Building thermal process simulation method based on Lagrange interpolation integral method

Abstract

The invention relates to the technical field of building environments, and discloses a building thermal process simulation method based on a Lagrange interpolation integral method, which comprises the following steps of: obtaining building physical parameters and building boundary conditions of a target building, constructing a thermal process state space model of the target building, and performing model analysis and eigenvalue decomposition to obtain a thermal process state space model of the target building; determining an eigenvalue matrix and an eigenvector matrix; constructing an integral coefficient matrix according to the first moment and the second moment; temperature vectors are calculated according to the eigenvalue matrix, the eigenvector matrix and the integral coefficient matrix and serve as building thermal process simulation output of the target building, the problem that a thermal process simulation program lacks a high-precision matrix calculation format can be solved, the prediction precision of temperature changes of the building under different environment conditions is guaranteed, and the prediction efficiency is improved. Data support is provided for building heat distribution simulation and building heating system design optimization, energy waste is reduced, and scientific basis and technical support are provided for establishment of energy-saving buildings and comfortable environments.

Description

Technical Field

[0001] The present invention relates to the technical field of building environment, and more particularly, to a building thermal process simulation method based on Lagrange interpolation integration method. Background Art

[0002] Building thermal process simulation is a method for evaluating thermal performance by simulating the changes in the internal thermal environment of a building. Its basic principle is based on heat transfer processes such as heat conduction, heat convection, and heat radiation in physics. Through detailed modeling of building structures, materials, and environmental characteristics, simulation software can calculate the temperature changes of a building under different environmental conditions. It is then used to evaluate the thermal performance and energy consumption of the building. It is usually applied in fields such as building performance design optimization, evaluation of building energy consumption and energy-saving schemes, and analysis of building environmental adaptability.

[0003] Currently common building temperature change simulation software includes: DeST based on state space models and EnergyPlus and eQuest derived from DOE series software. These models are usually based on methods such as the energy balance method and the weight coefficient method, etc. However, these methods have the following problems: 1. Calculating equations separately for each node makes it difficult to utilize tools such as high-performance linear algebra calculation libraries like Blas. 2. For the thermal disturbance in a unit time period, linear interpolation is usually only done between two time points, and it is impossible to ensure accuracy while being compatible with building thermal inertia, resulting in a usually short simulation time step and slow simulation calculation speed. Therefore, traditional methods cannot accurately predict the temperature changes of a building under different environmental conditions. Summary of the Invention

[0004] In view of this, the present invention proposes a building thermal process simulation method based on Lagrange interpolation integration method, which can solve the problem of the lack of a high-precision matrix calculation format in the thermal process simulation program, ensure the prediction accuracy of the temperature changes of a building under different environmental conditions, provide data support for building thermal distribution simulation and optimization of building heating system design, and further improve energy efficiency, reduce energy waste, and provide a scientific basis and technical support for the creation of energy-saving buildings and comfortable environments.

[0005] The present invention proposes a building thermal process simulation method based on Lagrange interpolation integration method, including: Obtaining the building physical parameters and building boundary conditions of the target building, and constructing a thermal process state space model of the target building based on the building physical parameters and building boundary conditions; Performing model analysis and eigenvalue decomposition on the thermal process state space model to determine an eigenvalue matrix and an eigenvector matrix; Obtaining a first moment and a second moment, and constructing an integral coefficient matrix according to the first moment and the second moment; Calculate the temperature vector of the target building according to the eigenvalue matrix, eigenvector matrix, and integral coefficient matrix, and use the temperature vector as the output of the building thermal process simulation of the target building.

[0006] Further, when obtaining the building physical parameters and building boundary conditions of the target building and constructing the thermal process state space model of the target building based on the building physical parameters and building boundary conditions, it includes: Construct a first building parameter matrix C and a second building parameter matrix A based on the building physical parameters, where the building physical parameters include building density, building specific heat capacity, building volume, and building temperature; ; ; where i and j represent the nodes after spatial discretization of the target building, is the building density of the i-th node, is the building specific heat capacity of the i-th node, is the building volume of the i-th node, is the second-order symmetric tensor between the i-th node and the j-th node; ; where, is the heat exchange coefficient between the i-th node and the j-th node, is the heat exchange coefficient between the i-th node and the building external disturbance, and k is the building external disturbance; Construct a building condition matrix B based on the building boundary conditions; ; where, is the influence coefficient of the building external disturbance k on the i-th node; Construct the thermal process state space model of the target building according to the first building parameter matrix C, the second building parameter matrix A, and the building condition matrix B.

[0007] Further, when constructing the thermal process state space model of the target building according to the first building parameter matrix C, the second building parameter matrix A, and the building condition matrix B, it includes: ; where, is the thermal process state space model of the target building, , is the building temperature of the i-th node, Q is the building internal and external disturbance input, , is the value of the building external disturbance k.

[0008] Further, when performing model analysis and eigenvalue decomposition on the thermal process state space model to determine the eigenvalue matrix and eigenvector matrix, it includes: Performing model analysis on the thermal process state space model to construct a first coefficient matrix and a second coefficient matrix ; Performing eigenvalue decomposition on the first coefficient matrix and the second coefficient matrix to obtain an eigenvalue matrix and an eigenvector matrix P, where the eigenvalue matrix is a diagonal matrix, and the eigenvector matrix satisfies , and the eigenvalue decomposition satisfies .

[0009] Further, when obtaining a first moment and a second moment and constructing an integral coefficient matrix according to the first moment and the second moment, it includes: Obtaining a first moment , obtaining a second moment ; wherein, is the integral coefficient matrix, is the i-th eigenvalue of the eigenvalue matrix , is the allowable error coefficient, e is a constant, x is an integration variable, ∏ is the product symbol, 0 ≤ m ≤ N, 0 ≤ N ≤ 4, 0 ≤ n ≤ N.

[0010] Further, when calculating the temperature vector of the target building according to the eigenvalue matrix, eigenvector matrix, and integral coefficient matrix, it includes: ; wherein, is the temperature vector of the target building at the second moment , is the temperature vector corresponding to the first moment, is the input of the disturbances inside and outside the building from the first moment to the second moment.

[0011] Further, before using the temperature vector as the simulation output of the building thermal process of the target building, it further includes: Obtaining a preset moment set value, determining whether the second moment is greater than the moment set value, and if so, using the temperature vector as the simulation output of the building thermal process of the target building; If not, determine whether the thermal process state space model is a time-invariant model. If so, re-obtain a new first moment and a new second moment, and construct a new integral coefficient matrix according to the new first moment and the new second moment; If not, re-obtain the new building physical parameters and new building boundary conditions of the target building, and construct a new thermal process state space model of the target building based on the new building physical parameters and new building boundary conditions.

[0012] Further, after using the temperature vector as the output of the building thermal process simulation of the target building, it further includes: Collect the temperature vectors corresponding to the target building at multiple moments; Sort all the temperature vectors by numerical magnitude, and determine the maximum temperature vector and the minimum temperature vector; Calculate the mean value of the temperature vectors corresponding to all the temperature vectors; Select the first ΔR1 temperature vectors between the maximum temperature vector and the mean value of the temperature vectors, and select the first ΔR2 temperature vectors between the minimum temperature vector and the mean value of the temperature vectors; Construct a first temperature vector sequence according to the first ΔR1 temperature vectors and the first ΔR2 temperature vectors; Construct a second temperature vector sequence according to all the remaining temperature vectors; Calculate the temperature change factor of the target building based on the first temperature vector sequence and the second temperature vector sequence.

[0013] Further, when calculating the temperature change factor of the target building based on the first temperature vector sequence and the second temperature vector sequence, it includes: Sort the first temperature vector sequence by numerical magnitude, and extract the first maximum temperature vector and the first minimum temperature vector corresponding to the first temperature vector sequence; Sort the second temperature vector sequence by numerical magnitude, and extract the second maximum temperature vector and the second minimum temperature vector corresponding to the second temperature vector sequence; Calculate the mean value of the first temperature vector sequence corresponding to the first temperature vector sequence, and mark the mean value of the first temperature vector sequence in the first temperature vector sequence as the first temperature vector marking point; Calculate the mean value of the second temperature vector sequence corresponding to the second temperature vector sequence, and mark the mean value of the second temperature vector sequence in the second temperature vector sequence as the second temperature vector marking point; Count the number of first temperature vectors Δa1 between the first temperature vector marking point and the first minimum temperature vector; Count the number of second temperature vectors Δa2 between the marked points of the second temperature vector and the second maximum temperature vector; Calculate the first temperature vector calculation coefficient a3 according to the first temperature vector number Δa1 and the second temperature vector number Δa2, where , is the total number of all temperature vectors; Count the number of third temperature vectors Δw1 between the marked points of the first temperature vector and the first maximum temperature vector; Count the number of fourth temperature vectors Δw2 between the marked points of the second temperature vector and the second minimum temperature vector; Calculate the second temperature vector calculation coefficient w3 according to the third temperature vector number Δw1 and the fourth temperature vector number Δw2, where ; Calculate the absolute value of the coefficient difference between the first temperature vector calculation coefficient and the second temperature vector calculation coefficient, and calculate the sum value of the first temperature vector calculation coefficient and the second temperature vector calculation coefficient; Calculate the ratio of the absolute value of the coefficient difference and the sum value of the coefficients, and use it as the temperature change factor of the target building.

[0014] Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention discloses a building thermal process simulation method based on the Lagrange interpolation integral method, which obtains the building physical parameters and building boundary conditions of the target building, and constructs a thermal process state space model of the target building; performs model analysis and eigenvalue decomposition on the thermal process state space model to determine the eigenvalue matrix and eigenvector matrix; constructs an integral coefficient matrix according to the first moment and the second moment; calculates the temperature vector of the target building according to the eigenvalue matrix, eigenvector matrix and integral coefficient matrix, and uses it as the output of the building thermal process simulation of the target building, which can solve the problem that the thermal process simulation program lacks a high-precision matrix calculation format, ensure the prediction accuracy of the temperature change of the building under different environmental conditions, provide data support for the building thermal distribution simulation and the optimization of the building heating system design, reduce energy waste, and provide a scientific basis and technical support for the creation of energy-saving buildings and comfortable environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] By reading the detailed description of the preferred embodiments below, various other advantages and benefits will become clear to those of ordinary skill in the art. The drawings are only for the purpose of showing the preferred embodiments and are not considered to be a limitation of the present invention. Moreover, throughout the drawings, the same reference numerals are used to represent the same components. In the drawings: Figure 1Schematic flowchart of the building thermal process simulation method based on the Lagrange interpolation integration method provided by the embodiments of the present invention. Detailed implementation manners

[0016] Hereinafter, exemplary embodiments of the present disclosure will be described in more detail with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be fully conveyed to those skilled in the art. It should be noted that, without conflict, the embodiments in the present invention and the features in the embodiments can be combined with each other. Hereinafter, the present invention will be described in detail with reference to the drawings and in combination with the embodiments.

[0017] As Figure 1 shown, in some embodiments of the present application, this embodiment provides a building thermal process simulation method based on the Lagrange interpolation integration method, including: S110: Obtain the building physical parameters and building boundary conditions of the target building, and construct a thermal process state space model of the target building based on the building physical parameters and building boundary conditions; In some embodiments of the present application, when obtaining the building physical parameters and building boundary conditions of the target building and constructing a thermal process state space model of the target building based on the building physical parameters and building boundary conditions, it includes: Construct a first building parameter matrix C and a second building parameter matrix A based on the building physical parameters, where the building physical parameters include building density, building specific heat capacity, building volume, and building temperature; ; ; where i and j represent the nodes after spatial discretization of the target building, is the building density of the i-th node, is the building specific heat capacity of the i-th node, is the building volume of the i-th node, is the second-order symmetric tensor between the i-th node and the j-th node; ; where, is the heat exchange coefficient between the i-th node and the j-th node, is the heat exchange coefficient between the i-th node and the building external disturbance, and k is the building external disturbance; Construct a building condition matrix B based on the building boundary conditions; ; where, The influence coefficient of the building external disturbance k on the i-th node; Construct the state space model of the thermal process of the target building according to the first building parameter matrix C, the second building parameter matrix A, and the building condition matrix B.

[0018] In this embodiment, the building physical parameters include building density, building specific heat capacity, building volume, and building temperature.

[0019] In this embodiment, the building boundary conditions include outdoor temperature, solar radiation, heat dissipation from people and equipment, etc.

[0020] In this embodiment, the enclosure structures such as walls, ceilings, and floors are divided into 3 - 5 nodes along the thickness, and the entire volume area of the room is divided into one node.

[0021] In this embodiment, is the heat exchange coefficient between the i-th node and the j-th node, and the heat exchange coefficient is used to quantify the energy exchange rate occurring between two nodes through various heat transfer forms. The heat exchange relationships include: conduction, convective heat transfer on the inner wall surface, air infiltration through indoor doors and windows, radiation on the inner wall surface, etc.

[0022] Specific explanations: Conduction: Direct heat transfer through solid materials (such as walls, floors), described by Fourier's law.

[0023] Convective heat transfer on the inner wall surface (Convection): Heat exchange between air and the wall surface, described by the convective heat transfer coefficient, which depends on air velocity, temperature difference, surface roughness, etc.

[0024] Air Infiltration through indoor doors and windows: Heat exchange brought about by air infiltration through gaps, related to air flow rate and density.

[0025] Radiation on the inner wall surface: Radiation heat transfer between surfaces, described by the Stefan - Boltzmann law.

[0026] It should be noted that the above specific descriptions and corresponding mathematical formulas have been publicly disclosed and are mature in traditional methods, so they will not be repeated here.

[0027] In this embodiment, the heat exchange coefficient between node i and the external disturbance k is a comprehensive parameter, used to quantify the heat exchange rate between node i and the external disturbance k.

[0028] Specific explanations: External Convection: Convective heat transfer between outdoor air and the outer surface of the building (such as exterior walls, roofs), determined by wind speed, surface roughness, etc.

[0029] Air infiltration through doors and windows: Cold air infiltrates into the room through the gaps in doors and windows and directly mixes with the air at node i (indoor air), resulting in heat loss.

[0030] It should be noted that the above specific descriptions and corresponding mathematical formulas have been publicly disclosed and are mature in traditional methods, so they will not be repeated here.

[0031] In this embodiment, the influence coefficient is usually represented by a heat exchange coefficient.

[0032] In some embodiments of the present application, when constructing the thermal process state space model of the target building according to the first building parameter matrix C, the second building parameter matrix A, and the building condition matrix B, it includes: ; Among them, is the thermal process state space model of the target building, , is the building temperature of the i-th node, Q is the input of internal and external disturbances of the building, , is the value of the external disturbance k of the building.

[0033] The beneficial effects of the above technical solutions are: The present invention can accurately determine the thermal process state space model of the target building, lay a foundation for the thermal process simulation of the target building, and significantly improve the calculation efficiency of the thermal process simulation of the target building.

[0034] S120: Perform model parsing and eigenvalue decomposition on the thermal process state space model to determine the eigenvalue matrix and the eigenvector matrix; In some embodiments of the present application, when performing model parsing and eigenvalue decomposition on the thermal process state space model to determine the eigenvalue matrix and the eigenvector matrix, it includes: Perform model parsing on the thermal process state space model to construct the first coefficient matrix and the second coefficient matrix ; Perform eigenvalue decomposition on the first coefficient matrix and the second coefficient matrix to obtain the eigenvalue matrix and the eigenvector matrix P. Among them, the eigenvalue matrix is a diagonal matrix, and the eigenvector matrix satisfies , and the eigenvalue decomposition satisfies .

[0035] In this embodiment, the eigenvalue decomposition method is relatively mature and will not be repeated here. It satisfies That's all.

[0036] The beneficial effects of the above technical solution are as follows: In the present invention, eigenvalue decomposition is performed on the first coefficient matrix and the second coefficient matrix to obtain the eigenvalue matrix and the eigenvector matrix P, thereby providing data support for calculating the temperature vector of the target building and ensuring the simulation accuracy of the temperature vector of the target building.

[0037] S130: Obtain the first moment and the second moment, and construct an integral coefficient matrix according to the first moment and the second moment; In some embodiments of the present application, when obtaining the first moment and the second moment and constructing an integral coefficient matrix according to the first moment and the second moment, it includes: Obtain the first moment , obtain the second moment ; wherein, is the integral coefficient matrix, is the i-th eigenvalue of the eigenvalue matrix , is the allowable error coefficient, e is a constant, x is the integration variable, ∏ is the symbol of successive multiplication, 0 ≤ m ≤ N, 0 ≤ N ≤ 4, 0 ≤ n ≤ N.

[0038] In this embodiment, is preferably 10 -5 .

[0039] In this embodiment, the number of eigenvalues is the same as the number of nodes, so i is also used to represent it.

[0040] In this embodiment, successive multiplication means multiplying the result when m takes the value of 0 and the results when m takes the values of 1, 2, 3,..., N, where the result when m takes the value of n is ignored.

[0041] The beneficial effects of the above technical solution are as follows: The present invention constructs an integral coefficient matrix according to the first moment and the second moment, ensuring the accuracy of the subsequent solution format, and further ensuring the calculation accuracy of the temperature change of the target building.

[0042] S140: Calculate the temperature vector of the target building according to the eigenvalue matrix, the eigenvector matrix and the integral coefficient matrix, and use the temperature vector as the output of the building heat process simulation of the target building.

[0043] In some embodiments of the present application, when calculating the temperature vector of the target building according to the eigenvalue matrix, the eigenvector matrix and the integral coefficient matrix, it includes: ; Among them, is the temperature vector of the target building at the second moment ; is the temperature vector corresponding to the first moment, and is the input of the disturbances inside and outside the building from the first moment to the second moment.

[0044] The beneficial effects of the above technical solution are as follows: The present invention adopts a display recurrence algorithm in a linear matrix format, which can make full use of high-performance linear algebra calculation libraries such as Blas in computer implementation to achieve efficient solution of the model and ensure extremely high integration accuracy. It can solve the problem that the thermal process simulation program lacks a high-precision matrix calculation format and ensure the prediction accuracy of the temperature change of the building under different environmental conditions.

[0045] In some embodiments of the present application, before using the temperature vector as the output of the building thermal process simulation of the target building, it further includes: Obtain a preset moment setting value, and judge whether the second moment is greater than the moment setting value. If so, use the temperature vector as the output of the building thermal process simulation of the target building; If not, judge whether the thermal process state space model is a time-invariant model. If so, re-obtain a new first moment and a new second moment, and construct a new integral coefficient matrix according to the new first moment and the new second moment; If not, re-obtain the new building physical parameters and new building boundary conditions of the target building, and construct a new thermal process state space model of the target building based on the new building physical parameters and the new building boundary conditions.

[0046] In this embodiment, the moment setting value is preferably 24 hours, and can be specifically adjusted according to the actual situation.

[0047] In this embodiment, a time-invariant model refers to a mathematical model whose parameters, input-output relationships, or dynamic characteristics do not change with time.

[0048] In this embodiment, constructing a new integral coefficient matrix according to the new first moment and the new second moment, the specific steps are the same as above, and only need to be adjusted adaptively.

[0049] In this embodiment, constructing a new thermal process state space model of the target building based on the new building physical parameters and the new building boundary conditions, the specific steps are the same as above, and only need to be adjusted adaptively.

[0050] In some embodiments of the present application, after using the temperature vector as the output of the building thermal process simulation of the target building, it further includes: Collect the temperature vectors corresponding to the target building at multiple moments; Sort all the temperature vectors by their numerical values, and determine the maximum temperature vector and the minimum temperature vector; Calculate the mean value of the temperature vectors corresponding to all the temperature vectors; Select the first ΔR1 temperature vectors between the maximum temperature vector and the mean value of the temperature vectors, and select the first ΔR2 temperature vectors between the minimum temperature vector and the mean value of the temperature vectors; Construct a first temperature vector sequence based on the first ΔR1 temperature vectors and the first ΔR2 temperature vectors; Construct a second temperature vector sequence based on all the remaining temperature vectors; Calculate the temperature change factor of the target building based on the first temperature vector sequence and the second temperature vector sequence.

[0051] In this embodiment, through the above steps, temperature vectors corresponding to multiple moments can be obtained. The number of moments is preferably 20, and can also be adjusted according to the actual situation specifically.

[0052] In this embodiment, ΔR1 is preferably 8, and ΔR2 is preferably 4, and can also be adjusted according to the actual situation specifically.

[0053] In some embodiments of the present application, when calculating the temperature change factor of the target building based on the first temperature vector sequence and the second temperature vector sequence, it includes: Sort the first temperature vector sequence by its numerical values, and extract the first maximum temperature vector and the first minimum temperature vector corresponding to the first temperature vector sequence; Sort the second temperature vector sequence by its numerical values, and extract the second maximum temperature vector and the second minimum temperature vector corresponding to the second temperature vector sequence; Calculate the mean value of the first temperature vector sequence corresponding to the first temperature vector sequence, and mark the mean value of the first temperature vector sequence in the first temperature vector sequence as the first temperature vector marking point; Calculate the mean value of the second temperature vector sequence corresponding to the second temperature vector sequence, and mark the mean value of the second temperature vector sequence in the second temperature vector sequence as the second temperature vector marking point; Count the number of the first temperature vectors Δa1 between the first temperature vector marking point and the first minimum temperature vector; Count the number of the second temperature vectors Δa2 between the second temperature vector marking point and the second maximum temperature vector; Calculate a first temperature vector calculation coefficient a3 according to the number of the first temperature vectors Δa1 and the number of the second temperature vectors Δa2, where, , is the total number of all temperature vectors; Count the number of third temperature vectors Δw1 between the first temperature vector marker points and the first maximum temperature vector; Count the number of fourth temperature vectors Δw2 between the second temperature vector marker points and the second minimum temperature vector; Calculate the second temperature vector calculation coefficient w3 according to the number of third temperature vectors Δw1 and the number of fourth temperature vectors Δw2, where, ; Calculate the absolute value of the coefficient difference between the first temperature vector calculation coefficient and the second temperature vector calculation coefficient, and calculate the coefficient sum value of the first temperature vector calculation coefficient and the second temperature vector calculation coefficient; Calculate the ratio of the absolute value of the coefficient difference and the coefficient sum value, and use it as the temperature change factor of the target building.

[0054] In this embodiment, when counting the number of temperature vectors, the temperature vectors equal to the average value of the temperature vectors are not counted.

[0055] The beneficial effects of the above technical solutions are: The present invention calculates the temperature change factor of the target building based on the first temperature vector sequence and the second temperature vector sequence, which ensures the calculation accuracy of the temperature change factor. The temperature change factor can reflect the temperature change situation of the target building, provide data support for building thermal distribution simulation and optimizing the design of the building heating system, reduce energy waste, and provide a scientific basis and technical support for the creation of energy-saving buildings and comfortable environments.

[0056] Those skilled in the art should understand that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can adopt the form of a computer program product implemented 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.

[0057] The present application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowcharts and / or block diagrams can be implemented by computer program instructions, and the combination of the processes and / or blocks in the flowcharts and / or block diagrams. These computer program instructions can be provided to the processors of general-purpose computers, special-purpose computers, embedded processors, or other programmable data processing devices to generate a machine, so that the instructions executed by the processors of the computer or other programmable data processing devices generate for implementing in the process Figure 1 one process or multiple processes and / or blocks Figure 1a device for the functions specified in one or more boxes.

[0058] These computer program instructions can also be stored in a computer-readable memory capable of guiding a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured article including an instruction device, and the instruction device implements the functions specified in one Figure 1 process or multiple processes and / or boxes Figure 1 a box or multiple boxes.

[0059] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one Figure 1 process or multiple processes and / or boxes Figure 1 a box or multiple boxes.

[0060] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: the specific implementation manners of the present invention can still be modified or equivalently replaced, and any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the protection scope of the present invention.

Claims

1. A building thermal process simulation method based on Lagrange interpolation integration method, characterized in that Including: Obtain the building physical parameters and building boundary conditions of the target building, and construct a thermal process state - space model of the target building based on the building physical parameters and building boundary conditions; Perform model analysis and eigenvalue decomposition on the thermal process state - space model to determine the eigenvalue matrix and eigenvector matrix; Obtain the first moment and the second moment, and construct an integral coefficient matrix according to the first moment and the second moment; Calculate the temperature vector of the target building according to the eigenvalue matrix, eigenvector matrix and integral coefficient matrix, and use the temperature vector as the simulation output of the building thermal process of the target building.

2. The building thermal process simulation method based on the Lagrange interpolation integration method according to claim 1, wherein When obtaining the building physical parameters and building boundary conditions of the target building and constructing the thermal process state - space model of the target building based on the building physical parameters and building boundary conditions, it includes: Construct a first building parameter matrix C and a second building parameter matrix A based on the building physical parameters, where the building physical parameters include building density, building specific heat capacity, building volume and building temperature; ； ； where i and j represent the nodes after spatial discretization of the target building, is the building density of the i-th node, is the specific heat capacity of the building of the i-th node, is the building volume of the i-th node, is the second-order symmetric tensor between the i-th node and the j-th node; ； Among them, is the heat exchange coefficient between the i-th node and the j-th node, is the heat exchange coefficient between the i-th node and the external disturbance of the building, and k is the external disturbance of the building; Construct a building condition matrix B based on the building boundary conditions; ； Among them, is the influence coefficient of the building external disturbance k on the i-th node; Construct the thermal process state - space model of the target building according to the first building parameter matrix C, the second building parameter matrix A and the building condition matrix B.

3. The building thermal process simulation method based on the Lagrange interpolation integration method according to claim 2, characterized in that When constructing the thermal process state - space model of the target building according to the first building parameter matrix C, the second building parameter matrix A and the building condition matrix B, it includes: ； Among them, is the state space model of the thermal process of the target building, , is the building temperature of the i-th node, Q is the input of internal and external disturbances of the building, , is the value of the external disturbance k of the building.

4. The building thermal process simulation method based on the Lagrange interpolation integration method according to claim 2, characterized in that When performing model analysis and eigenvalue decomposition on the thermal process state - space model to determine the eigenvalue matrix and eigenvector matrix, it includes: Perform model analysis on the thermal process state space model to construct a first coefficient matrix and a second coefficient matrix ; Perform eigenvalue decomposition on the first coefficient matrix and the second coefficient matrix to obtain an eigenvalue matrix and an eigenvector matrix P, where the eigenvalue matrix is a diagonal matrix, and the eigenvector matrix satisfies , and the eigenvalue decomposition satisfies .

5. The building thermal process simulation method based on the Lagrange interpolation integration method according to claim 4, characterized in that, When obtaining the first moment and the second moment and constructing an integral coefficient matrix according to the first moment and the second moment, it includes: Obtain the first moment and obtain the second moment ; Among them, is the integral coefficient matrix, is the eigenvalue matrix is the i-th eigenvalue of is the allowable error coefficient, e is a constant, x is the integration variable, ∏ is the symbol for continued multiplication, 0 ≤ m ≤ N, 0 ≤ N ≤ 4, 0 ≤ n ≤ N.

6. The building thermal process simulation method based on the Lagrange interpolation integration method according to claim 5, characterized in that, When calculating the temperature vector of the target building according to the eigenvalue matrix, eigenvector matrix and integral coefficient matrix, it includes: ； wherein, is the temperature vector of the target building at the second moment , is the temperature vector corresponding to the first moment is the input of internal and external disturbances of the building from the first moment to the second moment.

7. The building thermal process simulation method based on the Lagrange interpolation integration method according to claim 1, characterized in that Before using the temperature vector as the simulation output of the building thermal process of the target building, it further includes: Obtain a preset moment set value, judge whether the second moment is greater than the moment set value, if so, use the temperature vector as the simulation output of the building thermal process of the target building; If not, judge whether the thermal process state - space model is a time - invariant model, if so, re - obtain a new first moment and a new second moment, and construct a new integral coefficient matrix according to the new first moment and the new second moment; If not, re - obtain the new building physical parameters and new building boundary conditions of the target building, and construct a new thermal process state - space model of the target building based on the new building physical parameters and new building boundary conditions.

8. The building thermal process simulation method based on the Lagrange interpolation integration method according to claim 1, characterized in that After using the temperature vector as the simulation output of the building thermal process of the target building, it further includes: Collect the temperature vectors corresponding to the target building at multiple moments; Sort all the temperature vectors by numerical magnitude, and determine the maximum temperature vector and the minimum temperature vector; Calculate the mean value of the temperature vectors corresponding to all temperature vectors; Select the first ΔR1 temperature vectors between the maximum temperature vector and the mean value of the temperature vectors, and select the first ΔR2 temperature vectors between the minimum temperature vector and the mean value of the temperature vectors; Construct a first temperature vector sequence based on the first ΔR1 temperature vectors and the first ΔR2 temperature vectors; Construct a second temperature vector sequence based on all the remaining temperature vectors; Calculate the temperature change factor of the target building based on the first temperature vector sequence and the second temperature vector sequence.

9. The building thermal process simulation method based on the Lagrange interpolation integration method according to claim 8, characterized in that When calculating the temperature change factor of the target building based on the first temperature vector sequence and the second temperature vector sequence, it includes: Sort the first temperature vector sequence by numerical magnitude, and extract the first maximum temperature vector and the first minimum temperature vector corresponding to the first temperature vector sequence; Sort the second temperature vector sequence by numerical magnitude, and extract the second maximum temperature vector and the second minimum temperature vector corresponding to the second temperature vector sequence; Calculate the mean value of the first temperature vectors corresponding to the first temperature vector sequence, and mark the mean value of the first temperature vectors in the first temperature vector sequence as the first temperature vector marking point; Calculate the mean value of the second temperature vectors corresponding to the second temperature vector sequence, and mark the mean value of the second temperature vectors in the second temperature vector sequence as the second temperature vector marking point; Count the number of first temperature vectors Δa1 between the first temperature vector marking point and the first minimum temperature vector; Count the number of second temperature vectors Δa2 between the second temperature vector marking point and the second maximum temperature vector; Calculate a first temperature vector calculation coefficient a3 based on the first temperature vector quantity Δa1 and the second temperature vector quantity Δa2, where, , is the total quantity of all temperature vectors; Count the number of third temperature vectors Δw1 between the first temperature vector marking point and the first maximum temperature vector; Count the number of fourth temperature vectors Δw2 between the second temperature vector marking point and the second minimum temperature vector; Calculate a second temperature vector calculation coefficient w3 according to the third temperature vector quantity Δw1 and the fourth temperature vector quantity Δw2, where, ; Calculate the absolute value of the coefficient difference between the first temperature vector calculation coefficient and the second temperature vector calculation coefficient, and calculate the sum value of the first temperature vector calculation coefficient and the second temperature vector calculation coefficient; Calculate the ratio of the absolute value of the coefficient difference and the sum value of the coefficients, and use it as the temperature change factor of the target building.

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