Mechanism-data reconfiguration granular flow system temperature time series prediction method and device

Abstract

The application provides a temperature time sequence prediction method and device for a mechanism-data reconstructed particle flow system, and the method comprises the following steps: a new physical information neural network method (PINN) is constructed by combining normalized time, dimensionless particle temperature and a local weight adjustment method; a theoretical comprehensive loss function is constructed by a control equation, initial conditions and limited observation data sample points; a simple and uniform cooling particle flow system is taken as an example; three types of inverse problems, i.e., missing energy loss coefficient, missing initial particle temperature and both missing, are determined one by one according to the corresponding comprehensive loss function; the specific comprehensive loss function is taken as a target for solving and iterative updating; and the missing values in the three types of inverse problems and the variation law of the particle temperature with time are accurately determined in sequence. The application considers that the PINN combines experimental data and simulation methods, and has high accuracy and strong robustness in predicting flow field information, closing control equations and initial boundary value conditions.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of reconstruction of flow field characteristics of particle flow system and mining of physical laws, and particularly relates to a mechanism-data reconstruction particle flow system temperature time sequence prediction method and device. BACKGROUND

[0002] Particle flow systems exist widely in nature and industrial production: rapid prediction of debris flow phenomena in nature can directly protect the safety of tens of thousands of lives and property, and full study of particle flow phenomena in debris flow can provide effective debris flow impact force prediction and effective ideas for protection structure design; in industrial production, such as chemical processes, the flow behavior of catalysts directly determines the efficiency of catalytic reactions and the yield of final products; in the field of metallurgical minerals, particle flow characteristics directly determine the efficiency of ore separation and metallurgical efficiency; in the pharmaceutical process, ensuring that the binder and the pharmaceutical preparation are fully mixed is crucial to the quality of the drug. Therefore, in-depth and systematic study of the flow characteristics of particle flow systems is of great significance for reducing the impact of natural disasters, improving industrial production efficiency and economic benefits.

[0003] Common methods for understanding the flow characteristics of particle flow systems mainly include experimental measurement and numerical simulation methods. Experimental measurement methods mainly include invasive and non-invasive measurement. Invasive measurement mainly uses probe measurement, such as Prior Art 1 (Publication No.: CN114367319B) and Prior Art 2 (CN103097015B). This invasive probe measurement method significantly affects the basic flow information of the particle flow field, because the inserted probe directly limits the range of particle motion, which is quite different from the particle motion characteristics in the real system. Non-invasive measurement, such as PIV technology in Prior Art 3 (Publication No.: CN113311186B), can only measure relatively simple two-dimensional flow field information, and cannot accurately capture three-dimensional flow characteristics. In summary, common experimental measurement methods can only obtain local and two-dimensional particle flow characteristics, and cannot accurately describe the overall flow field characteristics. With the rapid development of computer technology, numerical simulation methods have become one of the main technical means for understanding the flow characteristics of particle flow systems. The coarse-grained calculation method in Prior Art 4 (Publication No.: CN112131633B) can reduce the number of discrete units by packing a group of small particles with larger coarse particles, but the interaction mechanism between coarse particles is still difficult to determine by analytical means, which may reduce the accuracy of the coarse-grained method.

[0004] In summary, considering the importance of particle flow systems, but the existing measurement and numerical simulation methods have limitations, therefore, it is urgent to develop a new method to systematically study the flow field characteristics and internal physical mechanisms of particle flow systems. SUMMARY

[0005] One purpose of the present application is to provide a mechanism-data reconstructed granular flow system temperature time sequence prediction method, which can comprehensively reconstruct the granular flow full flow field motion information, combine numerical experimental data with incomplete control equations and initial conditions based on a physical information neural network method, accurately predict the granular temperature time sequence characteristics in the granular flow system, and assist in determining the complete control equation form and initial conditions, and has high robustness, accuracy, universality and scalability. Another purpose of the present application is to provide a mechanism-data reconstructed granular flow system temperature time sequence prediction device. Still another purpose of the present application is to provide a computer readable medium. Still another purpose of the present application is to provide a computer device.

[0006] In order to achieve the above purposes, the present application discloses a mechanism-data reconstructed granular flow system temperature time sequence prediction method, comprising:

[0007] The granular flow system is simulated by the discrete element method to generate experimental granular temperatures at different times, and the reconstructed granular flow system is a uniformly cooled smooth granular flow system;

[0008] A theoretical comprehensive loss function is constructed based on the experimental granular temperatures at different times and the physical control equation by the constructed physical information-based neural network;

[0009] According to the system missing condition, the target comprehensive loss function is determined according to the theoretical comprehensive loss function, and the network parameters of the physical information-based neural network are iteratively updated to minimize the target comprehensive loss function, and a granular temperature time sequence characteristic prediction model is constructed. The system missing condition includes missing energy dissipation coefficient, missing initial time granular temperature or missing energy dissipation coefficient and initial time granular temperature;

[0010] Based on the granular temperature time sequence characteristic prediction model, the granular temperature is predicted according to the input predicted time information to obtain the target granular temperature time sequence.

[0011] Preferably, the granular flow system is simulated by the discrete element method to generate experimental granular temperatures at different times, comprising:

[0012] According to the preset granular velocity distribution function, and based on Newton's law of motion and the linear interaction force model between particles, the granular motion information in the reconstructed granular flow system is simulated, and the experimental granular velocities at different times are counted;

[0013] The experimental granular fluctuation velocity is determined according to the experimental granular velocities at different times combined with the average granular velocity;

[0014] According to the experimental granular fluctuation velocity, the average value of the fluctuation energy of all particles in the system is counted to generate experimental granular temperatures at different times.

[0015] Preferably, a theoretical comprehensive loss function is constructed according to the experimental particle temperature at different time and the physical control equation through the constructed neural network based on physical information, including:

[0016] The experimental particle temperature at different time is dimensionless processed to obtain dimensionless experimental particle temperature;

[0017] A data sample loss function and an initial condition loss function are constructed according to the dimensionless experimental particle temperature through the neural network based on physical information;

[0018] The time information at different time is normalized to obtain normalized time information;

[0019] A mechanism-data dual-driven control equation loss function is constructed according to the preset local weight coefficient, the normalized time information, the dimensionless experimental particle temperature and the physical control equation through the neural network based on physical information, and the local weight coefficient is determined according to the reciprocal of the predicted dimensionless particle temperature at the corresponding time;

[0020] The data sample loss function, the initial condition loss function and the mechanism-data dual-driven control equation loss function are weighted according to the preset global weight coefficient to generate the theoretical comprehensive loss function.

[0021] Preferably, a data sample loss function and an initial condition loss function are constructed according to the dimensionless experimental particle temperature through the neural network based on physical information, including:

[0022] The dimensionless experimental particle temperature is randomly sampled to obtain dimensionless sparse particle temperature;

[0023] A data sample loss function is constructed according to the dimensionless sparse particle temperature and the corresponding time at different time through the neural network based on physical information;

[0024] An initial condition loss function is constructed according to the dimensionless experimental particle temperature at the initial time through the neural network based on physical information.

[0025] Preferably, the system missing condition is missing energy dissipation coefficient;

[0026] According to the theoretical comprehensive loss function, a target comprehensive loss function is determined according to the system missing condition, and the network parameters of the neural network based on physical information are iteratively updated to minimize the target comprehensive loss function, and a particle temperature time sequence feature prediction model is constructed, including:

[0027] The theoretical comprehensive loss function is determined as the target comprehensive loss function;

[0028] The neural network based on physical information is trained and optimized by a preset optimization algorithm, network parameters including network weights and network biases are iteratively updated, and a particle temperature time series feature prediction model and an optimized energy dissipation coefficient are generated, with the objective of minimizing the target comprehensive loss function.

[0029] Preferably, the system missing condition is missing the initial time particle temperature.

[0030] According to the system missing condition, the target comprehensive loss function is determined according to the theoretical comprehensive loss function, and the network parameters of the neural network based on physical information are iteratively updated to build a particle temperature time series feature prediction model, including:

[0031] The initial condition loss function is removed from the theoretical comprehensive loss function to generate the target comprehensive loss function.

[0032] The neural network based on physical information is trained and optimized by a preset optimization algorithm, network parameters including network weights and network biases are iteratively updated, and a particle temperature time series feature prediction model and an optimized energy dissipation coefficient are generated, with the objective of minimizing the target comprehensive loss function.

[0033] Preferably, the system missing condition is missing the energy dissipation coefficient and the initial time particle temperature.

[0034] According to the system missing condition, the target comprehensive loss function is determined according to the theoretical comprehensive loss function, and the network parameters of the neural network based on physical information are iteratively updated to build a particle temperature time series feature prediction model, including:

[0035] The initial condition loss function is removed from the theoretical comprehensive loss function to generate the target comprehensive loss function.

[0036] The neural network based on physical information is trained and optimized by a preset optimization algorithm, network parameters including network weights and network biases are iteratively updated, and a particle temperature time series feature prediction model, an optimized energy dissipation coefficient and an initial time optimized particle temperature are generated, with the objective of minimizing the target comprehensive loss function.

[0037] Preferably, based on the particle temperature time series feature prediction model, the particle temperature is predicted according to the input predicted time information to obtain a target particle temperature time series, including:

[0038] The predicted time information is normalized to generate normalized predicted time information.

[0039] The normalized to-be-predicted time information is calculated through a particle temperature time sequence prediction model to generate a dimensionless particle temperature time sequence, which is a corresponding relationship between the dimensionless particle temperature and the normalized to-be-predicted time information.

[0040] The dimensionless particle temperature is time de-normalized and the particle temperature is dimensioned according to the normalized to-be-predicted time information to generate a target particle temperature time sequence.

[0041] Preferably, the method further comprises:

[0042] The predicted target particle temperature time sequence and the obtained real particle temperature time sequence are compared to generate a particle temperature time sequence feature error result.

[0043] Preferably, the method further comprises:

[0044] The optimized energy dissipation coefficient and the pre-calculated theoretical energy dissipation coefficient are compared to generate an energy dissipation coefficient error result, and the optimized energy dissipation coefficient is obtained through iterative calculation based on the physical control equation in the particle temperature time sequence prediction model.

[0045] Preferably, the method further comprises:

[0046] The initial time optimized particle temperature and the obtained initial time real particle temperature are compared to generate an initial time particle temperature error result, and the initial time optimized particle temperature is obtained through iterative calculation based on the particle temperature time sequence prediction model.

[0047] The application also discloses a mechanism-data reconstructed particle flow system temperature time sequence prediction device, comprising:

[0048] A numerical simulation unit is configured to perform numerical simulation on the reconstructed particle flow system through a discrete element method to generate experimental particle temperatures at different times, and the reconstructed particle flow system is a uniformly cooled smooth particle flow system.

[0049] A theoretical comprehensive loss function construction unit is configured to construct a theoretical comprehensive loss function according to the experimental particle temperatures at different times and the physical control equation through a constructed neural network based on physical information.

[0050] A particle temperature time sequence feature prediction model training unit is configured to determine a target comprehensive loss function according to the theoretical comprehensive loss function under a system missing condition, and iteratively update network parameters of the neural network based on physical information to construct a particle temperature time sequence feature prediction model, with the minimum target comprehensive loss function as the target, and the system missing condition includes missing energy dissipation coefficient, missing initial time particle temperature, or missing energy dissipation coefficient and initial time particle temperature.

[0051] The particle temperature time sequence feature prediction unit is configured to predict particle temperature based on a particle temperature time sequence feature prediction model according to input time information to be predicted, and obtain a target particle temperature time sequence.

[0052] The application further discloses a computer readable medium, which stores a computer program, and the program is executed by a processor to realize the method.

[0053] The application further discloses a computer device, which comprises a memory and a processor, the memory is configured to store information comprising program instructions, and the processor is configured to control execution of the program instructions, and the processor realizes the method when executing the program.

[0054] The application further discloses a computer program product, which comprises computer program / instructions, and the computer program / instructions are executed by a processor to realize the method.

[0055] The application generates experimental particle temperatures at different times by numerically simulating a reconstructed granular flow system by using a discrete element method, and reconstructs the granular flow system into a uniform cooling smooth granular flow system; a neural network based on physical information is constructed by time normalization, particle temperature dimensionless and local weight design, a theoretical comprehensive loss function is constructed according to experimental particle temperatures at different times, physical control equations and initial particle temperatures; according to a system missing condition, the target comprehensive loss function is determined according to the theoretical comprehensive loss function, and the network parameters of the neural network based on physical information are iteratively updated to minimize the target comprehensive loss function, a particle temperature time sequence feature prediction model is constructed, and accurate prediction and supplement of the missing condition are realized; the system missing condition includes missing energy dissipation coefficient, missing initial time particle temperature or missing energy dissipation coefficient and initial time particle temperature; based on the particle temperature time sequence feature prediction model, particle temperature prediction is performed according to input time information to be predicted, and a target particle temperature time sequence is obtained, which can fully reconstruct the granular flow field motion information, and the physical information neural network method combines numerical experimental data with incomplete control equations and initial conditions, which can not only accurately predict the particle temperature time sequence feature in the granular flow system, but also assist in determining the complete control equation and constitutive relation, energy dissipation loss parameter and initial condition, and has high robustness, accuracy, universality and expansibility. BRIEF DESCRIPTION OF DRAWINGS

[0056] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings described below are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained from these drawings without creative labor.

[0057] Figure 1 A flow chart of a temperature time series prediction method of a mechanism-data reconstructed granular flow system provided for an embodiment of the present application;

[0058] Figure 2 A flow chart of another temperature time series prediction method of a mechanism-data reconstructed granular flow system provided for an embodiment of the present application;

[0059] Figure 3 A structure schematic diagram of a PINN suitable for uniformly cooling a granular flow system provided for an embodiment of the present application;

[0060] Figure 4 A comparison diagram of a target granular temperature time series and a real granular temperature time series when the energy dissipation coefficient A is missing provided for an embodiment of the present application;

[0061] Figure 5 A time series diagram of the relative error between a target granular temperature time series and a real granular temperature time series when the energy dissipation coefficient A is missing provided for an embodiment of the present application;

[0062] Figure 6 A structure schematic diagram of a temperature time series prediction device of a mechanism-data reconstructed granular flow system provided for an embodiment of the present application;

[0063] Figure 7 A structure schematic diagram of a computer device provided for an embodiment of the present application. DETAILED DESCRIPTION

[0064] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the protection scope of the present application.

[0065] It should be noted that the mechanism-data reconstructed particle flow system temperature time sequence prediction method and device disclosed in the application can be used in the field of artificial intelligence technology, and can also be used in any field other than the field of artificial intelligence technology. The application field of the mechanism-data reconstructed particle flow system temperature time sequence prediction method and device disclosed in the application is not limited.

[0066] In order to facilitate understanding of the technical solutions provided in the present application, the related contents of the technical solutions of the present application will be described first. Accurate and comprehensive capture of the flow field characteristics of the particle flow system is of great significance to ensure the safety of daily life and improve the quality and efficiency of industrial production. With the vigorous development of machine learning technology, the Physics-Informed Neural Networks (PINN) method based on physical information is one of the typical data and physical law driven machine learning methods. The method combines local measurement data with part / all physical laws in the numerical simulation process, and through reasonable architecture of neural network form and fusion of the flow field characteristics and other physical information composed of data and equations into the learning process, it is expected to quickly realize accurate reconstruction of the flow characteristics of the particle flow system, and further improve the physical laws satisfied by the system through the learning results.

[0067] The present application simulates the uniform cooling smooth particle flow system by the discrete element method, generates the size of the particle temperature at different times in the system, and uses it as a high-precision numerical experiment point. The normalized time sequence is used as the input layer, the dimensionless particle temperature is used as the output layer, the fully connected neural network is combined, the incomplete particle temperature control equation, the initial particle temperature and the particle temperature at part of the time are coupled into the loss function as physical information, and the local weight of the sample point is adjusted based on the physical characteristics. The one-to-one correspondence between the normalized time and the dimensionless particle temperature is determined by the optimization parameter algorithm, and the physical information neural network method is further used to realize the change relationship of the particle temperature with time in the uniform cooling smooth particle flow system, the missing physical law and the initial particle temperature. The present application combines numerical experimental data with part of the physical law such as the incomplete control equation and the missing initial condition. Not only can the change of the particle temperature in the particle flow system be accurately described, but also the complete control equation form and the initial condition can be determined, the accuracy of the prediction result is improved, and the generalization ability is good. The present application provides a solution for industrial complex particle flow system.

[0068] It is worth noting that, Figure 1 Or Figure 2 The application scenario of the method shown is a uniform cooling smooth particle flow system.

[0069] The implementation process of the mechanism-data reconstructed particle flow system temperature time sequence prediction method provided by the embodiments of the present application will be described below by taking a mechanism-data reconstructed particle flow system temperature time sequence prediction device as an execution subject. It can be understood that the execution subject of the mechanism-data reconstructed particle flow system temperature time sequence prediction method provided by the embodiments of the present application includes but is not limited to the mechanism-data reconstructed particle flow system temperature time sequence prediction device.

[0070] Figure 1 The flowchart of the mechanism-data reconstructed particle flow system temperature time sequence prediction method provided by the embodiments of the present application is shown in Figure 1 The method comprises the following steps.

[0071] In step 101, the discrete element method (DEM) is used to perform numerical simulation on the reconstructed particle flow system to generate experimental particle temperatures at different times.

[0072] In the embodiments of the present application, the reconstructed particle flow system is a uniformly cooled smooth particle flow system. Specifically, the uniformly cooled smooth particle flow system is simulated by DEM to generate experimental particle temperatures at different times, which are used as high-precision numerical experimental points.

[0073] In step 102, a physical information based neural network (PINN) is constructed, and a theoretical comprehensive loss function is constructed according to the experimental particle temperatures at different times and the physical control equation.

[0074] In the embodiments of the present application, the data sample loss function of the dimensionless particle temperature is constructed by a non-replacement random sampling method Based on the initialized PINN, the initial condition loss function is constructed according to the initial time particle temperature predicted by the output layer of the network The input time data set of the PINN is obtained by a uniform distribution strategy, that is, the normalization is performed before the hidden layer at different times, and the mechanism-data dual-drive control equation loss function is determined according to the physical control equation of the uniformly cooled particle flow system The data sample loss function, the initial condition loss function and the mechanism-data dual-drive control equation loss function are weighted and summed to obtain the theoretical comprehensive loss function.

[0075] In step 103, the target comprehensive loss function is determined according to the theoretical comprehensive loss function under the system missing condition, and the network parameters of the PINN are iteratively updated to construct a particle temperature time sequence feature prediction model.

[0076] In the embodiments of the present application, the system missing condition includes missing energy dissipation coefficient, missing initial time particle temperature or missing energy dissipation coefficient and initial time particle temperature.

[0077] It is worth noting that if the system missing condition is missing the initial time particle temperature or missing the energy dissipation coefficient and the initial time particle temperature, the initial condition loss function does not exist, the initial condition loss function is removed from the theoretical comprehensive loss function to obtain the target comprehensive loss function; the network weight and the network bias of the PINN are iteratively updated to minimize the target comprehensive loss function, and a particle temperature time sequence feature prediction model is constructed.

[0078] In step 104, based on the particle temperature time sequence feature prediction model, the particle temperature is predicted according to the input time information to be predicted to obtain the target particle temperature time sequence.

[0079] In the embodiment of the present application, the particle temperature time sequence feature prediction model can identify a complete uniform cold particle flow system and has good generalization performance, which can successfully predict the system missing condition, such as the energy dissipation coefficient and / or the initial time particle temperature; on the other hand, the particle temperature time sequence feature prediction model is applied to reconstruct the particle flow system flow field characteristic prediction, and the target particle temperature time sequence can be obtained according to the input time information to be predicted, which is the law of the evolution of the particle temperature in the system with time.

[0080] In the technical scheme provided by the embodiment of the present application, the discrete element method is used to numerically simulate the reconstructed particle flow system to generate experimental particle temperatures at different times, and the reconstructed particle flow system is a uniform cooling smooth particle flow system; the theoretical comprehensive loss function is constructed based on the physical information neural network according to the experimental particle temperatures at different times and the physical control equation; the target comprehensive loss function is determined according to the theoretical comprehensive loss function according to the system missing condition, and the network parameters of the physical information neural network are iteratively updated to minimize the target comprehensive loss function, and the particle temperature time sequence feature prediction model is constructed, the system missing condition includes missing the energy dissipation coefficient, missing the initial time particle temperature or missing the energy dissipation coefficient and the initial time particle temperature; based on the particle temperature time sequence feature prediction model, the particle temperature is predicted according to the input time information to be predicted to obtain the target particle temperature time sequence, which can fully reconstruct the particle flow full flow field motion information, and the physical information neural network method combines the numerical experimental data with the incomplete control equation and the initial condition, which can not only accurately predict the particle temperature time sequence feature in the particle flow system, but also can assist in determining the complete control equation form and the initial condition, and has high robustness, accuracy, universality and scalability.

[0081] Figure 2 The flow chart of another mechanism-data reconstructed particle flow system temperature time sequence prediction method provided by the embodiment of the present application is shown as Figure 2 The method comprises the following steps.

[0082] Step 201, according to the preset particle velocity distribution function, and based on Newton's law of motion and the linear interaction force model between particles, simulate the particle motion information in the reconstructed granular flow system, and count the experimental particle velocity at different times.

[0083] In the embodiment of the application, each step is executed by a mechanism-data reconstructed granular flow system temperature time sequence prediction device.

[0084] In the embodiment of the application, the reconstructed granular flow system is a uniformly cooled smooth granular flow system, and the system characteristic parameters include but are not limited to simulation domain length, particle number, particle diameter, particle density, regional solid content, recovery coefficient, total simulation time, Young's modulus, Poisson's ratio, DEM time step and simulation initial temperature.

[0085] For example: the simulation domain length L is 0.064 m, the particle number N is 5w, the particle diameter size is 0.001 m, the particle density is 1500 kg / m 3 , the regional solid content is 0.1, the recovery coefficient is 0.95, the total simulation time is 5 s, the Young's modulus is 1×10 8 , the Poisson's ratio is 0.3, the DEM time step is s, and the simulation initial temperature is 0.01 m 2 / s 2 .

[0086] Specifically, under the condition of three-dimensional periodic boundary conditions, the smooth particles are uniformly distributed, and the particle velocity distribution function is:

[0087]

[0088] wherein, is the particle velocity distribution function, which gives the number density of particles at a specific velocity c; n is the number density; c is the particle velocity, which is a velocity vector containing the size and direction of particle motion; is the experimental particle temperature.

[0089] The initial particle temperature in the system is determined to ensure that the average particle velocity is 0.

[0090] It is worth noting that DEM is a method based on Newton's law of motion combined with a linear interaction force model between particles, which is used to describe the particle motion information in the system.

[0091] In this process, the particles in the system are uniformly distributed over a period of time. In the reconstructed granular flow system, the experimental particle velocity at different times is counted.

[0092] Step 202, generating experimental particle temperatures at different times according to experimental particle velocities at different times.

[0093] Step 202 specifically includes:

[0094] Step 2021, determining experimental particle fluctuation velocities according to experimental particle velocities at different times and average particle velocities.

[0095] In the embodiment of the present application, by , the numerical experimental particle velocities and the average particle velocities at different times are calculated to obtain the particle fluctuation velocities. Wherein, N is the number of particles; is the experimental particle fluctuation velocity of the i-th particle; is the experimental particle velocity of the i-th particle; is the average particle velocity.

[0096] Step 2022, generating experimental particle temperatures at different times according to experimental particle fluctuation velocities.

[0097] In the embodiment of the present application, with the passage of time, due to the free inelastic collision between particles, the particle fluctuation energy in the system, i.e. the particle temperature, will gradually decrease over time. The particle temperature is defined as the average kinetic energy level of the particle velocity fluctuation in the system, and this energy loss reflects the energy attenuation process caused by inelastic collision inside the particle system. This phenomenon can be quantitatively described by the change of particle temperature, and the particle temperature is defined as:

[0098]

[0099] Wherein, is the experimental particle temperature; is the fluctuation velocity; N is the number of particles; is the fluctuation velocity of the i-th particle; is the average value of the fluctuation energy of all particles.

[0100] In the embodiment of the present application, the experimental particle temperatures at different times simulated by the discrete element method above are taken as high-precision numerical experimental points.

[0101] Step 203, dimensionless processing of experimental particle temperatures at different times to obtain dimensionless experimental particle temperatures.

[0102] In the embodiment of the present application, by , the experimental particle temperatures at different times are dimensionless processed to obtain dimensionless experimental particle temperatures. Wherein, is the dimensionless particle temperature, is the experimental particle temperature, is the particle temperature at the initial time.

[0103] Step 204, constructing a data sample loss function and an initial condition loss function according to the dimensionless experimental particle temperature through the PINN.

[0104] In the embodiment of the application, the PINN is pre-constructed, and the PINN is composed of a full connection neural network, including an input layer, a plurality of hidden layers and an output layer; the input layer receives normalized time information, the number of neurons of each layer is matched with the feature dimension, and the PINN model can fully capture the law of evolution of the average particle temperature of the system with time; the hidden layer performs nonlinear transformation through an activation function, and extracts and converts high-level abstract representations of input features layer by layer; the output layer adopts a Softplus activation function to smoothly output the data from the hidden layer, and ensures that the predicted particle temperature is positive and conforms to the physical reality. The constructed PINN can be initialized for subsequent training and application.

[0105] As an optional solution, the input layer of the PINN is in the form of layer=[1, 128, 128, 128, 128, 1], indicating that the input layer is composed of only one variable of time, the time step is 0.01s in the training process, the hidden layer is 4 layers (L=4), and the output layer is a particle temperature related variable layer; the hidden layer performs nonlinear transformation through an activation function, and extracts and converts high-level abstract representations of input features layer by layer; the output layer adopts a Softplus activation function to smoothly output the data from the hidden layer and keep it positive, which conforms to the physical reality, and the form of the entire full connection neural network is as follows:

[0106]

[0107]

[0108]

[0109] wherein, is the input normalized time information, is the output of the kth layer, is the network weight of the kth layer, is the output of the input layer, is the output of the (k-1)th layer, is the network bias of the kth layer, is the activation function, and L is the total number of layers, is a nonlinear transformation function.

[0110] In the embodiment of the application, step 204 specifically includes:

[0111] ​​Step 2041, random sampling is performed on the dimensionless experimental particle temperature to obtain the dimensionless sparse particle temperature.

[0112] In the embodiment of the present application, the dimensionless experimental particle temperature is collected by the non-replacement random sampling method, and the dimensionless experimental particle temperature corresponding to a specified number of different time points is obtained; the dimensionless experimental particle temperature corresponding to the different time points is determined as the dimensionless sparse particle temperature.

[0113] It is worth noting that the specified number can be determined according to actual needs, and the embodiment of the present application does not limit this. As an optional solution, the specified number is 21, that is, 21 dimensionless experimental particle temperatures corresponding to different time points are randomly sampled as the dimensionless sparse real particle temperature.

[0114] Step 2042, a data sample loss function is constructed by PINN according to the dimensionless sparse particle temperature and the corresponding different time points.

[0115] In the embodiment of the present application, the different time points corresponding to the dimensionless sparse particle temperature are normalized to obtain normalized sparse time information, and the normalized sparse time information is used as the input of PINN. According to the dimensionless particle temperature prediction value output by PINN and the dimensionless sparse real particle temperature, the data sample loss function constructed is as follows:

[0116]

[0117] Wherein, is the data sample loss function, N Data is the number of dimensionless sparse particle temperatures, is the dimensionless particle temperature prediction value of the i-th time point predicted, is the real value of the i-th time point of the dimensionless particle temperature randomly sampled from the dimensionless experimental particle temperature.

[0118] In the embodiment of the present application, the data residual set of the data sample loss function is composed of the data loss of the dimensionless sparse particle temperature corresponding to only a small number of random time points.

[0119] Step 2043, an initial condition loss function is constructed by PINN according to the dimensionless initial time point experimental particle temperature.

[0120] In the embodiment of the present application, according to the initial time point particle temperature prediction value output by PINN and the dimensionless sparse particle temperature, the initial condition loss function constructed is as follows:

[0121]

[0122] wherein, is the initial condition loss function, is the dimensionless particle temperature prediction value at the initial moment, 1 is the dimensionless particle temperature true value at the initial moment, that is, the dimensionless experimental particle temperature at the initial moment.

[0123] Step 205, normalizing different moments to obtain normalized time information.

[0124] In the embodiment of the application, by , the current moment, the maximum moment and the minimum moment are calculated to generate normalized time information. Wherein, is the normalized time information, t is the current moment, t min is the minimum moment in different moments, t max is the maximum moment in different moments.

[0125] In the embodiment of the application, by normalizing the time information, the optimal input range of the PINN hidden layer activation function can be met, so as to improve the model convergence speed and prediction accuracy.

[0126] Step 206, constructing a mechanism-data double-drive control equation loss function by PINN according to the preset local weight coefficient, the normalized time information, the dimensionless experimental particle temperature and the physical control equation.

[0127] In the embodiment of the application, the physical control equation, that is, the particle temperature equation. According to the normalized time information and the dimensionless experimental particle temperature, the corresponding uniform cooling state particle temperature control equation is generated as follows:

[0128]

[0129] wherein, t min is the minimum moment in different moments, t max is the maximum moment in different moments, is the differential function of the dimensionless experimental particle temperature, A is the energy dissipation coefficient, is the particle temperature at the initial moment, is the dimensionless experimental particle temperature.

[0130] It is worth noting that the energy dissipation coefficient A can be taken as an unknown value participating in PINN iteration optimization when missing. If it needs to be closed in advance, it can be obtained by solving the following kinetic theory of particles:

[0131]

[0132]

[0133] wherein, is the energy dissipation coefficient obtained by solving the kinetic theory of granular flow, e is the restitution coefficient of the granules, is the average solid content in the system, is the radial distribution function, is the diameter of the granules.

[0134] In the embodiment of the present application, the loss function related to the mechanism-data dual-driven control equation is constructed according to the normalized time information, the dimensionless experimental granule temperature, the physical control equation and the local weight coefficient preset according to the physical characteristics of the system as follows:

[0135]

[0136] wherein, is the loss function of the mechanism-data dual-driven control equation, A is the energy dissipation coefficient, is the initial granule temperature, is the i-th dimensionless predicted experimental granule temperature, is the number of granules, is the differential function of the i-th dimensionless predicted experimental granule temperature, t min is the minimum time in different time, t max is the maximum time in different time, is the local weight coefficient.

[0137] It is worth mentioning that the local weight coefficient is determined according to the reciprocal of the dimensionless granule temperature predicted at the corresponding time, and the local weight coefficient preset according to the physical characteristics of the system is specifically that the physical model of the local weight is selected as the reciprocal of the dimensionless granule temperature predicted at the corresponding time.

[0138] The present application embeds physical information by incorporating physical laws as constraint conditions into the loss function, part of which includes the mechanism-data dual-driven control equation loss function corresponding to the control equation, and the physical law in the uniform cooling granule flow system is the granule temperature equation. According to the partial differential equation term given by the physical law, the state variable differential is calculated in turn by using automatic differentiation technology, and then the mechanism-data dual-driven control equation loss function combined with data and physics is composed . The local weight coefficient of the physically driven loss part is adjusted to the reciprocal of the dimensionless granule temperature predicted at the corresponding time, so as to enhance the contribution of the corresponding control equation when the granule temperature is low, thereby being able to well capture the granule temperature variation law when the granule temperature is low, and further improve the network prediction accuracy.

[0139] Step 207, according to the preset global weight coefficient, the data sample loss function, the initial condition loss function and the mechanism-data double drive control equation loss function are weighted to generate a theoretical comprehensive loss function.

[0140] In the embodiment of the application, the global weight coefficient includes a data sample weight coefficient, an initial condition weight coefficient and a mechanism-data double drive control equation weight coefficient.

[0141] It should be noted that the global weight coefficient can be set according to actual needs, and the embodiment of the application does not limit this.

[0142] Specifically, the data sample loss function, the initial condition loss function and the mechanism-data double drive control equation loss function are weighted and summed to obtain the theoretical comprehensive loss function as follows:

[0143]

[0144] wherein, is the mechanism-data double drive control equation weight coefficient, is the initial condition weight coefficient, is the data sample weight coefficient, A is an energy dissipation coefficient, is the initial particle temperature, is the dimensionless particle temperature true value of the i th particle, is the number of input layer different time points, is the differential function prediction value of the i th dimensionless experimental particle temperature, t min is the minimum time point in different time points, t max is the maximum time point in different time points, is the local weight coefficient, N Data is the number of dimensionless sparse particle temperatures, is the dimensionless particle temperature prediction value at the i th time point, is the dimensionless sparse particle temperature true value at the i th time point, is the dimensionless particle temperature prediction value at the initial time point, 1 is the dimensionless particle temperature true value at the initial time point.

[0145] Step 208, according to the system missing condition, the target comprehensive loss function is determined according to the theoretical comprehensive loss function, and the network parameters of the neural network based on physical information are iteratively updated to construct a particle temperature time sequence feature prediction model.

[0146] In the embodiment of the present application, the specific process of determining the target comprehensive loss function and model training is described in turn for three types of system missing conditions. The system missing conditions include: missing energy dissipation source term parameters, resulting in incomplete control equations as physical laws, missing initial particle temperature, and missing both energy dissipation source term parameters and initial particle temperature.

[0147] If the system missing condition is missing energy dissipation coefficient, step 208 specifically includes:

[0148] Step 2081, determine the theoretical comprehensive loss function as the target comprehensive loss function.

[0149] In the embodiment of the present application, if the system missing condition is missing energy dissipation coefficient, there is no influence on the theoretical comprehensive loss function, and the theoretical comprehensive loss function with missing energy dissipation coefficient is determined as the target comprehensive loss function.

[0150] Step 2082, through a preset optimization algorithm, the neural network based on physical information is trained and optimized to minimize the target comprehensive loss function, the network parameters are iteratively updated, and the particle temperature time series feature prediction model and the optimized energy dissipation coefficient are generated.

[0151] In the embodiment of the present application, the optimization algorithm iteratively updates and adjusts the network parameters to minimize the target comprehensive loss function. The optimization algorithm strategy is coupled with ADAM and L-BFGS-B, specifically, the ADAM optimizer is used to make the parameters globally converge quickly, and then the L-BFGS-B algorithm is used to realize fine optimization and parameter adjustment, finally the model converges, and the particle temperature time series feature prediction model and the optimized energy dissipation coefficient are generated.

[0152] It is worth noting that the initial learning rate and the number of training steps of the ADAM optimizer are determined by specific examples, and the embodiment of the present application does not limit this.

[0153] In the embodiment of the present application, the network parameters include network weights and network biases.

[0154] Further, based on the comparison between the optimized energy dissipation coefficient and the energy dissipation coefficient determined in advance by the kinetic theory of granular particles, the energy dissipation coefficient error result is generated. The optimized energy dissipation coefficient is obtained by iterative calculation based on the physical control equation in the particle temperature time series feature prediction model, and the theoretical energy dissipation coefficient is obtained by solving the kinetic theory of granular particles.

[0155] In one specific embodiment, the calculated optimized energy dissipation coefficient is -57.3452, and the theoretical energy dissipation coefficient obtained by solving the kinetic theory of granular particles is -57.3452. The relative error is 0.0044%, which proves that the present application has the ability to accurately predict the core constitutive relationship parameters.

[0156] If the system missing condition is the missing particle temperature at the initial time, step 208 specifically comprises:

[0157] Step 3081, removing the initial condition loss function from the theoretical comprehensive loss function to generate a target comprehensive loss function.

[0158] In the embodiment of the application, if the system missing condition is the missing particle temperature at the initial time, there is no initial condition loss function, and the target comprehensive loss function obtained by removing the initial condition loss function from the theoretical comprehensive loss function is as follows:

[0159]

[0160] wherein, is a mechanism-data double-drive control equation weight coefficient, is a data sample weight coefficient, A is an energy dissipation coefficient, is the predicted value of the experimental particle temperature at the i-th time, N t is the number of different time points in the input layer, is the predicted value of the differential function of the experimental particle temperature at the i-th time, t min is the minimum time in different time points, t max is the maximum time in different time points, is a local weight coefficient, N Data is the number of sparsely sampled particle temperatures, is the predicted value of the particle temperature at the i-th time, is the true value of the particle temperature at the i-th time after sparse sampling.

[0161] Step 3082, by a preset optimization algorithm, taking minimizing the target comprehensive loss function as the goal, model training and optimization are performed on the neural network based on physical information, network parameters are iteratively updated, a particle temperature time series feature prediction model and an optimized particle temperature at the initial time are generated.

[0162] In the embodiment of the application, the network parameters are iteratively updated and adjusted by using the optimization algorithm, so that the target comprehensive loss function is minimized, wherein only the energy dissipation coefficient is an unknown parameter for network iterative optimization. The optimization algorithm strategy is coupled by ADAM and L-BFGS-B, specifically, the ADAM optimizer is used to make the parameters globally converge quickly, and then the L-BFGS-B algorithm is used to realize fine optimization and parameter adjustment, so that the final model converges, and a particle temperature time series feature prediction model and an optimized particle temperature at the initial time are generated.

[0163] It is worth noting that the initial learning rate and the number of training steps of the ADAM optimizer are determined by specific examples, and the embodiment of the application does not limit this.

[0164] The network parameters include network weights and network biases in the embodiment of the application.

[0165] Further, the initial time particle temperature error result is generated by comparing the optimized initial time particle temperature and the obtained initial time real particle temperature, the optimized initial time particle temperature is obtained based on iterative calculation of the particle temperature time sequence feature prediction model, and the initial time real particle temperature is actually obtained.

[0166] If the system missing condition is missing energy dissipation coefficient and initial time particle temperature, step 208 specifically includes:

[0167] Step 4081, removing the initial condition loss function from the theoretical comprehensive loss function to generate a target comprehensive loss function.

[0168] In the embodiment of the application, if the energy dissipation coefficient and the initial time particle temperature are missing, the initial condition loss function does not exist, the initial condition loss function is removed from the theoretical comprehensive loss function of the missing energy dissipation coefficient to obtain a target comprehensive loss function, that is, the data sample loss function and the mechanism-data dual-drive control equation loss function containing the missing energy dissipation coefficient are integrated to generate a target comprehensive loss function. The generated target comprehensive loss function is as follows:

[0169]

[0170] Wherein, is a mechanism-data dual-drive control equation weight coefficient, is a data sample weight coefficient, A is an energy dissipation coefficient, is an experimental particle temperature prediction value at the i th time, N t is the number of different times in the input layer, is a differential function prediction value of the experimental particle temperature at the i th time, t min is the minimum time in different times, t max is the maximum time in different times, is a local weight coefficient, N Data is the number of sparsely sampled particle temperatures, is a particle temperature prediction value at the i th time, is a real value of the particle temperature at the i th time after sparse sampling.

[0171] Step 4082, by a preset optimization algorithm, taking minimizing the target comprehensive loss function as the goal, the PINN is model trained and optimized, the network parameters are iteratively updated, the particle temperature time sequence feature prediction model, the optimized energy dissipation coefficient and the optimized initial time particle temperature are generated.

[0172] Figure 3A structure diagram of a PINN suitable for a uniform cooling particle flow system is provided for an embodiment of the present application, as shown in Figure 3 The time information t enters the PINN, and first time normalization processing is performed on the normalized time information The normalized time information is input into a neural network (Neural Networks) for calculation, and a dimensionless particle temperature prediction value is output; differential calculation (AD) is performed on the normalized time information to obtain a differential calculation result , and a comprehensive loss function Loss is constructed; model training and optimization are performed on the PINN with the goal of minimizing the comprehensive loss function, and if the loss function result is less than a preset loss threshold , the particle temperature time series feature prediction model is constructed; if the loss function result is greater than or equal to the preset loss threshold , the network parameter bias result is calculated and the network parameters W k and b k are iteratively updated until the loss function result is less than the preset loss threshold . Further, the dimensionless particle temperature prediction value at any time i and the corresponding real particle temperature prediction value have a linear mapping relationship , and the trained particle temperature time series feature prediction model can output the particle temperature prediction value .

[0173] In an embodiment of the present application, the network parameters are iteratively updated and adjusted using an optimization algorithm to minimize the target comprehensive loss function. The optimization algorithm strategy is coupled with ADAM and L-BFGS-B, specifically, the ADAM optimizer is used to make the parameters globally converge quickly, and then the L-BFGS-B algorithm is used to realize fine optimization and parameter adjustment, and finally the model converges to generate the particle temperature time series feature prediction model, the optimized energy dissipation coefficient, and the optimized particle temperature at the initial time.

[0174] It should be noted that the initial learning rate and the number of training steps of the ADAM optimizer are determined by specific examples, and the present application does not limit this.

[0175] In an embodiment of the present application, the network parameters include network weights and network biases.

[0176] Further, based on the comparison between the optimized energy dissipation coefficient and the theoretically calculated energy dissipation coefficient, an energy dissipation coefficient error result is generated, the optimized energy dissipation coefficient is obtained by iterative calculation based on the physical control equation in the particle temperature time series feature prediction model, and the theoretically calculated energy dissipation coefficient is obtained by solving the kinetic theory of particles.

[0177] Further, based on the initial time point optimized particle temperature and the obtained initial time point real particle temperature, an initial time point particle temperature error result is generated, the initial time point optimized particle temperature is obtained based on the particle temperature time sequence feature prediction model iterative calculation, and the initial time point real particle temperature is actually obtained.

[0178] Step 209, normalizing the to-be-predicted time information to generate normalized to-be-predicted time information.

[0179] In the embodiment of the application, by , the current time, the maximum time and the minimum time in the to-be-predicted time information are calculated to generate the normalized to-be-predicted time information. Wherein, is the normalized to-be-predicted time information, t is the current time in the to-be-predicted time information, t min is the minimum time in the to-be-predicted time information, t max is the maximum time in the to-be-predicted time information.

[0180] Step 210, calculating the normalized to-be-predicted time information by the particle temperature time sequence feature prediction model to generate the dimensionless particle temperature time sequence.

[0181] In the embodiment of the application, the dimensionless particle temperature time sequence specifically refers to the dimensionless particle temperature size at each normalized time, and the dimensionless particle temperature time sequence is obtained by arranging the dimensionless particle temperature in sequence according to the dimensionless time. The dimensionless particle temperature time sequence is the corresponding relationship between the dimensionless particle temperature and the normalized to-be-predicted time information.

[0182] Specifically, the normalized to-be-predicted time information is input into the particle temperature time sequence feature prediction model for prediction, and the dimensionless particle temperature time sequence is output.

[0183] Step 211, performing reverse normalization processing on the dimensionless particle temperature time sequence to generate a target particle temperature time sequence.

[0184] In the embodiment of the application, the dimensionless particle temperature is time-reverse normalized with the normalized to-be-predicted time information and the dimensionless particle temperature is dimensioned to generate the target particle temperature time sequence.

[0185] In the embodiment of the application, the reverse normalization strategy is: . Wherein, is the target particle temperature time sequence, is the mapping function of the PINN network, is the normalized to-be-predicted time information, is the initial time point particle temperature, The dimensionless particle temperature time series.

[0186] It is worth noting that the initial particle temperature can be the initial optimized particle temperature or the initial real particle temperature.

[0187] Further, based on the predicted target particle temperature time series and the obtained real particle temperature time series, a particle temperature time series feature error result is generated. To verify the ability of the PINN to solve the inverse problem of the uniform cooling particle flow system and the generalization performance of the verifier, according to the law that the real particle temperature of the uniform cooling particle flow system changes with time:

[0188]

[0189] wherein, is the real particle temperature time series, is an intermediate parameter, is the initial particle temperature, t is the time, and A is the energy dissipation coefficient.

[0190] The target particle temperature time series predicted by the particle temperature time series feature prediction model trained based on the PINN at each time is compared with the real particle temperature time series, and the relative error between the two is defined as:

[0191]

[0192] wherein, R(t i ) is the relative error between the target particle temperature time series and the real particle temperature time series at time t, is the target particle temperature time series at time t, is the real particle temperature time series at time t. The average error of the two is:

[0193]

[0194]

[0195] wherein, is the average error between the target particle temperature time series and the real particle temperature time series at time t, N t is the number of particles, is the target particle temperature time series at time t, is the real particle temperature time series at time t.

[0196] ​​​​​​In a specific embodiment, the input layer time interval involved in the PINN training here is 0.01s. But the time interval involved in the generalization process is further encrypted to 0.005s.

[0197] In a specific embodiment, Figure 4 A comparison chart between the target particle temperature time series and the real particle temperature time series in the uniform cooling particle flow system when the energy dissipation coefficient A is missing is provided for the embodiment of the present application, as shown in Figure 4 The particle recovery coefficient e is 0.95, the average solid content in the system is 0.1, the number of particles N is 50000, and the particle temperature at the initial time is 0.01 m 2 / s 2 . The horizontal axis is time, unit: s, range: [10 -4 ,10 0 ]; the vertical axis is the temperature time series, unit: m 2 / s 2 , range: [10 -4 ,10 -2 ]; the red solid line represents the target particle temperature time series predicted by the particle temperature time series feature prediction model based on PINN training, and the black dotted line represents the real particle temperature time series.

[0198] In a specific embodiment, Figure 5 A time series chart of the relative error between the target particle temperature time series and the real particle temperature time series in the uniform cooling particle flow system when the energy dissipation coefficient A is missing is provided for the embodiment of the present application, as shown in Figure 5 The horizontal axis is time, unit: s, range: [0,5]; the vertical axis is the relative error, unit: %, range: [0.0,0.4].

[0199] As can be seen from Figure 4 and Figure 5 , the prediction accuracy of the particle temperature of the present application is high. The average relative error of the particle temperature determined in the training process for relatively discrete data sample points is 0.0433%, and the average relative error of the particle temperature obtained by generalization for relatively dense data sample points is only 0.0437%. The generalization error is comparable to the training error, and the difference from the overall average relative error 0.0435% is not large, which shows that the training and generalization ability of the particle temperature time series feature prediction model based on PINN training is good.

[0200] Further, the inverse problem of the uniform cooling particle flow system with missing initial particle temperature and energy dissipation coefficient and initial conditions is investigated in turn in the embodiment of the present application. The overall loss function involved in both is:

[0201]

[0202] in, The weighting coefficients for the mechanism-data dual-drive control equations are... Here, A represents the data sample weighting coefficient, and A represents the energy dissipation coefficient. Let N be the predicted temperature of the experimental particle at time i. t The number of input layers at different time points. The predicted value of the differential function of the experimental particle temperature at time i, t min Let t be the minimum time among different times. max For the maximum time among different times, N represents the local weighting coefficients. Data The number of sparsely sampled particles at temperature. Let i be the predicted particle temperature at time i. This represents the true particle temperature at the i-th time step after sparse sampling.

[0203] Furthermore, for the missing initial particle temperature, a global weight is set as follows: The initial learning rate for ADAM was set to 0.0001, and the number of model iterations (Niter) was 5 × 10⁻⁶. 4 Second-rate.

[0204] Furthermore, for the missing energy dissipation coefficient and the initial particle temperature, a global weight is set as follows: The initial learning rate for ADAM was set to 0.001, and the number of model iterations (Niter) was 2 × 10⁻⁶. 5 Second-rate.

[0205] In summary, the results of solving the inverse problem of the uniformly cooled granular flow system are summarized as follows:

[0206]

[0207] This application employs a strategy of input information normalization, dimensionless particle temperature, and increased weighting of local physical constraints at low temperatures. This improves the solution accuracy by 2-3 orders of magnitude compared to the standard PINN, demonstrating excellent generalization performance. While traditional methods can only solve positive problems where "both the governing equations and initial conditions are complete," PINN can effectively reconstruct the flow field and identify incomplete governing equations and full flow field information based on prior physical knowledge and sparse flow field data. In this embodiment, even with incomplete governing equation sets or missing initial conditions, the entire flow field information can still be obtained through network iterative optimization, and complete governing equations can be identified. For the already trained PINN network, driven by both mechanism and data, the learning process is highly interpretable and exhibits excellent generalization ability; therefore, the entire flow field information can be accurately obtained directly by inputting time information.

[0208] It is worth noting that the acquisition, storage, use, and processing of data in the technical solution of this application all comply with relevant laws and regulations. The user information in the embodiments of this application was obtained through legal and compliant means, and the acquisition, storage, use, and processing of user information have been authorized and agreed upon by the client.

[0209] It is worth noting that the information collected in this application is information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data all comply with the relevant laws, regulations and standards of the relevant countries and regions, necessary confidentiality measures have been taken, and they do not violate public order and good morals. Corresponding operation portals are provided for users to choose to authorize or refuse.

[0210] It is worth noting that the technical solution provided in this application provides users with a corresponding operation entry point, allowing users to choose to agree to or reject the automated decision-making result; if the user chooses to reject, the process will proceed to the expert decision-making process.

[0211] Figure 6 This is a schematic diagram of a temperature time-series prediction device for a mechanism-data reconstructed granular flow system provided in an embodiment of the present invention. This device is used to execute the aforementioned mechanism-data reconstructed granular flow system temperature time-series prediction method, such as... Figure 6 As shown, the device includes:

[0212] The numerical simulation unit 11 is used to perform numerical simulation of the reconstructed particle flow system using the discrete element method, generating experimental particle temperatures at different times, and reconstructing the particle flow system as a uniformly cooled smooth particle flow system.

[0213] The theoretical synthesis loss function construction unit 12 is used to construct the theoretical synthesis loss function based on the experimental particle temperature and physical control equation at different times through the constructed physical information-based neural network;

[0214] The particle temperature time series feature prediction model training unit 13 is used to determine the target comprehensive loss function according to the theoretical comprehensive loss function based on the system missing conditions, and to iteratively update the network parameters of the physical information-based neural network with the goal of minimizing the target comprehensive loss function, so as to construct the particle temperature time series feature prediction model. The system missing conditions include missing energy dissipation coefficient, missing particle temperature at the initial moment, or missing both energy dissipation coefficient and particle temperature at the initial moment.

[0215] The particle temperature time series feature prediction unit 14 is used to predict particle temperature based on the particle temperature time series feature prediction model and the input time information to be predicted, so as to obtain the target particle temperature time series.

[0216] In this embodiment of the invention, the numerical simulation unit 11 is specifically used to simulate and reconstruct the particle motion information in the particle flow system according to a preset particle velocity distribution function and based on Newton's laws of motion and the linear force model between particles, and to statistically calculate the experimental particle velocity at different times; based on the experimental particle velocity at different times, to statistically calculate the average value of the pulsating energy of all particles in the system, and to determine the experimental particle pulsating velocity in combination with the average particle velocity; and to generate the experimental particle temperature at different times based on the experimental particle pulsating velocity.

[0217] In this embodiment of the invention, the theoretical synthesis loss function construction unit 12 is specifically used to perform dimensionless processing on the experimental particle temperature at different times to obtain the dimensionless experimental particle temperature; construct a data sample loss function and an initial condition loss function based on the dimensionless experimental particle temperature using a neural network based on physical information; perform normalization processing on different times to obtain normalized time information; construct a mechanism-data dual-drive control equation loss function based on the preset local weight coefficients, the normalized time information, the dimensionless experimental particle temperature, and the physical control equation using a neural network based on physical information, where the local weight coefficients are determined based on the reciprocal of the predicted dimensionless particle temperature at the corresponding time; and weight the data sample loss function, the initial condition loss function, and the mechanism-data dual-drive control equation loss function according to the preset global weight coefficients to generate the theoretical synthesis loss function.

[0218] In this embodiment of the invention, the theoretical synthesis loss function construction unit 12 is specifically used to randomly sample the dimensionless experimental particle temperature to obtain the dimensionless sparse particle temperature; construct a data sample loss function based on the dimensionless sparse particle temperature and the corresponding different times using a neural network based on physical information; and construct an initial condition loss function based on the experimental particle temperature at the initial time after dimensionless measurement using a neural network based on physical information.

[0219] In this embodiment of the invention, the missing condition of the system is the missing energy dissipation coefficient; the particle temperature time series feature prediction model training unit 13 is specifically used to determine the theoretical comprehensive loss function as the target comprehensive loss function; through a preset optimization algorithm, with the goal of minimizing the target comprehensive loss function, the model of the physical information-based neural network is trained and optimized, the network parameters are iteratively updated, and the particle temperature time series feature prediction model and the optimized energy dissipation coefficient are generated. The network parameters include network weights and network biases.

[0220] In this embodiment of the invention, the missing condition of the system is the missing particle temperature at the initial moment; the particle temperature time series feature prediction model training unit 13 is specifically used to remove the initial condition loss function from the theoretical comprehensive loss function and generate the target comprehensive loss function; through a preset optimization algorithm, with the goal of minimizing the target comprehensive loss function, the model of the physical information-based neural network is trained and optimized, the network parameters are iteratively updated, and the particle temperature time series feature prediction model and the optimized particle temperature at the initial moment are generated. The network parameters include network weights and network biases.

[0221] In this embodiment of the invention, the missing conditions of the system are the missing energy dissipation coefficient and the particle temperature at the initial moment; the particle temperature time series feature prediction model training unit 13 is specifically used to remove the initial condition loss function from the theoretical comprehensive loss function of the missing energy dissipation coefficient to generate the target comprehensive loss function; through a preset optimization algorithm, with the goal of minimizing the target comprehensive loss function, the model of the physical information-based neural network is trained and optimized, the network parameters are iteratively updated, and a particle temperature time series feature prediction model, an optimized energy dissipation coefficient and an optimized particle temperature at the initial moment are generated. The network parameters include network weights and network biases.

[0222] In this embodiment of the invention, the particle temperature time series feature prediction unit 14 is specifically used to normalize the time information to be predicted, generating normalized time information to be predicted; through the particle temperature time series feature prediction model, the normalized time information to be predicted is calculated to generate a dimensionless particle temperature time series, which is the correspondence between the dimensionless particle temperature and the normalized time information to be predicted; the dimensionless particle temperature is subjected to time inverse normalization and particle temperature dimensioning processing with the normalized time information to be predicted to generate the target particle temperature time series.

[0223] In this embodiment of the invention, the device further includes a first comparison unit 15.

[0224] The first comparison unit 15 is used to compare the predicted target particle temperature time series with the acquired real particle temperature time series to generate particle temperature time series feature error results.

[0225] In this embodiment of the invention, the device further includes a second comparison unit 16.

[0226] The second comparison unit 16 is used to compare the optimized energy dissipation coefficient with the pre-calculated theoretical energy dissipation coefficient to generate an energy dissipation coefficient error result. The optimized energy dissipation coefficient is obtained by iterative calculation based on the physical control equation in the particle temperature time series characteristic prediction model.

[0227] In this embodiment of the invention, the device further includes a third comparison unit 17.

[0228] The third comparison unit 17 is used to compare the optimized particle temperature at the initial time with the actual particle temperature at the initial time, and generate the particle temperature error result at the initial time. The optimized particle temperature at the initial time is obtained by iterative calculation based on the particle temperature time series feature prediction model.

[0229] In this embodiment of the invention, a numerical simulation of the reconstructed granular flow system is performed using the discrete element method to generate experimental particle temperatures at different times, reconstructing the granular flow system as a uniformly cooled, smooth granular flow system. A theoretical comprehensive loss function is constructed based on the experimental particle temperatures at different times and the physical control equations using a physically based neural network. According to the system's missing conditions, a target comprehensive loss function is determined based on the theoretical comprehensive loss function. The network parameters of the physically based neural network are iteratively updated with the goal of minimizing the target comprehensive loss function, constructing a particle temperature time-series feature prediction model. The system's missing conditions include missing energy dissipation coefficients, missing initial particle temperatures, or missing both energy dissipation coefficients and initial particle temperatures. Based on the particle temperature time-series feature prediction model, particle temperatures are predicted according to the input time information to be predicted, obtaining the target particle temperature time series. This method can comprehensively reconstruct the motion information of the entire granular flow field. By combining numerical experimental data with incomplete control equations and initial conditions using the physically based neural network method, it can not only accurately predict the particle temperature time-series features in the granular flow system but also assist in determining the complete form of the control equations and initial conditions, exhibiting high robustness, accuracy, versatility, and scalability.

[0230] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer device, specifically, a computer device can be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or any combination of these devices.

[0231] This invention provides a computer device, including a memory and a processor. The memory is used to store information including program instructions, and the processor is used to control the execution of the program instructions. When the program instructions are loaded and executed by the processor, they implement the steps of the above-described embodiment of the mechanism-data reconstructed granular flow system temperature time series prediction method. For a detailed description, please refer to the above-described embodiment of the mechanism-data reconstructed granular flow system temperature time series prediction method.

[0232] The following is for reference. Figure 7 It shows a schematic diagram of the structure of a computer device 600 suitable for implementing the embodiments of this application.

[0233] like Figure 7 As shown, the computer device 600 includes a central processing unit (CPU) 601, which can perform various appropriate tasks and processes based on programs stored in read-only memory (ROM) 602 or programs loaded from storage section 608 into random access memory (RAM) 603. The RAM 603 also stores various programs and data required for the operation of the computer device 600. The CPU 601, ROM 602, and RAM 603 are interconnected via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0234] The following components are connected to I / O interface 605: an input section 606 including a keyboard, mouse, etc.; an output section 607 including a cathode ray tube (CRT), liquid crystal feedback (LCD), etc., and speakers, etc.; a storage section 608 including a hard disk, etc.; and a communication section 609 including a network interface card such as a LAN card, modem, etc. The communication section 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to I / O interface 605 as needed. A removable medium 611, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on drive 610 as needed so that computer programs read from it can be installed in storage section 608 as needed.

[0235] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program tangibly embodied on a machine-readable medium, the computer program including program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 609, and / or installed from removable medium 611.

[0236] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information by any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0237] For ease of description, the above devices are described separately by function as various units. Of course, in implementing this application, the functions of each unit can be implemented in one or more software and / or hardware.

[0238] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0239] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0240] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0241] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0242] The acquisition, storage, use, and processing of data in this application all comply with the relevant provisions of national laws and regulations.

[0243] It should be noted that in the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, it does not mean that the applicant has used or necessarily used the solution.

[0244] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0245] This application can be described in the general context of computer-executable instructions, such as program modules, that are executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform a specific task or implement a specific abstract data type. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0246] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0247] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for time-series temperature prediction of a mechanism-data reconstructed granular flow system, characterized in that, The method includes: The reconstructed particle flow system was numerically simulated using the discrete element method to generate experimental particle temperatures at different times. The reconstructed particle flow system is a uniformly cooled, smooth particle flow system. By constructing a physical information-based neural network, a theoretical comprehensive loss function is built based on the experimental particle temperature and physical control equations at different times. Based on the system missing conditions, the target comprehensive loss function is determined according to the theoretical comprehensive loss function. With the goal of minimizing the target comprehensive loss function, the network parameters of the physical information-based neural network are iteratively updated to construct a particle temperature time series feature prediction model. The system missing conditions include missing energy dissipation coefficient, missing particle temperature at the initial moment, or missing both energy dissipation coefficient and particle temperature at the initial moment. Based on the particle temperature time series feature prediction model, particle temperature is predicted according to the input time information to be predicted, and the target particle temperature time series is obtained. The aforementioned neural network based on physical information constructs a theoretical comprehensive loss function according to the experimental particle temperature and physical control equations at different times, including: The experimental particle temperatures at different times were processed to obtain dimensionless experimental particle temperatures. Using the physical information-based neural network, a data sample loss function and an initial condition loss function are constructed based on the dimensionless experimental particle temperature. The different times are normalized to obtain normalized time information; Using the physical information-based neural network, a mechanism-data dual-drive control equation loss function is constructed based on preset local weight coefficients, the normalized time information, the dimensionless experimental particle temperature, and the physical control equation. The local weight coefficients are determined based on the reciprocal of the dimensionless particle temperature predicted at the corresponding time. The data sample loss function, the initial condition loss function, and the mechanism-data dual-drive control equation loss function are weighted according to the preset global weight coefficients to generate the theoretical comprehensive loss function.

2. The method for predicting the temperature time series of granular flow systems based on mechanism-data reconstruction according to claim 1, characterized in that, The numerical simulation of the reconstructed granular flow system using the discrete element method to generate experimental particle temperatures at different times includes: According to the preset particle velocity distribution function, and based on Newton's laws of motion and the linear force model between particles, the particle motion information in the particle flow system is simulated and reconstructed, and the experimental particle velocities at different times are statistically determined. The experimental particle pulsation velocity was determined based on the experimental particle velocity at different times combined with the average particle velocity. Based on the pulsation velocity of the experimental particles, the average value of the pulsation energy of all particles in the system is calculated to generate the experimental particle temperature at different times.

3. The method for predicting the temperature time series of granular flow systems based on mechanism-data reconstruction according to claim 1, characterized in that, The process of constructing a data sample loss function and an initial condition loss function based on the dimensionless experimental particle temperature using the physical information-based neural network includes: The temperature of the dimensionless experimental particles was randomly sampled to obtain the temperature of the dimensionless sparse particles. Using the physical information-based neural network, a data sample loss function is constructed based on the dimensionless sparse particle temperature and the corresponding different times. Using the physical information-based neural network, an initial condition loss function is constructed based on the experimental particle temperature at the initial moment after dimensionless measurement.

4. The method for predicting the temperature time series of granular flow systems based on mechanism-data reconstruction according to claim 1, characterized in that, The missing condition of the system is the missing energy dissipation coefficient; The process involves determining a target comprehensive loss function based on the theoretical comprehensive loss function according to the system's missing conditions, and iteratively updating the network parameters of the physical information-based neural network with the goal of minimizing the target comprehensive loss function, thereby constructing a particle temperature time-series feature prediction model. This includes: The theoretical comprehensive loss function is determined as the target comprehensive loss function; Using a preset optimization algorithm, with the goal of minimizing the target comprehensive loss function, the physical information-based neural network is trained and optimized, the network parameters are iteratively updated, and a particle temperature time series feature prediction model and an optimized energy dissipation coefficient are generated. The network parameters include network weights and network biases.

5. The method for predicting the temperature time series of granular flow systems based on mechanism-data reconstruction according to claim 1, characterized in that, The missing condition of the system is the particle temperature at the initial moment; The process involves determining a target comprehensive loss function based on the theoretical comprehensive loss function according to the system's missing conditions, and iteratively updating the network parameters of the physical information-based neural network with the goal of minimizing the target comprehensive loss function, thereby constructing a particle temperature time-series feature prediction model. This includes: The initial conditional loss function is removed from the theoretical comprehensive loss function to generate the target comprehensive loss function; Using a preset optimization algorithm, with the goal of minimizing the target comprehensive loss function, the physical information-based neural network is trained and optimized, the network parameters are iteratively updated, and a particle temperature time series feature prediction model and the optimized particle temperature at the initial time are generated. The network parameters include network weights and network biases.

6. The method for predicting the temperature time series of granular flow systems based on mechanism-data reconstruction according to claim 1, characterized in that, The missing conditions of the system are the missing energy dissipation coefficient and the particle temperature at the initial moment; The process involves determining a target comprehensive loss function based on the theoretical comprehensive loss function according to the system's missing conditions, and iteratively updating the network parameters of the physical information-based neural network with the goal of minimizing the target comprehensive loss function, thereby constructing a particle temperature time-series feature prediction model. This includes: The initial condition loss function is removed from the theoretical comprehensive loss function that lacks energy dissipation coefficients to generate the target comprehensive loss function; Using a preset optimization algorithm, with the goal of minimizing the target comprehensive loss function, the neural network based on physical information is trained and optimized, the network parameters are iteratively updated, and a particle temperature time series feature prediction model is generated, the energy dissipation coefficient is optimized, and the particle temperature at the initial time is optimized. The network parameters include network weights and network biases.

7. The method for predicting the temperature time series of a granular flow system based on mechanism-data reconstruction according to claim 1, characterized in that, The particle temperature time series feature prediction model predicts particle temperature based on the input time information to be predicted, and obtains the target particle temperature time series, including: The time information to be predicted is normalized to generate normalized time information to be predicted; The particle temperature time series feature prediction model is used to calculate the normalized time information to be predicted to generate a dimensionless particle temperature time series. The dimensionless particle temperature time series is the correspondence between the dimensionless particle temperature and the normalized time information to be predicted. The dimensionless particle temperature is subjected to time inverse normalization and particle temperature dimensioning processing after normalization of the predicted time information to generate the target particle temperature time series.

8. The method for predicting the temperature time series of granular flow systems based on mechanism-data reconstruction according to claim 1, characterized in that, The method further includes: The predicted target particle temperature time series is compared with the obtained actual particle temperature time series to generate particle temperature time series feature error results.

9. The method for predicting the temperature time series of granular flow systems based on mechanism-data reconstruction according to claim 4 or 6, characterized in that, The method further includes: An energy dissipation coefficient error result is generated by comparing the optimized energy dissipation coefficient with the pre-calculated theoretical energy dissipation coefficient. The optimized energy dissipation coefficient is obtained by iterative calculation based on the physical control equation in the particle temperature time series characteristic prediction model.

10. The method for predicting the temperature time series of a granular flow system based on mechanism-data reconstruction according to claim 5 or 6, characterized in that, The method further includes: The optimized particle temperature at the initial moment is compared with the actual particle temperature at the initial moment to generate an error result for the particle temperature at the initial moment. The optimized particle temperature at the initial moment is obtained by iterative calculation based on the particle temperature time series feature prediction model.

11. A temperature time-series prediction device for a mechanism-data reconstruction granular flow system, characterized in that, The device includes: The numerical simulation unit is used to perform numerical simulation of the reconstructed granular flow system using the discrete element method, generating experimental particle temperatures at different times. The reconstructed granular flow system is a uniformly cooled smooth granular flow system. The theoretical synthesis loss function construction unit is used to construct a theoretical synthesis loss function based on the experimental particle temperature and physical control equation at different times by constructing a physical information-based neural network. The particle temperature time series feature prediction model training unit is used to determine the target comprehensive loss function according to the theoretical comprehensive loss function based on the system missing conditions, and to iteratively update the network parameters of the physical information-based neural network with the goal of minimizing the target comprehensive loss function, thereby constructing the particle temperature time series feature prediction model. The system missing conditions include missing energy dissipation coefficient, missing particle temperature at the initial moment, or missing both energy dissipation coefficient and particle temperature at the initial moment. The particle temperature time series feature prediction unit is used to predict particle temperature based on the particle temperature time series feature prediction model and the input time information to be predicted, so as to obtain the target particle temperature time series. The theoretical comprehensive loss function construction unit is specifically used to perform dimensionless processing on the experimental particle temperature at different times to obtain the dimensionless experimental particle temperature; construct a data sample loss function and an initial condition loss function based on the dimensionless experimental particle temperature using a physical information-based neural network; normalize the different times to obtain normalized time information; construct a mechanism-data dual-drive control equation loss function based on the preset local weight coefficients, normalized time information, dimensionless experimental particle temperature, and physical control equation using a physical information-based neural network, where the local weight coefficients are determined based on the reciprocal of the predicted dimensionless particle temperature at the corresponding time; and weight the data sample loss function, initial condition loss function, and mechanism-data dual-drive control equation loss function according to the preset global weight coefficients to generate the theoretical comprehensive loss function.

12. A computer-readable medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the time-series temperature prediction method for reconstructed granular flow systems based on the mechanism-data as described in any one of claims 1 to 10.

13. A computer device comprising a memory and a processor, the memory for storing information including program instructions, and the processor for controlling the execution of the program instructions, characterized in that, When the program instructions are loaded and executed by the processor, they implement the temperature time-series prediction method for the reconstructed granular flow system based on the mechanism-data as described in any one of claims 1 to 10.

14. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by the processor, they implement the temperature time-series prediction method for the reconstructed granular flow system of the mechanism-data as described in any one of claims 1 to 10.

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