furnace heating zone is determined, and then the total energy consumption of the foaming furnace heating zone during the energy consumption responsibility period is determined. Since the total energy consumption includes production energy consumption and basic energy consumption caused by equipment idling, the proportion of the furnace length occupied by the target slab in the foaming furnace is introduced to make more accurate allocation and accurately calculate the slab production energy consumption, thereby achieving refined energy management.

[0080] See Figure 4 As shown, it is a structural schematic diagram of the energy consumption analysis module in an embodiment of the present invention;

[0081] Specifically, the energy data also includes power curves;

[0082] The energy consumption analysis module includes an energy consumption comparison unit, a calibration unit, and a time series analysis unit.

[0083] The energy consumption comparison unit is used to compare the energy consumption of slab production with the standard energy consumption baseline range to obtain a first comparison result and a second comparison result.

[0084] The calibration unit is used to mark the energy consumption aggregation point as an abnormal energy consumption event when the first comparison result is obtained;

[0085] The time series analysis unit is used to determine the energy consumption decrease node based on the power curve, and to calculate the actual time deviation based on the energy consumption decrease node and the departure time.

[0086] The first comparison result shows that the energy consumption of slab production is not within the standard energy consumption baseline range;

[0087] The second comparison result shows that the energy consumption for slab production is within the standard energy consumption baseline range.

[0088] In this embodiment, based on historical normal production data, normal ranges for unit product energy consumption are established for slabs of different product specifications (such as different thicknesses and core materials). Generally, the standard energy consumption baseline range is the mean ± 3 times the standard deviation of historical normal production data. By comparing the calculated slab production energy consumption with the established standard energy consumption baseline range, if the calculated unit energy consumption does not exceed the baseline range of its corresponding specification, it indicates that the energy consumption in the current production process is normal. However, to ensure the accuracy of the collected data, it is also necessary to verify the accuracy of slab tracking. That is, by taking images of the slab through an industrial vision system and identifying the slab edge, it is analyzed whether the slab is slipping or deviating, thereby verifying whether the RFID reading is correct, thus ensuring the accuracy of energy consumption collection. When the calculated unit energy consumption exceeds the baseline range of its corresponding specification, it is marked as an abnormal energy consumption event. In this case, by analyzing the power curve of the foaming furnace, the energy consumption drop node where the power begins to drop significantly is found, and the difference between the energy consumption drop node and the departure time recorded by the vision system is calculated to obtain the actual time deviation, thereby verifying the accuracy of slab tracking.

[0089] Specifically, the timing verification unit is used to correct the energy consumption responsibility period of the next target slab when the actual time deviation is greater than the standard time deviation;

[0090] The root cause diagnosis unit is used to perform root cause diagnosis on the operating status of the composite process based on the operating equipment parameter curve when the actual time deviation is less than or equal to the standard time deviation, so as to output a fault warning prompt.

[0091] Specifically, the timing verification unit includes a first correction subunit and a second correction subunit;

[0092] The first correction subunit is used to increase the initial time compensation parameter to the correction time compensation parameter when the actual time deviation is greater than the standard time deviation.

[0093] The second correction subunit is used to calculate the sum of the correction time compensation parameter and the departure time to obtain the correction departure time, and to determine the energy consumption responsibility period of the next slab based on the correction departure time.

[0094] In this embodiment, since the energy consumption of the heating furnace continues for a short period after the slab leaves the heating zone before returning to zero, it is possible that energy consumption for slab A is under-counted while energy consumption for slab B is over-counted. Therefore, through the superposition analysis of energy consumption and equipment parameters, it was found that if the inflection point of energy consumption decrease is always later than the time point when the slab leaves the heating zone recorded by the tracking system, this is because although the slab has left the detection point, its tail is still being heated. Therefore, by adjusting the time compensation parameter to correct the energy consumption curve of the production slab, the tracking parameters are automatically fine-tuned, ultimately achieving self-optimization of the entire system. That is, by comparing the actual time deviation with the standard time deviation, if it is determined that the actual time deviation is greater than the standard time deviation, it indicates that the departure time recorded by the visual tracking system is too early and there is a systematic deviation. Then, the time compensation parameter is automatically increased, that is, adjusted from 0 seconds to the actual time deviation, and this parameter is used to correct the end time of energy consumption collection of subsequent slabs, thereby achieving self-calibration of the tracking system.

[0095] Specifically, the feedback optimization module includes a timing verification unit and a root cause diagnosis unit;

[0096] The root cause diagnosis unit includes a superimposed curve acquisition unit and an anomaly analysis unit;

[0097] The superimposed curve acquisition unit is used to acquire the key parameter curves corresponding to the curves calibrated as abnormal energy consumption events in the time series database;

[0098] The anomaly analysis unit is used to adjust the corresponding operating parameters or output alarm prompts based on the key parameter curves.

[0099] Specifically, the anomaly analysis unit includes a gas flow analysis subunit, a temperature analysis subunit, a velocity analysis subunit, and a raw material pressure subunit;

[0100] The gas flow analysis subunit is used to determine whether the gas flow exceeds the limit based on the comparison between the actual gas flow and the upper limit of gas flow.

[0101] The temperature analysis subunit is used to obtain the difference between the actual temperature value and the set temperature value when the gas flow exceeds the standard, as the real-time temperature difference value, and to determine whether to lower the heating furnace temperature set value based on the comparison result between the real-time temperature difference value and the allowable temperature difference value.

[0102] The speed analysis subunit is used to compare the actual operating speed of the production line with the historical average speed when the real-time temperature difference is less than or equal to the allowable temperature difference, and to determine whether the gas flow exceeding the standard is a reasonable fluctuation based on the comparison results.

[0103] The raw material pressure subunit is used to analyze the fluctuation of raw material injection pressure and to issue the highest priority alarm when it is determined that the raw material injection pressure fluctuates drastically.

[0104] In this embodiment, the actual gas flow rate is compared with the upper limit of the gas flow rate. If the actual gas flow rate is greater than the upper limit, the gas flow rate is determined to be excessive; if the actual gas flow rate is less than or equal to the upper limit, the gas flow rate is determined to be within the limit, and the fluctuation of the raw material injection pressure is analyzed. When the gas flow rate is excessive, the difference between the actual temperature value and the temperature setpoint is obtained as the real-time temperature difference. The real-time temperature difference is compared with the allowable temperature difference: if the real-time temperature difference is greater than the allowable temperature difference, the heating furnace temperature setpoint is lowered; if the real-time temperature difference is less than or equal to the allowable temperature difference, the actual operating speed of the production line is compared with the historical average speed: if the actual operating speed of the production line is greater than the historical average speed, it is determined that the increase in total gas flow rate is reasonable and expected, and the fluctuation of the raw material injection pressure is analyzed; if the actual operating speed of the production line is less than or equal to the historical average speed, the increase in total gas flow rate is determined to be reasonable and expected, and the fluctuation of the raw material injection pressure is analyzed; if the actual operating speed of the production line is less than or equal to the historical average speed, the increase in total gas flow rate is determined to be reasonable and expected, and the fluctuation of the raw material injection pressure is analyzed. The average speed is recorded, and an alarm is issued to prompt on-site inspectors to check the cause of the speed reduction. When it is determined that the raw material injection pressure fluctuates drastically, it indicates that the raw material formula is improper or the injection system is malfunctioning, leading to unstable foaming reaction and a surge in heat absorption. In this case, the highest priority alarm is issued, prompting immediate inspection of the raw material mixing system and injection pump. The process of analyzing the fluctuation of raw material injection pressure is as follows: the raw material injection pressure corresponding to each sampling point is obtained, the curve of the raw material injection pressure changing with time is plotted, and the percentage of sampling points on the curve that do not fall within the standard fluctuation range is obtained to get the pressure fluctuation ratio. The pressure fluctuation ratio is compared with the fluctuation ratio threshold: if the pressure fluctuation ratio is less than or equal to the fluctuation ratio threshold, the raw material injection pressure is determined to be stable, and the step of verifying the accuracy of slab tracking is then initiated; if the pressure fluctuation ratio is greater than the fluctuation ratio threshold, the raw material injection pressure is determined to be drastically fluctuating.

[0105] The allowable temperature difference refers to the acceptable normal deviation range between the actual temperature and the set temperature of the heating furnace. If the actual temperature is consistently lower than the set temperature and exceeds this range, it indicates a significant problem with the heating system. The allowable temperature difference is set between 5℃ and 10℃, preferably 6.5℃. The setting process is as follows: after the system is put into operation, under stable production conditions, the distribution of the temperature difference ΔT in historical data is statistically analyzed, including the average value μ and the standard deviation σ. The initial threshold can then be set to μ+2σ or μ+3σ. If the statistics show that the average ΔT is 2℃ and the standard deviation of fluctuation is 1.5℃ when normal, the threshold can be set to 2+(3×1.5)=6.5℃.

[0106] The fluctuation percentage threshold represents the maximum allowable proportion of pressure sampling points that exceed the standard fluctuation range within the analysis time window. If this threshold is exceeded, the pressure is considered to be fluctuating drastically. The standard fluctuation range can be set to ±5% or ±10% of the pressure set value. For example, if the pressure is set to 100 bar, the standard fluctuation range is 90 bar to 110 bar, and the fluctuation percentage threshold is set between 20% and 30%, preferably 25%.

[0107] The stability of furnace temperature, the speed of the production line, the ratio of raw materials and the injection volume, etc., must be strictly controlled in this process. Any deviation may lead to quality problems. Through root cause analysis, we can avoid quality problems caused by insufficient foaming, over-cooking or coking, and poor adhesion, while improving the overall production efficiency.

[0108] Specifically, the feedback optimization module also includes a verification unit;

[0109] The verification unit is used to verify whether the slab tracking is normal based on the actual offset distance and actual dwell time of the target slab.

[0110] In this embodiment, the actual offset distance of the target slab determines whether it has been stationary for an extended period. The actual dwell time of the target slab determines whether its centerline has significantly deviated from the theoretical centerline. When a prolonged stationary period is detected and the calculated centerline significantly deviates from the theoretical centerline, an alarm is issued indicating abnormal slab movement. The edges and deviations of the target slab are detected using a visual sensor. A visual sensor is installed on the RFID reader adjacent to key nodes. The visual sensor is a planar array camera. Simultaneously, an LED strip light source or backlight is installed to ensure high-contrast, shadow-free images are obtained under different ambient light conditions. The visual sensor continuously acquires images at regular intervals to obtain continuous slab images. For each slab image, the camera is pre-calibrated to establish the image pixel coordinates and the actual physical coordinates (in millimeters). The conversion relationship is as follows: the pixel position of the slab's centerline in the image coordinate system is calculated based on the left and right edges, and then converted into the actual physical position of the slab on the production line. The actual offset distance between the calculated slab centerline and the theoretical centerline is determined. If the actual offset distance is greater than the safety distance threshold, the target slab is determined to be off-track. If the actual offset distance is less than or equal to the safety distance threshold, the actual dwell time of the target slab is obtained and compared with the dwell time threshold. If the actual dwell time is greater than the dwell time threshold, the slab is determined to be slipping. If the actual dwell time is less than or equal to the dwell time threshold, the slab is determined to be tracking normally. The safety distance threshold is set to 20mm, and the dwell time threshold is set to 2s. This ensures that the time window on which energy consumption collection depends is accurate, thereby ensuring that energy consumption data can be accurately mapped to each slab, achieving refined energy management.

[0111] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

[0112] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A smart management system for color steel composite panel production data, characterized in that, include: The data acquisition module is used to collect energy data and operating parameters of key equipment on the production line; A slab tracking module, which is connected to the data acquisition module, is used to identify and track each target slab entering the heating zone of the foaming furnace, and output the tracking event stream of any target slab; An energy consumption collection module, which is connected to the slab tracking module, is used to determine the energy consumption responsibility period and slab production energy consumption of the target slab at the energy consumption collection point based on the tracking event flow. An energy consumption analysis module, which is connected to the energy consumption collection module and the data acquisition module, is used to determine whether to mark the energy consumption collection point as an abnormal energy consumption event based on the comparison results between the slab production energy consumption and the standard energy consumption baseline, and to calculate the corresponding actual time deviation based on the energy data and the energy consumption responsibility period. The feedback optimization module, which is connected to the energy consumption analysis module and the data acquisition module, is used to correct the energy consumption responsibility period or issue an alarm based on the comparison result between the actual time deviation and the standard time deviation. The energy data includes gas flow rate; the energy consumption collection module includes a time period determination unit, an energy consumption extraction unit, and an energy consumption calculation unit. The time period determination unit is used to determine the entry time and exit time of the target slab in the foaming furnace based on the tracking event flow of the target slab, as the energy consumption responsibility period. The energy consumption extraction unit is used to extract the total gas flow curve of the foaming furnace during the energy consumption responsibility period from the time-series database. The energy consumption calculation unit is used to calculate the energy consumption of slab production based on the total gas flow curve and slab length. The energy consumption calculation unit includes a first calculation subunit and a second calculation subunit; The first calculation subunit integrates the total gas flow curve over the energy consumption responsibility period to obtain the total gas consumption during the energy consumption responsibility period, which is taken as the total energy consumption. The second calculation subunit is used to calculate the product of the total energy consumption and the proportional coefficient to obtain the energy consumption for slab production; Wherein, the proportionality coefficient is the ratio of the slab length to the effective length of the furnace chamber; The energy data also includes power curves; The energy consumption analysis module includes an energy consumption comparison unit, a calibration unit, and a time series analysis unit. The energy consumption comparison unit is used to compare the energy consumption of slab production with the standard energy consumption baseline range to obtain a first comparison result and a second comparison result. The calibration unit is used to mark the energy consumption aggregation point as an abnormal energy consumption event when the first comparison result is obtained; The time series analysis unit is used to determine the energy consumption decrease node based on the power curve, and to calculate the actual time deviation based on the energy consumption decrease node and the departure time. The first comparison result shows that the energy consumption of slab production is not within the standard energy consumption baseline range; The second comparison result shows that the energy consumption for slab production is within the standard energy consumption baseline range.

2. The intelligent management system for color steel composite panel production data according to claim 1, characterized in that, The slab tracking module includes a slab identification unit, a position synchronization unit, and an event output unit; The slab identification unit is used to capture the identification code of each slab in real time and generate the corresponding slab ID. The position synchronization unit is used to synchronously record the encoder count value at the moment when the slab ID is identified, so as to bind the slab ID with the production line position. The event output unit is used to generate a tracking event stream based on the slab ID, timestamp, and encoder count value.

3. The intelligent management system for color steel composite panel production data according to claim 1, characterized in that, The feedback optimization module includes a timing verification unit and a root cause diagnosis unit; The root cause diagnosis unit includes a superimposed curve acquisition unit and an anomaly analysis unit; The superimposed curve acquisition unit is used to acquire the key parameter curves corresponding to the curves calibrated as abnormal energy consumption events in the time series database; The anomaly analysis unit is used to adjust the corresponding operating parameters or output alarm prompts based on the key parameter curves.

4. The intelligent management system for color steel composite panel production data according to claim 3, characterized in that, The timing verification unit is used to correct the energy consumption responsibility period of the next target slab when the actual time deviation is greater than the standard time deviation; The root cause diagnosis unit is used to perform root cause diagnosis on the operating status of the composite process based on the operating equipment parameter curve when the actual time deviation is less than or equal to the standard time deviation, so as to output a fault warning prompt.

5. The intelligent management system for color steel composite panel production data according to claim 4, characterized in that, The timing verification unit includes a first correction subunit and a second correction subunit; The first correction subunit is used to increase the initial time compensation parameter to the correction time compensation parameter when the actual time deviation is greater than the standard time deviation. The second correction subunit is used to calculate the sum of the correction time compensation parameter and the departure time to obtain the correction departure time, and to determine the energy consumption responsibility period of the next slab based on the correction departure time.

6. The intelligent management system for color steel composite panel production data according to claim 3, characterized in that, The anomaly analysis unit includes a gas flow analysis subunit, a temperature analysis subunit, a velocity analysis subunit, and a raw material pressure subunit; The gas flow analysis subunit is used to determine whether the gas flow exceeds the limit based on the comparison between the actual gas flow and the upper limit of gas flow. The temperature analysis subunit is used to obtain the difference between the actual temperature value and the set temperature value when the gas flow exceeds the standard, as the real-time temperature difference value, and to determine whether to lower the heating furnace temperature set value based on the comparison result between the real-time temperature difference value and the allowable temperature difference value. The speed analysis subunit is used to compare the actual operating speed of the production line with the historical average speed when the real-time temperature difference is less than or equal to the allowable temperature difference, and to determine whether the gas flow exceeding the standard is a reasonable fluctuation based on the comparison results. The raw material pressure subunit is used to analyze the fluctuation of raw material injection pressure and to issue the highest priority alarm when it is determined that the raw material injection pressure fluctuates drastically.

7. The intelligent management system for color steel composite panel production data according to claim 3, characterized in that, The feedback optimization module also includes a verification unit; The verification unit is used to verify whether the slab tracking is normal based on the actual offset distance and actual dwell time of the target slab.

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