Grain pile temperature calibration method and system based on thermodynamics

Abstract

The invention relates to the technical field of grain temperature, discloses a thermodynamics-based grain pile temperature calibration method and system, and aims to solve the problems of high cost and poor accuracy of an existing grain pile temperature detection method. Obtaining the temperature of the point position detected by each sensor in the sensor matrix; discretizing the space where the grain pile is located into grain pile grids, determining the temperature of each grain pile grid according to the temperature detected by each sensor, and generating a temperature distribution field for representing temperature distribution; constructing a thermodynamic equation for each grain pile grid, discretizing the thermodynamic equation to generate a discrete equation, and converting the discrete equation into a linear equation set in a matrix form; and solving the system of linear equations by taking the temperature of the point location detected by each sensor as a boundary condition, and calibrating the temperature according to a solving result. The method reduces the cost, improves the accuracy, and is especially suitable for granaries.

Description

Technical Field

[0001] The present invention relates to the technical field of grain temperature detection, and particularly relates to a method and system for calibrating the temperature of a grain pile based on thermodynamics. Background Art

[0002] Grain pile temperature detection refers to the process of real-time or periodic monitoring of the temperature inside and on the surface of a grain pile during the grain storage process. Through grain pile temperature detection, grain spoilage can be prevented, pest risk can be controlled, storage conditions can be optimized, and the safety and stability of stored grain can be ensured. However, as a porous granular medium, the temperature field distribution of a grain pile is affected by multiple factors such as environmental humidity, microbial activity, and bulk density, resulting in significant temperature differences inside, and uneven heat transfer of grains, making it difficult for a single detection point to reflect the overall temperature distribution.

[0003] In the prior art, the main solution for grain pile temperature detection is to set up a temperature sensor matrix in the grain pile. First, the temperatures at different positions of the grain pile are collected by multiple temperature sensors in the temperature sensor matrix, and then a three-dimensional temperature distribution map of the grain pile is generated based on an interpolation algorithm. However, this method has at least the following problems: First, the accuracy of the temperature field completely depends on the number and distribution of temperature sensors. A large number of temperature sensors need to be buried inside the grain pile, which may damage the airtightness of the grain pile, and the probes are vulnerable to mechanical damage and fumigation corrosion, resulting in high maintenance costs. If the temperature sensors are arranged sparsely, the interpolation algorithm will cause a smoothing effect due to insufficient data and cannot capture local anomalies. At the same time, the temperature sensors may be sparsely or densely arranged due to installation restrictions. In this case, the interpolation results are prone to problems such as over-weighting in the central region and error amplification in the edge region, resulting in a decrease in the accuracy of temperature field reconstruction. Second, the interpolation algorithm only depends on mathematical statistical laws and ignores the physical characteristics of the grain pile, and cannot reflect the dynamic changes of the actual temperature field. Third, if there are local high-temperature or low-temperature abnormal points in a certain part of the grain pile, the interpolation algorithm is likely to form a concentric circular temperature distribution around the abnormal temperature point, which does not conform to the actual heat conduction law of the grain pile and has poor accuracy. Summary of the Invention

[0004] The present invention aims to solve the problems of high cost and poor accuracy existing in the existing grain pile temperature detection methods, and proposes a method and system for calibrating the temperature of a grain pile based on thermodynamics.

[0005] The technical solutions adopted by the present invention to solve the above technical problems are as follows: In the first aspect, the present invention provides a method for calibrating the temperature of a grain pile based on thermodynamics, and the method includes: Install a sensor matrix in the grain pile, and obtain the temperature of the position where each temperature probe of the sensor matrix is located; The space where the grain pile is located is discretized into grain pile grids, the temperature of each grain pile grid is determined according to the temperature detected by each sensor, and a temperature distribution field is generated to represent the temperature distribution; Constructing a thermodynamic equation for each grain pile grid, discretizing the thermodynamic equation to generate a discrete equation, and converting the discrete equation into a linear equation system in matrix form; The temperature of the point where each sensor is located is used as a boundary condition to solve the linear equations, and the temperature of each grain pile grid is calibrated according to the solution results.

[0006] Furthermore, the thermodynamic equation is as follows: ; in, represents the effective density of the grain pile, represents the equivalent specific heat capacity of the grain pile, represents the effective thermal conductivity of the grain pile, represents the heat source term, represents the temperature of the grain pile grid, represents the temperature gradient of the grain pile grid, represents the divergence operator, Represents the first-order derivative of the temperature of the grain pile grid with respect to time.

[0007] Furthermore, the discrete equation is as follows: ; in, Indicates the The grain pile grid is in the time step temperature, Indicates the The grain pile grid is in the time step temperature, Indicates the The grain pile grid is in the time step The temperature, The grain pile grid is The adjacent grids of the grain pile grid, represents the time step, Indicates the The grain pile grid and the The contact area of the grain pile grid, Indicates the The grain pile grid and the The spacing between grain pile grids.

[0008] Furthermore, the linear equations are as follows: ; in, represents the coefficient matrix, represents the temperature vector, including the temperatures of each grain pile grid at the time step ; represents the right - hand side vector, including the sum of the heat storage term and the heat source term of each grain pile grid at the time step ; In the coefficient matrix : ; ; where represents the diagonal elements of the coefficient matrix ; represents the non - diagonal elements of the coefficient matrix ; ; In the right - hand side vector : ; where represents the sum of the heat storage term and the heat source term of the th grain pile grid at the time step ;

[0009] Furthermore, solve the linear equation set, and calibrate the temperatures of each grain pile grid according to the solution results, including: If there is a sensor in the grain pile grid, use the temperature detected by the sensor as the temperature of this grain pile grid. After substituting the temperature of this grain pile grid into the linear equation set, use the pre - conditioned conjugate gradient method to solve, obtain the temperatures of each grain pile grid, and update the temperature distribution field according to the obtained temperatures of each grain pile grid to complete the temperature calibration.

[0010] Furthermore, determine the temperatures of each grain pile grid according to the temperatures detected at the positions where each sensor is located, including: If there is a sensor in the grain pile grid, use the temperature detected by the sensor as the temperature of this grain pile grid; if there is no sensor in the grain pile grid, determine the temperature of this grain pile grid according to the temperatures detected by adjacent sensors and based on the interpolation algorithm.

[0011] Furthermore, each sensor in the sensor matrix also respectively includes an electromagnetic wave emission module and an electromagnetic wave reception module; Determining the temperatures of each grain pile grid according to the temperatures detected at the positions where each sensor is located also includes: Under experimental conditions, detect the electromagnetic parameters of the grain pile at different temperatures, and construct a mapping relationship between temperature and electromagnetic parameters; Control each sensor to transmit and receive electromagnetic wave signals, and the frequency of the electromagnetic wave signal transmitted by each sensor corresponds to the temperature at the location where it is located; Construct the electromagnetic wave propagation equation between any two sensors in the grain pile to generate an electromagnetic wave propagation equation set; Determine the electromagnetic parameters of each grain pile grid according to the frequency of the electromagnetic wave signal transmitted by each sensor, the amplitude and phase of the received electromagnetic wave signal, and based on the electromagnetic wave propagation equation set; Determine the temperature of each grain pile grid according to the electromagnetic parameters of each grain pile grid and based on the mapping relationship between temperature and electromagnetic parameters.

[0012] Furthermore, the electromagnetic parameters are permittivity, permeability or conductivity.

[0013] Furthermore, the electromagnetic wave propagation equation between any two sensors is as follows: ; ; ; ; ; Where, represents the phase delay when the electromagnetic wave of the th sensor propagates to the th sensor, represents the propagation path of the electromagnetic wave of the th sensor propagating to the th sensor, represents the arc length parameter of the propagation path, which is used to describe the position on the propagation path, represents the position at which the phase constant is located, represents the integration variable, represents the amplitude attenuation when the electromagnetic wave of the th sensor propagates to the th sensor, represents the natural exponential function, represents the position at which the attenuation coefficient is located, represents the real part of the permittivity at the position , represents the permeability, represents the conductivity, represents the angular frequency of the electromagnetic wave signal, represents the pi, represents the frequency of the electromagnetic wave signal.

[0014] Second aspect, the present invention provides a thermodynamic-based temperature calibration system for a grain pile, which is used to implement the thermodynamic-based temperature calibration method for a grain pile as described in the first aspect. The system includes: a sensor matrix and a main control device; The sensor matrix is installed in the grain pile; The main control device is configured to obtain the temperature of each location detected by the temperature probe of each sensor in the sensor matrix; discretize the space where the grain pile is located into grain pile grids, determine the temperature of each grain pile grid according to the temperature detected by each sensor, and generate a temperature distribution field for representing the temperature distribution; construct a thermodynamic equation for each grain pile grid, discretize the thermodynamic equation to generate a discrete equation, and convert the discrete equation into a linear equation system in matrix form; use the temperature of each location detected by each sensor as a boundary condition, solve the linear equation system, and calibrate the temperature of each grain pile grid according to the solution result.

[0015] The beneficial effects of the present invention are as follows: The thermodynamic-based temperature calibration method and system provided by the present invention, based on the initial temperature distribution field, extend the local temperature detected by sensors to the global based on the heat conduction law, thereby realizing the calibration of the temperature distribution field. Even if the sensor arrangement is sparse, the temperature of the unmonitored area can still be estimated with high precision, reducing the equipment cost and maintenance cost of the sensors. At the same time, the degree of damage to the airtightness of the grain pile by the sensors is reduced, and the temperature calibration is carried out using the thermodynamic equation, which conforms to the actual heat conduction law of the grain pile, realizing low-cost and high-resolution monitoring of the grain pile temperature field; the present invention also dynamically correlates the electromagnetic wave signal propagation characteristics with the temperature field. Based on the propagation characteristics of electromagnetic waves, using the phase delay and amplitude attenuation data between multiple pairs of sensors, combined with the electromagnetic wave propagation model constructed based on Maxwell's equations, the electromagnetic parameters in the grain pile are inversely calculated, and then the electromagnetic parameters are converted into temperature to generate an initial temperature distribution field. The electromagnetic wave propagation path covers the entire grain pile, and a single path can penetrate multiple areas. Combining the cross data of multiple pairs of sensors, even if the number of sensors is limited, temperature detection with a resolution of millimeters to centimeters can still be achieved, improving the accuracy of the initial temperature distribution field. And by inversely calculating the electromagnetic parameters to determine the temperature, it is more in line with the real physical process. When the temperature gradient in the grain pile changes steeply due to uneven ventilation, electromagnetic inversion can accurately reflect the mutation boundary, further improving the accuracy of the initial temperature distribution field. Description of the Drawings

[0016] Figure 1 It is a schematic flowchart of the thermodynamic-based temperature calibration method for a grain pile provided in the embodiment; Figure 2 It is a schematic diagram of the sensor installation structure provided in the embodiment; Figure 3 It is a schematic diagram of the transmission of electromagnetic wave signals provided in the embodiment; Figure 4 Structural schematic diagram of the thermodynamics-based grain bulk temperature calibration system provided for the embodiment. Specific implementation manners

[0017] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solution in this embodiment will be clearly and completely described below in conjunction with the accompanying drawings in this embodiment.

[0018] In some processes described in the specification of the present invention and the above-mentioned accompanying drawings, a plurality of operations that appear in a specific order are included. However, it should be clearly understood that these operations may not be executed in the order in which they appear in this article or may be executed in parallel. The serial numbers of the operations are only used to distinguish different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel.

[0019] The technical solution of the present invention is applicable to application scenarios that require temperature detection of grain bulks, such as temperature detection of rice, wheat, corn, etc. in granaries.

[0020] Since the existing solutions for grain bulk temperature detection usually use a temperature sensor matrix to collect the temperatures at multiple points in the grain bulk, and then use an interpolation algorithm to determine the temperatures at other points, and then generate a temperature distribution field of the grain bulk. The inventor found through research on the existing grain bulk temperature detection methods that the accuracy of the temperature distribution field in the prior art completely depends on the number and distribution of temperature sensors, a large number of temperature sensors need to be arranged, the equipment cost and maintenance cost are high, the degree of damage to the airtightness of the grain bulk is large, and the accuracy is poor.

[0021] Based on this, the technical solution of the present invention is proposed. In the present invention, a sensor matrix is installed in the grain bulk, and the temperature of the location where each temperature probe of the sensors in the sensor matrix is detected is obtained; the space where the grain bulk is located is discretized into grain bulk grids, the temperature of each grain bulk grid is determined according to the temperature detected by each sensor, and a temperature distribution field for representing the temperature distribution is generated; a thermodynamic equation is constructed for each grain bulk grid, the thermodynamic equation is discretized to generate a discrete equation, and the discrete equation is converted into a linear equation set in matrix form; the temperature of the location where each sensor is detected is used as a boundary condition, the linear equation set is solved, and the temperature of each grain bulk grid is calibrated according to the solution result.

[0022] It can be understood that the change in the temperature of the grain pile is essentially the result of the combined action of the heat conduction process and internal heat generation (such as microbial metabolism). As a practical object, the temperature change of the grain pile is affected by heat conduction, heat sources, and boundary conditions. Based on this, the present invention first uses the temperature probe of the sensor to detect the temperature at the location where it is located, divides the space where the grain pile is located into grids, and constructs an initial temperature distribution field according to the detected temperature. Then, a heat conduction equation that follows the law of conservation of energy is constructed for each grain pile grid, and a linear equation system for describing the heat balance of the entire grain pile is generated based on the heat conduction equation. Finally, the temperature detected by the sensor and the relevant thermodynamic parameters of the grain pile are embedded into the linear equation system, and the temperature calibration of the grain pile grid can be carried out according to the solution result. Based on the initial temperature distribution field, the above process extends the local temperature detected by the sensor to the global based on the heat conduction law, thereby realizing the calibration of the temperature distribution field. Even if the sensors are sparsely arranged, the temperature of the unmonitored area can still be estimated with high precision, reducing the equipment cost and maintenance cost of the sensors. At the same time, the degree of damage to the airtightness of the grain pile by the sensors is reduced, and the temperature calibration is carried out using the thermodynamic equation, which conforms to the actual heat conduction law of the grain pile, realizing low-cost and high-resolution monitoring of the temperature field of the grain pile.

[0023] Next, the technical solutions in this embodiment will be clearly and completely described in conjunction with the accompanying drawings in this embodiment. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments.

[0024] Figure 1 The flowchart of a method for calibrating the temperature of a grain pile based on thermodynamics is shown. Please refer to Figure 1 This method includes the following steps: Step 1: Install a sensor matrix in the grain pile and obtain the temperature at the location detected by the temperature probe of each sensor in the sensor matrix.

[0025] Please refer to Figure 2 In practical applications, multiple sensors can be distributed and set in the grain pile, and the distance between adjacent sensors can be dynamically adjusted according to the size of the grain pile and the requirements of monitoring resolution. Each sensor integrates a temperature probe (such as a thermocouple or a thermistor), and the temperature probe is used to detect the temperature at its own location. The temperature data is transmitted to the main control device through analog-to-digital conversion or a direct digital interface. Each sensor is communicatively connected to the main control device, and the main control device is used to receive the temperature data detected by the sensor. In this embodiment, each sensor in the sensor matrix uses a passive sensor and does not require battery power.

[0026] Step 2: Discretize the space where the grain pile is located into grain pile grids, determine the temperature of each grain pile grid according to the temperature detected by each sensor, and generate a temperature distribution field for representing the temperature distribution.

[0027] In practical applications, first divide the space of the grain pile into grain pile grids. The specific size of the grain pile grids can be set according to the detection resolution requirements (such as 1 cm³), and then determine the temperature of each grain pile grid according to the temperature at the position detected by each sensor.

[0028] In one embodiment, in step 2, determining the temperature of each grain pile grid according to the temperature at the position detected by each sensor includes: If there is a sensor in the grain pile grid, take the temperature detected by the sensor as the temperature of the grain pile grid; if there is no sensor in the grain pile grid, determine the temperature of the grain pile grid according to the temperatures detected by adjacent sensors and based on the interpolation algorithm.

[0029] In practical applications, traverse each grain pile grid. If there is a sensor in the grain pile grid, directly record the temperature detected by the sensor. For the grain pile grids without sensors, find the surrounding adjacent sensors and use the interpolation algorithm to calculate the temperature. For example, use the inverse distance weighted algorithm: for each adjacent sensor, calculate the distance to the current grid point, and calculate the weighted average temperature according to the reciprocal of the distance or other weight functions. Finally, map the temperature of each obtained grain pile grid to the three-dimensional space coordinates of the grain pile, and an initial temperature distribution field representing the temperature distribution of the grain pile can be obtained.

[0030] In another embodiment, in step 2, determining the temperature of each grain pile grid according to the temperature at the position detected by each sensor includes steps 21 to 25.

[0031] Step 21: Under the experimental environment, detect the electromagnetic parameters of the grain pile at different temperatures and construct a mapping relationship between the temperature and the electromagnetic parameters.

[0032] It can be understood that the temperature dependence of the electromagnetic parameters of the grain pile stems from the coupling effect of multiple physical fields. The temperature change of the grain pile will cause changes in moisture phase change, thermal expansion, biological metabolism and electromagnetic wave propagation characteristics, and further cause changes in the electromagnetic parameters. Based on this, in this embodiment, a mapping relationship between the temperature and the electromagnetic parameters is constructed under the experimental environment.

[0033] In this embodiment, the electromagnetic parameters can be dielectric constant, magnetic permeability or conductivity. Specifically, the thermal expansion of the grain will reduce the porosity and increase the bulk density, which leads to an increase in the number of scattering interfaces in the electromagnetic wave propagation path and an increase in the equivalent dielectric constant; high temperature (>20 °C) activates the respiration of microorganisms in the grain pile, releases and moisture, and at the same time increases the ion concentration, significantly improving the conductivity; the magnetic permeability of trace metal impurities (such as iron filings) in the grain pile will be slightly adjusted due to temperature changes. For example, high temperature may reduce the ordered arrangement of magnetic impurities, resulting in a decrease in magnetic permeability.

[0034] In this embodiment, to detect the electromagnetic parameters of the grain pile at different temperatures, the amplitude and phase of the electromagnetic wave before and after penetrating the grain pile can be measured by a vector network analyzer at different temperatures. The attenuation coefficient and phase delay of the electromagnetic wave can be determined based on the amplitude and phase of the electromagnetic wave before and after penetrating the grain pile, and the electromagnetic parameters at the corresponding temperature can be calculated according to the attenuation coefficient and phase delay.

[0035] In practical applications, the grain pile can be compacted into a uniform cuboid or cylinder. At different temperatures, when the electromagnetic wave vertically penetrates the grain pile, by measuring the transmission coefficient of the grain pile at a specific frequency and combining the amplitude and phase of the electromagnetic wave before and after penetration, the attenuation coefficient and phase delay of the electromagnetic wave can be calculated, and then the electromagnetic parameters of the grain pile can be deduced inversely. The amplitude attenuation is directly related to the loss characteristics of the medium. The greater the loss, the more obvious the amplitude attenuation of the electromagnetic wave; while the phase change is related to the influence of the medium on the propagation speed of the electromagnetic wave. Different electromagnetic parameters will change the propagation speed of the electromagnetic wave, resulting in a phase change. Using a vector network analyzer, accurately measuring the amplitude and phase differences between the incident wave and the penetrated wave, and then through mathematical processing, the electromagnetic parameters such as the complex permittivity and complex permeability of the medium can be accurately calculated. At the same time, by measuring the ambient temperature of the grain medium and corresponding the temperature with the electromagnetic parameters, the mapping relationship between the temperature and electromagnetic parameters of the grain pile can be finally constructed.

[0036] Step 22: Control each sensor to transmit and receive electromagnetic wave signals, and the frequency of the electromagnetic wave signal transmitted by each sensor corresponds to the temperature at its location.

[0037] In this embodiment, each sensor is also integrated with an electromagnetic wave transmitting module and an electromagnetic wave receiving module. The electromagnetic wave transmitting module is used to transmit electromagnetic wave signals, and the electromagnetic wave receiving module is used to receive electromagnetic wave signals. The main control device is also used to control the sensor to transmit electromagnetic wave signals and receive the relevant data of the received electromagnetic wave signals sent by the sensor.

[0038] In this embodiment, the sensor is encapsulated with a wave-transparent material (such as polypropylene) to reduce interference to the electromagnetic wave signal and further improve the accuracy of temperature detection.

[0039] The temperature probe of each sensor real-time detects the temperature at its location, and the temperature data is transmitted to the main control device through analog-to-digital conversion or a direct digital interface. Please refer to Figure 3 , for any two sensors, the main control device generates an excitation signal corresponding to the electromagnetic wave frequency according to the temperature value to control the electromagnetic wave transmitting module of the sensor to transmit electromagnetic wave signals of the corresponding frequency. The electromagnetic wave receiving module of each sensor receives the electromagnetic wave signals transmitted by other sensors and returns the relevant data of the received electromagnetic wave signals to the main control device.

[0040] Step 23: Construct the electromagnetic wave propagation equation between any two sensors in the grain pile to generate an electromagnetic wave propagation equation set.

[0041] In this embodiment, constructing the electromagnetic wave propagation equation between any two sensors in the grain pile specifically includes: Based on Maxwell's equations, determine the wave equation of the electric field intensity of the electromagnetic wave in the grain pile, and construct the electromagnetic wave propagation equation according to the wave equation of the electric field intensity of the electromagnetic wave in the grain pile.

[0042] Maxwell's equations are a set of partial differential equations that describe the relationship between electric fields, magnetic fields, charge density, and current density. This set of equations consists of four equations, namely Gauss's law that describes how charges generate electric fields, Gauss's magnetic law that indicates the non-existence of magnetic monopoles, Faraday's law of electromagnetic induction that explains how time-varying magnetic fields generate electric fields, and Maxwell-Ampere's law that shows how currents and time-varying electric fields generate magnetic fields.

[0043] In this embodiment, in a passive, linear, and isotropic grain pile medium, the differential form of Maxwell's equations is as follows: ; ; ; ; From Maxwell's equations, the wave equation of the electric field intensity of the electromagnetic wave in the grain pile can be deduced as follows: ; Among them, represents the divergence operator, represents the curl operator, represents the Laplace operator, represents the electric displacement vector, represents the magnetic induction intensity, represents the electric field intensity, represents the magnetic field intensity, represents the current density, represents the magnetic permeability, represents the dielectric constant, represents the conductivity, represents the first-order derivative of the magnetic induction intensity with respect to time, represents the first-order derivative of the electric displacement vector with respect to time, represents the second-order derivative of the electric field intensity with respect to time, represents the first-order derivative of the electric field intensity with respect to time.

[0044] When electromagnetic waves propagate in the grain pile medium, the phase and amplitude changes are related to the path integral. Based on this, according to the wave equation of the electric field intensity of electromagnetic waves in the grain pile, for any two sensors , an electromagnetic wave propagation equation can be established to describe the electromagnetic wave propagation characteristics (phase delay and amplitude attenuation) from the th sensor to the th sensor as follows: ; ; ; ; ; where, represents the phase delay when the electromagnetic wave of the th sensor propagates to the th sensor, represents the propagation path of the electromagnetic wave of the th sensor to the th sensor, represents the arc length parameter of the propagation path, used to describe the position on the propagation path, represents the position where the phase constant is located, characterizing the phase change per unit length when the electromagnetic wave propagates in the medium, represents the integration variable, represents the amplitude attenuation when the electromagnetic wave of the th sensor propagates to the th sensor, represents the natural exponential function, represents the position where the attenuation coefficient is located, characterizing the energy attenuation per unit length when the electromagnetic wave propagates in the medium, represents the real part of the dielectric constant at the position , represents the angular frequency of the electromagnetic wave signal, represents the pi, represents the frequency of the electromagnetic wave signal.

[0045] In the above electromagnetic wave propagation equation, the phase delay and amplitude attenuation of electromagnetic wave propagation are related to the medium characteristics at each point on the path. By integrating along the path, the propagation characteristics of electromagnetic waves in inhomogeneous media can be accurately described, further improving the accuracy of temperature detection. In addition, the attenuation of electromagnetic waves is determined by the absorption characteristics of the medium, and the absorption process usually follows an exponential law. Therefore, the linear integral can be mapped to the actual attenuation amplitude through an exponential function. Through integration and exponential functions, the propagation behavior of electromagnetic waves in complex media can be accurately modeled, providing a mathematical basis for subsequent temperature inversion.

[0046] Step 24: Determine the electromagnetic parameters of each grain pile grid based on the frequency of the electromagnetic wave signal emitted by each sensor, the amplitude and phase of the received electromagnetic wave signal, and based on the electromagnetic wave propagation equations.

[0047] In practical applications, first, it is necessary to determine the position information of each grain pile grid relative to the sensor to determine the position of the electromagnetic wave on the propagation path; then, for any two sensors, discretize the path integral for each grain pile grid on the electromagnetic wave propagation path; then, determine the phase delay and amplitude attenuation of the electromagnetic wave signal on the propagation path according to the amplitude and phase of the received electromagnetic wave signal, and substitute the frequency of the electromagnetic wave signal emitted by each sensor and the phase delay and amplitude attenuation of the electromagnetic wave signal on the propagation path into the corresponding equations in the electromagnetic wave propagation equations, and the electromagnetic parameters of each grain pile grid can be inversely obtained.

[0048] By discretizing the grain pile into grids, constructing electromagnetic wave propagation equations, and inversely solving them, the finally generated electromagnetic parameter distribution field can reflect the electromagnetic parameter distribution inside the grain pile with high resolution.

[0049] Step 25: Determine the temperature of each grain pile grid based on the electromagnetic parameters of each grain pile grid and based on the mapping relationship between temperature and electromagnetic parameters.

[0050] Specifically, based on the electromagnetic parameters of each grain pile grid and based on the mapping relationship between temperature and electromagnetic parameters, the temperature of each grain pile grid can be obtained. Finally, map the temperature of each obtained grain pile grid to the three-dimensional space coordinates of the grain pile, and the initial temperature distribution field representing the temperature distribution of the grain pile can be obtained.

[0051] The initial temperature distribution field is constructed through the above steps. Since the electromagnetic wave propagation path covers the entire grain pile and a single path can penetrate multiple regions, combined with the cross data of multiple sensor pairs, even with a limited number of sensors, temperature detection with a resolution of millimeters to centimeters can be achieved, reducing the equipment cost and maintenance cost of the sensors and reducing the degree of damage to the airtightness of the grain pile by the sensors. Determining the temperature by inverting the electromagnetic parameters is more in line with the real physical process. When the temperature gradient in the grain pile changes abruptly due to uneven ventilation, electromagnetic inversion can accurately reflect the mutation boundary, further improving the accuracy of the initial temperature distribution field.

[0052] Step 3: Construct a thermodynamic equation for each grain pile grid, discretize the thermodynamic equation to generate a discrete equation, and convert the discrete equation into a system of linear equations in matrix form.

[0053] In this embodiment, first, a thermodynamic equation is constructed for each grain pile grid as follows: ; where represents the effective density of the grain pile, represents the equivalent specific heat capacity of the grain pile, represents the effective thermal conductivity of the grain pile, represents the heat source term, represents the temperature of the grain pile grid, represents the temperature gradient of the grain pile grid, represents the divergence operator, represents the first-order derivative of the temperature of the grain pile grid with respect to time.

[0054] After constructing the thermodynamic equation, the finite element method (FEM) or the finite difference method (FDM) is used to discretize the thermodynamic equation to obtain a discrete equation as follows: ; where represents the temperature of the th grain pile grid at time step , represents the temperature of the th grain pile grid at time step , represents the temperature of the th grain pile grid at time step , the th grain pile grid is an adjacent grid of the th grain pile grid, represents the time step size, represents the th grain pile grid and the th grain pile grid's contact area, Indicates the The grain pile grid and the The spacing between grain pile grids.

[0055] Finally, a linear equation is generated for each grain pile grid, forming a linear equation system in the form of a sparse matrix, as follows: ; in, represents the coefficient matrix, Represents the temperature vector, including each grain pile grid at the time step temperature, Represents the right side vector, including each grain pile grid at the time step The sum of the heat storage term and the heat source term; In the coefficient matrix middle: ; ; in, Representation coefficient matrix The diagonal elements of Representation coefficient matrix The off-diagonal elements of ; On the right vector middle: ; in, Indicates the The grain pile grid is in the time step The sum of the heat storage term and the heat source term.

[0056] Step 4: Using the temperature of the point where each sensor is located as a boundary condition, the linear equations are solved, and the temperature of each grain pile grid is calibrated according to the solution results.

[0057] In practical applications, the grain pile parameters are first input into the linear equation system. The grain pile parameters mainly include the effective density of the grain pile , equivalent specific heat capacity , effective thermal conductivity and heat source terms , where the effective density The equivalent specific heat capacity can be calculated by porosity and component density. The effective thermal conductivity can be calculated by porosity and specific heat capacity. and heat source terms It can be measured through experiments; then, the preset time step and the temperatures at the locations detected by each sensor in the historical time step are used as boundary conditions and input into a system of linear equations; finally, an iterative method (such as the preconditioned conjugate gradient method) is used to solve the system of linear equations to obtain the temperatures of each grain pile grid, and the temperature distribution field is updated according to the temperatures of each grain pile grid obtained by the solution to complete the temperature calibration.

[0058] In summary, the grain pile temperature calibration method and system based on thermodynamics provided in this embodiment expand the local temperature detected by the sensors to the global based on the heat conduction law on the basis of the initial temperature distribution field, thereby realizing the calibration of the temperature distribution field. Even if the sensors are sparsely arranged, the temperature of the unmonitored area can still be estimated with high precision, reducing the equipment cost and maintenance cost of the sensors. At the same time, the damage degree of the sensors to the airtightness of the grain pile is reduced, and the temperature calibration is carried out using the thermodynamic equation, which conforms to the actual heat conduction law of the grain pile, realizing low-cost and high-resolution monitoring of the grain pile temperature field; by dynamically correlating the electromagnetic wave signal propagation characteristics with the temperature field, based on the propagation characteristics of electromagnetic waves, using the phase delay and amplitude attenuation data between multiple pairs of sensors, combined with the electromagnetic wave propagation model constructed based on Maxwell's equations, the electromagnetic parameters in the grain pile are inversely calculated, and then the electromagnetic parameters are converted into temperature to generate the initial temperature distribution field. The electromagnetic wave propagation path covers the entire grain pile, and a single path can penetrate multiple regions. Combining the cross data of multiple pairs of sensors, even if the number of sensors is limited, temperature detection with a resolution of millimeters to centimeters can still be achieved, improving the accuracy of the initial temperature distribution field. And by inversely calculating the electromagnetic parameters to determine the temperature, it is more in line with the real physical process. When the temperature gradient in the grain pile changes abruptly due to uneven ventilation, the electromagnetic inversion can accurately reflect the mutation boundary, further improving the accuracy of the initial temperature distribution field.

[0059] Based on the above technical solutions, this embodiment also proposes a grain pile temperature calibration system based on thermodynamics for implementing the grain pile temperature calibration method based on thermodynamics as described in the embodiment. Please refer to Figure 4 , the system includes: a sensor matrix and a main control device; The sensor matrix is installed in the grain pile; The main control device is used to obtain the temperature at the location detected by the temperature probe of each sensor in the sensor matrix; discretize the space where the grain pile is located into grain pile grids, determine the temperatures of each grain pile grid according to the temperatures detected by each sensor, and generate a temperature distribution field for representing the temperature distribution; construct a thermodynamic equation for each grain pile grid, discretize the thermodynamic equation to generate a discrete equation, and convert the discrete equation into a system of linear equations in matrix form; use the temperature at the location detected by each sensor as a boundary condition, solve the system of linear equations, and calibrate the temperatures of each grain pile grid according to the solution results.

[0060] It can be understood that since the thermodynamic-based grain pile temperature calibration system described in this embodiment is a system for implementing the thermodynamic-based grain pile temperature calibration method described in the embodiment, for the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple. For relevant parts, refer to the partial description of the method, and details will not be elaborated here.

Claims

1. A grain pile temperature calibration method based on thermodynamics, characterized in that: The method comprises: A sensor matrix is installed in the grain pile, and the temperature of the point detected by the temperature probe of each sensor in the sensor matrix is obtained; The space where the grain pile is located is discretized into grain pile grids, the temperature of each grain pile grid is determined according to the temperature detected by each sensor, and a temperature distribution field is generated to represent the temperature distribution; Constructing a thermodynamic equation for each grain pile grid, discretizing the thermodynamic equation to generate a discrete equation, and converting the discrete equation into a linear equation system in matrix form; The temperature of the point where each sensor is detected is used as a boundary condition to solve the linear equations, and the temperature of each grain pile grid is calibrated according to the solution results.

2. The thermodynamics-based grain pile temperature calibration method according to claim 1, characterized in that: The thermodynamic equation is as follows: ； in, represents the effective density of the grain pile, represents the equivalent specific heat capacity of the grain pile, represents the effective thermal conductivity of the grain pile, represents the heat source term, represents the temperature of the grain pile grid, represents the temperature gradient of the grain pile grid, represents the divergence operator, Represents the first-order derivative of the temperature of the grain pile grid with respect to time.

3. The thermodynamically based grain pile temperature calibration method according to claim 2, characterized in that: The discrete equation is as follows: ； in, Indicates the The grain pile grid is in the time step temperature, Indicates the The grain pile grid is in the time step temperature, Indicates the The grain pile grid is in the time step The temperature, The grain pile grid is The adjacent grids of the grain pile grid, represents the time step, Indicates the The grain pile grid and the The contact area of the grain pile grid, Indicates the The grain pile grid and the The spacing between grain pile grids.

4. The thermodynamics-based grain pile temperature calibration method according to claim 3, characterized in that: The linear equations are as follows: ； in, represents the coefficient matrix, Represents the temperature vector, including each grain pile grid at the time step temperature, Represents the right side vector, including each grain pile grid at the time step The sum of the heat storage term and the heat source term; In the coefficient matrix middle: ； ； in, Representation coefficient matrix The diagonal elements of Representation coefficient matrix The off-diagonal elements of ; On the right vector middle: ； in, Indicates the The grain pile grid is in the time step The sum of the heat storage term and the heat source term.

5. The thermodynamics-based grain pile temperature calibration method according to claim 4, characterized in that: Solving the linear equations and calibrating the temperature of each grain pile grid according to the solution results includes: If there is a sensor in the grain pile grid, the temperature detected by the sensor is used as the temperature of the grain pile grid. After substituting the temperature of the grain pile grid into the linear equation system, the preconditioned conjugate gradient method is used to solve it to obtain the temperature of each grain pile grid. The temperature distribution field is updated according to the temperature of each grain pile grid obtained by the solution to complete the temperature calibration.

6. The thermodynamics-based grain pile temperature calibration method according to claim 1, characterized in that: The temperature of each grain pile grid is determined based on the temperature of the point detected by each sensor, including: If there is a sensor in the grain pile grid, the temperature detected by the sensor is used as the temperature of the grain pile grid; if there is no sensor in the grain pile grid, the temperature of the grain pile grid is determined based on the temperatures detected by adjacent sensors and an interpolation algorithm.

7. The thermodynamics-based grain pile temperature calibration method according to claim 1, characterized in that: Each sensor in the sensor matrix further includes an electromagnetic wave transmitting module and an electromagnetic wave receiving module; The temperature of each grain pile grid is determined based on the temperature of the point detected by each sensor, and also includes: In an experimental environment, the electromagnetic parameters of grain piles at different temperatures are detected, and a mapping relationship between temperature and electromagnetic parameters is established; Control each sensor to transmit and receive electromagnetic wave signals. The frequency of the electromagnetic wave signal transmitted by each sensor corresponds to the temperature at the location. Construct the electromagnetic wave propagation equation between any two sensors in the grain pile and generate the electromagnetic wave propagation equation group; Determining the electromagnetic parameters of each grain pile grid based on the frequency of the electromagnetic wave signal emitted by each sensor and the amplitude and phase of the received electromagnetic wave signal and based on the electromagnetic wave propagation equations; The temperature of each grain pile grid is determined according to the electromagnetic parameters of each grain pile grid and based on a mapping relationship between temperature and electromagnetic parameters.

8. The thermodynamics-based grain pile temperature calibration method according to claim 7, characterized in that: The electromagnetic parameter is dielectric constant, magnetic permeability or electrical conductivity.

9. The thermodynamics-based grain pile temperature calibration method according to claim 8, characterized in that: The electromagnetic wave propagation equation between any two sensors is as follows: ； ； ； ； ； in, Indicates the The electromagnetic wave of the first sensor propagates to the The phase delay of the sensor is Indicates the The electromagnetic wave of the first sensor propagates to the The propagation path of each sensor, Represents the arc length parameter of the propagation path, which is used to describe the position on the propagation path. Indicates location The phase constant at represents the integration variable, Indicates the The electromagnetic wave of the first sensor propagates to the The amplitude attenuation when the sensor is represents the natural exponential function, Indicates location The attenuation coefficient at Indicates location The real part of the dielectric constant at represents the magnetic permeability, represents the conductivity, represents the angular frequency of the electromagnetic wave signal, represents pi, Indicates the frequency of the electromagnetic wave signal.

10. A grain pile temperature calibration system based on thermodynamics, characterized in that: For implementing the thermodynamics-based grain pile temperature calibration method according to any one of claims 1 to 9, the system comprises: a sensor matrix and a main control device; The sensor matrix is installed in the grain pile; The main control device is used to obtain the temperature of the point detected by the temperature probe of each sensor in the sensor matrix; discretize the space where the grain pile is located into grain pile grids, determine the temperature of each grain pile grid according to the temperature detected by each sensor, and generate a temperature distribution field for representing the temperature distribution; construct a thermodynamic equation for each grain pile grid, discretize the thermodynamic equation to generate a discrete equation, and convert the discrete equation into a linear equation group in matrix form; use the temperature of the point detected by each sensor as a boundary condition, solve the linear equation group, and calibrate the temperature of each grain pile grid according to the solution result.

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