Grain pile temperature calibration method and system based on thermodynamics

Abstract

The present invention relates to the field of grain temperature technology and discloses a thermodynamically based grain pile temperature calibration method and system. This method aims to address the high cost and poor accuracy of existing grain pile temperature detection methods. The solution primarily includes: installing a sensor matrix in the grain pile and obtaining the temperature of each sensor-detected point in the sensor matrix; discretizing the space where the grain pile resides into grain pile grids, determining the temperature of each grain pile grid based on the temperature detected by each sensor, and generating a temperature distribution field representing 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; solving the linear equation system using the temperature of each sensor-detected point as a boundary condition, and calibrating the temperature based on the solution results. This method reduces cost and improves accuracy, making it particularly suitable for granaries.

Description

Technical Field

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

[0002] Grain pile temperature monitoring refers to the process of real-time or periodic monitoring of the internal and surface temperatures of grain piles during storage. This helps prevent grain spoilage, control pest risks, optimize storage conditions, and ensure the safety and stability of stored grain. However, as a porous, granular medium, the temperature distribution of a grain pile is influenced by multiple factors, including ambient humidity, microbial activity, and bulk density. This results in significant temperature differences within the grain pile, and uneven heat transfer within the grain, making it difficult for a single monitoring point to reflect the overall temperature distribution.

[0003] In the prior art, the main approach to grain pile temperature detection is to install a temperature sensor matrix within the grain pile. Multiple temperature sensors in the matrix first collect temperatures at different locations in the grain pile, and then generate a three-dimensional temperature distribution map of the grain pile based on an interpolation algorithm. However, this approach presents at least the following problems: First, the accuracy of the temperature field is entirely dependent on the number and distribution of temperature sensors. This requires burying a large number of temperature sensors within the grain pile, potentially compromising its sealing. Furthermore, the probes are susceptible to mechanical damage and fumigation corrosion, resulting in high maintenance costs. If the temperature sensors are sparsely arranged, the interpolation algorithm will experience a smoothing effect due to insufficient data, failing to capture local anomalies. Furthermore, due to installation constraints, the temperature sensors may be sparsely or clustered. In this case, the interpolation result is prone to overweighting the central region and amplifying errors in the edge regions, resulting in reduced temperature field reconstruction accuracy. Second, the interpolation algorithm relies solely on mathematical statistics, ignoring the physical properties of the grain pile and failing to reflect the dynamic changes in the actual temperature field. Third, if a localized high or low temperature anomaly exists somewhere in the grain pile, the interpolation algorithm will easily create a concentric temperature distribution around the anomaly, which is inconsistent with the actual heat conduction patterns of the grain pile and results in poor accuracy. Summary of the Invention

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

[0005] The technical solution adopted by the present invention to solve the above technical problems is:

[0006] In a first aspect, the present invention provides a method for calibrating grain pile temperature based on thermodynamics, the method comprising:

[0007] 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;

[0008] 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;

[0009] 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;

[0010] 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.

[0011] Furthermore, the thermodynamic equation is as follows:

[0012] ;

[0013] 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.

[0014] Furthermore, the discrete equation is as follows:

[0015] ;

[0016] 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.

[0017] Furthermore, the linear equations are as follows:

[0018] ;

[0019] 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;

[0020] In the coefficient matrix middle:

[0021] ;

[0022] ;

[0023] in, Representation coefficient matrix The diagonal elements of Representation coefficient matrix The off-diagonal elements of ;

[0024] On the right vector middle:

[0025] ;

[0026] in, Indicates the The grain pile grid is in the time step The sum of the heat storage term and the heat source term.

[0027] Furthermore, the linear equations are solved, and the temperature of each grain pile grid is calibrated according to the solution results, including:

[0028] 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.

[0029] Furthermore, the temperature of each grain pile grid is determined based on the temperature of the point detected by each sensor, including:

[0030] 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.

[0031] Furthermore, each sensor in the sensor matrix further comprises an electromagnetic wave transmitting module and an electromagnetic wave receiving module;

[0032] The temperature of each grain pile grid is determined based on the temperature of the point detected by each sensor, and also includes:

[0033] 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;

[0034] 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.

[0035] Construct the electromagnetic wave propagation equation between any two sensors in the grain pile and generate the electromagnetic wave propagation equation group;

[0036] 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;

[0037] 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.

[0038] Furthermore, the electromagnetic parameter is dielectric constant, magnetic permeability or electrical conductivity.

[0039] Furthermore, the electromagnetic wave propagation equation between any two sensors is as follows:

[0040] ;

[0041] ;

[0042] ;

[0043] ;

[0044] ;

[0045] 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.

[0046] In a second aspect, the present invention provides a thermodynamically based grain pile temperature calibration system for implementing the thermodynamically based grain pile temperature calibration method as described in the first aspect, the system comprising: a sensor matrix and a main control device;

[0047] The sensor matrix is ​​installed in the grain pile;

[0048] 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.

[0049] The beneficial effects of the present invention are as follows: the grain pile temperature calibration method and system provided by the present invention, based on the thermodynamics, expands the local temperature detected by the sensor to the global temperature based on the law of heat conduction 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, thereby reducing the equipment cost and maintenance cost of the sensor, while reducing the degree of damage to the sealing of the grain pile by the sensor. The temperature calibration is carried out using thermodynamic equations, which conforms to the actual heat conduction law of the grain pile, thereby realizing low-cost and high-resolution monitoring of the grain pile temperature field. The present invention also dynamically associates the propagation characteristics of electromagnetic wave signals with the temperature field, based on the propagation characteristics of electromagnetic waves, and utilizes multiple The phase delay and amplitude attenuation data between sensor pairs are combined with the electromagnetic wave propagation model constructed based on Maxwell's equations to invert the electromagnetic parameters in the grain pile, and then convert the electromagnetic parameters 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. Combined with the cross-data of multiple sensor pairs, even if the number of sensors is limited, temperature detection with millimeter to centimeter resolution can still be achieved, which improves the accuracy of the initial temperature distribution field. In addition, determining the temperature by inverting electromagnetic parameters is more in line with the real physical process. When the temperature gradient of the grain pile changes sharply due to uneven ventilation, the electromagnetic inversion can accurately reflect the mutation boundary, further improving the accuracy of the initial temperature distribution field. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 A schematic flow chart of a thermodynamically based grain pile temperature calibration method provided in an embodiment;

[0051] Figure 2 A schematic diagram of the sensor installation structure provided in the embodiment;

[0052] Figure 3 A schematic diagram of the transmission of electromagnetic wave signals provided in an embodiment;

[0053] Figure 4 A schematic structural diagram of a thermodynamics-based grain pile temperature calibration system provided in an embodiment. DETAILED DESCRIPTION

[0054] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solution of this embodiment will be clearly and completely described below in conjunction with the drawings in this embodiment.

[0055] Some of the processes described in the specification of the present invention and the figures above include multiple operations that appear in a specific order. However, it should be understood that these operations may not be performed in the order in which they appear in this document or may be performed in parallel. The sequence numbers of the operations are merely used to distinguish different operations and do not represent any order of execution. In addition, these processes may include more or fewer operations, and these operations may be performed sequentially or in parallel.

[0056] The technical solution of the present invention is applicable to application scenarios where temperature detection of grain piles is required, such as temperature detection of rice, wheat, corn, etc. in granaries.

[0057] Existing methods for detecting grain pile temperature typically use a matrix of temperature sensors to collect temperatures at multiple points in the pile, then use an interpolation algorithm to determine the temperatures at other points to generate a temperature distribution field. Research into existing methods for detecting grain pile temperature revealed that the accuracy of the temperature distribution field in this technology is entirely dependent on the number and distribution of temperature sensors. This requires deploying a large number of temperature sensors, resulting in high equipment and maintenance costs, significant damage to the grain pile's sealing, and poor accuracy.

[0058] Based on this, the technical solution of the present invention is proposed. In the present invention, 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 for representing the temperature distribution is generated; a thermodynamic equation is constructed for each grain pile grid, the thermodynamic equation is discretized to generate a discrete equation, and the discrete equation is converted into a linear equation group in matrix form; the temperature of the point detected by each sensor is used as a boundary condition, the linear equation group is solved, and the temperature of each grain pile grid is calibrated according to the solution result.

[0059] It can be understood that changes in grain pile temperature are essentially the result of the combined effects of heat conduction and internal heat production (such as microbial metabolism). As a physical object, a grain pile's temperature changes are influenced by heat conduction, heat sources, and boundary conditions. Based on this, the present invention first uses sensor temperature probes to detect the temperature at a specific location, divides the space within the grain pile into grids, and constructs an initial temperature distribution field based on the detected temperatures. Then, for each grain pile grid, a heat conduction equation that adheres to the law of conservation of energy is constructed. Based on this heat conduction equation, a linear system of equations describing the heat balance of the entire grain pile is generated. Finally, the sensor-detected temperatures and relevant thermodynamic parameters of the grain pile are embedded in the linear system of equations. The resulting solution allows for temperature calibration of the grain pile grids. Based on the initial temperature distribution field, the above process expands the local temperature detected by the sensors to a global temperature distribution based on the laws of heat conduction, thereby achieving calibration of the temperature distribution field. Even with sparsely spaced sensors, high-precision temperature estimates of unmonitored areas can be achieved, reducing sensor equipment and maintenance costs while minimizing the risk of sensor damage to the grain pile's seal. Furthermore, temperature calibration using thermodynamic equations conforms to the actual laws of heat conduction in grain piles, enabling low-cost, high-resolution monitoring of the grain pile's temperature field.

[0060] The technical solution of this embodiment will be clearly and completely described below in conjunction with the drawings in this embodiment. Obviously, the described embodiment is only a part of the embodiments of the present invention, rather than all the embodiments.

[0061] Figure 1 A flow chart showing a method for calibrating grain pile temperature based on thermodynamics is shown in Figure 1 , the method comprises the following steps:

[0062] Step 1: Install a sensor matrix in the grain pile and obtain the temperature of the point detected by the temperature probe of each sensor in the sensor matrix.

[0063] See also Figure 2 In practical applications, multiple sensors can be distributed throughout the grain pile, and the spacing between adjacent sensors can be dynamically adjusted based on the size of the grain pile and the required monitoring resolution. Each sensor incorporates a temperature probe (such as a thermocouple or thermistor) that detects the temperature at its location. This temperature data is transmitted to the main control unit via analog-to-digital conversion or a direct digital interface. Each sensor is separately connected to the main control unit for communication, and the main control unit receives the temperature data detected by the sensors. In this embodiment, each sensor in the sensor matrix is ​​a passive sensor and does not require battery power.

[0064] 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 to represent the temperature distribution.

[0065] In practical application, the grain pile space is first divided into The specific size of the grain pile grid can be set according to the detection resolution requirement (such as 1cm³), and then the temperature of each grain pile grid is determined according to the temperature of the point detected by each sensor.

[0066] In one embodiment, in step 2, determining the temperature of each grain pile grid based on the temperature of the point detected by each sensor includes:

[0067] 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.

[0068] In practice, each grain pile grid is traversed. If a sensor is present in the grid, the temperature detected by that sensor is directly recorded. For grids without sensors, neighboring sensors are searched and the temperature is calculated using an interpolation algorithm, such as an inverse distance weighted algorithm. For each neighboring sensor, the distance to the current grid point is calculated, and a weighted average temperature is calculated based on the inverse of the distance or another weighting function. Finally, the obtained temperature of each grain pile grid is mapped to the three-dimensional spatial coordinates of the grain pile to obtain an initial temperature distribution field representing the temperature distribution of the grain pile.

[0069] In another embodiment, in step 2, the temperature of each grain pile grid is determined according to the temperature of the point detected by each sensor, including steps 21 to 25.

[0070] Step 21: Under the experimental environment, detect the electromagnetic parameters of the grain pile at different temperatures and establish a mapping relationship between temperature and electromagnetic parameters.

[0071] It can be understood that the temperature dependence of the electromagnetic parameters of the grain pile originates from the coupling 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, thereby causing the electromagnetic parameters to change. Based on this, this embodiment constructs a mapping relationship between temperature and electromagnetic parameters in an experimental environment.

[0072] In this embodiment, the electromagnetic parameter can be dielectric constant, magnetic permeability or electrical conductivity. Specifically, the thermal expansion of grains will reduce porosity and increase bulk density, which will lead to an increase in 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, releasing and moisture, while increasing the ion concentration and significantly improving the electrical conductivity; the magnetic permeability of trace metal impurities (such as iron filings) in the grain pile will undergo slight adjustments due to temperature changes. For example, high temperature may reduce the orderly arrangement of magnetic impurities, resulting in a decrease in magnetic permeability.

[0073] In this embodiment, the electromagnetic parameters of the grain pile at different temperatures are detected. 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. The attenuation coefficient and phase delay of the electromagnetic wave are determined based on the amplitude and phase before and after penetrating the grain pile. The electromagnetic parameters at the corresponding temperature are calculated based on the attenuation coefficient and phase delay.

[0074] In practical applications, a grain pile can be compacted into a uniform rectangular parallelepiped or cylinder. At different temperatures, when electromagnetic waves penetrate the grain pile vertically, the transmission coefficient at specific frequencies is measured. Combining the amplitude and phase of the electromagnetic waves before and after penetration, the attenuation coefficient and phase delay of the electromagnetic waves can be calculated, and the electromagnetic parameters of the grain pile can be inferred. Amplitude attenuation is directly related to the loss characteristics of the medium: greater loss results in greater amplitude attenuation. Phase shift, on the other hand, is related to the medium's effect on the propagation speed of the electromagnetic wave. Different electromagnetic parameters can alter the propagation speed of the electromagnetic wave, leading to phase shifts. Using a vector network analyzer, the amplitude and phase differences between the incident and penetrating waves are precisely measured. Through mathematical processing, electromagnetic parameters such as the complex permittivity and complex permeability of the medium can be accurately calculated. Furthermore, by measuring the ambient temperature of the grain medium, the temperature is correlated with the electromagnetic parameters, ultimately establishing a mapping between the temperature and electromagnetic parameters of the grain pile.

[0075] Step 22: Control each sensor to transmit and receive electromagnetic wave signals, where the frequency of the electromagnetic wave signal transmitted by each sensor corresponds to the temperature at the location.

[0076] 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 relevant data of the received electromagnetic wave signals sent by the sensor.

[0077] In this embodiment, the sensor is packaged with a wave-transmitting material (such as polypropylene) to reduce interference with electromagnetic wave signals and further improve the accuracy of temperature detection.

[0078] The temperature probe of each sensor detects the temperature of the point in real time, and the temperature data is transmitted to the main control device through analog-to-digital conversion or direct digital interface. Figure 3 For any two sensors, the main control device generates an excitation signal of the corresponding electromagnetic wave frequency according to the temperature value to control the electromagnetic wave transmitting module of the sensor to transmit the electromagnetic wave signal of the corresponding frequency. The electromagnetic wave receiving module of each sensor receives the electromagnetic wave signal transmitted by other sensors and returns the relevant data of the received electromagnetic wave signal to the main control device.

[0079] Step 23: Construct the electromagnetic wave propagation equation between any two sensors in the grain pile to generate the electromagnetic wave propagation equation group.

[0080] In this embodiment, the electromagnetic wave propagation equation between any two sensors in the grain pile is constructed, specifically including:

[0081] The wave equation of the electric field intensity of the electromagnetic wave in the grain pile is determined based on Maxwell's equations, and the electromagnetic wave propagation equation is constructed according to the wave equation of the electric field intensity of the electromagnetic wave in the grain pile.

[0082] Maxwell's equations are a set of partial differential equations that describe the relationship between electric fields, magnetic fields, charge density, and current density. The equations consist of four equations: Gauss's law describing how charges generate electric fields; Gauss's law of magnetism, which shows that magnetic monopoles do not exist; Faraday's law of induction, which explains how time-varying magnetic fields generate electric fields; and Maxwell-Ampere's law, which explains how current and time-varying electric fields generate magnetic fields.

[0083] In this embodiment, in a passive, linear, isotropic grain pile medium, the differential form of Maxwell's equations is as follows:

[0084] ;

[0085] ;

[0086] ;

[0087] ;

[0088] The wave equation of the electric field intensity of electromagnetic waves in the grain pile can be derived from Maxwell's equations as follows:

[0089] ;

[0090] in, 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 strength, represents the magnetic field strength, represents the current density, represents the magnetic permeability, represents the dielectric constant, represents the conductivity, represents the first-order derivative of 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, It represents the first derivative of the electric field strength with respect to time.

[0091] When electromagnetic waves propagate in the grain pile medium, the phase and amplitude changes are related to the path integral. Based on this, we can use the wave equation of the electric field intensity of the electromagnetic wave in the grain pile to calculate the path integral of any two sensors. , establish an electromagnetic wave propagation equation to describe the electromagnetic wave from the The sensor propagates to the The electromagnetic wave propagation characteristics (phase delay and amplitude attenuation) of each sensor are as follows:

[0092] ;

[0093] ;

[0094] ;

[0095] ;

[0096] ;

[0097] 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 , which represents the phase change per unit length when the electromagnetic wave propagates in the medium, 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 the point represents the energy attenuation per unit length when the electromagnetic wave propagates in the medium. Indicates location The real part of the dielectric constant at represents the angular frequency of the electromagnetic wave signal, represents pi, Indicates the frequency of the electromagnetic wave signal.

[0098] In the above electromagnetic wave propagation equation, the phase delay and amplitude attenuation of electromagnetic wave propagation are related to the medium properties at each point along the path. By integrating 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 properties of the medium. The absorption process generally follows an exponential law, so 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 foundation for subsequent temperature inversion.

[0099] Step 24: Determine the electromagnetic parameters of each grain pile grid according to the frequency of the electromagnetic wave signal transmitted by each sensor and the amplitude and phase of the received electromagnetic wave signal and based on the electromagnetic wave propagation equations.

[0100] In practical applications, it is first 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, the path integral of each grain pile grid on the electromagnetic wave propagation path is discretized; then, based on the amplitude and phase of the received electromagnetic wave signal, the phase delay and amplitude attenuation of the electromagnetic wave signal on the propagation path are determined, and 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 are substituted into the corresponding equations in the electromagnetic wave propagation equation group to obtain the electromagnetic parameters of each grain pile grid by inversion.

[0101] By discretizing the grain pile into grids, constructing a set of electromagnetic wave propagation equations and inversely solving them, the electromagnetic parameter distribution field finally generated can reflect the electromagnetic parameter distribution inside the grain pile with high resolution.

[0102] Step 25: 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.

[0103] Specifically, according to 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, the temperature of each grain pile grid is mapped to the three-dimensional spatial coordinates of the grain pile to obtain the initial temperature distribution field representing the temperature distribution of the grain pile.

[0104] The above steps are used to construct the initial temperature distribution field. Since the electromagnetic wave propagation path covers the entire grain pile, a single path can penetrate multiple areas. Combined with the cross-data of multiple sensor pairs, temperature detection with millimeter to centimeter resolution can be achieved even if the number of sensors is limited, reducing the equipment and maintenance costs of the sensors and the degree of damage to the sealing of the grain pile by the sensors. Determining the temperature by inverting electromagnetic parameters is more consistent with the real physical process. When the temperature gradient of the grain pile changes sharply due to uneven ventilation, electromagnetic inversion can accurately reflect the mutation boundary, further improving the accuracy of the initial temperature distribution field.

[0105] 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 linear equation system in matrix form.

[0106] In this embodiment, the thermodynamic equation for each grain pile grid is first constructed as follows:

[0107] ;

[0108] 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.

[0109] After constructing the thermodynamic equation, the finite element method (FEM) or finite difference method (FDM) is used to discretize the thermodynamic equation to obtain the discrete equation as follows:

[0110] ;

[0111] 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.

[0112] 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:

[0113] ;

[0114] 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;

[0115] In the coefficient matrix middle:

[0116] ;

[0117] ;

[0118] in, Representation coefficient matrix The diagonal elements of Representation coefficient matrix The off-diagonal elements of ;

[0119] On the right vector middle:

[0120] ;

[0121] in, Indicates the The grain pile grid is in the time step The sum of the heat storage term and the heat source term.

[0122] 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.

[0123] 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 experimentally; then the preset time step and the temperature of the point where each sensor is detected in the historical time step are used as boundary conditions and input into the linear equation system; finally, an iterative method (such as the preconditioned conjugate gradient method) is used to solve the linear equation system to obtain the temperature of each grain pile grid, and the temperature distribution field is updated according to the solved temperature of each grain pile grid to complete the temperature calibration.

[0124] In summary, the thermodynamic-based grain pile temperature calibration method and system provided in this embodiment, based on the initial temperature distribution field, expands the local temperature detected by the sensor to the global temperature based on the law of heat conduction, 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, which reduces the equipment cost and maintenance cost of the sensor, and at the same time reduces the degree of damage to the sealing of the grain pile by the sensor. The temperature calibration is performed using thermodynamic equations, which conforms to the actual heat conduction law of the grain pile, and realizes low-cost and high-resolution monitoring of the grain pile temperature field. By dynamically correlating the propagation characteristics of the electromagnetic wave signal with the temperature field, based on the propagation characteristics of the electromagnetic wave, multiple sensors are used to The phase delay and amplitude attenuation data between the pairs are combined with the electromagnetic wave propagation model constructed based on Maxwell's equations to invert the electromagnetic parameters in the grain pile, and then convert the electromagnetic parameters 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. Combined with the cross-data of multiple sensor pairs, even if the number of sensors is limited, temperature detection with millimeter to centimeter resolution can still be achieved, which improves the accuracy of the initial temperature distribution field. In addition, determining the temperature by inverting electromagnetic parameters is more in line with the real physical process. When the temperature gradient of the grain pile changes sharply due to uneven ventilation, the electromagnetic inversion can accurately reflect the mutation boundary, further improving the accuracy of the initial temperature distribution field.

[0125] Based on the above technical solution, this embodiment also proposes a grain pile temperature calibration system based on thermodynamics, which is used to implement the grain pile temperature calibration method based on thermodynamics as described in the embodiment. Figure 4 , the system includes: a sensor matrix and a main control device;

[0126] The sensor matrix is ​​installed in the grain pile;

[0127] 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.

[0128] It can be understood that since the thermodynamics-based grain pile temperature calibration system described in this embodiment is a system for implementing the thermodynamics-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 matters, please refer to the partial description of the method, and no further details will be given 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 each sensor point 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; 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, including: 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.

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 thermodynamics-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: The electromagnetic parameter is dielectric constant, magnetic permeability or electrical conductivity.

8. The thermodynamics-based grain pile temperature calibration method according to claim 7, 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.

9. 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 8, 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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