Data optimization and control method and system for thermal cycle of annealing furnace

Abstract

The invention discloses a data optimization and control method and system for a thermal cycle of an annealing furnace, and relates to the field of data identification and processing. The system comprises an infrared temperature measuring device, a temperature sensor and a server. The method obtains temperature information of a working interval by a temperature sensor and writes a position code to obtain first data and form a first data set. The first data set is sorted according to the position code and made into a temperature change curve chart, the temperature change curve chart is analyzed and it is judged whether there is an abnormality in the data fluctuation; if there is an abnormality, the first data corresponding to the peak and the trough in the curve table are extracted to obtain the node data; the difference between the glass temperature data and the node data is obtained, and the working state of a waste heat recovery system is controlled by the server; if there is no abnormality, the server configures a first strategy, and the server controls the temperature sensor and the infrared temperature measuring device to collect data regularly under the first strategy, so as to improve the problem of delayed discovery of data abnormalities.

Description

Technical Field

[0001] The invention relates to data identification and processing, and in particular to a method and system for data optimization and control of a thermal cycle of an annealing furnace. Background Art

[0002] At present, in order to refine the temperature management of the annealing kiln, temperature sensors are usually used to collect the temperature of each working area before, during and after. Due to the long production process of the entire annealing kiln, there are huge sampling components and control units as a whole. The annealing kiln is mainly divided into the annealing section and the cooling section. Usually, a waste heat recovery system is installed on one side of the annealing kiln. The waste heat recovery system adjusts the hot air flow recovery of each working area and inputs the appropriate temperature air flow to control the temperature. In this way, the air flow is used to reasonably utilize the heat, thereby further stabilizing the energy consumption while meeting the production process.

[0003] In the existing float glass production process, due to the continuity of the float glass process, each sampling element and control system will have an impact on the molding effect of the entire process. Currently, sensitive fault monitoring methods are mainly used to obtain abnormalities and make adjustments in the first time. With the development and diversification of computer equipment, the control of abnormal data risks of glass products has begun to introduce time information and space information for collaborative analysis (such as texture recognition, historical data analysis for prediction) to improve the efficiency of abnormal handling. However, when the temperature in a certain area is intuitively abnormal, it is usually possible that there is an adverse effect on the process. Therefore, how to make risk discovery more advanced, especially before the glass product has abnormalities, how to reduce risks is worth exploring. Summary of the invention

[0004] The object of the present invention is to provide a method and system for optimizing and regulating data of a thermal cycle of an annealing furnace, so as to improve the problem of delayed discovery of data anomalies when time information and space information are analyzed in a coordinated manner.

[0005] An embodiment of the present invention provides a method for determining anomalies using an influence coefficient when data fluctuates, in the hope of improving risk assessment in multiple dimensions such as temperature, time, and space.

[0006] An embodiment of the present invention provides a method for fast processing by combining a processing list with an influence coefficient, in order to improve the efficiency of handling abnormal situations.

[0007] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0008] A data optimization and control method for the thermal cycle of an annealing furnace, which is used for abnormal cold end data of a float glass production line and for regulating a waste heat recovery system, comprises the following steps: S100, distributing a temperature sensor array in each working area of ​​the annealing furnace and connecting it to a server signal, obtaining temperature information of the working area by the temperature sensor and writing a position code to obtain first data. S200, the server obtains the first data of each temperature sensor to obtain a first data set; the first data in the first data set is sorted according to the position code and made into a temperature change curve; wherein the position code indicates the position of the temperature sensor in the annealing furnace.

[0009] S300, the server analyzes the temperature change curve to determine whether there is any abnormality in the data fluctuation.

[0010] If the temperature change curve increases or has peaks and valleys, it indicates that the data fluctuation is abnormal. The first data corresponding to the peaks and valleys in the curve table are extracted to obtain the node data, and S400 is executed.

[0011] If the temperature change curve graph decreases gradually and no peaks or valleys appear, it means that the data fluctuation is normal, and S500 is executed.

[0012] S400, determine the working range of the node data, obtain the difference between the glass temperature data and the node data, and adjust the working state of the waste heat recovery system by the server, and repeat S200 to S300 until the data fluctuation is normal.

[0013] S500, the server configures a first strategy, which is a normal strategy. The server controls the temperature sensor and the infrared temperature measuring device to collect data regularly under the first strategy (normal strategy). After each collection, a temperature change curve is drawn again to determine whether there is any abnormality in the data fluctuation.

[0014] Among them, the first strategy is the normal strategy.

[0015] A further technical solution is that at S300, if there is an abnormality in the data fluctuation, the above-mentioned server presets the safety parameters, and the safety parameters are range values; when the above-mentioned server executes S400, the difference is compared with the safety parameters, and it is determined whether the difference is within the set range of the safety parameters. If the difference is within the range of the safety parameters, it means that the risk is controllable, and S400 continues to be executed. If the difference is outside the range of the safety parameters, it means that the risk is uncontrollable, and the execution of S400 is suspended; the above-mentioned server obtains the node data that exceeds the safety parameters, and the work area corresponding to the node data is extracted as the abnormal area, and the server obtains the influence coefficient of the abnormal area, and adjusts the air supply volume of the waste heat recovery system according to the influence coefficient.

[0016] A further technical solution is that, when executing S400, the server further obtains the influence coefficient of the abnormal area, and the acquisition of the influence coefficient includes at least the following steps:

[0017] S410, the server extracts the location corresponding to the node data, obtains the coverage area of ​​the abnormal area; determines the number of temperature sensors within the coverage area, extracts the coverage impact value of the number of temperature sensors and calculates the regional impact coefficient; the calculation formula of the regional impact coefficient A is:

[0018] ;

[0019] Where n is the number of temperature sensors within the abnormal area, is the impact value of the jth temperature sensor coverage area within the abnormal area, is the weight of the impact value of the coverage area of ​​the jth temperature sensor;

[0020] S420, determining the interval between two adjacent first data of the same temperature sensor in the abnormal area; the server retrieves the weighted factor table and obtains the time impact coefficient corresponding to the interval time to obtain the time impact number. The weighted factor table is a relationship table preset by the server to check the interval time and the time impact coefficient.

[0021] S430, obtaining all first data of the abnormal area, obtaining difference data between the glass temperature data and each first data, and obtaining a difference influence number through the difference data, wherein the calculation formula of the difference influence number U is:

[0022] ;

[0023] Where M is the number of temperature sensors within the abnormal area. is the first data of the i-th temperature sensor in the abnormal area; It is the glass temperature data of abnormal area.

[0024] S440, the server inputs the time impact number, the difference impact number and the abnormal area into the evaluation model to calculate and obtain the impact coefficient. The calculation formula of the impact coefficient Te is:

[0025] ;

[0026] Wherein, Te is the influence coefficient within the abnormal area; U is the difference influence number of the temperature sensor within the abnormal area; P is the time influence number of the sampling interval of the temperature sensor within the abnormal area; A is the regional influence coefficient of the abnormal area.

[0027] A further technical solution is that the above-mentioned server sets a processing list, in which several control parameters are set, and each control parameter corresponds to an influence coefficient. The above-mentioned server extracts the control parameters in the processing list according to the influence coefficient, and adjusts the exhaust and supply air volumes of the waste heat recovery system according to the control parameters.

[0028] Preferably, the return air inlet and the exhaust air inlet of the annealing furnace and the waste heat recovery system are respectively provided with the first indicator and the second indicator, and the return air inlet and the exhaust air inlet of each working interval are set to the working temperature that meets the working conditions. Among them, the first indicator is set at the return air inlet, and the color change temperature of the first indicator is less than or equal to the lower limit of the working temperature of the return gas. Among them, the second indicator is placed at the exhaust air inlet, and the color change temperature of the second indicator is higher than the upper limit of the working temperature of the exhaust airflow in the working interval of the annealing furnace.

[0029] A further technical solution is that transparent peepholes are provided at the above-mentioned return air outlet and exhaust air outlet, and the above-mentioned transparent peepholes are matched with visual devices. The above-mentioned visual devices are connected with server signals, and the above-mentioned server is used to call the visual device to obtain image data of the first indicator and the second indicator.

[0030] A further technical solution is that the image data of the first indicator and the second indicator both include color markings. The server determines whether the airflow temperature in the connection zone is lower than the lower limit through the color marking of the first indicator; when the color marking of the image data of the first indicator does not change color, it indicates that the return air temperature is insufficient, and the server marks the working interval corresponding to the first indicator as abnormal, and the server adjusts the parameters of the waste heat recovery system and reduces the air supply. The server determines whether the temperature of the corresponding working interval exceeds the upper limit through the image data of the second indicator; when the color marking of the image data of the second indicator changes color, it indicates that the temperature of the working interval is too high, and the server marks the working interval corresponding to the second indicator as abnormal, and the server adjusts the parameters of the waste heat recovery system and increases the exhaust volume.

[0031] A further technical solution is that the server records the number of times S300 is executed and configures a verification number. When the number of times S300 is executed recorded by the server reaches the verification number, the server calls the visual device to obtain image data of the first indicator and the second indicator, and the server performs directional monitoring.

[0032] The present invention also discloses a system for thermal circulation of an annealing furnace, the system comprising an infrared temperature measuring device, a temperature sensor and a server; the infrared temperature measuring device is distributed at the exit of each working interval, the infrared temperature measuring device is connected to the server by signal, the infrared temperature measuring device collects the glass temperature and uploads it to the server, the server is connected to the control unit of a waste heat recovery system by signal, the server sends instructions to the control unit of the waste heat recovery system, wherein the temperature sensor is installed in the working interval of the annealing furnace, the temperature sensor is connected to the server by signal, the temperature sensor collects the temperature information of the working interval of the annealing furnace; the temperature sensor performs device numbering, the temperature sensor writes the position code of the device number into the temperature information to obtain the first data, and the server executes the data optimization and control method of the thermal circulation of the annealing furnace.

[0033] Preferably, the system also includes a visual device, which is placed at the perspective peephole of each working area. The visual device is connected to the server signal, and is used to collect image data and upload it to the server, which is used to analyze the image data.

[0034] Compared with the prior art, the beneficial effects of the present invention are at least one of the following:

[0035] Compared with the discovery efficiency of traditional temperature monitoring systems, the present invention has significant advantages in the immediacy and predictability of data processing and temperature control. The identification of abnormal conditions is relatively intuitive, and abnormalities can be found when the temperature fluctuates (without exceeding or falling below the threshold of a reasonable temperature). By continuously collecting temperature data and forming a temperature change curve, by setting a first strategy, it is beneficial to effectively discover any abnormal fluctuations in the normalized working process, and by analyzing the temperature curve to identify the first data that may be at risk, thereby reducing the impact of emergencies on the production line.

[0036] At the same time, compared with traditional monitoring methods, the control method of the present invention is less dependent on the prediction model, and its model computing resources can be tended to be used for subsequent data analysis, thereby reducing the computing resources required for abnormality discovery. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 It is a schematic diagram of the process of the present invention.

[0038] Figure 2 It is a schematic diagram of the abnormality handling process of the present invention.

[0039] Figure 3 It is a schematic diagram of the system structure of the present invention. DETAILED DESCRIPTION

[0040] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0041] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative position relationship, movement status, etc. in a certain working state. If the specific posture changes, the directional indication will also change accordingly. In the present invention, unless otherwise clearly specified and limited, the term "connection" and the like should be understood in a broad sense. For example, "connection" can be an electrical signal connection or a signal connection; it can also be the internal connection of two elements or the interaction relationship between two elements, unless otherwise clearly defined. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0042] If there are descriptions involving "first", "second", etc. in the embodiments of the present invention, the descriptions of "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the meaning of "and / or" appearing in the full text includes three parallel schemes. Taking "A and / or B" as an example, it includes scheme A, or scheme B, or a scheme that satisfies both A and B. In addition, the technical solutions between the various embodiments can be combined with each other, but it must be based on the ability of ordinary technicians in the field to implement. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0043] In the preparation process of float glass, annealing furnace is usually used to cool the glass products. Annealing furnace is one of the three major thermal equipment in the float glass production line. The temperature process of annealing furnace mainly involves annealing section and cooling section. The temperature of the continuous glass strip drawn from the tin bath is about 600 degrees Celsius. It enters the annealing furnace through the transition roller table and is properly cooled by the annealing furnace. The temperature of the glass output from the annealing furnace is about 230 degrees Celsius. It is based on the heat utilization and thermal cycle design of the annealing furnace. The annealing section is usually a sealed environment. The working interval is usually represented by zone A, zone B and zone C. The temperature from zone A to zone C usually decreases from 600 degrees Celsius to 200 degrees Celsius. The cooling section is usually a semi-open environment. The working interval is usually represented by zone T and zone F. And each zone is usually connected to the waste heat recovery system. The high temperature of the working area is recovered or supplemented with air flow for neutralization through the waste heat recovery system. In order to achieve controllable temperature in the working area and reduce energy consumption.

[0044] In the actual temperature difference abnormality control process, the temperature of different positions (working areas) within the working range is currently mainly collected through thermocouples or other thermal sensors. When the temperature is lower than the set temperature, the waste heat recovery system is linked to perform temperature processing. The triggering timing of its regulation usually requires the temperature to be lower than the lower limit of the set temperature. When triggered, the temperature usually has a certain adverse effect on the glass, and it also takes a period of time to restore the normal temperature. As a result, when regulation occurs, it is usually necessary to re-inspect the product or partially cut it.

[0045] refer to Figure 1 As shown, one embodiment of the present invention is a method for optimizing and regulating the data of an annealing furnace thermal cycle, which is used for abnormal cold end data of a float glass production line and regulating a waste heat recovery system, and the method comprises the following steps: S100, distributing a temperature sensor array in each working interval of the annealing furnace and connecting it to a server signal, and obtaining the temperature information of the working interval by the temperature sensor and writing the position code to obtain the first data.

[0046] Among them, the temperature sensors are evenly distributed in each working area of ​​the annealing furnace, so that the real-time working data of each working area can be obtained; since each temperature sensor is connected to the server, the temperature sensor can send the collected temperature information to the server in real time.

[0047] Among them, the position coding marked in the temperature information can make the temperature data better reflect the temperature sensor to which the data corresponds and the specific position in the annealing kiln represented by the temperature. The temperature data of different positions of the entire annealing kiln can be obtained through the server, providing guidance for subsequent data analysis and regulation.

[0048] Exemplarily, the temperature sensor may be an existing thermocouple or other existing high temperature resistant sensor. In principle, the temperature sensor needs to meet the working reliability requirements in a high temperature environment.

[0049] Considering that the recognition of temperature anomaly has hysteresis, therefore, in step S200, the server obtains the first data of each temperature sensor to obtain a first data set; the first data in the first data set is sorted according to the position code and made into a temperature change curve chart. Among them, the position code represents the position of the temperature sensor in the annealing furnace. The above curve chart is based on the sorting of the position code, and the sorting principle of the position code is generally arranged according to the path of the glass in the working interval (the path of the glass flowing in the annealing furnace). The data collected by the temperature sensor is collected by the server, and the server combines all the collected first data with position changes into a first data set. The above first data set contains several first data, and the first data collected by each sensor contains a temperature value and a corresponding position code. The above server sorts the data according to the position code, and the sorting of the position code is usually arranged in the order of the path of the glass flowing in the annealing furnace to ensure that the data reflects the real spatial temperature distribution. The first data of the first data set is used to generate a temperature change curve chart after sorting.

[0050] For reference, in the graph, the x-axis is sorted in sequence according to the position code, that is, the position of the sensor in the annealing furnace is arranged in sequence, and the y-axis represents the corresponding temperature value. By connecting the points, a continuous curve is formed, which is convenient for visually observing the temperature changes in the entire annealing furnace. Since the server sorts according to the position code of the temperature sensor, the graph can intuitively express the flow path of the glass in the annealing furnace to show the temperature change trend. That is, the temperature distribution and change trend of the entire annealing furnace are observed through the temperature change curve, so as to identify possible abnormal temperature fluctuations. It should be pointed out that abnormal fluctuations are usually caused by temperature fluctuations. Generally speaking, the stress change of glass usually occurs only after it is lower than the threshold. Short-term temperature fluctuations, as long as they are not lower than the threshold, can have a negligible impact on glass molding. Identifying abnormal fluctuations is more to determine abnormal areas and eliminate possible risk factors in advance.

[0051] To determine the area of ​​abnormal fluctuation, in step S300, the server analyzes the temperature change curve to determine whether there is an abnormality in the data fluctuation. Since the design of the curve is mainly based on position coding, under normal circumstances, the temperature process corresponding to the position coding of the annealing furnace should decrease steadily, and the uniform cooling of the glass is ensured by decreasing, thereby avoiding stress concentration during the cooling process of the glass. When the temperature change curve increases or has peaks and valleys, it indicates that the data fluctuation is abnormal, mainly because such a situation usually reflects the uneven temperature distribution inside the annealing furnace or problems with the temperature control system.

[0052] Among them, the temperature change curve is decreasing, and there are no peaks and valleys, indicating that the data fluctuation is normal, the temperature control is in line with expectations and the temperature distribution is uniform. Because the data fluctuation is normal, the curve shows a decreasing trend, indicating a working state in which the glass gradually drops from high temperature to low temperature, avoiding physical stress caused by excessive temperature difference. In principle, there is no need for adjustment or intervention. Therefore, the glass can be processed in the established manner during the entire annealing process, and then S500 is executed.

[0053] Among them, the temperature change curve is increasing or has peaks and troughs, which means that there is an abnormality in the data fluctuation. Usually, the appearance of peaks and troughs means that the temperature in the local area is abnormally high or abnormally low. The node data is obtained by extracting the first data corresponding to the peaks and troughs in the curve table, and then executing S400.

[0054] The node data corresponds to the first data of the peak and the valley, and the node data facilitates accurate finding of the specific location and temperature value where the temperature fluctuation occurs, so as to correct these anomalies by taking temperature control measures for the abnormal area by executing S400 to adjust the waste heat recovery system, thereby restoring normal heat distribution and temperature stability.

[0055] In order to reduce the impact of abnormal fluctuations, S400 determines the working range of the node data, obtains the difference between the glass temperature data and the node data, and the server adjusts the working state of the waste heat recovery system, and repeats S200 to S300 until the data fluctuation is normal.

[0056] The difference is the absolute value of the difference between the two temperatures. The working state of the waste heat recovery system is mainly to increase or decrease the air supply to the working area, and to increase or decrease the air extraction volume in the working area (the heat recovery efficiency of the area can be adjusted laterally). The recovered heat energy is guided to other working areas through pipelines to increase the temperature of the incoming gas.

[0057] Among them, after confirming the working interval corresponding to the abnormal data node through the peaks and valleys, the server will adjust the temperature control in the working interval where the abnormal data node is located, and use the server to adjust the working state of the waste heat recovery system, and input air flow or extract hot air to the corresponding working interval through the waste heat recovery system to compensate for the temperature difference, so that the working interval can restore the normal temperature distribution. After each adjustment of the waste heat recovery system is completed, steps S200 to S300 are re-executed until all temperature data appear normal. The purpose of this step is to achieve precise control of the temperature distribution in the annealing kiln by dynamically adjusting the waste heat recovery system, optimize energy utilization, and ensure the quality of glass production.

[0058] For reference, the above-mentioned waste heat recovery system can be an existing system, and the structural part will not be described in detail here. In principle, the waste heat recovery system is connected to each working area through a pipeline system, and the working area is at least provided with a return air port and an exhaust port, wherein the return air port and the exhaust port are respectively connected to different pipes in the pipeline system. Since a heat exchanger is provided on the pipeline system, the heat energy extracted from the exhaust port passes through the pipeline system to the heat exchanger to exchange heat with the air flow in the pipeline system, and its heat energy can be sent back to different working areas through the pipeline system in conjunction with the return air port. It should be noted that a blower and a one-way valve are respectively provided at the return air port and the exhaust port, and the blower and the one-way valve are used to promote the flow of hot air inside the kiln to ensure that the heat can be evenly distributed, while removing excess hot air or replenishing hot air to specific areas.

[0059] In order to meet the continuous work requirements, S500 is performed, and the server configures the first strategy (normal strategy). The server controls the temperature sensor and the infrared temperature measuring device to collect data regularly under the first strategy (normal strategy). After each collection, a temperature change curve is made again and it is judged whether the data fluctuation is abnormal. The server judges that the temperature fluctuation is normal, and the server configures and executes the first strategy. Under the first strategy, its temperature sensor and infrared temperature measuring device collect data regularly, make a temperature change curve again and judge whether the data fluctuation is abnormal, so as to repeat S300 periodically. Ensure that the entire control logic still has a certain early warning function under normal conditions, so that when an abnormality occurs, it can quickly respond to any new or unexpected temperature changes and reduce the production risks caused by abnormal temperature. By evenly arranging high-precision temperature sensors in each working area of ​​the annealing kiln and transmitting data back to the server in real time, accurate monitoring of the temperature in the kiln is achieved. Through the refined temperature monitoring and control process, it is expected to improve the hysteresis of data anomaly recognition commonly seen in the annealing process of float glass production, and significantly improve production efficiency and product quality.

[0060] It should be noted that the system design combines the adaptability of the existing heat recovery system with the intelligent adjustment function of the existing heat recovery system. The server adjusts the working mode of the heat recovery system according to temperature changes, and accurately performs temperature compensation or heat extraction treatment on the working range by dynamically modifying the operating status of the heat circulation system. Under the premise of improving the response speed to abnormalities, the processing efficiency is relatively high, ensuring the uniformity of temperature and the quality stability of products during the glass production process.

[0061] refer to Figure 2As shown, based on the above embodiment, another embodiment of the present invention is that at S300, if there is an abnormal data fluctuation, the server presets a security parameter, and the security parameter is a range value; when the server executes S400, the difference is compared with the security parameter, and it is determined whether the difference is within the set range of the security parameter.

[0062] Among them, the safety parameter is a parameter range, because the temperature in the working range cannot be completely avoided without a small fluctuation. Although some temperature fluctuations have abnormal tendencies, the parameter range of the safety parameter setting takes into account normal production fluctuations and the sensitivity (deviation) of the equipment. Therefore, the fluctuations within this range are considered normal and predictable by the system and will not have an adverse effect on the production process or product quality. Some fluctuations may also be short-term temporary fluctuations caused by external interference (which do not affect product quality). In this case, a certain fluctuation tolerance is reserved through the safety parameter, so as to adaptively judge whether measures need to be taken for abnormal data fluctuations.

[0063] If the difference is within the range of the safety parameter, it means that the risk is controllable, and S400 is continued to be executed. Since it is within the tolerance range, the occurrence of the difference can be regarded as a controllable risk.

[0064] If the difference is outside the range of safety parameters, it means that the risk is uncontrollable. Since the difference exceeds the set safety parameters, it is generally considered not a normal phenomenon. This phenomenon may lead to subsequent large temperature differences and affect the physical properties of the glass. Therefore, in order to avoid continuing to work and causing serious equipment problems or product defects (excessive temperature differences in the glass cause uneven stress and increase the risk of breakage), it is necessary to immediately identify the root cause of the problem and suspend the execution of S400 to avoid further aggravation of temperature abnormalities or impact on other production areas.

[0065] Among them, in order to conduct a more detailed analysis of the anomaly, after identifying the specific risk location, the waste heat recovery system can be adjusted to deal with the risk. The above-mentioned server obtains node data that exceeds the safety parameters, and the working area corresponding to the node data is extracted as the abnormal area. The server obtains the influence coefficient of the abnormal area and adjusts the air supply volume of the waste heat recovery system according to the influence coefficient.

[0066] For reference, the above server must first confirm the specific node that exceeds the safety parameters, and determine the specific working range where the abnormality occurs through position coding, and calculate the impact coefficient according to the severity of the abnormality (such as the size of the temperature difference, the scope of impact and duration, etc.). The impact coefficient is used to evaluate the potential danger and impact degree, so as to adjust the parameters of the waste heat recovery system.

[0067] For reference, when the impact coefficient is high, it means that the risk is relatively high, and the waste heat recovery system needs to adjust the abnormal working range as soon as possible, and even needs to assist in adjusting the areas adjacent to the abnormal working range. For example, if the temperature in the abnormal area is too high, the cooling intensity at the junction of the adjacent areas can be appropriately increased to help alleviate the heat in the abnormal area.

[0068] On the contrary, when the impact coefficient is assessed to be low, it indicates that the risk in this area is relatively small, and gentle measures can be taken to make adjustments to avoid unexpected fluctuations caused by sudden changes. Therefore, when the impact coefficient is low, the above-mentioned waste heat recovery system can slowly adjust the temperature of the abnormal working range, and avoid unexpected fluctuations during the adjustment process by gradually increasing or decreasing the air supply. Specifically, the adjustment of the waste heat recovery system is divided into several small steps, and the air supply volume is adjusted in stages by less than 5% in each small step, and the temperature of the area and its surrounding areas is continuously monitored during the adjustment to ensure response to any sudden changes.

[0069] Based on the above embodiment, another embodiment of the present invention is that when executing S400, the server further obtains the influence coefficient of the abnormal area, and the acquisition of the influence coefficient includes at least the following steps:

[0070] S410, the server extracts the location corresponding to the node data, obtains the coverage area of ​​the abnormal area; determines the number of temperature sensors within the coverage area, extracts the coverage impact value of the number of temperature sensors and calculates the regional impact coefficient; the calculation formula of the regional impact coefficient A is:

[0071] ;

[0072] Where n is the number of temperature sensors within the abnormal area, is the impact value of the jth temperature sensor coverage area within the abnormal area, is the weight corresponding to the impact value of the coverage area of ​​the jth temperature sensor.

[0073] Among them, the regional impact coefficient is mainly used to reflect the risk of abnormal diffusion. The larger the area of ​​the abnormal area, the greater the impact of the temperature fluctuation on the overall production process. Calculating the regional impact coefficient A can help assess the potential risk level. The specific coverage area of ​​the abnormal area and the number of temperature sensors in the area are helpful to confirm the scope of the impact. The weight calculation is mainly based on the coverage impact of the temperature sensor within the coverage area, so that when calculating the regional impact coefficient, it can reflect the impact of the sensor on the entire abnormal area, which is convenient for effectively adjusting the waste heat recovery system.

[0074] Since the system has sampling behavior, in order to avoid the impact caused by sampling delay, S420 is performed to determine the interval time between two adjacent first data of the same temperature sensor in the abnormal area; the above-mentioned server calls the weighted factor table and obtains the time impact coefficient corresponding to the interval time to obtain the time impact number.

[0075] The weighted factor table is a table comparing the intervals preset by the server with the time influence coefficients. During the production process, temperature changes require time to reflect their impact on the physical properties of the glass. A longer interval may mean that temperature changes fail to receive timely feedback, thereby increasing the risk to product quality.

[0076] For example, the table is usually preset according to historical data. Generally speaking, the longer the time interval, the larger the blind spot, so that the time influence coefficient will gradually increase.

[0077] Among them, the weighted factor table (interval time and time influence coefficient) is as follows:

[0078] Time interval (minutes) Time influence coefficient Time interval (minutes) Time influence coefficient 0-1 1 5-6 1.6 1-2 1.1 6-7 1.8 2-3 1.2 7-8 2 3-4 1.3 8-9 2.5 4-5 1.4 10 or more 3.0

[0079] Among them, 0-1 minute: the data update frequency is high, and the temperature monitoring is more accurate, so the time impact coefficient is 1.0. Specifically, there may be a small blind spot in the interval of 1-5 minutes, and the impact coefficient increases slowly over time. The interval of 5-8 minutes is relatively long, which is easy to cause the delay of monitoring to increase, and the impact of temperature changes is more obvious, so the time impact coefficient is greatly increased. The monitoring interval of more than 8 minutes is long, which is easy to cause information lag, the impact of temperature fluctuations increases sharply, and the growth rate of the time impact coefficient is more obvious. More than 10 minutes, usually the monitoring interval can no longer avoid risks, and the information lag is obvious, so the time impact coefficient of temperature fluctuations takes the highest value. The potential impact of temperature fluctuations on glass production is reflected by the time impact number.

[0080] In order to better evaluate the impact of temperature anomaly on glass production quality, S430 is performed to obtain all first data in the abnormal area, obtain the difference data between the glass temperature data and each first data, and obtain the difference impact number through the difference data. The calculation formula of the difference impact number U is:

[0081] ;

[0082] Where M is the number of temperature sensors within the abnormal area. is the first data of the i-th temperature sensor in the abnormal area; It is the glass temperature data of abnormal area.

[0083] It should be explained that the abnormal region range is usually equal to the abnormal region area, and the number of temperature sensors n is equal to the number of temperature sensors M. If a safety factor is preset for the abnormal region range, the abnormal region range will be expanded by percentage based on the abnormal region area. At this time, the number of temperature sensors M in the abnormal region range is greater than or equal to the number of temperature sensors n in the abnormal region area.

[0084] The magnitude of the difference impact number can reflect the intensity of the temperature anomaly and the health status of the temperature control process in the abnormal area. The calculation of the difference impact number usually results in a large range of values, which is helpful in judging whether control measures need to be taken and the urgency and scope of control. Especially when there are multiple abnormal areas, the area with a larger impact coefficient can be preferentially processed.

[0085] S440, the server inputs the time impact number, the difference impact number and the area of ​​the abnormal region into the evaluation model for calculation to obtain the impact coefficient.

[0086] Among them, the calculation formula of the influence coefficient Te is: ;

[0087] In the formula, Te is the impact coefficient within the abnormal area; U is the difference impact number of the temperature sensor within the abnormal area; P is the time impact number of the temperature sensor sampling interval within the abnormal area; A is the regional impact coefficient of the abnormal area. The impact coefficient Te takes into account the risks in the time and space dimensions, and can better evaluate the actual impact of a specific abnormal area on the entire production environment.

[0088] Furthermore, considering that different influence coefficients have different adjustments, the server sets a processing list, in which several control parameters are set, and each control parameter corresponds to an influence coefficient, and a mapping relationship between the influence coefficient and the control parameter is established through the processing list. The server extracts the control parameter in the processing list according to the influence coefficient, and adjusts the exhaust and supply air volume of the waste heat recovery system according to the control parameter.

[0089] Among them, the processing list contains several control parameters, and each control parameter corresponds to a specific influence coefficient range. The specific data of the control parameters include exhaust volume, supply air volume, wind speed, temperature setting, etc. The server sets a start threshold for the influence coefficient. When working, the server calculates and monitors the influence coefficient in real time. When it is found that the influence coefficient reaches the start threshold in the processing list, the control parameter corresponding to the influence coefficient is immediately extracted from the processing list. Then, according to the extracted control parameters, the exhaust and supply air volumes in the waste heat recovery system are adjusted. It should be noted that in the process of adjusting the exhaust and supply air volumes of the waste heat recovery system, the general adjustment method is linear or staged adjustment.

[0090] Based on the above embodiments, another embodiment of the present invention is that since the sensors in the working area and the blower of the waste heat recovery system are electronic devices, the risk of damage to the electronic devices cannot be avoided during long-term use. When the electronic device is damaged, it may not be possible to directly obtain relevant data and control. Therefore, the return air inlet and the exhaust port of the above-mentioned annealing kiln and the waste heat recovery system are respectively provided with a first indicator and a second indicator, and the return air inlet and the exhaust port of each working area are set to an operating temperature that meets the working conditions. It is expected that a physical warning will be provided through the first indicator and the second indicator.

[0091] Among them, the first indicator is set at the return air inlet, and the color change temperature of the first indicator is less than or equal to the lower limit of the working temperature of the return gas. Among them, the first indicator is set at the return air inlet mainly for physical monitoring of the return air temperature. Too low return air temperature usually directly affects the thermal efficiency of the system, causing damage to equipment or products. Therefore, the color change temperature of the first indicator is designed to be less than or equal to the lower limit of the working temperature of the return gas, ensuring that the indicator can effectively change color when the return air temperature is lower than the normal working temperature range.

[0092] Among them, the second indicator is placed at the exhaust port, and the color change temperature of the second indicator is higher than the upper limit of the working temperature of the exhaust airflow in the working area of ​​the annealing furnace. Considering that the exhaust port directly obtains the temperature in the working area, when the exhaust port obtains too high a temperature, it may indicate that the equipment in the working area is damaged (temperature recognition abnormality) or the heat energy loss exceeds expectations. Therefore, the second indicator is set to capture potential risks. By placing the second indicator at the exhaust port, the color change temperature is set to be higher than the upper limit of the working temperature of the exhaust airflow in the working area of ​​the annealing furnace, ensuring that once the exhaust gas temperature exceeds the specified upper limit temperature, the indicator will change color, quickly alerting the system operator or control system.

[0093] Among them, the first indicator is an existing thermosensitive material. Since the air flow temperature at the return air outlet is usually above 200 degrees Celsius, the lower limit of the temperature of the return air outlet usually needs to reach above 200°C to change color. Therefore, the color-changing material of the first indicator can be a silicon-based coating or a borosilicate coating.

[0094] Among them, the return air temperature of the second indicator is usually above 400 degrees Celsius, and some working intervals need to reach a return air temperature of more than 500 degrees Celsius. Therefore, the color-changing material of the second indicator can be ceramic-based coatings or high-temperature liquid crystal materials. It should be noted that high-temperature liquid crystal materials are mainly based on their unique molecular structure and show different order at different temperatures. As the temperature of the material increases, the thermal motion of the liquid crystal molecules increases, which reduces the order between molecules (from "lamellar phase" to "nematic phase"). The structural change will change the scattering or reflection characteristics of the material to light, thereby causing color changes. When using high-temperature liquid crystal materials, it is necessary to customize the color for different working interval temperatures. Although the cost is high, it has good applicability.

[0095] Furthermore, in order to better identify the working conditions of the first indicator and the second indicator, transparent peepholes are provided at the return air port and the exhaust port, and the transparent peepholes are matched with visual devices, and the visual devices are connected to the server by signal, and the server is used to call the visual device to obtain the image data of the first indicator and the second indicator. Among them, the transparent peephole is an existing product, and its transparent peephole in principle needs to be made of transparent materials that can withstand high temperatures and heat insulation, so that the visual device can obtain image data in real time through the transparent peephole, and transmit the image data to the server for analysis. Among them, the visual device and the server can be connected by signal through a wired or wireless connection, and transmit the image data in real time.

[0096] For reference, the server stores all image data at the same time and adds a timestamp to facilitate traceability analysis. The image data can be used for later machine learning when necessary.

[0097] Furthermore, the image data of the first indicator and the second indicator both include color markings. The color markings refer to the specific colors of the first indicator and the second indicator before and after the color change, and the color markings are used to determine whether the color of the specific areas of the first indicator and the second indicator has changed (whether the temperature threshold has been reached or exceeded). When the temperature reaches a certain point, the first indicator and / or the second indicator is triggered to change its color significantly. After the visual device captures the image data of these color changes, the server interprets this information as an intuitive indication of the temperature status.

[0098] Among them, the above-mentioned server determines whether the air flow temperature in the connection area is lower than the lower limit through the color mark of the first indicator; the first indicator is usually set at the return air outlet to monitor whether the return air flow temperature is lower than the lower limit of the temperature of the working interval. When the color mark of the image data of the first indicator does not change color, it means that the temperature has not reached its color change threshold, which means that the return air temperature is insufficient. The above-mentioned server marks the working interval corresponding to the first indicator as abnormal, and the server adjusts the parameters of the waste heat recovery system and reduces the air supply. By reducing the amount of air entering the annealing kiln or related working intervals, the further drop in temperature can be alleviated, helping to maintain the working interval temperature at a more appropriate level to stabilize the system operation.

[0099] Among them, the second indicator is usually set at the air exhaust port to monitor whether the temperature of the working interval exceeds the upper limit. The server determines whether the temperature of the corresponding working interval exceeds the upper limit through the image data of the second indicator; when the color mark of the image data of the second indicator changes color, it indicates that the temperature has exceeded the color change threshold of its reaction, which means that the temperature of the working interval is too high. The server marks the working interval corresponding to the second indicator as abnormal, and the server adjusts the parameters of the waste heat recovery system and increases the exhaust volume. By increasing the exhaust volume, excess heat can be discharged more effectively, the temperature of the working interval can be reduced, and safety and quality risks caused by excessive temperature can be prevented.

[0100] Based on the above embodiments, another embodiment of the present invention is that, considering that during normal operation, temperature sensors are mainly used, and image data can assist temperature sensors in linkage monitoring, therefore, the server records the number of times S300 is executed and configures a verification number. When the number of times the server records the execution of S300 reaches the verification number, the server calls the visual device to obtain the image data of the first indicator and the second indicator, and the server performs directional monitoring.

[0101] Under normal conditions, the operating temperature of the annealing kiln and the waste heat recovery system is continuously monitored through a relatively sensitive temperature sensor. The temperature sensor provides real-time data to the server, which can be used to instantly monitor the working status and adjust the parameters of the waste heat recovery system to ensure that the operating temperature remains within a safe and ideal range. At the same time, the server uses the number of times S300 is executed and the check number as an auxiliary means of image data linkage. The number of executions is recorded by the server, and the check number is used as a preset threshold to determine when the image data auxiliary triggering timing is required. When the number of executions of S300 reaches the check number, the server automatically calls the visual device to obtain the image data of the first indicator and the second indicator. Supplementary inspection of equipment wear, aging or changes in environmental factors through image data can reduce abnormal phenomena that may be missed by a single sensing mode.

[0102] It should be noted that after each image data is involved, the server recalculates the number of times S300 is executed, so that the visual devices can work periodically in coordination under normal conditions by setting the check number cycle.

[0103] refer to Figure 3 As shown, another embodiment of the present invention is a system for heat circulation of an annealing furnace, characterized in that: the system includes an infrared temperature measuring device, a temperature sensor and a server; the infrared temperature measuring device is distributed at the exit of each working interval, the infrared temperature measuring device is connected to the server signal, the infrared temperature measuring device collects the glass temperature and uploads it to the server; the server is connected to the control unit of a waste heat recovery system signal, and the server sends instructions to the control unit of the waste heat recovery system.

[0104] The server, as the center of the system, is responsible for receiving data from infrared temperature measuring equipment and temperature sensors and performing data processing and analysis. The analysis results are used to control the waste heat recovery system. At the same time, the server also stores historical data and historical instructions sent.

[0105] Among them, the infrared temperature measuring equipment is installed at the exit of each working area, and the infrared technology is used to measure the temperature of the glass non-contactly. Among them, the temperature sensors are installed in the working area of ​​the annealing furnace in an array to measure and monitor the actual ambient temperature in these areas. Among them, the above-mentioned temperature sensor is installed in the working area of ​​the annealing furnace, and the temperature sensor is connected to the server signal, and the temperature information of the working area of ​​the annealing furnace is collected by the temperature sensor; the above-mentioned temperature sensor is numbered, and the temperature sensor writes the position code of the equipment number into the temperature information to obtain the first data, and the server executes the above-mentioned data optimization and control method of the thermal cycle of the annealing furnace.

[0106] Furthermore, the system also includes visual devices, which are placed at the perspective peephole of each working area, and are connected to the server signal. The visual devices are used to collect image data and upload them to the server, which is used to analyze the image data. Among them, the visual devices are placed at the perspective peephole of each working area, and they provide visual image data inside the working area, and the image data of the visual devices are uploaded to the server, so that the image data can be used to monitor the physical conditions inside the kiln (such as the position and state of the glass), and can also jointly verify whether the linkage status between the kiln and the waste heat recovery system is normal.

[0107] The "one embodiment", "another embodiment", "embodiment", "preferred embodiment" and the like mentioned in this specification refer to the specific features, structures or characteristics described in conjunction with the embodiment as included in at least one embodiment generally described in this application. The same expression appearing in multiple places in the specification does not necessarily refer to the same embodiment. Further, when a specific feature, structure or characteristic is described in conjunction with any embodiment, it is claimed that the realization of such feature, structure or characteristic in conjunction with other embodiments also falls within the scope of the present invention.

[0108] Although the present invention is described herein with reference to a number of illustrative embodiments of the present invention, it will be appreciated that those skilled in the art may devise many other modifications and implementations that fall within the scope and spirit of the principles disclosed herein. More specifically, within the scope of the present disclosure, drawings, and claims, a variety of variations and modifications may be made to the components and / or layout of the subject combination layout. In addition to the variations and modifications made to the components and / or layout, other uses will also be apparent to those skilled in the art.

Claims

1. A data optimization and control method for the thermal cycle of an annealing furnace, which is used to detect abnormal cold end data of a float glass production line and to control the waste heat recovery system, characterized in that: The method comprises the following steps: S100, distributing a temperature sensor array in each working area of ​​the annealing furnace and connecting the array to a server signal, wherein the temperature sensor obtains temperature information of the working area and writes the position code to obtain first data; S200, the server obtains the first data of each temperature sensor to obtain a first data set; sorts the first data in the first data set according to the position code and generates a temperature change curve chart, Wherein, the position code indicates the position of the temperature sensor in the annealing furnace; S300, the server analyzes the temperature change curve to determine whether there is an abnormality in the data fluctuation; If the temperature change curve graph increases or has peaks and valleys, it indicates that the data fluctuation is abnormal, and the first data corresponding to the peaks and valleys in the curve table are extracted to obtain the node data, and S400 is executed; If the temperature change curve is decreasing and no peaks or valleys appear, it means that the data fluctuation is normal, and S500 is executed; S400, determining the working range of the node data, obtaining the difference between the glass temperature data and the node data, and adjusting the working state of the waste heat recovery system by the server, and repeatedly executing S200 to S300 until the data fluctuation is normal; S500, the server configures a first strategy, and the server controls the temperature sensor and the infrared temperature measuring device to collect data regularly under the first strategy, and after each collection, a temperature change curve is drawn again to determine whether there is an abnormality in the data fluctuation.

2. The data optimization and control method for the thermal cycle of an annealing furnace according to claim 1, characterized in that: At S300, if data fluctuation is abnormal, the server presets a safety parameter, and the safety parameter is a range value; when the server executes S400, the difference is compared with the safety parameter, and it is determined whether the difference is within the set range of the safety parameter; If the difference is within the range of safety parameters, it means that the risk is controllable and S400 is continued; If the difference is outside the range of safety parameters, it means that the risk is uncontrollable, and S400 is suspended; the server obtains node data that exceeds the safety parameters, and extracts the working area corresponding to the node data as the abnormal area. The server obtains the influence coefficient of the abnormal area and adjusts the air supply volume of the waste heat recovery system according to the influence coefficient.

3. The data optimization and control method for the thermal cycle of an annealing furnace according to claim 2, characterized in that: When executing S400, the server further obtains the influence coefficient of the abnormal area, and the obtaining of the influence coefficient includes at least the following steps: S410, the server extracts the location corresponding to the node data, obtains the coverage area of ​​the abnormal area; determines the number of temperature sensors within the coverage area, extracts the coverage impact value of the number of temperature sensors and calculates the regional impact coefficient; The calculation formula of its regional influence coefficient A is: ； Where n is the number of temperature sensors within the abnormal area, is the impact value of the jth temperature sensor coverage area within the abnormal area, is the weight of the impact value of the coverage area of ​​the jth temperature sensor; S420, determining the interval between two adjacent first data of the same temperature sensor in the abnormal area; the server calls the weighted factor table and obtains the time influence coefficient corresponding to the interval time to obtain the time influence number; The weighted factor table is a relation table comparing the interval time preset by the server with the time influence coefficient; S430, acquiring all first data of the abnormal area, acquiring difference data between the glass temperature data and each first data, and acquiring a difference influence number through the difference data; Among them, the calculation formula of the difference influence number U is: ； Where M is the number of temperature sensors within the abnormal area. is the first data of the i-th temperature sensor in the abnormal area; The glass temperature data for the abnormal area; S440, the server inputs the time impact number, the difference impact number and the abnormal area into the evaluation model for calculation to obtain the impact coefficient; Among them, the calculation formula of the influence coefficient Te is: ； Wherein, Te is the influence coefficient within the abnormal area; U is the difference influence number of the temperature sensor within the abnormal area; P is the time influence number of the sampling interval of the temperature sensor within the abnormal area; A is the regional influence coefficient of the abnormal area.

4. The data optimization and control method for the thermal cycle of an annealing furnace according to claim 3 is characterized in that: The server sets a processing list, in which several control parameters are set, and each control parameter corresponds to an influence coefficient. The server extracts the control parameters in the processing list according to the influence coefficient, and adjusts the exhaust and supply air volumes of the waste heat recovery system according to the control parameters.

5. The data optimization and control method for the thermal cycle of an annealing furnace according to claim 1, characterized in that: The return air inlet and the exhaust air inlet of the annealing furnace and the waste heat recovery system are respectively provided with a first indicator and a second indicator, and the return air inlet and the exhaust air inlet of each working interval are set to a working temperature that meets the working conditions; Wherein, the first indicator is arranged at the return air port, and the color change temperature of the first indicator is less than or equal to the lower limit of the working temperature of the return gas; The second indicator is placed at the exhaust port, and the color change temperature of the second indicator is higher than the upper limit of the working temperature of the exhaust air flow in the working interval of the annealing furnace.

6. The data optimization and control method for the thermal cycle of an annealing furnace according to claim 5, characterized in that: The return air port and the exhaust port are both provided with transparent peepholes, and the transparent peepholes are matched with visual devices. The visual devices are connected with server signals, and the server is used to call the visual devices to obtain image data of the first indicator and the second indicator.

7. The data optimization and control method for the thermal cycle of an annealing furnace according to claim 6, characterized in that: The image data of the first indicator and the second indicator both include color markings, The server determines whether the air flow temperature of the connection area is lower than the lower limit through the color mark of the first indicator; when the image data color mark of the first indicator does not change color, it means that the return air temperature is insufficient, and the server marks the working interval corresponding to the first indicator as abnormal, and the server adjusts the parameters of the waste heat recovery system and reduces the air supply volume; Among them, the server determines whether the temperature of the corresponding working interval exceeds the upper limit through the image data of the second indicator; when the color mark of the image data of the second indicator changes color, it means that the temperature of the working interval is too high, and the server marks the working interval corresponding to the second indicator as abnormal, and the server adjusts the parameters of the waste heat recovery system and increases the exhaust volume.

8. The data optimization and control method for the thermal cycle of an annealing furnace according to claim 6, characterized in that: The server records the number of times S300 is executed and configures a verification number. When the number of times S300 is executed recorded by the server reaches the verification number, the server calls the visual device to obtain image data of the first indicator and the second indicator, and the server performs directional monitoring.

9. A system for thermal circulation of an annealing furnace, characterized in that: The system includes an infrared temperature measuring device, a temperature sensor and a server; the infrared temperature measuring device is distributed at the exit of each working area, the infrared temperature measuring device is connected to the server by signal, and the infrared temperature measuring device collects the glass temperature and uploads it to the server; the server is connected to the control unit of the waste heat recovery system by signal, and the server sends instructions to the control unit of the waste heat recovery system; Wherein, the temperature sensor is installed in the working area of ​​the annealing furnace, the temperature sensor is connected to the server signal, and the temperature sensor collects the temperature information of the working area of ​​the annealing furnace; the temperature sensor performs equipment numbering, and the temperature sensor writes the position code of the equipment number into the temperature information to obtain the first data, and the server executes the data optimization and control method of the annealing furnace thermal cycle described in any one of claims 1 to 8.

10. The annealing furnace thermal cycle system according to claim 9, characterized in that: The system also includes a visual device, which is placed at the perspective peephole of each working area. The visual device is connected to the server signal. The visual device is used to collect image data and upload it to the server, which is used to analyze the image data.

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