Intelligent illumination heating method and device

Abstract

The invention relates to the technical field of illumination heating, and discloses an intelligent illumination heating method and device, and the method comprises the steps: analyzing the temperature gradient and optical characteristics of the surface of a target object through a preset stage recognition model according to a pre-obtained surface hot spot image of the target object, and recognizing a first stage of the target object; analyzing the photo-thermal demand and the energy state of the target object through a preset illumination heating matching mechanism to perform photo-thermal coupling, and calculating corresponding illumination heating parameters; the illumination heating device is controlled to perform illumination heating on the target object, and the real-time state change condition of the target object is analyzed; the illumination heating parameters are optimized in real time through a preset parameter optimization model, and the illumination heating device is adjusted in real time; the accuracy of the calculated illumination heating parameters can be improved, the illumination heating process is controlled by adjusting the illumination heating parameters in real time, and the illumination heating effect is improved.

Description

Technical Field

[0001] The present application relates to the field of light heating technology, and more specifically to an intelligent light heating method and device. Background Art

[0002] During the incubation process of poultry eggs, light heating technology is needed to regulate the incubation process and improve the incubation effect. Current incubation equipment mostly uses fixed temperature and light intensity, which does not take into account the different requirements of temperature and light intensity for the embryos in the eggs at different developmental stages, resulting in poor incubation results.

[0003] The existing technology has the following problems: the incubation stage of the hatching eggs is analyzed based on single data and fixed methods, and the obtained incubation stage cannot reflect the current actual state of the eggs, resulting in inaccurate setting of the illumination and heating parameters, affecting the illumination and heating effect; during the illumination and heating process, the illumination parameters and heating parameters are set separately, ignoring the correlation between illumination and heating, resulting in inaccurate set illumination parameters and heating parameters; during the illumination and heating process, the state inside the eggs is constantly changing, and the existing illumination and heating control method lacks real-time adjustment of the illumination and heating parameters according to the real-time state, resulting in poor illumination and heating effect; in order to solve at least one of the above problems, the present application proposes an intelligent illumination and heating method and device. Summary of the Invention

[0004] In view of the shortcomings of the existing technology, the purpose of this application is to provide an intelligent light heating method and device that can effectively solve the problems in the background technology. The specific technical solutions of this application are as follows:

[0005] An intelligent light heating method, comprising:

[0006] Based on the pre-acquired hot spot image of the target object surface, the temperature gradient and optical characteristics of the target object surface are analyzed by a preset stage recognition model to identify the first stage of the target object;

[0007] Based on the first stage, the light and heat requirements and energy state of the target object are analyzed through the preset light and heat matching mechanism to perform light and heat coupling and calculate the corresponding light and heat parameters;

[0008] Using the light heating parameters to control the light heating device to perform light heating on the target object, and analyzing the real-time state changes of the target object;

[0009] According to the real-time state changes, the light heating parameters are optimized in real time through a preset parameter optimization model, and the light heating device is adjusted in real time to perform intelligent light heating on the target object.

[0010] Specifically, the method of analyzing the temperature gradient and optical properties of the target object surface using a preset stage recognition model based on the pre-acquired hot spot image of the target object surface to identify the first stage of the target object includes:

[0011] The temperature and transmittance of the target object surface are collected through a preset sensor group to obtain a hot spot image of the target object surface;

[0012] According to the hot spot image, the temperature gradient and optical characteristics of the target object surface are analyzed by a preset stage recognition model to obtain a fusion feature;

[0013] Based on the fused features, the first stage of the target object is identified.

[0014] Specifically, based on the hot spot image, the temperature gradient and optical properties of the target object surface are analyzed by a preset stage recognition model to obtain fusion features, including:

[0015] Calculating the temperature gradient field in the hot spot image through a preset stage recognition model, and extracting a temperature feature set from the temperature gradient field;

[0016] performing optical characteristic analysis on the hot spot image to extract an optical feature set;

[0017] Extracting a set of biometric features by performing wavelet transform on the hot spot image;

[0018] The temperature feature set, the optical feature set, and the biological feature set are fused according to different channels to obtain fused features.

[0019] Specifically, based on the first stage, the light and heat requirements and energy state of the target object are analyzed through a preset light and heat matching mechanism to perform light and heat coupling, and the corresponding light and heat parameters are calculated, including:

[0020] Analyze the first stage according to the preset stage parameter mapping rules and match the corresponding first parameters;

[0021] The light and heat requirements and energy state of the target object are analyzed through the preset light and heat matching mechanism, and light and heat coupling is performed to generate a coupling matrix.

[0022] Analyzing the real-time state of the target object, compensating and correcting the first parameter according to the real-time state, and obtaining a second parameter;

[0023] The coupling matrix and the second parameter are combined to generate corresponding light heating parameters.

[0024] Specifically, the method of analyzing the light and heat requirements and energy state of the target object through a preset light and heat matching mechanism and performing light and heat coupling to generate a coupling matrix includes:

[0025] A parameter coordinate system is constructed based on the first parameter, and the preset light and heat matching mechanism analyzes the parameters and parameter weights of the target object to obtain the light and heat demand parameters and energy state parameters corresponding to the target object;

[0026] The light and heat demand parameter is split according to a plurality of preset dimensions, and the split light and heat demand parameters are weightedly fused according to corresponding parameter weights to obtain a first fused parameter vector;

[0027] The energy state parameter is split according to a plurality of preset dimensions, and the split energy state parameters are weightedly fused according to corresponding parameter weights to obtain a second fused parameter vector;

[0028] A matrix coupling operation is performed on the first fusion parameter vector and the second fusion parameter vector to generate a coupling matrix.

[0029] Specifically, combining the coupling matrix and the second parameter to generate corresponding light heating parameters includes:

[0030] Analyze the correlation strength of the light and heat demand parameters in the coupling matrix and construct light and heat constraints;

[0031] Extracting multiple features from the second parameter, and mapping the extracted feature dimensions to be consistent with the vector dimensions in the coupling matrix to obtain a second parameter feature vector;

[0032] The second parameter eigenvector is optimized through the light-heat constraint to generate corresponding light-heating parameters.

[0033] Specifically, using the light heating parameters to control the light heating device to perform light heating on the target object and analyzing the real-time state changes of the target object includes:

[0034] Controlling the light heating device to perform light heating on the target object according to the light heating parameters;

[0035] Through the preset sensor group, the status data of the target object is collected in real time to obtain real-time status data;

[0036] According to the real-time status data, the temperature change and energy absorption of the target object are analyzed to obtain the real-time status change.

[0037] Specifically, according to the real-time state change, the light heating parameters are optimized in real time through a preset parameter optimization model, and the light heating device is adjusted in real time to perform intelligent light heating on the target object, including:

[0038] Compare and analyze the real-time status changes with the preset status to obtain the degree of status deviation;

[0039] According to the state deviation degree, the light heating parameters are optimized in real time through a preset parameter optimization model to obtain updated light heating parameters;

[0040] The updated light heating parameters are used to adjust the light heating device in real time to perform intelligent light heating on the target object.

[0041] Specifically, according to the state deviation degree, the light heating parameters are optimized in real time by a preset parameter optimization model to obtain updated light heating parameters, including:

[0042] According to the degree of state deviation, a set of candidate optimization parameters for optimizing the light heating parameters is calculated through a preset parameter optimization model;

[0043] By analyzing the adjustment of the candidate optimization parameters to the state deviation degree, the key optimization parameters are screened out from the candidate optimization parameter set;

[0044] The light heating parameters are optimized in real time according to the key optimization parameters to obtain updated light heating parameters.

[0045] An intelligent light heating device, used to implement the intelligent light heating method, comprising:

[0046] The stage recognition module analyzes the temperature gradient and optical properties of the target object surface based on the pre-acquired hot spot image of the target object surface through a preset stage recognition model to identify the first stage of the target object;

[0047] A parameter calculation module, based on the first stage, analyzes the light and heat requirements and energy state of the target object through a preset light and heat matching mechanism to perform light and heat coupling and calculate the corresponding light and heat parameters;

[0048] A light heating module controls the light heating device to heat the target object using the light heating parameters and analyzes the real-time state changes of the target object;

[0049] The light heating optimization module optimizes the light heating parameters in real time according to the real-time state changes through a preset parameter optimization model, and adjusts the light heating device in real time to perform intelligent light heating on the target object.

[0050] The beneficial effects of the present application are as follows: temperature characteristics and optical characteristics are analyzed based on the hot spot image on the surface of the target object, the current stage of the target object is identified, and the light and heat requirements and energy status of the target object in the current stage are analyzed. The corresponding light and heating parameters are matched through light and heat coupling combined with the correlation between the light parameters and the heating parameters, thereby improving the pertinence and effectiveness of the light and heating parameter matching. At the same time, the light and heating parameters are adjusted in real time according to the real-time state changes of the target object, the light and heating process of the target object is controlled, and intelligent light heating of the target object is realized, thereby improving the accuracy of the calculated light and heating parameters, and controlling the light and heating process by real-time adjustment of the light and heating parameters to improve the light heating effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 This is a workflow diagram of an intelligent light heating method in an embodiment of the present application;

[0052] Figure 2 Schematic diagram of the parameter coordinate system in the embodiment of the present application;

[0053] Figure 3 is a schematic diagram of a coupling matrix in an embodiment of the present application;

[0054] Figure 4 This is a structural diagram of an intelligent light heating device in an embodiment of the present application. DETAILED DESCRIPTION

[0055] The present application is further described below in conjunction with the accompanying drawings and embodiments. Identical components are denoted by the same reference numerals. It should be noted that the terms "front," "rear," "left," "right," "upper," and "lower" used in the following description refer to directions in the accompanying drawings, and the terms "bottom," "top," "inner," and "outer" refer to directions toward or away from the geometric center of a particular component, respectively.

[0056] In the embodiments of this application, words such as "exemplary" or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of this application should not be construed as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0057] Hereinafter, the terms "first," "second," and the like are used in general terms for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of such features. In the description of the embodiments of this application, unless otherwise specified, "plurality" means two or more.

[0058] refer to Figure 1 As shown in FIG, a specific embodiment of an intelligent light heating method of the present application includes:

[0059] S101, based on a pre-acquired hot spot image of the target object surface, analyzing the temperature gradient and optical properties of the target object surface using a preset stage recognition model to identify the first stage of the target object;

[0060] S102: Based on the first stage, the light and heat requirements and energy state of the target object are analyzed through a preset light and heat matching mechanism to perform light and heat coupling and calculate corresponding light and heat parameters;

[0061] S103, using the light heating parameters to control the light heating device to perform light heating on the target object, and analyzing the real-time state change of the target object;

[0062] S104 , optimizing the light heating parameters in real time through a preset parameter optimization model according to the real-time state change, and adjusting the light heating device in real time to perform intelligent light heating on the target object.

[0063] This embodiment collects hot spot images on the egg surface, analyzes the hot spot images, extracts temperature gradients, and analyzes the optical properties of the eggs to identify the first stage the eggs are in. Based on the first stage, the light and heat requirements of the eggs are determined. Combined with the initial energy state of the eggs, light and heat coupling analysis is performed through the light heating matching mechanism to calculate light intensity, heating time, light wavelength and other light heating parameters; according to the calculated parameters, the light heating device is controlled to light heat the eggs, and the state changes of the eggs are collected in real time. The light heating parameters are optimized in real time, including adjusting the light intensity and heating time, and continuously monitoring and optimizing until the eggs achieve the expected light heating effect. Compared with the traditional method of single light heating of eggs, the present application combines the current stage and real-time state of the eggs to calculate the light heating parameters at the same time, and adjusts and optimizes the light heating parameters in real time, so that the eggs can be accurately light heated and the light heating effect can be improved.

[0064] In this embodiment, based on a pre-acquired hot spot image of the target object's surface, a preset stage recognition model is used to analyze the temperature gradient and optical properties of the target object's surface. The temperature gradient reflects the temperature difference at different locations on the object's surface, and the optical properties include characteristic parameters such as reflectivity and absorptivity. The temperature gradient and optical properties vary with the object's growth stage. The stage recognition model includes, but is not limited to, a random forest model. The random forest model is trained using a large number of historical hot spot images and the corresponding growth stages of the target object to obtain a pre-trained random forest model. The hot spot image of the target object's surface is input into the pre-trained random forest model, and the model outputs the first stage the target object is currently in. By identifying the current stage of the target object, the initial state of the target object can be accurately grasped, providing a basis for calculating the illumination and heating parameters, thereby avoiding improper illumination and heating caused by inaccurate judgment of the object's initial state, which could affect the normal growth process of the target object.

[0065] After determining the first stage of the target object, the preset light and heating matching mechanism is used to analyze the amount of light and heat required for the target object to achieve the expected light and heating effect, and the light and heat demand is obtained. The current energy level of the target object is analyzed to obtain the energy state. Combined with the correlation between the light and heat demand and the energy state, a light and heat coupling analysis is performed to calculate the corresponding light and heating parameters, including light intensity, heating time, light wavelength and other parameters. The light and heating parameters calculated through light and heat coupling are more in line with the actual needs of the target object, can improve the efficiency and accuracy of light heating, and avoid energy waste.

[0066] According to the calculated light heating parameters, the light heating device is controlled to perform light heating on the target object. At the same time, the detection equipment is used to monitor the state changes of the target object in real time, including temperature changes, changes in surface optical properties, etc. By monitoring the state changes of the target object during the light heating process in real time, problems arising in the light heating process can be discovered in time, and a state reference can be provided for the optimization of the light heating parameters. If it is found that the temperature of the egg rises too quickly, the heating parameters can be adjusted in time to prevent the egg from being overheated and affecting the normal growth process of the egg.

[0067] Furthermore, according to the real-time state changes of the target object, the current light heating parameters are analyzed and optimized through a preset parameter optimization model. The parameter optimization model includes but is not limited to a particle swarm optimization model. The particle swarm optimization model is trained using a large amount of historical state data to obtain a pre-trained particle swarm optimization model. The real-time state changes of the target object and the expected light heating effect are input into the pre-trained particle swarm optimization model, and the model outputs the optimized light heating parameters. The light heating parameters are dynamically adjusted and optimized according to the real-time state of the target object, which can ensure that the light heating process is always in the best state, improve the quality and stability of the light heating, and avoid local excessive light exposure or uneven heating of the eggs during the light heating process of the eggs by optimizing the parameters in real time.

[0068] This application analyzes the temperature characteristics and optical characteristics based on the hot spot image on the surface of the target object, identifies the current stage of the target object, and analyzes the light and heat requirements and energy status of the target object in the current stage. Through light and heat coupling combined with the correlation between the illumination parameters and heating parameters, the corresponding illumination and heating parameters are matched to improve the pertinence and effectiveness of the illumination and heating parameter matching. At the same time, the illumination and heating parameters are adjusted in real time according to the real-time state changes of the target object to control the illumination and heating process of the target object, realize intelligent illumination heating of the target object, improve the accuracy of the calculated illumination and heating parameters, and control the illumination and heating process by real-time adjustment of the illumination and heating parameters to improve the illumination heating effect.

[0069] Furthermore, based on the pre-acquired hot spot image of the target object surface, the temperature gradient and optical characteristics of the target object surface are analyzed by a preset stage recognition model to identify the first stage of the target object, including:

[0070] S201, collecting the temperature and transmittance of the target object surface through a preset sensor group to obtain a hot spot image of the target object surface;

[0071] S202: Analyze the temperature gradient and optical properties of the target object surface using a preset stage recognition model based on the hot spot image to obtain a fusion feature;

[0072] S203: Based on the fusion features, identify the first stage of the target object.

[0073] In this embodiment, a preset sensor group is used to collect temperature data and transmittance data from the egg surface, and a hot spot image of the egg surface is generated. The hot spot image is input into a preset stage recognition model. The model extracts temperature gradient features and optical characteristic features from the hot spot image, and performs feature fusion processing on the temperature gradient features and optical characteristic features to obtain fused features. The first stage of the target object is identified based on the fused features. By fusing different types of features, a more comprehensive feature basis is provided for target object stage recognition, avoiding the one-sidedness of single feature data. The stage recognition of the target object based on the fused features can improve the accuracy of the first stage recognition result of the egg, provide an initial basis for the calculation of the illumination heating parameters, and ensure the effectiveness of the illumination heating process.

[0074] In this embodiment, for light heating of eggs, the sensor group includes an infrared temperature sensor and a transmittance sensor. The infrared temperature sensor collects the temperature of the egg surface, and the transmittance sensor collects the transmittance of different positions on the egg surface. The sensor group is installed in a position that can fully cover the egg surface to collect data from various areas of the egg. The collected temperature data and transmittance data are superimposed and fused, and the temperature data is converted into different colors, with high temperature areas being red and low temperature areas being blue, to generate a temperature image. The transmittance data is set, with a large grayscale value for high transmittance and a small grayscale value for low transmittance. The grayscale value is superimposed on the temperature image to generate a hot spot image of the egg surface. Through collaborative collection by multiple sensors, temperature and transmittance information on the egg surface can be obtained, a more complete hot spot image can be generated, and accurate data can be provided for stage recognition of the target object.

[0075] Specifically, based on the generated hot spot image, a preset stage recognition model is used to analyze the temperature change rate in the hot spot image to obtain temperature gradient information, and the transmittance is analyzed to obtain optical characteristic information, and features related to the temperature gradient and optical characteristics are extracted. The temperature gradient features and the optical characteristic features are fused to obtain fused features that can reflect the state of the target object; through feature fusion, temperature information and optical information can be combined. Compared with a single temperature feature or optical feature, the fused feature can more comprehensively reflect the state of the target object and improve the accuracy of stage recognition of the target object.

[0076] After obtaining the fusion feature, the fusion feature is compared with the preset feature template. By analyzing the growth stage of the target object, the corresponding feature template is set according to the different feature performances of different growth stages. The fusion feature is compared with each feature template and the similarity is calculated. The stage corresponding to the feature template with the highest similarity is selected as the first stage of the target object. By identifying the stage of the target object through the fusion feature, the current stage of the target object can be determined more accurately, providing an initial state basis for the target object's illumination and heating process.

[0077] Furthermore, based on the hot spot image, the temperature gradient and optical properties of the target object surface are analyzed by a preset stage recognition model to obtain fusion features, including:

[0078] S301, calculating the temperature gradient field in the hot spot image using a preset stage recognition model, and extracting a temperature feature set from the temperature gradient field;

[0079] S302, performing optical characteristic analysis on the hot spot image to extract an optical feature set;

[0080] S303, extracting a set of biometric features by performing wavelet transform on the hot spot image;

[0081] S304: Fusing the temperature feature set, the optical feature set, and the biometric feature set according to different channels to obtain fused features.

[0082] In this embodiment, based on the hot spot image, the temperature gradient field in the hot spot image is calculated by a preset stage recognition model. The stage recognition model includes but is not limited to a neural network model. The neural network model is trained using a large number of hot spot images to obtain a pre-trained neural network model. The hot spot image is input into the pre-trained neural network model. The model calculates the temperature difference and spatial distance between each pixel point in the hot spot image and the surrounding pixels to obtain the temperature gradient of the corresponding pixel point. The temperature gradient field is constructed according to the temperature gradient distribution of each pixel point. Multiple features are extracted from the temperature gradient field, including the maximum value, minimum value, average value, and variance of the temperature gradient. The extracted multiple features are combined to form a temperature feature set. By constructing the temperature gradient field and extracting the corresponding temperature features, the temperature state of the target object can be analyzed in combination with the degree of change and distribution trend of the surface temperature of the target object, and the corresponding heating parameters can be set.

[0083] Specifically, by analyzing the reflection and absorption of different light rays by the surface of the target object, the optical properties of the target object are analyzed, and the hot spot image of the egg is converted into a format that can analyze optical properties, including but not limited to RGB format. The optical property analysis of the converted RGB format image includes counting the grayscale values ​​of different areas on the egg surface, calculating the average values ​​of different color channels, etc., and extracting the corresponding optical features, including the maximum value, minimum value, average value, average value of each color channel, standard deviation of the grayscale value, etc. The optical features are combined to form an optical feature set; the optical properties of the egg are related to its internal structure, such as the thickness of the eggshell and the state of the egg membrane. By analyzing the optical properties of the egg, the internal condition of the egg can be obtained, providing data support for identifying the stage of the egg.

[0084] Furthermore, the hot spot image is subjected to wavelet transformation and decomposed into components of different frequencies. The surface thermal distribution and optical properties of the egg will show different biological characteristics at different frequencies, including the frequency characteristics corresponding to the eggshell texture and the local frequency changes caused by the internal embryo. In this embodiment, the db4 wavelet basis function is selected to perform a secondary wavelet decomposition on the hot spot image of the egg to obtain low-frequency components and high-frequency components, wherein the low-frequency component reflects the overall outline of the hot spot image, and the high-frequency component reflects the detailed characteristics of the hot spot image; the energy value of the high-frequency component, the entropy value of different high-frequency components, the peak value and other characteristics are extracted, and the extracted features are combined to form a biological feature set; by extracting the biological features, the characteristics of the egg as a biological object can be obtained, and the biological state of the egg can be analyzed in combination with the biological features, thereby improving the accuracy of identifying the stage of the egg. The extracted temperature feature sets, optical feature sets, and biological feature sets are standardized separately. A weight is assigned to each feature set based on its importance for egg stage identification. The temperature features, optical features, and biological features are fused according to different channels and corresponding weights to calculate the corresponding fused features. By fusing temperature information, optical information, and biological information, the limitations of single feature analysis are avoided. The fused features can more comprehensively and accurately reflect the state of the egg, improve the accuracy of the stage identification results, and provide an accurate state reference for setting the illumination and heating parameters of the target object.

[0085] Furthermore, based on the first stage, the light and heat requirements and energy state of the target object are analyzed through a preset light and heat matching mechanism to perform light and heat coupling, and the corresponding light and heat parameters are calculated, including:

[0086] S401: Analyze the first phase according to a preset phase parameter mapping rule and match the corresponding first parameter;

[0087] S402, analyzing the light and heat requirements and energy state of the target object through a preset light and heat matching mechanism and performing light and heat coupling to generate a coupling matrix;

[0088] S403, analyzing the real-time state of the target object, compensating and correcting the first parameter according to the real-time state, and obtaining a second parameter;

[0089] S404: Combine the coupling matrix and the second parameter to generate corresponding illumination heating parameters.

[0090] This embodiment maps and matches the corresponding first parameter according to the preset stage parameter mapping rules based on the first stage of the identified target object, analyzes the light and heat requirements and energy status during the egg growth stage, and performs light and heat coupling analysis in combination with the correlation between the light parameters and the thermal parameters to generate a coupling matrix; at the same time, the real-time status of the egg is analyzed, and the first parameter is compensated and corrected in combination with the real-time status to obtain the second parameter, and the second parameter is adjusted and optimized using the coupling coefficient in the coupling matrix to obtain the light heating parameter; by associating and coupling the light parameters and the thermal parameters, more accurate light heating parameters can be calculated in combination with the correlation between light and heat, and the parameters can be corrected based on the real-time status of the target object, which can improve the flexibility of the light heating process and enhance the light heating effect.

[0091] In this embodiment, based on the first stage of the identified target object, the first parameters of the corresponding stage are matched according to the preset stage parameter mapping rules; the basic illumination and heating parameters required for target objects at different growth stages are different, and the stage parameter mapping rules are set based on a large number of historical parameter data of target objects at different stages. Corresponding basic illumination and heating parameters are set for each growth stage, and the stage parameter mapping rules are matched according to the first stage of the target object to obtain the corresponding first parameters; through stage and parameter mapping, the basic illumination and heating parameters suitable for the current stage can be quickly matched for the target object, which improves the efficiency of parameter determination and provides a basis for parameter optimization.

[0092] Specifically, the light energy and thermal energy required for the target object to achieve the expected heating effect are analyzed to obtain the light and heat demand, the current energy level of the target object is analyzed to obtain the energy state, and the light and heat demand and the energy state are light-thermally coupled through a preset light and heating matching mechanism combined with the correlation between light energy and thermal energy and the absorption and conversion efficiency of the target object to light and heat to obtain a coupling matrix, wherein the elements in the coupling matrix represent the degree of correlation and conversion efficiency between different light parameters and thermal parameters; by analyzing the light and heat demand and energy state, the light and heat energy required for the egg and the initial energy situation can be determined, the generated coupling matrix analyzes and quantifies the correlation between the light parameters and thermal parameters, and the light and heating parameters calculated in combination with the coupling matrix are more accurate.

[0093] At the same time, the real-time state of the target object is monitored, and the state of the target object is monitored in real time by using a sensor group to obtain real-time state data. The real-time state is analyzed in combination with the degree of influence of the parameters on the state, and the state difference between the real-time state and the expected state is corrected according to the corresponding compensation rules, and the compensation correction amount is calculated. The compensation correction amount is superimposed on the first parameter to obtain the second parameter; the first parameter is corrected in combination with the real-time state change of the target object to avoid inappropriate light heating parameters due to real-time state differences, so that the parameters are more in line with the actual state of the target object and the effect of light heating is improved.

[0094] Specifically, the coupling matrix and the second parameter are combined. The coupling matrix reflects the correlation degree and conversion efficiency between the light parameters and the thermal parameters. The second parameter is the parameter corrected by the real-time state. By combining the information in the coupling matrix and the second parameter, the comprehensive light-thermal coupling relationship and the real-time light heating parameters are obtained to improve the stability and reliability of the light heating effect.

[0095] Furthermore, the light and heat requirements and energy state of the target object are analyzed through the preset light and heat matching mechanism, and light and heat coupling is performed to generate a coupling matrix, including:

[0096] S501: Construct a parameter coordinate system based on the first parameter, and use a preset light and heat matching mechanism to analyze the parameters and parameter weights of the target object to obtain light and heat demand parameters and energy state parameters corresponding to the target object;

[0097] S502: Split the solar thermal demand parameter according to multiple preset dimensions, and perform weighted fusion on the split solar thermal demand parameters according to corresponding parameter weights to obtain a first fusion parameter vector;

[0098] S503: Split the energy state parameter according to a plurality of preset dimensions, and perform weighted fusion on the plurality of split energy state parameters according to corresponding parameter weights to obtain a second fusion parameter vector;

[0099] S504 : Perform a matrix coupling operation on the first fusion parameter vector and the second fusion parameter vector to generate a coupling matrix.

[0100] like Figure 2As shown, a parameter coordinate system is constructed according to the three dimensions of light intensity, heating time, and light wavelength. The position of the first parameter in the parameter coordinate system is determined according to the corresponding parameter value in the first parameter. In the preset light heating matching mechanism, the influence of each parameter on the light and heat demand and energy state is analyzed, and the parameter weights corresponding to the light and heat demand and energy state are determined respectively. Each parameter value is extracted from the first parameter, and the light and heat demand parameters and energy state parameters are obtained according to the corresponding parameter weights. By constructing the parameter coordinate system and calculating the corresponding parameter weights, the corresponding parameters can be accurately extracted, providing a basis for parameter coupling.

[0101] The solar thermal demand parameters are split according to multiple preset dimensions, and the solar thermal demand parameters are split according to three dimensions: total energy, energy density, and energy conversion efficiency. A corresponding weight is set for each dimension according to the calculation accuracy requirements of the solar thermal demand parameters. The split solar thermal demand parameters are weighted with the corresponding weights, and the solar thermal demand parameters of each dimension after weighted calculation are combined to obtain a first fused parameter vector. By splitting the solar thermal demand parameters, the split parameters can reflect different aspects of the solar thermal demand. The first fused parameter vector obtained after weighted fusion combines information from each dimension, which facilitates matrix operations in the coupling process.

[0102] The energy state parameters are split according to multiple preset dimensions. The energy state parameters are split according to three dimensions: total energy, energy density, and energy conversion efficiency. A corresponding weight is set for each dimension based on the calculation accuracy requirements of the energy state parameters. The split energy state parameters are weighted with the corresponding weights. The energy state parameters of each dimension after weighted calculation are combined to obtain a second fusion parameter vector. By splitting the energy state parameters, the split parameters can reflect different aspects of the energy state. The second fusion parameter vector obtained after weighted fusion combines the information of each dimension, which facilitates the matrix operation of the coupling process.

[0103] like Figure 3 As shown, after the first fusion parameter vector and the second fusion parameter vector are calculated, the first fusion parameter vector is used as a column vector and the second fusion parameter vector is used as a row vector through a vector outer product operation, and they are multiplied to obtain a coupling matrix. The element values ​​in the coupling matrix reflect the degree of coupling between the light and heat demand and the energy state in different dimensions; the light and heat demand and the energy state are correlated and coupled through the coupling operation, which provides a basis for analyzing the correlation between the lighting parameters and the heating parameters, and calculates more accurate lighting and heating parameters.

[0104] Furthermore, the coupling matrix and the second parameter are combined to generate corresponding light heating parameters, including:

[0105] S601. Analyze the correlation strength of the light and heat demand parameters in the coupling matrix and construct light and heat constraints.

[0106] S602: parse multiple features from the second parameter, and map the parsed feature dimensions to be consistent with the vector dimensions in the coupling matrix to obtain a second parameter feature vector;

[0107] S603: Optimize the second parameter eigenvector using the light-heat constraint to generate corresponding light-heating parameters.

[0108] In this embodiment, the correlation strength of the light and thermal demand parameters in the coupling matrix is ​​analyzed. The element values ​​in the coupling matrix reflect the correlation strength between the light and thermal demand parameters. The larger the element value, the closer the correlation between the light and thermal demand parameters. The element values ​​in the coupling matrix are analyzed, the mean and standard deviation of the element values ​​are calculated, and the relationship range between the light and thermal demand parameters is determined, including the ratio limit of light intensity and heating time, and the corresponding relationship limit between wavelength and energy conversion efficiency. The determined relationship range is used as the light and thermal constraint. By constructing the light and thermal constraint to define the range for parameter optimization, the unreasonable parameter calculation results are avoided, and the accuracy and effectiveness of the calculated parameter results are improved.

[0109] Specifically, the second parameter includes multiple characteristic information, including but not limited to light intensity, heating time, and light wavelength. According to the vector dimension of the coupling matrix, the second parameter is analyzed and mapped into three dimensions: total energy, energy density, and energy conversion efficiency. The mapped parameters are combined to obtain the second parameter characteristic vector; through dimensional mapping, the second parameter characteristic vector is made consistent with the dimension of the coupling matrix, which facilitates the optimization operation of the coupling matrix and accelerates the parameter optimization process.

[0110] The photothermal constraint limits the range of the parameter, and the photothermal constraint is used to optimize the second parameter eigenvector. If it does not meet the requirements, the second parameter eigenvector is adjusted according to the constraint conditions, including but not limited to improving the corresponding energy conversion efficiency by increasing the wavelength, so that the energy absorption of the egg is more efficient. After the photothermal constraint optimization, the final light heating parameters are obtained; the parameters are optimized through the photothermal constraint, so that the optimized light heating parameters meet the initial characteristics of the second parameter and meet the correlation relationship of the photothermal demand parameters, avoiding the irrationality of the parameter setting. The final parameters can meet the heating requirements of the eggs and ensure the heating effect.

[0111] Furthermore, the light heating parameters are used to control the light heating device to perform light heating on the target object, and the real-time state change of the target object is analyzed, including:

[0112] S701, controlling the light heating device to perform light heating on the target object according to the light heating parameters;

[0113] S702, collecting status data of the target object in real time through a preset sensor group to obtain real-time status data;

[0114] S703: Analyze the temperature change and energy absorption of the target object according to the real-time status data to obtain the real-time status change.

[0115] This embodiment controls the light heating device to perform light heating on the target object according to the calculated light heating parameters, and uses the sensor group to collect the state of the target object in real time, analyzes the temperature change and energy absorption of the target object, and obtains the real-time state change; by monitoring the real-time state change of the target object, heating anomalies can be quickly discovered and the light heating effect can be improved.

[0116] Specifically, the illumination heating parameters include illumination intensity, heating time, illumination wavelength, etc. The illumination heating parameters are used to control the illumination heating device to illuminate and heat the target object. The various sensors in the sensor group are used to collect parameters such as the surface temperature and surface reflectivity of the target object in real time during the heating process. The temperature change reflects the heating effect, and the reflectivity change reflects the energy absorption. The surface temperature and surface reflectivity are analyzed to obtain the real-time state changes of the target object, providing a state basis for subsequent parameter optimization.

[0117] Furthermore, according to the real-time state change, the light heating parameters are optimized in real time through a preset parameter optimization model, and the light heating device is adjusted in real time to perform intelligent light heating on the target object, including:

[0118] S801, comparing and analyzing the real-time state change with the preset state to obtain the state deviation degree;

[0119] S802: Optimize the illumination and heating parameters in real time using a preset parameter optimization model according to the state deviation degree to obtain updated illumination and heating parameters;

[0120] S803: Use the updated light heating parameters to adjust the light heating device in real time to perform intelligent light heating on the target object.

[0121] In this embodiment, the real-time state change of the target object is compared and analyzed with the preset state to obtain the state deviation degree. The preset state is the standard state parameter set according to the ideal heating process of the target object, including the expected temperature change curve, energy absorption rate range, etc. The state data in the real-time state change is compared with the standard state parameters, and the difference is calculated to obtain the state deviation degree. By analyzing the state deviation degree, the gap between the actual heating process and the ideal heating state is quantitatively analyzed, providing a basis for parameter optimization.

[0122] Specifically, according to the calculated degree of state deviation, the light heating parameters are optimized in real time through a preset parameter optimization model. The parameter optimization model includes but is not limited to a multi-objective optimization model. A large number of historical light heating parameters are used to train the multi-objective optimization model to obtain a pre-trained multi-objective optimization model. Combined with the calculated degree of state deviation and light heating parameters, the model outputs the optimized light heating parameters to obtain updated light heating parameters. Optimizing the light heating parameters according to the degree of state deviation can improve the accuracy of the parameter optimization results, and the optimized parameters can improve the light heating effect.

[0123] Furthermore, the operating state of the illumination heating device is adjusted according to the updated illumination heating parameters, so that the illumination heating device operates according to the updated illumination heating parameters. The device can respond to the state changes of the target object in real time through real-time adjustment, avoiding the accumulation of illumination heating deviations due to fixed parameters, ensuring that the illumination heating process is always in the optimal state, and improving the adaptability and effectiveness of intelligent illumination heating.

[0124] Furthermore, according to the state deviation degree, the light heating parameters are optimized in real time by a preset parameter optimization model to obtain updated light heating parameters, including:

[0125] S901. Calculate a set of candidate optimization parameters for optimizing the light heating parameters using a preset parameter optimization model based on the degree of state deviation.

[0126] S902: Filter out key optimization parameters from the candidate optimization parameter set by analyzing how the candidate optimization parameters adjust the state deviation degree;

[0127] S903: Optimize the illumination and heating parameters in real time according to the key optimization parameters to obtain updated illumination and heating parameters.

[0128] In this embodiment, the state deviation degree of the target object reflects the gap between the actual state of the target object and the preset state. According to the state deviation degree, the illumination and heating parameters are optimized by a preset parameter optimization model. The parameter optimization model is a multi-objective optimization model. The optimization goals are set to include increasing the illumination intensity by 3% to 5% and increasing the heating time by 10 minutes to 30 minutes. The illumination and heating parameters are optimized according to the set optimization goals, and multiple parameter combinations are generated within the set range to obtain a set of candidate optimization parameters. By optimizing the parameters, the limitations of a single adjustment scheme can be avoided, more options can be provided for the screening of optimal parameters, the flexibility and reliability of parameter optimization can be improved, and it can be ensured that the adjustment scheme most suitable for the current state can be found.

[0129] Specifically, the candidate optimization parameters in the candidate optimization parameter set are analyzed. Each candidate optimization parameter has a different effect on the adjustment of the state deviation. By analyzing the adjustment and improvement of the state deviation of each candidate optimization parameter in a simulated light heating environment, the parameters that can minimize the state deviation and consume less energy are screened out to obtain the key optimization parameters. By screening out the key optimization parameters, the state deviation can be improved to the greatest extent, and unnecessary energy consumption can be reduced, thereby improving the effectiveness of the parameter optimization process. The original light heating parameters are replaced with the key optimization parameters, and the light heating parameters are optimized in real time to obtain updated light heating parameters. By updating and optimizing the light heating parameters, the effectiveness of the updated parameters can be improved, so that the target object can quickly reach the preset state during the light heating process, thereby improving the effectiveness and stability of light heating.

[0130] like Figure 4 As shown, an intelligent light heating device is used to implement an intelligent light heating method, including:

[0131] The stage recognition module analyzes the temperature gradient and optical properties of the target object surface based on the pre-acquired hot spot image of the target object surface through a preset stage recognition model to identify the first stage of the target object;

[0132] A parameter calculation module, based on the first stage, analyzes the light and heat requirements and energy state of the target object through a preset light and heat matching mechanism to perform light and heat coupling and calculate the corresponding light and heat parameters;

[0133] A light heating module controls the light heating device to heat the target object using the light heating parameters and analyzes the real-time state changes of the target object;

[0134] The light heating optimization module optimizes the light heating parameters in real time according to the real-time state changes through a preset parameter optimization model, and adjusts the light heating device in real time to perform intelligent light heating on the target object.

[0135] In this embodiment, the stage recognition module analyzes the pre-acquired hot spot image of the target object surface through a preset stage recognition model, extracts the temperature gradient and optical characteristic information in the hot spot image, and thus identifies the first stage in which the target object is currently located; by identifying the stage in which the target object is located, a basis is provided for calculating the light heating parameters, and reasonable initial parameters are set according to the current state of the target object; the parameter calculation module is based on the first stage determined by the stage recognition module, and through a preset light heating matching mechanism, analyzes the light and heat requirements and energy state of the target object, calculates the corresponding light heating parameters through light and heat coupling, and the parameters calculated in combination with the current state of the target object can meet the actual needs of the target object, thereby improving the targetedness and efficiency of light heating of the target object.

[0136] Specifically, the light heating module controls the light heating device to perform light heating on the target object according to the light heating parameters obtained by the parameter calculation module, and performs real-time analysis of the state changes of the target object during the heating process, including temperature changes, energy absorption, etc.; by controlling the light heating device through the light heating parameters, it can ensure that the heating process is carried out according to the preset parameters, and monitor the state changes of the target object in real time, so as to facilitate real-time adjustment of the light heating state of the light heating device and improve the light heating effect of the target object. The light heating optimization module optimizes the light heating parameters in real time according to the real-time state changes of the target object obtained by the light heating module analysis through a preset parameter optimization model, and adjusts the heating state of the light heating device in real time, realizing intelligent light heating of the target object. By dynamically adapting to the state changes of the target object, it can ensure that the heating process is always in the best state, improve the heating quality and stability, and during the heating process of the egg, if it is found that the temperature rises too quickly, the parameters can be optimized in time and the device can be adjusted to ensure that the egg is evenly heated to achieve the expected effect.

[0137] The above description is merely a preferred embodiment of the present application. The scope of protection of the present application is not limited to the above embodiments. All technical solutions based on the concept of the present application are within the scope of protection of the present application. It should be noted that for those skilled in the art, certain improvements and modifications that do not depart from the principles of the present application should also be considered within the scope of protection of the present application.

Claims

1. An intelligent light heating method, characterized in that: include: Based on the pre-acquired hot spot image of the target object surface, the temperature gradient and optical characteristics of the target object surface are analyzed by a preset stage recognition model to identify the first stage of the target object; Based on the first stage, the light and heat requirements and energy state of the target object are analyzed through the preset light and heat matching mechanism to perform light and heat coupling and calculate the corresponding light and heat parameters; Using the light heating parameters to control the light heating device to perform light heating on the target object, and analyzing the real-time state changes of the target object; According to the real-time state changes, the light heating parameters are optimized in real time through a preset parameter optimization model, and the light heating device is adjusted in real time to perform intelligent light heating on the target object.

2. The intelligent light heating method according to claim 1, characterized in that: The method of analyzing the temperature gradient and optical characteristics of the target object surface by using a preset stage recognition model based on the pre-acquired hot spot image of the target object surface to identify the first stage of the target object includes: The temperature and transmittance of the target object surface are collected through a preset sensor group to obtain a hot spot image of the target object surface; According to the hot spot image, the temperature gradient and optical characteristics of the target object surface are analyzed by a preset stage recognition model to obtain a fusion feature; Based on the fused features, the first stage of the target object is identified.

3. The intelligent light heating method according to claim 2, characterized in that: Based on the hot spot image, the temperature gradient and optical properties of the target object surface are analyzed using a preset stage recognition model to obtain fusion features, including: Calculating the temperature gradient field in the hot spot image through a preset stage recognition model, and extracting a temperature feature set from the temperature gradient field; performing optical characteristic analysis on the hot spot image to extract an optical feature set; Extracting a set of biometric features by performing wavelet transform on the hot spot image; The temperature feature set, the optical feature set, and the biological feature set are fused according to different channels to obtain fused features.

4. The intelligent light heating method according to claim 1, characterized in that: Based on the first stage, the light and heat requirements and energy state of the target object are analyzed through the preset light and heat matching mechanism to perform light and heat coupling and calculate the corresponding light and heat parameters, including: Analyze the first stage according to the preset stage parameter mapping rules and match the corresponding first parameters; The light and heat requirements and energy state of the target object are analyzed through the preset light and heat matching mechanism, and light and heat coupling is performed to generate a coupling matrix. Analyzing the real-time state of the target object, compensating and correcting the first parameter according to the real-time state, and obtaining a second parameter; The coupling matrix and the second parameter are combined to generate corresponding light heating parameters.

5. The intelligent light heating method according to claim 4, characterized in that: The method of analyzing the light and heat requirements and energy state of the target object through a preset light and heat matching mechanism and performing light and heat coupling to generate a coupling matrix includes: A parameter coordinate system is constructed based on the first parameter, and the preset light and heat matching mechanism analyzes the parameters and parameter weights of the target object to obtain the light and heat demand parameters and energy state parameters corresponding to the target object; The light and heat demand parameter is split according to a plurality of preset dimensions, and the split light and heat demand parameters are weightedly fused according to corresponding parameter weights to obtain a first fused parameter vector; The energy state parameter is split according to a plurality of preset dimensions, and the split energy state parameters are weightedly fused according to corresponding parameter weights to obtain a second fused parameter vector; A matrix coupling operation is performed on the first fusion parameter vector and the second fusion parameter vector to generate a coupling matrix.

6. The intelligent light heating method according to claim 4, characterized in that: Combining the coupling matrix and the second parameter to generate corresponding light heating parameters includes: Analyze the correlation strength of the light and heat demand parameters in the coupling matrix and construct light and heat constraints; Extracting multiple features from the second parameter, and mapping the extracted feature dimensions to be consistent with the vector dimensions in the coupling matrix to obtain a second parameter feature vector; The second parameter eigenvector is optimized through the light-heat constraint to generate corresponding light-heating parameters.

7. The intelligent light heating method according to claim 1, characterized in that: Using the light heating parameters to control the light heating device to perform light heating on the target object, and analyzing the real-time state changes of the target object, including: Controlling the light heating device to perform light heating on the target object according to the light heating parameters; Through the preset sensor group, the status data of the target object is collected in real time to obtain real-time status data; According to the real-time status data, the temperature change and energy absorption of the target object are analyzed to obtain the real-time status change.

8. The intelligent light heating method according to claim 1, characterized in that: According to the real-time state change, the light heating parameters are optimized in real time through a preset parameter optimization model, and the light heating device is adjusted in real time to perform intelligent light heating on the target object, including: Compare and analyze the real-time status changes with the preset status to obtain the degree of status deviation; According to the state deviation degree, the light heating parameters are optimized in real time through a preset parameter optimization model to obtain updated light heating parameters; The updated light heating parameters are used to adjust the light heating device in real time to perform intelligent light heating on the target object.

9. The intelligent light heating method according to claim 8, characterized in that: According to the state deviation degree, the light heating parameters are optimized in real time through a preset parameter optimization model to obtain updated light heating parameters, including: According to the degree of state deviation, a set of candidate optimization parameters for optimizing the light heating parameters is calculated through a preset parameter optimization model; By analyzing the adjustment of the candidate optimization parameters to the state deviation degree, the key optimization parameters are screened out from the candidate optimization parameter set; The light heating parameters are optimized in real time according to the key optimization parameters to obtain updated light heating parameters.

10. An intelligent light heating device, characterized in that: A method for implementing an intelligent light heating method according to any one of claims 1 to 9, comprising: The stage recognition module analyzes the temperature gradient and optical properties of the target object surface based on the pre-acquired hot spot image of the target object surface through a preset stage recognition model to identify the first stage of the target object; A parameter calculation module, based on the first stage, analyzes the light and heat requirements and energy state of the target object through a preset light and heat matching mechanism to perform light and heat coupling and calculate the corresponding light and heat parameters; A light heating module controls the light heating device to heat the target object using the light heating parameters and analyzes the real-time state changes of the target object; The light heating optimization module optimizes the light heating parameters in real time according to the real-time state changes through a preset parameter optimization model, and adjusts the light heating device in real time to perform intelligent light heating on the target object.

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