A multi-modal interactive control method and system for an automated high and low temperature tunnel furnace

Abstract

The present invention relates to the field of tunnel furnace temperature control, and discloses a multimodal interactive control method and system for an automated high and low temperature tunnel furnace, comprising the following steps: monitoring the output temperature of the automated high and low temperature tunnel furnace, first evaluating and optimizing the output temperature monitoring results, and judging whether the target high and low temperature tunnel furnace can continue to work based on the output temperature detection results. Otherwise, it is necessary to perform real-time status analysis of the target material on the target high and low temperature tunnel furnace, and generate a processing optimization plan. The present invention can realize interactive control of the automated high and low temperature tunnel furnace through multimodal combined interaction, such as image recognition, voice recognition, output temperature display, etc., so that the automated high and low temperature tunnel furnace can realize material processing within a specified time to meet processing requirements.

Description

Technical Field

[0001] The present invention relates to the field of tunnel furnace temperature control, and in particular to a multi-modal interactive control method and system for an automated high and low temperature tunnel furnace. Background Art

[0002] The automated high and low temperature tunnel furnace is an industrial equipment used for temperature control testing, heat treatment or drying of products or materials. It can work in different temperature ranges, from low temperature to high temperature, and the whole process can be automated. This equipment is widely used in electronics, automobiles, aerospace, materials science and other fields to simulate the performance of products under different temperature conditions or to carry out necessary heat treatment processes. During the operation of the automated high and low temperature tunnel furnace, since the products or materials need to be temperature controlled, heat treated or dried, the output temperature needs to be regulated, and the output temperature needs to be regulated according to the actual situation of the products or materials. The risk factor of manually regulating the output temperature is high, which is not conducive to protecting personal safety, and the regulation efficiency is extremely low. Therefore, it is necessary to interactively control the automated high and low temperature tunnel furnace in multiple modes to realize the intelligent and safe regulation of the output temperature of the automated high and low temperature tunnel furnace. For example, through touch screens, voice and other methods, the operation method can be simplified to improve user satisfaction. According to voice prompts, visual signals, tactile feedback and other warnings to the staff, equipment abnormalities can be discovered and handled in time. Through multi-modal interactive control, the automated high and low temperature tunnel furnace can adapt to different industrial production needs more intelligently and flexibly, while improving the work efficiency and safety of operators. Summary of the invention

[0003] The present invention overcomes the deficiencies of the prior art and provides a multi-modal interactive control method and system for an automated high and low temperature tunnel furnace.

[0004] In order to achieve the above object, the technical solution adopted by the present invention is:

[0005] A first aspect of the present invention provides a multi-modal interactive control method for an automated high and low temperature tunnel furnace, comprising the following steps:

[0006] S102: evaluating the monitoring accuracy of the output temperature monitoring sensor in the automated high and low temperature tunnel furnace, and optimizing the detection accuracy of the output temperature monitoring sensor after the monitoring accuracy evaluation;

[0007] S104: real-time monitoring of the output temperature of the target high and low temperature tunnel furnace is performed through the intelligent touch screen, and a danger warning is implemented in the target high and low temperature tunnel furnace according to the output temperature curve during the real-time monitoring of the output temperature;

[0008] S106: Performing in-furnace visual monitoring and analysis on the suspended target high and low temperature tunnel furnace, and generating a processing optimization plan for the target material in the target high and low temperature tunnel furnace based on the in-furnace visual monitoring and analysis results.

[0009] Furthermore, in a preferred embodiment of the present invention, the step S102 is specifically:

[0010] The automated high and low temperature tunnel furnace that needs to be multi-modal interactively controlled is calibrated as a target high and low temperature tunnel furnace, and a sensor that can be installed in the target high and low temperature tunnel furnace to monitor the output temperature of the target high and low temperature tunnel furnace is obtained and calibrated as an output temperature monitoring sensor;

[0011] Install the output temperature monitoring sensor in the target high and low temperature tunnel furnace, obtain the material to be processed by the target high and low temperature tunnel furnace, calibrate it as the target material, and extract the target material sample from the target material;

[0012] Preset a processing test time, obtain an output temperature control system of a target high and low temperature tunnel furnace, and based on the output temperature control system of the target high and low temperature tunnel furnace, control the target high and low temperature tunnel furnace to perform processing tests on target material samples within the processing test time, and monitor the output temperature of the target high and low temperature tunnel furnace in real time during the processing test through the output temperature monitoring sensor, and calibrate it as the test output temperature of the target high and low temperature tunnel furnace;

[0013] Introduce a thermometer, and during the processing test time, use the thermometer to monitor the output temperature of the target high and low temperature tunnel furnace in real time, and calibrate it as the standard output temperature of the target high and low temperature tunnel furnace;

[0014] Based on the test output temperature and the standard output temperature of the target high and low temperature tunnel furnace, respectively, a test output temperature curve and a standard output temperature curve are constructed, and the overlap rate of the test output temperature curve and the standard output temperature curve is calculated;

[0015] Based on the overlap rate between the output temperature curve and the standard output temperature curve, the monitoring accuracy of the output temperature monitoring sensor is evaluated and optimized.

[0016] Furthermore, in a preferred embodiment of the present invention, the output temperature monitoring sensor is evaluated and optimized for monitoring accuracy based on the overlap rate between the output temperature curve and the standard output temperature curve, specifically:

[0017] The overlap rate of the output temperature curve and the standard output temperature curve is analyzed. If the overlap rate of the output temperature curve and the standard output temperature curve is not less than a preset value within the processing test time, the output temperature monitoring sensor is calibrated as a qualified monitoring sensor;

[0018] If the overlap rate between the output temperature curve and the standard output temperature curve is less than a preset value during the processing test time, the time node of the processing test time corresponding to the overlap rate between the output temperature curve and the standard output temperature curve is less than the preset value is obtained and marked as the output temperature abnormal time node;

[0019] Acquire real-time operating parameters of different components in the output temperature monitoring sensor, and convert the format of the real-time operating parameters of different components in the output temperature monitoring sensor into a time series format;

[0020] Acquire the real-time working parameters of different components in the output temperature monitoring sensor in a time series format at the output temperature abnormality time node, calibrate them into a class of real-time working time series parameters, introduce a Markov chain algorithm, and calculate the fault transfer probability of the class of real-time working time series parameters of all components through the Markov chain algorithm;

[0021] If there is a type of real-time working time series parameter of a component whose fault transfer probability is greater than a preset value, the corresponding component is calibrated as a fault output temperature monitoring sensor component, and the type of the fault output temperature monitoring sensor component is obtained;

[0022] Acquire a big data network, retrieve the types of components that can be replaced in the output temperature monitoring sensor in the big data network, mark them as replaceable component types, and if the type of the faulty output temperature monitoring sensor component is a replaceable component type, replace the faulty output temperature monitoring sensor component in the output temperature monitoring sensor to obtain a qualified monitoring sensor;

[0023] If the type of the faulty output temperature monitoring sensor element is not a replaceable element type, the output temperature monitoring sensor is directly replaced to obtain a qualified monitoring sensor.

[0024] Furthermore, in a preferred embodiment of the present invention, the step S104 is specifically:

[0025] An intelligent touch screen is introduced, and the output temperature control system of the target high and low temperature tunnel furnace is connected to the intelligent touch screen to obtain an intelligent temperature control touch screen, wherein the intelligent temperature control touch screen can display the output temperature of the target high and low temperature tunnel furnace in real time, and can set the output temperature condition of the target high and low temperature tunnel furnace and realize danger warning;

[0026] Presetting a target material processing time and obtaining a processing requirement of the target material, wherein the processing requirement of the target material determines the real-time status of the target material at different time points when the target material is processed within the target material processing time;

[0027] Combined with the processing time of the target material, the output temperature condition required for processing the target material in the target high and low temperature tunnel furnace based on the processing requirements of the target material is retrieved based on the big data network and calibrated as the target output temperature condition;

[0028] Importing a target output temperature condition into the intelligent temperature control touch screen, setting the output temperature condition as the target output temperature condition in a target high and low temperature tunnel furnace through the intelligent temperature control touch screen, and processing the target material based on the target output temperature condition through the intelligent temperature control touch screen within the target material processing time;

[0029] During the processing of the target material, the target output temperature curve range is generated according to the target output temperature conditions in the intelligent temperature control touch screen, and the real-time output temperature curve of the target high and low temperature tunnel furnace is generated in real time and calibrated as the real-time output temperature curve;

[0030] Determine whether the real-time output temperature curve is maintained within the target output temperature curve range in real time. If not, generate a danger warning in the target high and low temperature tunnel furnace through the intelligent temperature control touch screen, calculate the duration of the target high and low temperature tunnel furnace generating the danger warning, and preset the maximum duration of the target high and low temperature tunnel furnace generating the danger warning;

[0031] If the duration of the danger warning generated by the target high and low temperature tunnel furnace is not greater than the maximum duration, the high and low temperature tunnel furnace to be analyzed is controlled based on the intelligent temperature control touch screen to continue processing the target material;

[0032] If the duration of the danger warning generated by the target high and low temperature tunnel furnace is greater than the maximum duration, the target high and low temperature tunnel furnace is controlled to suspend processing of the target material based on the intelligent temperature control touch screen.

[0033] Furthermore, in a preferred embodiment of the present invention, the step S106 is specifically:

[0034] The suspended target high and low temperature tunnel furnace is calibrated as the high and low temperature tunnel furnace to be analyzed, and a machine vision system is integrated into the high and low temperature tunnel furnace to be analyzed, wherein the machine vision system includes a camera and an image processing system;

[0035] Acquire a real-time processing image of the target material in the high-low temperature tunnel furnace to be analyzed through a camera, mark it as the real-time processing image of the target material, and import the real-time processing image of the target material into an image processing system for storage;

[0036] The image processing system integrates a median filter module, an image enhancement module and an image feature extraction module, obtains a median filter window in the median filter module, traverses and slides the target material real-time processing image through the median filter window, and performs pixel value median value output processing on the target material real-time processing image in the median filter window during the sliding process, so as to obtain a class of target material real-time processing images;

[0037] Importing the real-time processed image of the first type of target material into the image enhancement module for contrast adjustment, and presetting a standard contrast, when the real-time processed image of the first type of target material is equal to the standard contrast, stopping the image enhancement module from performing contrast adjustment on the real-time processed image of the first type of target material, and obtaining the real-time processed image of the second type of target material;

[0038] Integrating an edge detection algorithm in the image feature extraction module, and extracting image features of the real-time processing images of the two types of target materials based on the edge detection algorithm to obtain feature data of the real-time processing images of the target materials;

[0039] The high and low temperature tunnel furnace to be analyzed is overhauled, and feature analysis is performed on the real-time processing image feature data of the target material. Based on the feature analysis results, a plan for optimizing the processing of the target material using the overhauled high and low temperature tunnel furnace to be analyzed is generated.

[0040] Furthermore, in a preferred embodiment of the present invention, the high and low temperature tunnel furnace to be analyzed is overhauled, and feature analysis is performed on the real-time processing image feature data of the target material. Based on the feature analysis results, a solution for optimizing the processing of the target material using the overhauled high and low temperature tunnel furnace to be analyzed is generated, specifically:

[0041] Based on the processing time of the target material, the time node when the target high and low temperature tunnel furnace pauses to process the target material is obtained and marked as the pause time node.

[0042] The real-time working parameters of each component in the high-low temperature tunnel furnace to be analyzed at the pause time node are obtained, and a Markov model is introduced. The real-time working parameters of each component in the high-low temperature tunnel furnace to be analyzed are imported into the Markov model to calculate the fault state transition probability of different components, and the dangerous fault state transition probability is preset;

[0043] The components whose fault state transition probability is greater than the dangerous fault state transition probability are calibrated as faulty high and low temperature tunnel furnace components, and the maintenance plan for the faulty high and low temperature tunnel furnace components is retrieved and output based on the big data network, so that the duration of the high and low temperature tunnel furnace to be analyzed to generate a dangerous warning is not greater than the maximum duration, and a qualified high and low temperature tunnel furnace is obtained;

[0044] Perform feature analysis on the real-time processing image feature data of the target material, and based on the feature analysis results, generate a solution for optimizing the processing of the target material using a qualified high and low temperature tunnel furnace.

[0045] Furthermore, in a preferred embodiment of the present invention, the feature analysis is performed on the real-time processing image feature data of the target material, and based on the feature analysis results, a solution for optimizing the processing of the target material using a qualified high and low temperature tunnel furnace is generated, specifically:

[0046] Based on the real-time processing image feature data of the target material, determine the real-time state of the target material in the high-low temperature tunnel furnace to be analyzed at the pause time node, wherein the real-time state of the target material in the high-low temperature tunnel furnace to be analyzed includes the properties and processing degree of the target material, and based on the processing requirements of the target material, determine the standard state threshold of the target material in the high-low temperature tunnel furnace to be analyzed at the pause time node;

[0047] If the real-time state of the target material in the high-low temperature tunnel furnace to be analyzed is less than the standard state threshold at the pause time node, the target material is calibrated as a first-class state target material; if the real-time state of the target material in the high-low temperature tunnel furnace to be analyzed is greater than the standard state threshold at the pause time node, the target material is calibrated as a second-class state target material;

[0048] For a type of state target material, the real-time state of the type of state target material and the processing requirements of the target material are combined and paired for analysis, the output temperature corresponding to the real-time state of the type of state target material is determined, calibrated as a type of output temperature, and the first processing optimization plan is set and output in a qualified high and low temperature tunnel furnace;

[0049] The first processing optimization scheme is to divide the processing requirements of the target material by combining the pause time node and a type of output temperature, obtain the output temperature conditions required for continuing processing of the target material in a type of state, and, according to the output temperature conditions required for continuing processing of the target material in a type of state, to continue processing the target material in a type of state;

[0050] For the target materials in the second state, the real-time state of the target materials in the second state is combined with the processing requirements of the target materials to perform a pairing analysis, determine the output temperature corresponding to the real-time state of the target materials in the second state, calibrate it as the second-class output temperature, and set and output the second processing optimization plan in a qualified high and low temperature tunnel furnace;

[0051] Among them, the second processing optimization plan is to combine the pause time node and the two types of output temperatures to divide the processing requirements of the target material, obtain the output temperature conditions required for continued processing of the second-state target material, and according to the output temperature conditions required for continued processing of the second-state target material, a plan for continuing processing of the second-state target material is developed.

[0052] The second aspect of the present invention further provides a multimodal interactive control system for an automated high and low temperature tunnel furnace, the multimodal interactive control system comprising a memory and a processor, the memory storing a multimodal interactive control method, and when the multimodal interactive control method is executed by the processor, the following steps are implemented:

[0053] Conduct monitoring accuracy evaluation on the output temperature monitoring sensor in the automated high and low temperature tunnel furnace, and optimize the detection accuracy of the output temperature monitoring sensor after the monitoring accuracy evaluation;

[0054] The output temperature of the target high and low temperature tunnel furnace is monitored in real time through the intelligent touch screen, and a danger warning is realized in the target high and low temperature tunnel furnace according to the output temperature curve during the real-time monitoring of the output temperature;

[0055] Conduct in-furnace visual monitoring and analysis on the suspended target high and low temperature tunnel furnace, and generate a processing optimization plan for the target material in the target high and low temperature tunnel furnace based on the in-furnace visual monitoring and analysis results.

[0056] The present invention solves the technical defects existing in the background technology, and the present invention has the following beneficial effects: the output temperature of the automated high and low temperature tunnel furnace is monitored, the output temperature monitoring results are first evaluated and optimized, and the target high and low temperature tunnel furnace is judged whether it can continue to work according to the output temperature detection results. Otherwise, it is necessary to perform real-time status analysis of the target material on the target high and low temperature tunnel furnace, and generate a processing optimization plan. The present invention can realize interactive control of the automated high and low temperature tunnel furnace through multi-modal combined interaction, such as image recognition, voice recognition, output temperature display and other aspects, so that the automated high and low temperature tunnel furnace can realize material processing within the specified time to meet the processing requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, drawings of other embodiments can be obtained based on these drawings without paying creative work.

[0058] Figure 1 A flow chart showing a multi-modal interactive control method for an automated high and low temperature tunnel furnace is shown;

[0059] Figure 2 A flow chart showing a method for visually monitoring and analyzing a target high and low temperature tunnel furnace, overhauling the furnace, and generating a processing optimization plan for the target material;

[0060] Figure 3 The program view of a multi-modal interactive control system of an automated high and low temperature tunnel furnace is shown. DETAILED DESCRIPTION

[0061] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0062] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0063] Figure 1 A flow chart of a multi-modal interactive control method for an automated high and low temperature tunnel furnace is shown, comprising the following steps:

[0064] S102: evaluating the monitoring accuracy of the output temperature monitoring sensor in the automated high and low temperature tunnel furnace, and optimizing the detection accuracy of the output temperature monitoring sensor after the monitoring accuracy evaluation;

[0065] S104: real-time monitoring of the output temperature of the target high and low temperature tunnel furnace is performed through the intelligent touch screen, and a danger warning is implemented in the target high and low temperature tunnel furnace according to the output temperature curve during the real-time monitoring of the output temperature;

[0066] S106: Performing in-furnace visual monitoring and analysis on the suspended target high and low temperature tunnel furnace, and generating a processing optimization plan for the target material in the target high and low temperature tunnel furnace based on the in-furnace visual monitoring and analysis results.

[0067] Furthermore, in a preferred embodiment of the present invention, the step S102 is specifically:

[0068] The automated high and low temperature tunnel furnace that needs to be multi-modal interactively controlled is calibrated as a target high and low temperature tunnel furnace, and a sensor that can be installed in the target high and low temperature tunnel furnace to monitor the output temperature of the target high and low temperature tunnel furnace is obtained and calibrated as an output temperature monitoring sensor;

[0069] Install the output temperature monitoring sensor in the target high and low temperature tunnel furnace, obtain the material to be processed by the target high and low temperature tunnel furnace, calibrate it as the target material, and extract the target material sample from the target material;

[0070] Preset a processing test time, obtain an output temperature control system of a target high and low temperature tunnel furnace, and based on the output temperature control system of the target high and low temperature tunnel furnace, control the target high and low temperature tunnel furnace to perform processing tests on target material samples within the processing test time, and monitor the output temperature of the target high and low temperature tunnel furnace in real time during the processing test through the output temperature monitoring sensor, and calibrate it as the test output temperature of the target high and low temperature tunnel furnace;

[0071] A thermometer is introduced, and during the processing test time, the output temperature of the target high and low temperature tunnel furnace is monitored in real time by the thermometer and calibrated as the standard output temperature of the target high and low temperature tunnel furnace.

[0072] It should be noted that in order to perform multimodal interactive control on the automated high and low temperature tunnel furnace, it is first necessary to test the detection accuracy of the sensor of the automated high and low temperature tunnel furnace. If the detection accuracy of the sensor is abnormal, it will snowball in the subsequent multimodal interactive control of the tunnel furnace, gradually amplifying the detection accuracy, resulting in a decrease in the efficiency of the multimodal interactive control of the tunnel furnace, affecting the control effect and personal safety. The target material is the material that needs to be processed. The processing mainly involves temperature control, heat treatment and drying of the material. Therefore, a sample of the target material can be extracted and processed and tested on the sample of the target material during the processing test time, so as to obtain the output temperature of the tunnel furnace on the output temperature detection sensor during the processing test. The output temperature during the processing test is measured by a thermometer, which is standard, that is, the standard output temperature of the target high and low temperature tunnel furnace is obtained. By comparing the standard output temperature of the target high and low temperature tunnel furnace with the test output temperature of the target high and low temperature tunnel furnace, it can be judged whether the monitoring effect of the sensor is accurate.

[0073] Furthermore, in a preferred embodiment of the present invention, the output temperature monitoring sensor is evaluated and optimized for monitoring accuracy based on the overlap rate between the output temperature curve and the standard output temperature curve, specifically:

[0074] The overlap rate of the output temperature curve and the standard output temperature curve is analyzed. If the overlap rate of the output temperature curve and the standard output temperature curve is not less than a preset value within the processing test time, the output temperature monitoring sensor is calibrated as a qualified monitoring sensor;

[0075] If the overlap rate between the output temperature curve and the standard output temperature curve is less than a preset value during the processing test time, the time node of the processing test time corresponding to the overlap rate between the output temperature curve and the standard output temperature curve is less than the preset value is obtained and marked as the output temperature abnormal time node;

[0076] Acquire real-time operating parameters of different components in the output temperature monitoring sensor, and convert the format of the real-time operating parameters of different components in the output temperature monitoring sensor into a time series format;

[0077] Acquire the real-time working parameters of different components in the output temperature monitoring sensor in a time series format at the output temperature abnormality time node, calibrate them into a class of real-time working time series parameters, introduce a Markov chain algorithm, and calculate the fault transfer probability of the class of real-time working time series parameters of all components through the Markov chain algorithm;

[0078] If there is a type of real-time working time series parameter of a component whose fault transfer probability is greater than a preset value, the corresponding component is calibrated as a fault output temperature monitoring sensor component, and the type of the fault output temperature monitoring sensor component is obtained;

[0079] Acquire a big data network, retrieve the types of components that can be replaced in the output temperature monitoring sensor in the big data network, mark them as replaceable component types, and if the type of the faulty output temperature monitoring sensor component is a replaceable component type, replace the faulty output temperature monitoring sensor component in the output temperature monitoring sensor to obtain a qualified monitoring sensor;

[0080] If the type of the faulty output temperature monitoring sensor element is not a replaceable element type, the output temperature monitoring sensor is directly replaced to obtain a qualified monitoring sensor.

[0081] It should be noted that the comparison between the standard output temperature of the target high and low temperature tunnel furnace and the test output temperature of the target high and low temperature tunnel furnace requires converting the two into temperature curves before comparison, because after conversion to the temperature curve, the temperature change can be intuitively understood and it is easier to compare. According to the temperature curve, the overlap rate is calculated. The larger the overlap rate, the higher the monitoring accuracy of the output temperature monitoring sensor. When the overlap rate is lower than the preset value, it proves that the monitoring accuracy of the output temperature monitoring sensor is not always accurate, and there will be abnormal detection accuracy at the time node, so the output temperature abnormal time node is obtained. In the output temperature abnormal time node, the monitoring sensor may have a fault, resulting in low monitoring accuracy. The format of the working parameters of the components in the output temperature monitoring sensor is converted into a time series format, the purpose is to find the corresponding component working parameters at the output temperature abnormal time node when analyzing the components. The low monitoring accuracy of the output temperature monitoring sensor can be solved by repairing or replacing the components. First, it is necessary to determine the faulty components. The Markov chain algorithm is used to calculate the fault transfer probability of a class of real-time working time series parameters of different components. Among them, the core assumption of the Markov chain algorithm is that the future state of the system depends only on the current state, but not on the past state. For the working parameters of different components, the working parameter states of the components include normal working state, fault state and other intermediate states. The transition probability describes the possibility of transitioning from one state to another. The greater the transition probability, the greater the possibility of transitioning from one state to another, and the greater the probability of failure. Therefore, after calculating the fault transition probability of a class of real-time working time series parameters of all components, the faulty output temperature monitoring sensor component can be determined. At the same time, it is necessary to analyze the types of faulty output temperature monitoring sensor components to determine whether they can be replaced. If they can be replaced, the components should be replaced. If not, the sensors should be replaced directly.

[0082] Furthermore, in a preferred embodiment of the present invention, the step S104 is specifically:

[0083] An intelligent touch screen is introduced, and the output temperature control system of the target high and low temperature tunnel furnace is connected to the intelligent touch screen to obtain an intelligent temperature control touch screen, wherein the intelligent temperature control touch screen can display the output temperature of the target high and low temperature tunnel furnace in real time, and can set the output temperature condition of the target high and low temperature tunnel furnace and realize danger warning;

[0084] Presetting a target material processing time and obtaining a processing requirement of the target material, wherein the processing requirement of the target material determines the real-time status of the target material at different time points when the target material is processed within the target material processing time;

[0085] Combined with the processing time of the target material, the output temperature condition required for processing the target material in the target high and low temperature tunnel furnace based on the processing requirements of the target material is retrieved based on the big data network and calibrated as the target output temperature condition;

[0086] Importing a target output temperature condition into the intelligent temperature control touch screen, setting the output temperature condition as the target output temperature condition in a target high and low temperature tunnel furnace through the intelligent temperature control touch screen, and processing the target material based on the target output temperature condition through the intelligent temperature control touch screen within the target material processing time;

[0087] During the processing of the target material, the target output temperature curve range is generated according to the target output temperature conditions in the intelligent temperature control touch screen, and the real-time output temperature curve of the target high and low temperature tunnel furnace is generated in real time and calibrated as the real-time output temperature curve;

[0088] Determine whether the real-time output temperature curve is maintained within the target output temperature curve range in real time. If not, generate a danger warning in the target high and low temperature tunnel furnace through the intelligent temperature control touch screen, calculate the duration of the target high and low temperature tunnel furnace generating the danger warning, and preset the maximum duration of the target high and low temperature tunnel furnace generating the danger warning;

[0089] If the duration of the danger warning generated by the target high and low temperature tunnel furnace is not greater than the maximum duration, the high and low temperature tunnel furnace to be analyzed is controlled based on the intelligent temperature control touch screen to continue processing the target material;

[0090] If the duration of the danger warning generated by the target high and low temperature tunnel furnace is greater than the maximum duration, the target high and low temperature tunnel furnace is controlled to suspend processing of the target material based on the intelligent temperature control touch screen.

[0091] It should be noted that it is necessary to introduce an intelligent touch screen for visual display of the output temperature, and connect the intelligent touch screen to the control system so that the intelligent touch screen can set the output temperature conditions for the tunnel furnace and realize the purpose of danger alarm on the intelligent touch screen when the output temperature is abnormal. The intelligent touch screen can assist in judging the efficiency and effect of processing the target material in the tunnel furnace. Different types of target materials have different processing requirements. Within a certain processing time, according to the processing requirements, it can be known that the real-time status of the target material at different processing time points is also different. For example, if there is no abnormality in the tunnel furnace, the stamping effect at one minute and the stamping effect at two minutes should be constant when stamping the iron sheet. And the processing requirements of the target material are different, and the output temperature at different time points in the tunnel furnace is also different, so it is necessary to obtain the target output temperature condition, and import the target output temperature condition through the intelligent temperature control touch screen for analysis to determine whether the output temperature of the target high and low temperature tunnel furnace under the target output temperature condition is less similar to the standard value. Through the target output temperature condition, the target output temperature curve range is constructed, and the real-time output temperature curve is obtained. By judging the relationship between the real-time output temperature curve and the target output temperature curve range, it can be judged whether the target high and low temperature tunnel furnace has an abnormal output temperature. The abnormal output temperature may be caused by incomplete combustion in the furnace, abnormal power supply voltage, etc. If the duration is short, there is no need to repair the tunnel furnace, because it is normal for the tunnel furnace to be abnormal. If it does not last long, there is no need to correct the fault of the tunnel furnace. If it lasts for a long time, the tunnel furnace needs to be suspended to prevent it from processing all the time and causing the target material to deteriorate.

[0092] Figure 2 A flow chart of a method for visually monitoring and analyzing a target high and low temperature tunnel furnace, overhauling the furnace, and generating a processing optimization plan for a target material is shown, including the following steps:

[0093] S202: Acquire a real-time processing image of a target material through a machine vision system, and perform image processing on the real-time processing image of the target material to obtain feature data of the real-time processing image of the target material.

[0094] S204: inspecting and repairing the high and low temperature tunnel furnace to be analyzed, and obtaining a qualified high and low temperature tunnel furnace;

[0095] S206: Perform feature analysis on the real-time processing image feature data of the target material, and based on the feature analysis results, generate a solution for optimizing the processing of the target material using a qualified high and low temperature tunnel furnace.

[0096] Furthermore, in a preferred embodiment of the present invention, the step S202 is specifically:

[0097] The suspended target high and low temperature tunnel furnace is calibrated as the high and low temperature tunnel furnace to be analyzed, and a machine vision system is integrated into the high and low temperature tunnel furnace to be analyzed, wherein the machine vision system includes a camera and an image processing system;

[0098] Acquire a real-time processing image of the target material in the high-low temperature tunnel furnace to be analyzed through a camera, mark it as the real-time processing image of the target material, and import the real-time processing image of the target material into an image processing system for storage;

[0099] The image processing system integrates a median filter module, an image enhancement module and an image feature extraction module, obtains a median filter window in the median filter module, traverses and slides the target material real-time processing image through the median filter window, and performs pixel value median value output processing on the target material real-time processing image in the median filter window during the sliding process, so as to obtain a class of target material real-time processing images;

[0100] Importing the real-time processed image of the first type of target material into the image enhancement module for contrast adjustment, and presetting a standard contrast, when the real-time processed image of the first type of target material is equal to the standard contrast, stopping the image enhancement module from performing contrast adjustment on the real-time processed image of the first type of target material, and obtaining the real-time processed image of the second type of target material;

[0101] An edge detection algorithm is integrated in the image feature extraction module, and based on the edge detection algorithm, image features are extracted from the real-time processing images of two types of target materials to obtain feature data of the real-time processing images of the target materials.

[0102] It should be noted that it is necessary to conduct real-time state analysis on the target material in the high and low temperature tunnel furnace to be analyzed, and determine its state after the equipment is paused, so as to formulate a plan for secondary processing of the target material to avoid waste of the target material. To determine the real-time state of the target material, a machine vision system can be integrated in the high and low temperature tunnel furnace to be analyzed, and the current image of the target material can be obtained through the camera in the machine vision system, and the image can be preprocessed. Among them, image preprocessing includes filtering, enhancing and feature extraction processing of the image. Image filtering can use the median filtering method, and the image is traversed and slid through the median filtering window in the median filtering module. During the sliding process, since the median filtering method is to select the median of all pixel values ​​in the window, the pixel value is taken as the median to achieve the filtering purpose. The filtered image may have dark locations. When observing or extracting feature data, it may be dark, resulting in incomplete extraction of feature data, so the image needs to be enhanced. Among them, the image enhancement method is to enhance the contrast of the image. After contrast enhancement, the edge detection algorithm is used to obtain the real-time processing image feature data of the target material. Among them, the edge detection algorithm calculates the image gradient and compares and analyzes it with the standard gradient threshold to segment the image into foreground image and background image, and obtains the characteristic data of the target material in advance based on the shape characteristics of the foreground image.

[0103] Furthermore, in a preferred embodiment of the present invention, the step S204 is specifically:

[0104] Based on the processing time of the target material, the time node when the target high and low temperature tunnel furnace pauses to process the target material is obtained and marked as the pause time node.

[0105] The real-time working parameters of each component in the high-low temperature tunnel furnace to be analyzed at the pause time node are obtained, and a Markov model is introduced. The real-time working parameters of each component in the high-low temperature tunnel furnace to be analyzed are imported into the Markov model to calculate the fault state transition probability of different components, and the dangerous fault state transition probability is preset;

[0106] The components whose fault state transition probability is greater than the dangerous fault state transition probability are calibrated as faulty high and low temperature tunnel furnace components, and the maintenance plan for the faulty high and low temperature tunnel furnace components is retrieved and output based on the big data network, so that the duration of the high and low temperature tunnel furnace to be analyzed to generate a dangerous warning is not greater than the maximum duration, and a qualified high and low temperature tunnel furnace is obtained.

[0107] It should be noted that the reason for the abnormal real-time state of the target material in the high and low temperature tunnel furnace to be analyzed is that the temperature output in the furnace is inconsistent with the temperature adapted to the target material at different times. The reason for the above result is that there is a fault in the target high and low temperature tunnel furnace, and the high and low temperature tunnel furnace to be analyzed needs to be repaired. The pause time node is the time node occupied by the tunnel furnace during the processing time of the target material when it is suspended. At this time, the tunnel furnace is in an abnormal fault state, including but not limited to output temperature that is too high or too low, unable to output temperature, etc. Using the Markov model is equivalent to using the Markov chain algorithm, which can calculate the probability of dangerous fault state transition of the real-time working parameters of each component in the high and low temperature tunnel furnace to be analyzed, and classify the components based on the probability of dangerous fault state transition of the real-time working parameters of the components to obtain faulty high and low temperature tunnel furnace components. And continue to retrieve the maintenance plan output of the faulty high and low temperature tunnel furnace components based on the big data network to obtain a qualified high and low temperature tunnel furnace.

[0108] Furthermore, in a preferred embodiment of the present invention, the step S206 is specifically:

[0109] Based on the real-time processing image feature data of the target material, determine the real-time state of the target material in the high-low temperature tunnel furnace to be analyzed at the pause time node, wherein the real-time state of the target material in the high-low temperature tunnel furnace to be analyzed includes the properties and processing degree of the target material, and based on the processing requirements of the target material, determine the standard state threshold of the target material in the high-low temperature tunnel furnace to be analyzed at the pause time node;

[0110] If the real-time state of the target material in the high-low temperature tunnel furnace to be analyzed is less than the standard state threshold at the pause time node, the target material is calibrated as a first-class state target material; if the real-time state of the target material in the high-low temperature tunnel furnace to be analyzed is greater than the standard state threshold at the pause time node, the target material is calibrated as a second-class state target material;

[0111] For a type of state target material, the real-time state of the type of state target material and the processing requirements of the target material are combined and paired for analysis, the output temperature corresponding to the real-time state of the type of state target material is determined, calibrated as a type of output temperature, and the first processing optimization plan is set and output in a qualified high and low temperature tunnel furnace;

[0112] The first processing optimization scheme is to divide the processing requirements of the target material by combining the pause time node and a type of output temperature, obtain the output temperature conditions required for continuing processing of the target material in a type of state, and, according to the output temperature conditions required for continuing processing of the target material in a type of state, to continue processing the target material in a type of state;

[0113] For the target materials in the second state, the real-time state of the target materials in the second state is combined with the processing requirements of the target materials to perform a pairing analysis, determine the output temperature corresponding to the real-time state of the target materials in the second state, calibrate it as the second-class output temperature, and set and output the second processing optimization plan in a qualified high and low temperature tunnel furnace;

[0114] Among them, the second processing optimization plan is to combine the pause time node and the two types of output temperatures to divide the processing requirements of the target material, obtain the output temperature conditions required for continued processing of the second-state target material, and according to the output temperature conditions required for continued processing of the second-state target material, a plan for continuing processing of the second-state target material is developed.

[0115] It should be noted that when the tunnel furnace pauses processing the target material, the real-time state of the target material at this time is different from the standard state corresponding to the current time node in the processing requirements. In the pause time node, if the standard state of the target material is preset to 1, the real-time state of the target material may be less than 1 or greater than 1. Among them, the real-time state of the target material is less than 1, which proves that the processing degree of the target material is insufficient when the processing time reaches the pause time node, while the real-time state of the target material is greater than 1, which is the opposite, proving that the processing degree of the target material is too high when the processing time reaches the pause time node. Therefore, based on the relationship between the real-time state of the target material in the high and low temperature tunnel furnace to be analyzed at the pause time node and the standard state threshold, the target material is classified. After the target material is classified, it is necessary to find the output temperature of the tunnel furnace paired with the real-time state in the processing requirements according to the real-time state of the target material, and generate the first processing optimization plan and the second processing optimization plan accordingly.

[0116] In addition, the multi-modal interactive control method of the automated high and low temperature tunnel furnace further includes the following steps:

[0117] After the first processing optimization scheme and the second processing optimization scheme are obtained, the first processing optimization scheme and the second processing optimization scheme are imported into the smart touch screen, and a voice control sensor is installed in the smart touch screen, wherein the voice control sensor can receive and perform voice signal recognition on a voice signal with a control instruction, and transmit the voice signal recognition result to the smart touch screen, so that the smart touch screen executes the control instruction;

[0118] Marking a staff member who can perform voice control on the smart touch screen as a target staff member, and pre-recording the voiceprint information of the target staff member in the voice recognition system and marking it as the target voiceprint information;

[0119] Based on the big data network, all text information for controlling the device status of the intelligent touch screen is retrieved, wherein the text information includes text information for controlling the opening, closing, pausing and adjusting the output temperature of the target high and low temperature tunnel furnace;

[0120] Based on the voice control sensor, voice signals around the qualified high and low temperature tunnel furnace are received and calibrated as a type of voice signal, and Gaussian filtering is performed on the type of voice signal, and a Mel filter is introduced to perform frame processing on the type of voice signal after Gaussian filtering, and a Hamming window function is applied to the type of voice signals after Gaussian filtering of different frames for windowing, and at the same time, the type of voice signals after Gaussian filtering of different frames after windowing are combined for frame processing to obtain a type II voice signal;

[0121] Acquire the voiceprint information of the second-class voice signal, and calculate the Mahalanobis distance between the voiceprint information of the second-class voice signal and the target voiceprint information, and mark it as the first-class Mahalanobis distance. When the first-class Mahalanobis distance is greater than a preset value, the corresponding second-class voice signal is discarded. When the first-class Mahalanobis distance is less than the preset value, the corresponding second-class voice signal is marked as the target voice signal;

[0122] A speech-text recognition device is introduced, a target speech signal is imported into the speech-text recognition device, text recognition and extraction are performed on the target speech signal to obtain text information of the target speech signal, and the Mahalanobis distance between the text information of the target speech signal and all text information for controlling the device state of the intelligent touch screen is calculated, and calibrated as a second-class Mahalanobis distance;

[0123] When the second-class Mahalanobis distance is less than a preset value, the intelligent touch screen is controlled to execute corresponding control instructions according to the target voice signal text information.

[0124] It should be noted that, during the execution of the first processing optimization scheme or the second processing optimization scheme, if there is a need to start, pause, and other operations on the tunnel furnace, the tunnel furnace can be controlled to execute corresponding instructions by recognizing voice. The advantage is that it can be kept away from the tunnel furnace to prevent the temperature output by the tunnel furnace from causing harm to the human body. Therefore, a voice control sensor is installed on the intelligent touch screen of the tunnel furnace to recognize voice signals. Among them, only staff can perform voice control on the intelligent touch screen, so the voiceprint information of the staff is obtained, and it is determined whether the voiceprint information of the collected voice information is the voiceprint information of the staff. The judgment method is to calculate the Mahalanobis distance between the voiceprint information. The voice information contains a variety of control instructions, including but not limited to controlling the target high and low temperature tunnel furnace to start, close, pause, and adjust the output temperature, analyze the collected voice signal, perform voice text recognition on it, and control the tunnel furnace to execute corresponding instructions on the intelligent touch screen according to the voice text recognition information. Before the voice is collected and recognized, since the voice may be in a noisy area, the voice information needs to be filtered. First, use Gaussian filtering to filter, and then divide the voice signal into frames and add windows based on the Mel filter. Framing can analyze the voice signal more accurately, and windowing can improve the parts missed during voice signal collection. Finally, the voice signal after windowing and framing is combined to obtain voice information that can be used for voiceprint analysis and text recognition.

[0125] like Figure 3 As shown, the second aspect of the present invention further provides a multimodal interactive control system for an automated high and low temperature tunnel furnace, the multimodal interactive control system comprising a memory 31 and a processor 32, the memory 31 storing a multimodal interactive control method, and when the multimodal interactive control method is executed by the processor 32, the following steps are implemented:

[0126] Conduct monitoring accuracy evaluation on the output temperature monitoring sensor in the automated high and low temperature tunnel furnace, and optimize the detection accuracy of the output temperature monitoring sensor after the monitoring accuracy evaluation;

[0127] The output temperature of the target high and low temperature tunnel furnace is monitored in real time through the intelligent touch screen, and a danger warning is realized in the target high and low temperature tunnel furnace according to the output temperature curve during the real-time monitoring of the output temperature;

[0128] Conduct in-furnace visual monitoring and analysis on the suspended target high and low temperature tunnel furnace, and generate a processing optimization plan for the target material in the target high and low temperature tunnel furnace based on the in-furnace visual monitoring and analysis results.

[0129] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A multi-modal interactive control method for an automated high and low temperature tunnel furnace, characterized in that: The following steps are involved: S102: evaluating the monitoring accuracy of the output temperature monitoring sensor in the automated high and low temperature tunnel furnace, and optimizing the detection accuracy of the output temperature monitoring sensor after the monitoring accuracy evaluation; S104: real-time monitoring of the output temperature of the target high and low temperature tunnel furnace is performed through the intelligent touch screen, and a danger warning is implemented in the target high and low temperature tunnel furnace according to the output temperature curve during the real-time monitoring of the output temperature; S106: Performing visual monitoring and analysis on the suspended target high and low temperature tunnel furnace, and generating a processing optimization plan for the target material by the target high and low temperature tunnel furnace based on the results of the visual monitoring and analysis on the furnace; Wherein, the S106 is specifically: The suspended target high and low temperature tunnel furnace is calibrated as the high and low temperature tunnel furnace to be analyzed, and a machine vision system is integrated into the high and low temperature tunnel furnace to be analyzed, wherein the machine vision system includes a camera and an image processing system; Acquire a real-time processing image of the target material in the high-low temperature tunnel furnace to be analyzed through a camera, mark it as the real-time processing image of the target material, and import the real-time processing image of the target material into an image processing system for storage; The image processing system integrates a median filter module, an image enhancement module and an image feature extraction module, obtains a median filter window in the median filter module, traverses and slides the target material real-time processing image through the median filter window, and performs pixel value median value output processing on the target material real-time processing image in the median filter window during the sliding process, so as to obtain a class of target material real-time processing images; Importing the real-time processed image of the first type of target material into the image enhancement module for contrast adjustment, and presetting a standard contrast, when the real-time processed image of the first type of target material is equal to the standard contrast, stopping the image enhancement module from performing contrast adjustment on the real-time processed image of the first type of target material, and obtaining the real-time processed image of the second type of target material; Integrating an edge detection algorithm in the image feature extraction module, and extracting image features of the real-time processing images of the two types of target materials based on the edge detection algorithm to obtain feature data of the real-time processing images of the target materials; The high and low temperature tunnel furnace to be analyzed is overhauled, and feature analysis is performed on the real-time processing image feature data of the target material. Based on the feature analysis results, a plan for optimizing the processing of the target material using the overhauled high and low temperature tunnel furnace to be analyzed is generated.

2. According to the multi-modal interactive control method of an automated high and low temperature tunnel furnace as described in claim 1, it is characterized in that: The S102 is specifically: The automated high and low temperature tunnel furnace that needs to be multi-modal interactively controlled is calibrated as a target high and low temperature tunnel furnace, and a sensor that can be installed in the target high and low temperature tunnel furnace to monitor the output temperature of the target high and low temperature tunnel furnace is obtained and calibrated as an output temperature monitoring sensor; Install the output temperature monitoring sensor in the target high and low temperature tunnel furnace, obtain the material to be processed by the target high and low temperature tunnel furnace, calibrate it as the target material, and extract the target material sample from the target material; Preset a processing test time, obtain an output temperature control system of a target high and low temperature tunnel furnace, and based on the output temperature control system of the target high and low temperature tunnel furnace, control the target high and low temperature tunnel furnace to perform processing tests on target material samples within the processing test time, and monitor the output temperature of the target high and low temperature tunnel furnace in real time during the processing test through the output temperature monitoring sensor, and calibrate it as the test output temperature of the target high and low temperature tunnel furnace; Introduce a thermometer, and during the processing test time, use the thermometer to monitor the output temperature of the target high and low temperature tunnel furnace in real time, and calibrate it as the standard output temperature of the target high and low temperature tunnel furnace; Based on the test output temperature and the standard output temperature of the target high and low temperature tunnel furnace, respectively, a test output temperature curve and a standard output temperature curve are constructed, and the overlap rate of the test output temperature curve and the standard output temperature curve is calculated; Based on the overlap rate between the output temperature curve and the standard output temperature curve, the monitoring accuracy of the output temperature monitoring sensor is evaluated and optimized.

3. According to the multi-modal interactive control method of an automated high and low temperature tunnel furnace as described in claim 2, it is characterized in that: Based on the overlap rate between the output temperature curve and the standard output temperature curve, the output temperature monitoring sensor is evaluated for monitoring accuracy and optimized, specifically: The overlap rate of the output temperature curve and the standard output temperature curve is analyzed. If the overlap rate of the output temperature curve and the standard output temperature curve is not less than a preset value within the processing test time, the output temperature monitoring sensor is calibrated as a qualified monitoring sensor; If the overlap rate between the output temperature curve and the standard output temperature curve is less than a preset value during the processing test time, the time node of the processing test time corresponding to the overlap rate between the output temperature curve and the standard output temperature curve is less than the preset value is obtained and marked as the output temperature abnormal time node; Acquire real-time operating parameters of different components in the output temperature monitoring sensor, and convert the format of the real-time operating parameters of different components in the output temperature monitoring sensor into a time series format; Acquire the real-time working parameters of different components in the output temperature monitoring sensor in a time series format at the output temperature abnormality time node, calibrate them into a class of real-time working time series parameters, introduce a Markov chain algorithm, and calculate the fault transfer probability of the class of real-time working time series parameters of all components through the Markov chain algorithm; If there is a type of real-time working time series parameter of a component whose fault transfer probability is greater than a preset value, the corresponding component is calibrated as a fault output temperature monitoring sensor component, and the type of the fault output temperature monitoring sensor component is obtained; Acquire a big data network, retrieve the types of components that can be replaced in the output temperature monitoring sensor in the big data network, mark them as replaceable component types, and if the type of the faulty output temperature monitoring sensor component is a replaceable component type, replace the faulty output temperature monitoring sensor component in the output temperature monitoring sensor to obtain a qualified monitoring sensor; If the type of the faulty output temperature monitoring sensor element is not a replaceable element type, the output temperature monitoring sensor is directly replaced to obtain a qualified monitoring sensor.

4. According to the multi-modal interactive control method of an automated high and low temperature tunnel furnace as described in claim 1, it is characterized in that: The S104 is specifically: An intelligent touch screen is introduced, and the output temperature control system of the target high and low temperature tunnel furnace is connected to the intelligent touch screen to obtain an intelligent temperature control touch screen, wherein the intelligent temperature control touch screen can display the output temperature of the target high and low temperature tunnel furnace in real time, and can set the output temperature condition of the target high and low temperature tunnel furnace and realize danger warning; Presetting a target material processing time and obtaining a processing requirement of the target material, wherein the processing requirement of the target material determines the real-time status of the target material at different time points when the target material is processed within the target material processing time; Combined with the processing time of the target material, the output temperature condition required for processing the target material in the target high and low temperature tunnel furnace based on the processing requirements of the target material is retrieved based on the big data network and calibrated as the target output temperature condition; Importing a target output temperature condition into the intelligent temperature control touch screen, setting the output temperature condition as the target output temperature condition in a target high and low temperature tunnel furnace through the intelligent temperature control touch screen, and processing the target material based on the target output temperature condition through the intelligent temperature control touch screen within the target material processing time; During the processing of the target material, the target output temperature curve range is generated according to the target output temperature conditions in the intelligent temperature control touch screen, and the real-time output temperature curve of the target high and low temperature tunnel furnace is generated in real time and calibrated as the real-time output temperature curve; Determine whether the real-time output temperature curve is maintained within the target output temperature curve range in real time. If not, generate a danger warning in the target high and low temperature tunnel furnace through the intelligent temperature control touch screen, calculate the duration of the target high and low temperature tunnel furnace generating the danger warning, and preset the maximum duration of the target high and low temperature tunnel furnace generating the danger warning; If the duration of the danger warning generated by the target high and low temperature tunnel furnace is not greater than the maximum duration, the high and low temperature tunnel furnace to be analyzed is controlled based on the intelligent temperature control touch screen to continue processing the target material; If the duration of the danger warning generated by the target high and low temperature tunnel furnace is greater than the maximum duration, the target high and low temperature tunnel furnace is controlled to suspend processing of the target material based on the intelligent temperature control touch screen.

5. According to the multi-modal interactive control method of an automated high and low temperature tunnel furnace as described in claim 1, it is characterized in that: The high and low temperature tunnel furnace to be analyzed is overhauled, and feature analysis is performed on the real-time processing image feature data of the target material. Based on the feature analysis results, a solution for processing and optimizing the target material using the overhauled high and low temperature tunnel furnace to be analyzed is generated, specifically: Based on the processing time of the target material, the time node when the target high and low temperature tunnel furnace pauses to process the target material is obtained and marked as the pause time node. The real-time working parameters of each component in the high-low temperature tunnel furnace to be analyzed at the pause time node are obtained, and a Markov model is introduced. The real-time working parameters of each component in the high-low temperature tunnel furnace to be analyzed are imported into the Markov model to calculate the fault state transition probability of different components, and the dangerous fault state transition probability is preset; The components whose fault state transition probability is greater than the dangerous fault state transition probability are calibrated as faulty high and low temperature tunnel furnace components, and the maintenance plan for the faulty high and low temperature tunnel furnace components is retrieved and output based on the big data network, so that the duration of the high and low temperature tunnel furnace to be analyzed to generate a dangerous warning is not greater than the maximum duration, and a qualified high and low temperature tunnel furnace is obtained; Perform feature analysis on the real-time processing image feature data of the target material, and based on the feature analysis results, generate a solution for optimizing the processing of the target material using a qualified high and low temperature tunnel furnace.

6. According to the multi-modal interactive control method of an automated high and low temperature tunnel furnace as described in claim 5, it is characterized in that: The feature analysis is performed on the real-time processing image feature data of the target material, and based on the feature analysis results, a solution for optimizing the processing of the target material using a qualified high and low temperature tunnel furnace is generated, specifically: Based on the real-time processing image feature data of the target material, determine the real-time state of the target material in the high-low temperature tunnel furnace to be analyzed at the pause time node, wherein the real-time state of the target material in the high-low temperature tunnel furnace to be analyzed includes the properties and processing degree of the target material, and based on the processing requirements of the target material, determine the standard state threshold of the target material in the high-low temperature tunnel furnace to be analyzed at the pause time node; If the real-time state of the target material in the high-low temperature tunnel furnace to be analyzed is less than the standard state threshold at the pause time node, the target material is calibrated as a first-class state target material; if the real-time state of the target material in the high-low temperature tunnel furnace to be analyzed is greater than the standard state threshold at the pause time node, the target material is calibrated as a second-class state target material; For a type of state target material, the real-time state of the type of state target material and the processing requirements of the target material are combined and paired for analysis, the output temperature corresponding to the real-time state of the type of state target material is determined, calibrated as a type of output temperature, and the first processing optimization plan is set and output in a qualified high and low temperature tunnel furnace; The first processing optimization scheme is to divide the processing requirements of the target material by combining the pause time node and a type of output temperature, obtain the output temperature conditions required for continuing processing of the target material in a type of state, and, according to the output temperature conditions required for continuing processing of the target material in a type of state, to continue processing the target material in a type of state; For the target materials in the second state, the real-time state of the target materials in the second state is combined with the processing requirements of the target materials to perform a pairing analysis, determine the output temperature corresponding to the real-time state of the target materials in the second state, calibrate it as the second-class output temperature, and set and output the second processing optimization plan in a qualified high and low temperature tunnel furnace; Among them, the second processing optimization plan is to combine the pause time node and the two types of output temperatures to divide the processing requirements of the target material, obtain the output temperature conditions required for continued processing of the second-state target material, and according to the output temperature conditions required for continued processing of the second-state target material, a plan for continuing processing of the second-state target material is developed.

7. A multi-modal interactive control system for an automated high and low temperature tunnel furnace, characterized in that: The multimodal interaction control system includes a memory and a processor, wherein a multimodal interaction control method program is stored in the memory, and when the multimodal interaction control method program is executed by the processor, the multimodal interaction control method steps as described in any one of claims 1-6 are implemented.

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