Analysis, modeling, and monitoring of meat and food temperature and heat flow during cooking

Abstract

The invention profiles, models, and analyzes the temperature and heat flow of meat and food during cooking. The present invention relates to a system and method for monitoring a laser or LED device, a Scanning by the scanning mechanism, measuring the temperature of the bottom and top surfaces of the food being cooked, and moving the food horizontally. The isothermal slice is modeled in 3D as a vertically non-isothermal slice, and the temperature of this slice, in particular Internal T core Calculate.

Description

[Technical field]

[0001] The present invention relates to a system and method for controllable and measurable cooking of meat. More specifically, the present invention is directed to modeling, calculating and monitoring meat temperature. and methods and systems for indicating the timing of cooking steps, such as turning meat over, It is. [Background technology]

[0002] Observing the cooking process closely helps optimize cooking and produce good food. Although several devices and methods have been provided in this field, There is no complete analysis or model of it. It is an object of the present invention to provide a method and apparatus for overcoming the shortcomings of the prior art. do. Also, temperature and thermal profiling, modeling and analysis of meat or food during cooking process. It is also an object to provide a method for monitoring. Another object of the present invention is to provide a profile-based method for dynamically optimizing meat and food cooking. The objective of the present invention is to provide a filing, modeling and monitoring methodology. Another object of the present invention is to provide a method for minimizing energy consumption in cooking. This is what we should do. It is yet another object of the present invention to provide a method for predicting the temperature and heat flow of meat or food in general during cooking. The objective is to provide a filing, modeling and monitoring facility. This and other objects of the present invention will become apparent as the description proceeds. Summary of the Invention

[0003] The present invention relates to a system for controlling and calculating the cooking of meats such as beef, fish, and chicken. In some embodiments, the measurement is performed in a controlled and measurable manner on a relatively flat, homogenous food material. It also realizes cooking temperatures based on calculations. Also, before you start cooking, you need to check the ingredients and the cooker. How to measure the environment and conditions of ingredients and calculate the spatial temperature difference of ingredients that changes in real time It is also something to offer. In one embodiment, a 3D model of meat or food is created by slicing the meat or food horizontally along a temperature gradient. A mathematical method is used to perform the calculation. The heat flow equation is calculated using the finite element method. It calculates the temperature of one part of the food and the differential change between adjacent parts. Hands-free and sensor-free food monitoring is essential for automated cooking. To cook by temperature, you can use a thermometer-like sensor that you insert into the food, or a kitchen utensil to cook by hand. This will eliminate the need for human labor. It will be possible to tell when food is being heated in a fryer without the need for humans or sensors. This allows you to decide in real time whether to invert the image on the screen or not. This will make it possible to build robots that can carry out processing. In yet another embodiment, the numerical model is dynamically updated continuously over time. The sensors measure the values ​​of various parameters around the system and provide real-time Then the parameters are used to determine the temperature of the food, especially its core temperature Tcore. Note: The time required to heat the core (hereafter referred to as Tcore) is recalculated and dynamically reevaluated. The world is a dynamic, continuous process of updating, recalculating, and re-evaluating the values ​​of variables. By evaluating the temperature of the food at any time, namely Tcore and Tcore By controlling the temperature change over time, △Tcore, the cooking is optimized. . In yet another embodiment, multiple meats or ingredients, or multiple different types of meat, beef, chicken, The system is configured to simultaneously scan, monitor, and cook fish, etc. Each slice or ingredient is modeled separately from the other meat slices or ingredients and moves during the cooking process. The contents of the dishes, which change dynamically, can be set independently. [Brief description of the drawings]

[0004] [Figure 1] FIG. 1 is a schematic diagram of an optical head and beam for scanning meat or food on a cooking surface. [Diagram 2] FIG. 1 illustrates a temperature map of meat or food on a cooking surface. [Diagram 3] FIG. 1 is a schematic diagram of meat or food temperature differential modeling. [Figure 4] FIG. 1 illustrates the division of different components of meat or food. [Diagram 5] FIG. 1 shows a table of the relative contents of different meats. [Figure 6] FIG. 1 is a schematic diagram of the optical head and temperature analysis and profiling of scanned meat or food. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0005] Detailed explanation of the figure Scheme The diagram below illustrates cooking meat or other food on a flat, hot surface. FIG. 1 shows a 3D, thermal or holographic imaging system in which a slice of meat (200, see FIGS. 2 and 4) is scanned with a beam 105. The visible scan head 100 measures meat and surface temperatures depending on the shape, size, texture and surrounding area of ​​the meat. FIG. 2 shows a schematic of a system for measuring meat or food product 200 in real time. It is placed on a hot surface 400 with temperature Tsurface, where Ttop = Tbotto m=Tside, where Tside is the temperature of any side of the item 200. where Ttop is the temperature of the top exposed surface of the item 200 and Tbottom is the temperature of the heated cooking This is the temperature of the bottom surface of the item 200 facing the surface of the appliance. The object of the present invention is to provide a method for determining the ΔTcore, i.e., the meat or food product 200, which is defined and prepared by a 3D model. Calculate the temperature difference over time for the center of the food placed horizontally on a cooking surface of 400°. Tcore is the modeled center temperature of the meat or food product 200 and is the The material is sliced ​​so that all points are horizontally isothermal with respect to Tcore. The temperature has a temperature gradient from the bottom surface in contact with the cooking surface 400 to the exposed top surface of the item 200, with a vertical is non-isothermal. The Tcore is calculated based on measurements taken by 3D sensors monitoring the cookware and ingredients 200. ΔTcore is the relative relationship between the cooker parameters and the surrounding conditions for a given temperature change ΔT. An example of this is the temperature of a cooking surface. The non-isothermal characteristics of the conduct. method This method solves the heat flow equation of meat in real time. First, the flat meat is The meat is then heated through the hotter bottom surface and into the meat. The heat and temperature in the meat or food is transferred to the food through the exposed surfaces of the meat, i.e. the top and sides. The first approximation of the temperature analysis results is given below, assuming that the diffusion parameters of the meat and food are constant. It can be derived by solving the two-dimensional heat flow equation. JPEG2025506547000002.jpg1128, Here X is the axis on which the temperature is measured, T is the temperature of the object being measured. t is time D is the diffusion constant It is. This equation is given by the diffusion constant D and the rate proportional to the local second derivative of temperature along the X direction, In general, this partial differential equation is almost The actual boundary conditions of the equations cannot be solved analytically. Therefore, the standard for finite element calculations is Solve numerically using known methods such as Crank-Nicholson method, Forward Euler There are known methods, such as the Backward Euler method, and others, all of which are described here. These methods are related to the way in which space and time are constructed. Here, dx and dt are small relative to the size of the object, and dx =L / N, where L is the height of the object and N is the number of steps to take along the X axis. The Nicholson method can be expressed as follows, where dt and dx are the calculation steps: JPEG2025506547000003.jpg15169 where i represents the number of slices of food, each with a thickness of dx, and n is the nth slice of the calculation. represents the time step. After introducing the new constant, JPEG2025506547000004.jpg1426 The equation can be rearranged as follows: JPEG2025506547000005.jpg5114 The left side of this equation contains components of temperature at various locations on the meat or food, but at a single time. The right hand side of the equation is the temperature at time step n. Combinatorial. We assume constant boundary and initial conditions at n=0 and n=N. Therefore, the right hand side of the equation is known. Therefore, the left hand side is This can be expanded into a set of equations that can be solved for any n. Figure 3 shows the initial conditions for solving the above set of equations: At time step n=0, the boundary conditions shown in Figure 3 are applied to meat 200 on a heated cooking surface. will be done. The meat or food is placed on a hot surface at 1000°C. The temperature is in equilibrium with the ambient room temperature of 250°C. Therefore, the Ti All s are known and can be replaced by a constant vector Ai. On the left side of the equation , all For simplicity, we can replace the value of JPEG2025506547000006.jpg713 with the new representation Ui. Now the new equation becomes It will look like this. JPEG2025506547000007.jpg789 JPEG2025506547000008.jpg91Expanding this set of equations and substituting values ​​for index i gives us the following: JPEG2025506547000009.jpg785JPEG2025506547000010.jpg782JPEG2025506547000011.jpg782JPEG2025506547000012.jpg782 JPEG2025506547000013.jpg7103 In the first equation, U-1 is measured by a sensor head that continuously and remotely monitors the cooking. This is the temperature of the hot surface at the bottom of the Then, the following equation is obtained: JPEG2025506547000014.jpg7100 where C0 is a known value. Therefore, U1 can only be expressed as a function of U0. : JPEG2025506547000015.jpg1460Next, if we introduce this U1 formula into the second formula instead of U1 on the left side, we get the following second formula: Obtained. JPEG2025506547000016.jpg14121 Now we can express U2 as a function of U0 and introduce the expressions for U1 and U2 into a third equation. Then we get an equation that contains only U3 and U0. We continue this process of substitution along the entire group. If we repeat this process until we reach the last equation, we arrive at the last equation that contains only the equations for U0 and UN. So, UN= Remember JPEG2025506547000017.jpg713? This is a remote sensor head located on the cooking plate. This represents the measured temperature of the top surface of the meat or food at a given time n+1. By introducing it into the equation, U0 can be calculated and solved. After solving for U0, we can go back to the original equation, introduce the calculated value of U0, and calculate U1. Similarly, we iteratively calculate all U from U1 to UN-1. This will give us the time step. Accurate temperature analysis results can be obtained from the bottom to the top of meat or food in cup n=1. The analysis results are based on the heat flow calculations for the inside of meat and food, and the heat flow measured in real time by the sensor head. This is a combination of surface temperature and height analysis of the meat. After substituting the results of the temperature analysis for n=1 and n=2 into the right-hand side of the equation, you can calculate the temperatures for n=2, n=3, etc. This process can be repeated. This will allow the meat / food being cooked on the hot surface to cool down. The internal temperature of the product is predicted and monitored by the sensor head as described above. The method is to swap the positions of the top and bottom surfaces of the meat or food, i.e. to flip the meat or food during the cooking process. The sensor head measures the temperature of the top and bottom of the meat or food, This is because the value of is introduced into the model in real time. The process and outcome remain the same. Whether inverted or not, this model has a central warming This applies more frequently to Based on this measurement and calculation capability, the present invention reevaluates the calculated temperature value depending on the value. This allows for more accurate prediction of the temperature of the meat or food core. So, for example, when flipping meat, the system measures the actual temperature of the top surface and calculates Tcor This resulted in a more accurate core temperature calculation compared to the previous one used to calculate e. The method is configured to introduce measurements into a series of equations to Diffusion constant D is the diffusion constant of the heat flow equation, which consists of the following parameters: JPEG2025506547000018.jpg1221, where κ is the thermal conductivity of meat or food, p is the density cp is the specific heat capacity of meat. D is in mm2 / sec. The density and specific heat capacity are differentiated for the various components of meat. These components include bone, It contains fat, moisture and protein. D generally accounts for an average of 70% of the mass of the meat being cooked. D for lean meat such as red meat is set to 0.12-0.14m This would be in the range of m2 / sec. Using this value, accurate temperature calculations can be performed. https: / / www.tandfonline.com / doi / pdf According to / 10.1271 / bbb.58.1942, the diffusion value of fat in meat is low at 0.1 mm2 / sec. To improve the accuracy of our model, it is important to estimate the percentage of fat in cooked meat. Additionally, bones behave differently than meat, making it difficult to assign the correct core temperature to a slice of meat. It must be excluded from the calculation because it does not contribute to the ttps: / / meatscience.org / docs / default-source / publications-resources / rmc / 1975 / heat- transfer-in-meat.pdf?sfvrsn=e308bbb3_2 This reference shows that the direction of transfer is parallel to the grain of the meat and perpendicular to the grain. Since there may be a 10% difference in thermal conductivity even in the perpendicular direction, Cutting meat may be important for this purpose. Therefore, in the method of the present invention, the visual and thermal sensors of the system are part of the sensing head. Use sensors to identify areas of uncooked meat that are not controlled or measured in the process. By focusing on the meat-only area, a model for predicting the core temperature of meat can be developed. FIG. 4 shows how the meat 200 is viewed from above and the bone 220, fat 230, and meat 210 regions are detected. In this case, the model of the automatic cooking process surrounds the meat-only area 210. The visual image shows the type of meat cut (grain-based) It is also possible to detect the type of cleavage (parallel or perpendicular) and set the appropriate diffusion constant based on the type of cleavage. . Visible area image of meat from the sensor head - bone areas (thin lines) 220, pure fat areas ( Detect the area of ​​the skin (white line) 230 and the flesh area (black line) 210. The cooking model then runs only within the area outlined in black, providing the most accurate core temperature estimate. can be. Emissivity and actual temperature measurement The emissivity of a material surface is its effectiveness at emitting energy as thermal radiation. is the thermal radiation from a surface and is given by the Stefan-Boltzmann law: It is the ratio of the radiation from a black surface of the same temperature. This ratio varies between 0 and 1. Most organic materials have relatively high emissivity, but this parameter is not constant and may vary depending on the material being monitored. It varies depending on the surface temperature, content, and surface shape of the object. To measure the emissivity of a material, its emissivity must be estimated or measured. The power relationship is given by the Stefan-Boltzmann equation: JPEG2025506547000019.jpg749 Here P is the radiated power (watts), A is the luminous area (m2), is the emissivity, the Stefan-Boltzmann constant (5.67x 10-8 Wm-2K-4), T is the surface temperature (Kelvin), Tc is the ambient temperature in Kelvin. The thermal sensor, part of the scanning optical head that measures the meat from above, measures the P / A parameter (thermal The radiated power per unit area of ​​meat displayed at each pixel of the sensor is measured. Therefore, if the emissivity of the meat is known, the above formula can be used to calculate the exact temperature of the top surface of the meat. The ambient temperature can be simply measured at a specific predefined location in equilibrium with the environment. The measurement can be made by a pure thermometer or by a scanning optical head. Several methods have been proposed to estimate or measure emissivity. Estimation of emissivity based on visual structure of meat: captured by a visible sensor on a scanning optical head By analyzing the captured images, the percentage of lean meat in the slice and the amount of lean meat on the top surface of the slice can be determined. The percentage of fat and other tissues can be determined. This analysis is carried out using machine learning tools (Open CV library), and Nv You can use common image processing tools available from GPU manufacturers such as idia, or By calculating the area of ​​various parts and weighting the emissivity of each part in the overall emissivity of the meat, An example of this last method is shown in Figure 5. The emissivity of the surface is measured immediately after turning the meat over on the hot surface. The underside of the meat that was in contact with it will reach the same temperature as the hot surface. And in that short time, The scanning optical head captures a thermal image of the meat, allowing the actual meat temperature to be determined, Using the Stefan-Boltzmann equation, we can accurately calculate the emissivity of the surface. Then, using this emissivity value, we can distinguish between meat with a raw top surface and meat with burnt top surface (meat with denatured proteins). ) over time, it is possible to calculate the temperature at which the structure changes. The amount of moisture released from the meat is used to estimate and measure both the temperature and emissivity of the meat. The amount of moisture on the hot surface of the meat is directly related to the denaturation of proteins inside the meat and is known in the technical literature. The optical head with a visible range sensor detects moisture in the image. It is possible to detect water droplets and estimate their amount over time using simple image processing tools. After estimating the rate of change of the temperature, it is now time to estimate the temperature and, more importantly, the emissivity of the meeting. and update the heat flow model with the updated emissivity value. The emissivity values ​​of various meats, fish and poultry meats are compared against the actual core temperature calculated from the model above. By measuring the time course, we can get a better estimate. In this case, the model uses The emissivity value can be changed to a value that results in the same core temperature in the model and in the measurement. This can be done in a systematic way, by gradually changing the emissivity value until the core temperature results are the same. Once you have found the emissivity, you can use it to determine the type of meat, temperature range, and meat characteristics (fat, protein, etc.) quality, moisture, bone, color, etc.) and other important parameters of the cooking process This corrected emissivity measurement can be used for several cooking runs. Sporadic samples, either once per cycle or once out of dozens or hundreds of cooking cycles There are sufficient data points for emissivity across a range of meat types and cooking conditions. Once stored in a database, standard data mining algorithms can be used to mine the data. It searches for the correct emissivity for your meat and the utensil you are using and recommends the right emissivity for your upcoming cooking cycle. It is possible. Scanning Optical Head The scanning optical head 100, located above the cooking plate, uses the heat flow equations and heat flow model described above to This optical sensor measures all the important meat and utensil parameters in real time. One proposed head configuration is shown in Figure 6. This design allows the optical head 100 to be mounted on the cooking plate. Can be positioned at any distance and angle above the cooking plate and to the meat or food being cooked As long as there is a clear line of sight, the system can perform well. The cooking process is monitored using depth, range and visible image data. The system 100 can automatically calibrate the area for monitoring. It includes a memory and communication unit, and stores the data collected by the sensor in real time. It integrates, runs the heat flow model, solves it in real time, and captures different images from different sensors. into one unified database to make decisions about the monitored cooking processes. Support. The scanning optical head consists of: IR (infrared) chip 150. This is the infrared light that surrounds the food and hotplate and cooking area. The chip 150 is sensitive to thermal radiation emitted from other parts. It has its own optical system 145 for setting the focal length, and it also has variable focus, zoom, aperture and other such features. It can include any optical parameters used in the sensor. It takes into account the emissivity of the object. Considering this, the IR chip 150 and the optical system 145 can measure the surface temperature of the object to be observed with high accuracy and ultra-high resolution. , and can be measured at high frame rates of at least 25 Hz. A visible chip 125 with proprietary optics 130 that is sensitive in the visible region of the spectrum and has at least Detect, monitor and measure objects with color information in real time at 60Hz rate and HD resolution can be done. A high-resolution radar sensor 180 that combines a transmitter 155 and a receiver 165 with a unique optical system 160. It can measure the range and depth of any object with a depth resolution of 2mm or better, as well as horizontal and vertical resolution It can be measured with the same accuracy. Laser or LED device 120 with different color options. Collimated (collimator 14 0) A light source that has a beam and shines a different color or different flash on an object on a cooking surface from a distance. Mark with speed and information about the condition of the object or cooking surface, e.g. hot, cold Visually indicate when food is over temperature, ready, removed from cooking surface, flipped, etc. can, The mirror 135a, the dichroic mirror 135b, and the set of low-emissivity glass windows are the same as those described above. All sensors are aligned along the optical path, eliminating parallax between sensors for optimal efficiency. This mirror and reflector set allows the line of sight of multiple sensors to be combined into a single axis. This allows you to have a single opening in the system through which all sensors operate. And the images produced by all the sensors are perfectly aligned without the need for spatial calibration. It is placed just before the main aperture of the optical head, and various sensors contained in the optical head are It can look in multiple directions while still maintaining the high resolution of the sensor and the images it creates. Two scanning axes can be scanned to cover a large field of view. Two-dimensional scanners 110a and 110b equipped with mirrors 115a, 115b or other known scanning mechanisms This scanner operates at various speeds and scan rates and is a versatile scanner. The scanner has several functions. It acts as an individual scanner for another sensor while the other sensor is inactive. To avoid misreading the image, other sensors are shut down or blocked. During this time, the field of view of one sensor is scanned at the optimal scan rate and scan pattern. In this case, the scanner is aligned along a single optical axis and scans the field of view of all other sensors. In this mode, the scan rate and scan pattern are determined by the specific sensor. It is optimized for all sensors. Calibration mode - The scanning unit moves in small increments to reach the required scanning area. It can detect corners of an object or move to a specific point in space to provide the appropriate area for the optical head to monitor. Assign the rear. High Resolution Mode: Scans a specific area in very small steps and captures multiple images of the same area. The image is stored. Large Field of View Mode - Moves the scanning unit slowly and accumulates multiple images from different sensors. The composite image is created by taking the instantaneous images of each sensor. It has a similar resolution to the image but a wider coverage area. Mark Mode - The 2D scanner shines a laser or LED light, which is part of the optical head, on the cooking surface or It creates clear light markers around the food being cooked. The manipulation of laser beams is well known in the field of laser shows and is a well-known technique. It can be used to mark certain items or features with light. The CPU unit 170 may be integrated into the optical head or connected to that component via a communication line. The unit can be based on, for example, the NVIDIA Jetson Nano GPU processor. It uses advanced AI and neural network algorithms on a wide variety of large images. It is specifically designed to run the same algorithm as the NI PXI-8110, which connects to multiple cameras and sensors and It acquires images from these cameras in real time and performs powerful image processing and object classification across frames. The CPU unit can execute multiple It can control, communicate and send commands to external sensors and devices. The functions include the following: The CPU unit is connected to an optical head sensor, which captures images in real time and displays various The image is processed based on a sophisticated algorithm. By running the heat transfer model described above and using the stored data from all sensors, This allows the CPU to monitor, calculate, and maintain the internal core temperature of the food being cooked. Using AI algorithms, the CPU checks the cooking quality throughout the entire cooking process. Able to distinguish temperature, color, shape, amount of smoke, changes in size and shape, etc. The CPU communicates with external monitors and databases using physical or wireless communication lines. and communicate. The CPU can be used to improve the learning process of the algorithms and to improve the performance of the kitchen team while cooking. Collect data for future use, including to monitor your performance and other data uses. and save. It can implement powerful AI algorithms based on neural networks to improve cooking quality. It makes real-time decisions and redirects the energy flow to the cooker to optimize the cooking process. , save energy consumption (gas, electricity, etc.). Data can also be sent to a server in the cloud, and such data can include, for example, Includes pictures of the food being cooked, energy consumption data, gas level readings, and other relevant data points. Can be enjoyed. Smoke, fire, hot food items in front of the person using the stove, or spilled liquids on surfaces It uses sound, light and / or telephone signals to landlines and mobile phones to warn of dangerous situations, such as overcooked food. Alarms can be sent in a variety of ways, including by calling a phone. It can be connected to other sensors such as temperature, smoke, CO and other hazardous gases, earthquake sensors, etc. By connecting these sensors, the CPU can detect qualified personnel or dangerous situations within the process. It can alert you to a variety of situations that require human intervention, such as warning you about It is. Fixed line, Ethernet, Wifi, Bluetooth, NFC, RF, present during solution implementation and other commonly used wireless remote solutions. Connect to the By combining the functions of the scanning optical head with the modeling and mathematical calculations of the CPU, This allows for precise cooking temperatures and finely cooked meat and other foods. The canning optical head detects the food product, its part and contents, and the part of the meat / ingredient being monitored. It provides real-time data about the boundary, its surroundings, and cooking equipment. The diffusion constant, which depends on the thermal conductivity, density, and specific heat capacity of the meat or object being cooked, can be re-evaluated. By feeding the new value of the diffusion constant back into the set of equations, a more accurate estimate of Tcore can be obtained. The value can be obtained.

Claims

1. Profile, model, and monitor the temperature and heat flow of meat and food during the cooking process. A method for determining whether a particular component of a liquid is to be processed, comprising the steps of: Place the meat or food on the flat surface of the cooking appliance and make sure it and the flat surface are aligned with the appropriate sensors, all A secondary sensor with a set of mirrors or a scanning mechanism that combines the line of sight of the sensors into a single, parallax-free axis. The optical head of the original scanner must be sufficiently visible to be monitored. The temperature of the underside of the meat or food product that is in contact with the flat surface of the cooking device and the temperature of the food product that is opposite the underside and the periphery of the cooking device are measured. The temperature of the top surface of the meat or food exposed to the ambient temperature is measured. The meat or food is sliced ​​horizontally isothermally and vertically non-isothermally in a 3D model. Modeling step. Based on the measured temperatures of the bottom and top surfaces of the meat or food, a series of calculations are used to determine this speed. Calculate the temperature of the rice.

2. The method of claim 1, wherein the sensor is an IR sensor, a visible distance sensor, a high resolution lidar sensor, from a laser or LED device with parallel emitting beams of different colors The present invention is characterized by the following characteristics.

3. 2. The method of claim 1, wherein the scanning mechanism comprises two scanning mirrors. thing.

4. 2. The method of claim 1, further comprising the step of: calculating the temperature of the slice by measuring the center of the modeled meat or food product; Slice of T core This involves calculating

5. The method according to claim 1 core This also includes slicing during cooking. T in the center core This includes temperature changes over time and 3D models and modeling.

6. The method according to claim 5, wherein the T core is dynamically and continuously updated during the cooking process. The 3D model will also be updated to recalculate and reassess the temperature modeling of the meat and food. and surrounding the meat or food to optimize the cooking process and cooking of the meat or food. Dynamically and continuously supplying parameter values.

7. 7. The method of claim 6, wherein the parameters include bottom and top temperatures of the meat or food product; and the thickness of said meat or food product.

8. The method of claim 1, wherein the 3D model of the meat or food product distinguishes between different components of the meat or food product, The 3D model is built using only red meat.

9. 9. The method of claim 8, wherein the different components of the meat or food are bone, fat, moisture and protein. It consists of proteins.

10. The method of claim 1 is adapted to simultaneously scan, monitor and measure multiple meats or food products, or a variety of meats. Each meat or food item is modeled separately from the others and dynamically changes during the cooking process. and independently setting each cooking plan to be modified.

11. 11. The method of claim 10, wherein the various meats comprise beef, poultry and fish. How to do this.

12. The method according to claim 1, further comprising the steps of: and re-evaluating the calculated temperature value in response to the

13. 9. The method according to claim 8, further comprising: turning the meat or food product over and measuring the actual temperature of the current top surface; The actual temperature is T core Compare the actual temperature to the previous temperature used to calculate The actual temperature measurements of the current top surface are introduced into the set of equations to obtain a more accurate core temperature. Further comprising obtaining the calculated value.

14. The method of claim 1, wherein the scanning is combined with modeling and mathematical calculations in a CPU. The optical head detects the product, its parts and contents, the boundaries and surroundings of the meat or cooking object being monitored. Get real-time data on your cooking environment and appliances, and the thermal conductivity of your meat or cooking items. The diffusion constant, which depends on its density and specific heat capacity, is reevaluated to obtain more accurate cooking temperatures and textures. For finely cooked meats and foods, more precise T core To obtain the value of feeding back into an equation.

15. Profile, model and monitor the temperature and heat flow of meat and food during the cooking process 1. A system for monitoring a signal processing device, the system comprising: A cooking device having a flat surface suitable for cooking meat or other foods. a sensor and a set of mirrors that combine the lines of sight of all the sensors onto a single, parallax-free axis; A two-dimensional scanner with two scanning mirrors or other known scanning mechanisms. The optical head measures the temperature of the bottom and top surfaces of meat or food, as well as the three-dimensional Realistic analysis of dimensions, shapes, boundaries, heights, widths and 3D structures that are important for accurate solution of heat flow models. It is configured to be measured in time. Meat or food is sliced ​​horizontally across the bottom and top surfaces, with the slice defined by the center of the meat or food. Rice temperature T core and ΔT of the temperature change over time during cooking core Calculate, 3D modeling Software means for debugging. It receives data collected from sensors and performs calculations, core and ΔT core The CPU that generates the value.

16. 16. The system of claim 15, wherein the sensor is an IR sensor, a visible distance sensor, a high resolution 3. Lidar sensors, lasers or other sensors with beams that are set to be parallel The LED device is selected from LED devices having different colors.

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