Temperature control methods, devices, non-volatile storage media and electronic equipment

Abstract

This invention discloses a temperature control method, device, non-volatile storage medium, and electronic device. Relating to the field of power grid energy control, the method includes: obtaining a bus voltage-temperature relationship, wherein the bus voltage-temperature relationship represents the relationship between the DC bus voltage and the regulated temperature of a heat pump motor; determining a bus voltage drop objective function based on the bus voltage-temperature relationship, wherein the bus voltage drop objective function includes curve parameter variables obtained from a temperature transition curve to be determined; solving the bus voltage drop objective function using a particle swarm optimization algorithm to obtain the optimal parameter values ​​corresponding to the curve parameter variables, wherein the bus voltage drop objective function reaches its minimum value when the curve parameter variables reach their optimal values; and substituting the optimal parameter values ​​into the temperature transition curve to obtain the target temperature transition curve. This application solves the technical problem that the temperature regulation of a heat pump motor can easily cause transient impacts on the power grid.

Description

Technical Field

[0001] This invention relates to the field of power grid energy control, and more specifically, to a temperature control method, apparatus, non-volatile storage medium, and electronic device. Background Technology

[0002] In modern microgrid systems, the DC bus connects devices including energy storage, power consumption, and power distribution, responsible for the energy supply and storage of the entire system. Therefore, the stability of the bus voltage is paramount. Heat pump systems typically consume over 50% of the power in a building microgrid, having the greatest impact on its stability. Furthermore, during temperature regulation, the heat pump drive motor needs to balance the heat load from the indoor-outdoor temperature difference while adjusting the temperature to the target temperature. This process easily leads to significant current fluctuations, causing substantial voltage drops and significantly impacting other devices sharing the DC bus. Therefore, a control strategy is needed to smoothly and stably control the temperature transition, thereby suppressing transient bus voltage fluctuations and improving the stability of microgrid systems with heat pump motors sharing the DC bus.

[0003] There is currently no effective solution to the above problems. Summary of the Invention

[0004] This invention provides a temperature control method, apparatus, non-volatile storage medium, and electronic device to at least solve the technical problem that heat pump motors can easily cause transient impacts on the power grid when adjusting the temperature.

[0005] To achieve the above objectives, according to one aspect of the present invention, a temperature control method is provided, comprising: acquiring a bus voltage-temperature relationship, wherein the bus voltage-temperature relationship represents the relationship between the voltage of a DC bus and the regulated temperature of a heat pump motor, the DC bus being used to supply power to the heat pump motor, and the heat pump motor being used to regulate the temperature of a target space; determining a bus voltage drop objective function based on the bus voltage-temperature relationship, wherein the bus voltage drop objective function includes curve parameter variables, the curve parameter variables being obtained from a temperature transition curve to be determined; solving the bus voltage drop objective function using a particle swarm optimization algorithm to obtain optimal parameter values ​​corresponding to the curve parameter variables, wherein the bus voltage drop objective function reaches its minimum value when the curve parameter variables take the optimal parameter values; and substituting the optimal parameter values ​​into the temperature transition curve to obtain a target temperature transition curve, wherein the target temperature transition curve represents a control strategy for the heat pump motor.

[0006] Optionally, solving the objective function of the bus voltage drop using the particle swarm optimization (PSO) algorithm to obtain the optimal parameter values ​​corresponding to the curve parameter variables includes: generating multiple particles for the PSO algorithm based on the curve parameter variables, and generating the fitness function for the PSO algorithm based on the objective function of the bus voltage drop; iteratively solving the problem using the multiple particles and the fitness function in the following manner: in each stage of iteratively updating multiple particles using the PSO algorithm, the multiple particles are divided into a regular subgroup, a random chaotic subgroup, and a directed chaotic subgroup; a random chaotic variable is added to the position coordinates of the particles in the random chaotic subgroup; the velocity variable in the position iteration formula of the particles in the directed chaotic subgroup is replaced with a chaotic velocity variable; iterative updates are performed using the regular subgroup, the updated random chaotic subgroup, and the updated directed chaotic subgroup to obtain multiple particles for the next iteration stage; the above iterative process is repeated until the optimal parameter values ​​are obtained.

[0007] Optionally, a random chaotic variable is added to the position coordinates of each particle in the random chaotic subgroup, including: determining the initial value of the chaotic variable corresponding to the current iteration stage based on the current iteration number, the maximum iteration number, and the neighborhood radius; determining the random chaotic variable based on the initial value of the chaotic variable corresponding to the current iteration stage, the current position coordinates of the particles in the random chaotic subgroup, and the random coefficients of the chaotic variable; and adding a random chaotic variable to the current position coordinates of the particles in the random chaotic subgroup.

[0008] Optionally, the velocity variable in the position iteration formula of the particles in the directed chaotic subgroup is replaced with a chaotic velocity variable, including: determining the initial value of the chaotic variable corresponding to the current iteration stage based on the current iteration number, the maximum iteration number, and the neighborhood radius; determining the chaotic velocity variable based on the initial value of the chaotic variable corresponding to the current iteration stage and the velocity variable in the position iteration formula of the particles in the directed chaotic subgroup; and replacing the velocity variable in the position iteration formula of the particles in the directed chaotic subgroup with the chaotic velocity variable.

[0009] Optionally, the objective function for bus voltage sag is determined based on the bus voltage-temperature relationship, including: obtaining the undetermined temperature transition curve; representing the undetermined temperature transition curve using a Chebyshev polynomial to obtain a seven-segment polynomial; eliminating parameters from the seven-segment polynomial based on the thermal boundary conditions of the initial temperature, the final temperature, and the rate of change of the initial and final temperatures to obtain the curve parameter variables in the undetermined temperature transition curve; and constructing the objective function for target voltage sag based on the bus voltage-temperature relationship and the curve parameter variables.

[0010] Optionally, the above method further includes: determining a temperature adjustment time based on human comfort constraints, a starting temperature, and an ending temperature, wherein the temperature adjustment time is one of the constraints of the target voltage drop objective function.

[0011] Optionally, obtaining the bus voltage-temperature relationship includes: calculating the output power-temperature relationship of the heat pump motor based on the heat conduction parameters of the building window and wall structure and the heat parameters of the heating network; calculating the bus voltage-temperature relationship based on the equivalent circuit and operating efficiency of the heat pump motor; and determining the bus voltage-temperature relationship of the heat pump motor based on the output power-temperature relationship and the bus voltage-temperature relationship.

[0012] To achieve the above objectives, according to another aspect of the present invention, a temperature control device is also provided, comprising: an acquisition module, configured to acquire a bus voltage-temperature relationship, wherein the bus voltage-temperature relationship represents the relationship between the voltage of a DC bus and the regulating temperature of a heat pump motor, the DC bus being used to supply power to the heat pump motor, and the heat pump motor being used to regulate the temperature of a target space; a determination module, configured to determine a bus voltage drop objective function based on the bus voltage-temperature relationship, wherein the bus voltage drop objective function includes curve parameter variables, the curve parameter variables being obtained from a temperature transition curve to be determined; a solution module, configured to solve the bus voltage drop objective function using a particle swarm optimization algorithm to obtain optimal parameter values ​​corresponding to the curve parameter variables, wherein the bus voltage drop objective function obtains a minimum value when the curve parameter variables take the optimal parameter values; and a substitution module, configured to substitute the optimal parameter values ​​into the temperature transition curve to be determined to obtain a target temperature transition curve, wherein the target temperature transition curve represents a control strategy for the heat pump motor.

[0013] According to another aspect of the present invention, a non-volatile storage medium is also provided, the non-volatile storage medium including a stored program, wherein, when the program is executed, the device where the non-volatile storage medium is located is controlled to perform any of the above-described temperature control methods.

[0014] According to another aspect of the present invention, an electronic device is also provided, the electronic device including a memory and a processor, the memory being used to store a program, and the processor being used to run the program stored in the memory, wherein the program, when running, executes any of the above-described temperature control methods.

[0015] In this embodiment of the invention, a voltage drop objective function for the DC bus is constructed. The objective function is determined based on the relationship between the DC bus voltage and temperature. The particle swarm optimization algorithm is used to solve the curve parameter variables in the objective function. Then, based on the curve parameter variables, a target temperature transition curve that minimizes the DC bus voltage drop rate is determined. This achieves the purpose of generating a temperature control strategy for the heat pump motor, thereby reducing the DC bus voltage drop amplitude during the heat pump motor temperature adjustment process. This solves the technical problem that the heat pump motor temperature adjustment process can easily cause transient impacts on the power grid. Attached Figure Description

[0016] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0017] Figure 1 A hardware structure block diagram of a computer terminal for implementing a temperature control method is shown.

[0018] Figure 2 This is a schematic flowchart of a temperature control method provided according to an embodiment of the present invention;

[0019] Figure 3 This is a circuit connection diagram of a heat pump system according to an optional embodiment of the present invention;

[0020] Figure 4 This is a schematic diagram of the curve processing procedure for the temperature transition curve according to an optional embodiment of the present invention;

[0021] Figure 5 This is a schematic diagram showing the solution results of the objective function for bus voltage sag according to an optional embodiment of the present invention;

[0022] Figure 6 This is a structural block diagram of a temperature control device provided according to an embodiment of the present invention;

[0023] Figure 7 This is a structural block diagram of an electronic device provided according to an embodiment of the present invention. Detailed Implementation

[0024] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0025] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0026] According to an embodiment of the present invention, a method embodiment for temperature control is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0027] The method embodiment provided in Embodiment 1 of this application can be executed on a mobile terminal, computer terminal, or similar computing device. Figure 1 A hardware block diagram of a computer terminal for implementing a temperature control method is shown. Figure 1 As shown, the computer terminal 10 may include one or more processors (shown as 102a, 102b, ..., 102n in the figure) (the processor may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.) and a memory 104 for storing data. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of a BUS bus), a network interface, a power supply, and / or a camera. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the aforementioned electronic device. For example, computer terminal 10 may also include... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0028] It should be noted that the aforementioned one or more processors and / or other data processing circuits are generally referred to herein as "data processing circuits". These data processing circuits may be implemented wholly or partially as software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuits may be a single, independent processing module, or may be wholly or partially integrated into any other element in the computer terminal 10. As involved in the embodiments of this application, the data processing circuits serve as processor control (e.g., selection of a variable resistor termination path connected to an interface).

[0029] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the temperature control method in this embodiment of the invention. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory 104, thereby realizing the temperature control method of the application described above. The memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor, and these remote memories can be connected to the computer terminal 10 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0030] The display may be, for example, a touchscreen liquid crystal display (LCD) that allows the user to interact with the user interface of the computer terminal 10.

[0031] The purpose of this invention is to reduce the DC bus voltage drop caused by heat pump motor power fluctuations during temperature regulation in heat pump systems, thereby improving the stability of shared-bus microgrid systems while also considering human comfort requirements. To achieve the above objectives, this invention provides the following technical solution: Based on the relationship between temperature rise and bus voltage change during heat pump motor temperature regulation, a temperature transition model is established using a Chebyshev temperature transition strategy. The objective function for evaluating the bus voltage drop during the temperature transition process and the corresponding constraints are derived. An improved particle swarm optimization algorithm is then used to optimize the temperature transition parameters. Finally, the optimal temperature transition curve is obtained by substituting these parameters into the temperature control strategy.

[0032] The present invention will now be described in conjunction with preferred implementation steps. Figure 2 This is a schematic flowchart of a temperature control method provided according to an embodiment of the present invention, as shown below. Figure 2 As shown, the method includes the following steps:

[0033] Step S202: Obtain the bus voltage-temperature relationship, where the bus voltage-temperature relationship represents the relationship between the DC bus voltage and the regulated temperature of the heat pump motor. The DC bus is used to supply power to the heat pump motor, and the heat pump motor is used to regulate the temperature of the target space.

[0034] The target space can be a building, and the heat pump motor can be used to regulate the temperature of the building. Figure 3 This is a circuit connection diagram of a heat pump system according to an optional embodiment of the present invention. The heat pump system includes a heat pump motor, which can also be referred to as a heat pump drive motor, used to regulate the building temperature. The heat pump motor is connected to the DC bus via a controller. Therefore, when the heat pump motor regulates the building temperature along a temperature transition curve, the power of the heat pump motor will fluctuate, causing fluctuations and drops in the supply voltage of the DC bus. If the voltage drop of the DC bus is too drastic, it will affect the normal operation of other devices sharing the same bus. Therefore, it is necessary to control the operating curve of the heat pump motor and find the temperature transition curve of the heat pump motor that minimizes the voltage drop rate of the DC bus, i.e., determine the target temperature transition curve in this embodiment.

[0035] As an optional embodiment, the bus voltage-temperature relationship can be obtained by: calculating the output power-temperature relationship of the heat pump motor based on the heat conduction parameters of the building window and wall structure and the heat parameters of the heating network; calculating the bus voltage-temperature relationship based on the equivalent circuit and operating efficiency of the heat pump motor; and determining the bus voltage-temperature relationship of the heat pump motor based on the output power-temperature relationship and the bus voltage-temperature relationship.

[0036] In this step, the heat pump temperature can be calculated based on the heat conduction parameters of the building's window and wall structure and the heat parameters of the heating network, thereby obtaining the output power-temperature relationship of the heat pump motor. Furthermore, based on the equivalent circuit and operating efficiency of the heat pump motor, the bus voltage and power relationship of the DC bus can be calculated, thereby obtaining the bus voltage-temperature relationship of the DC bus. Finally, based on these two relationships, the bus voltage-temperature relationship of the heat pump motor can be determined.

[0037] Step S204: Determine the objective function for bus voltage drop based on the bus voltage-temperature relationship. The objective function for bus voltage drop includes curve parameter variables, which are obtained from the temperature transition curve to be determined.

[0038] Since the objective function for bus voltage sag includes the curve parameter variables in the temperature transition curve to be determined, when the objective function for bus voltage sag reaches its minimum value, the value of the curve parameter variables at this time can be determined as the optimal parameter value that minimizes the voltage sag rate of the DC bus. Then, the operating curve of the heat pump motor can be determined based on this optimal parameter value. By controlling the heat pump motor to operate along the target temperature transition curve corresponding to the optimal parameter value, it can be ensured that the DC bus voltage does not experience a sudden temperature drop, thus ensuring the voltage stability of the microgrid system.

[0039] As an optional embodiment, the objective function for bus voltage drop is determined based on the bus voltage-temperature relationship, including: obtaining the transition curve of the unknown temperature; representing the transition curve of the unknown temperature using Chebyshev polynomials to obtain a seven-segment polynomial; eliminating parameters from the seven-segment polynomial based on the thermal boundary conditions of the initial temperature, the final temperature, and the rate of change of the initial and final temperatures to obtain the curve parameter variables in the transition curve of the unknown temperature; and constructing the objective function for target voltage drop based on the bus voltage-temperature relationship and the curve parameter variables.

[0040] Chebyshev polynomials are a class of polynomial functions that satisfy certain specific conditions. Specifically, on the interval [-1, 1], a Chebyshev polynomial can be represented as T. n (t), where n is an integer representing the degree of the Chebyshev polynomial. n (t) can be calculated using the recursive formula:

[0041] T0(t)=1

[0042] T1(t) = x

[0043] T n (t)=2x*T n-1 (t)-T n-2 (t)

[0044] Chebyshev polynomials can be conveniently used to represent a temperature transition curve. For example, if we want to fit a given temperature transition curve with a quadratic function f(x) = ax^2 + bx + c, and want the transition to be as smooth as possible while minimizing the fitting error, we should choose Chebyshev polynomials as the basis functions and express f(x) as a linear combination of them:

[0045] f(x)=a0T0(t)+a1T1(t)+a2T2(t)

[0046] Where a i Let represent the coefficient to be determined, and t be the normalized time variable (-1 ≤ t ≤ 1). This effectively describes the transition relationship between the intensity of temperature changes in different time periods during the curve process.

[0047] Optionally, in this optional embodiment, the temperature can be adjusted from the current temperature T0 to the target temperature T. s Temperature transition curve T e (t) is passed through the Chebyshev polynomial φ e (t) is used to characterize, such as Figure 4 As shown, based on the property that the domain and range of Chebyshev polynomials are both located in [-1, 1], the temperature transition curve is normalized and its coordinates are shifted. After inverse normalization and coordinate shifting, T e (t) and φ e The relationship between (t) is shown in formula (1):

[0048] T e (0) = T0, T e (t s ) = T s

[0049] T e (t)=φ e [(2t / t s -1)*0.5+0.5]*(T s -T o )+T o (1)

[0050] Where t s For temperature conditioning time, φ e (t) is a seven-segment Chebyshev polynomial, i.e.:

[0051]

[0052]

[0053] As an optional embodiment, the temperature conditioning time can be determined based on human comfort constraints, the initial temperature, and the final temperature, wherein the temperature conditioning time is one of the constraints of the objective function of the target voltage drop.

[0054] Considering the constraints of human body temperature adaptability, an acceptable temperature change range is set as shown in formula (3). The temperature adjustment time t is then calculated using this formula. s .

[0055] t s <k t |T s -T0| (3)

[0056] Where, k t T represents the parameter corresponding to human comfort constraints. s T0 is the final temperature, and T0 is the initial temperature. Optionally, the temperature adjustment time t can be calculated using formula (3). sFor example, t s The maximum value that can be obtained is determined as the temperature adjustment time.

[0057] Optionally, parameter elimination can be performed as follows: by substituting the above conditions into formula (2) through the thermal boundary conditions of the initial temperature, the final temperature and the rate of change of the initial and final temperatures, p0 to p5 in formula (2) are all represented by p6 and p7, as shown in formula (4).

[0058]

[0059] Due to t in the above optional embodiments s Since this has been determined, the objective function for bus voltage drop can be defined as a function that only includes p6 and p7 as unknown variables. Subsequently, we can solve for p6 and p7 to obtain the minimum value of the objective function for bus voltage drop.

[0060] Alternatively, the objective function for bus voltage sag can take the form shown below:

[0061]

[0062] In the formula, the specific heat density and volume of the air inside the building are CρV, and the current bus voltage is U. dc .

[0063] Step S206: Solve the objective function of bus voltage drop using the particle swarm optimization algorithm to obtain the optimal parameter values ​​corresponding to the curve parameter variables. When the curve parameter variables take the optimal parameter values, the objective function of bus voltage drop reaches its minimum value.

[0064] As an optional embodiment, the optimal parameter values ​​corresponding to the curve parameter variables are obtained by solving the objective function of bus voltage drop using the particle swarm optimization algorithm. This includes: generating multiple particles for the particle swarm optimization algorithm based on the curve parameter variables, and generating the fitness function for the particle swarm optimization algorithm based on the objective function of bus voltage drop; iteratively solving the problem using multiple particles and the fitness function in the following manner: in each stage of iteratively updating multiple particles using the particle swarm optimization algorithm, the multiple particles are divided into a regular subgroup, a random chaotic subgroup, and a directed chaotic subgroup; a random chaotic variable is added to the position coordinates of each particle in the random chaotic subgroup; the velocity variable in the position iteration formula of the particles in the directed chaotic subgroup is replaced with a chaotic velocity variable; iterative updates are performed using the regular subgroup, the updated random chaotic subgroup, and the updated directed chaotic subgroup to obtain multiple particles for the next iteration stage; the above iterative process is repeated until the optimal parameter values ​​are obtained.

[0065] To solve for p6 and p7, an improved particle swarm optimization (PSO) algorithm can be designed. In this PSO algorithm, the fitness function can be constructed based on the bus voltage drop objective function, such that increasing the particle's fitness function decreases the objective function value of the voltage drop objective function. The number of particles in the algorithm can be denoted as N, and the two dimensions of each particle are p6 and p7. At the k-th iteration, the position x of the i-th particle... i k With velocity v i k The information is:

[0066]

[0067] For each particle, its two-dimensional coordinates are substituted into formula (5) as the two parameters p6 and p7 in the temperature transition curve to be determined, and the reciprocal of its maximum voltage drop is taken as the particle's current fitness pr. i (k), then the particle velocity is expressed as:

[0068]

[0069] pb i (k) represents the location of the particle with the highest local fitness, gb i (k) represents the position of the particle with the highest global fitness. Here, ω represents the inertia factor in the particle swarm optimization algorithm; c1 and c2 are the learning factors in the particle swarm optimization algorithm, which can also be called acceleration factors.

[0070] To improve the algorithm's fitness and take into account the actual voltage drop and theoretical deviation, directional chaotic subgroups and random chaotic subgroups can be introduced into the particle swarm optimization algorithm.

[0071] As an optional implementation, a random chaotic variable is added to the position coordinates of each particle in the random chaotic subgroup, including: based on the current iteration number k and the maximum iteration number k. max Given the neighborhood radius γ, determine the initial values ​​of the chaotic variables corresponding to the current iteration stage; based on the initial values ​​of the chaotic variables corresponding to the current iteration stage and the current position coordinates of the particles in the random chaotic subgroup... and chaotic variable random coefficient z i (k), determine the random chaotic variable η i z i (k); the current position coordinates of the particles in the random chaotic subgroup. Add a random chaotic variable η i z i (k), to obtain the updated position coordinates x i (k).

[0072] Optionally, in a random chaotic particle swarm, the particle position is calculated using formula (7) with the addition of a chaotic variable, as shown in formula (8):

[0073]

[0074] Optionally, the neighborhood radius γ can be 0.1; z i (k) is a chaotic variable with a value range of [-1, 1]. In a random chaotic particle swarm, 20% of the total particles can be selected to join the swarm, thus avoiding the algorithm getting trapped in local optima.

[0075] As an optional embodiment, the velocity variable in the position iteration formula of the particles in the directed chaotic subgroup is replaced with a chaotic velocity variable, including: determining the initial value of the chaotic variable corresponding to the current iteration stage based on the current iteration number, the maximum iteration number, and the neighborhood radius; determining the chaotic velocity variable based on the initial value of the chaotic variable corresponding to the current iteration stage and the velocity variable in the position iteration formula of the particles in the directed chaotic subgroup; and replacing the velocity variable in the position iteration formula of the particles in the directed chaotic subgroup with the chaotic velocity variable.

[0076] Optionally, 20% of the particles in the particle swarm algorithm can also be selected as a directed chaotic subgroup. This directed chaotic subgroup can replace the velocity variable in formula (7) with chaotic velocity, as shown in formula (9).

[0077]

[0078] Therefore, through k max In the next iteration, the maximum value of the fitness function of the particle swarm contraction position is the optimal value of the temperature transition parameter, and its corresponding coordinates are the optimal solution parameter values ​​of p6 and p7.

[0079] Step S208: Substitute the optimal parameter values ​​into the temperature transition curve to obtain the target temperature transition curve, where the target temperature transition curve represents the control strategy for the heat pump motor.

[0080] In this step, by substituting the values ​​of parameters p6 and p7 into the Chebyshev trajectory, we can obtain the temperature transition strategy that optimizes the voltage drop.

[0081] Through the above steps, a voltage drop objective function for the DC bus is constructed. This objective function is determined based on the relationship between the DC bus voltage and temperature. The particle swarm optimization algorithm is used to solve for the curve parameter variables in the objective function. Then, based on the curve parameter variables, a target temperature transition curve that minimizes the DC bus voltage drop rate is determined. This achieves the goal of generating a temperature control strategy for the heat pump motor, thereby reducing the DC bus voltage drop amplitude during the heat pump motor temperature adjustment process. This also solves the technical problem that the heat pump motor temperature adjustment process can easily cause transient impacts on the power grid.

[0082] This invention relates to a temperature-optimal transition method based on an improved particle swarm optimization algorithm, belonging to the field of microgrid energy control. It includes: a temperature smoothing transition model for a heat pump system based on a seventh-order Chebyshev polynomial; designing a value function that satisfies the stability requirements of the microgrid system during the transition process based on this model; and implementing particle space constraints that satisfy boundary condition constraints. Furthermore, it uses a method of allocating adaptive and chaotic subgroups of the multi-edge model to perform particle swarm iterations to find the optimal curve parameters for the transition process. The effectiveness and performance of the method are verified by constructing a simulation model containing a heat pump motor. This method, while employing the particle swarm optimization algorithm, considers the optimization deviation problem caused by model offset and local convergence. It can efficiently and accurately achieve the target temperature transition while minimizing the impact of transient shocks on the common bus equipment, improving the stability and reliability of the heat pump microgrid and possessing high practical value.

[0083] The following is a process for finding the optimal parameter values ​​based on an optional embodiment of the present invention:

[0084] S1: Based on the thermodynamic properties of the heat pump system and the temperature-controlled object, analyze and calculate the heat transfer coefficient k of the external wall of the temperature-controlled object. w S w With the heat dissipation coefficient k of the external window i S i 1. Determine the specific heat density and volume (CρV) of the air inside the building, and simultaneously determine the outdoor temperature, indoor temperature, and target temperature (T). S and the current bus voltage U dc ;

[0085] S2: Determine the objective function ΔU of the transition strategy based on relevant thermal and state parameters. dc (t), that is, the voltage drop during the temperature transition process is required to be a cost function. The ultimate goal of parameter design in the transition strategy is to minimize the voltage change, so as to minimize the impact of the heat pump operation on the common bus power system.

[0086] S3: Based on the thermal boundary conditions, the initial temperature and the initial temperature change rate constraints are substituted into the Chebyshev equation, and p0 to p5 are all expressed through p6 and p7, thus obtaining a polynomial with only two polynomial coefficients, p6 and p7, and the transition time t. s Temperature transition equation with three unknowns;

[0087] S4: Based on the human comfort temperature rise constraint, calculate the longest temperature rise time that meets the human comfort requirements, and use this time as the transition time t. s Thus, the temperature transition equation simplifies to a minimum voltage drop problem with only two unknowns.

[0088] S5: Set the boundary conditions for p6 and p7 to [-0.01, 0.01], and set 80 particles randomly distributed within the boundary conditions. Calculate the fitness function based on the maximum voltage drop, and find the optimal value of this iterative fitness function and the optimal value of the overall fitness as the local optimal particle pb. i (1) with the globally optimal particle gb i (1) The contraction velocity vector v(k+1) of each particle is calculated using the optimal particle, thus obtaining the position x(k+1) of the iterative particle. Before the iterative particle performs the next fitness calculation, two groups of particles are randomly selected as directed chaotic particles and random chaotic particles for position updates. Therefore, the above part is repeated using the chaotic particle swarm as the initial group for the next iteration, through k... max In the next iteration, the maximum value of the fitness function at the particle swarm contraction position is the optimal value of the temperature transition parameter, corresponding to pb. i (k max ), gb i (k max The coordinates represent the local and global optimal positions of the parameter values ​​p6 and p7.

[0089] Figure 5 This is a schematic diagram illustrating the solution results of the objective function for bus voltage sag according to an optional embodiment of the present invention, as shown below. Figure 5 As shown, the coordinate axes in the three-dimensional spatial coordinate system are p6, p7, and the bus voltage, respectively. The results of the particle swarm optimization can be visualized to obtain the desired solution. Figure 5 The figure clearly shows how to determine the optimal parameter values ​​for p6 and p7.

[0090] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the present invention is not limited to the described order of actions, because according to the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to the present invention.

[0091] Through the above description of the embodiments, those skilled in the art can clearly understand that the temperature control method according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platform. Of course, it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0092] According to an embodiment of the present invention, a temperature control device for implementing the above-described temperature control method is also provided. Figure 6 This is a structural block diagram of a temperature control device provided according to an embodiment of the present invention, such as... Figure 6 As shown, the temperature control device includes: an acquisition module 62, a determination module 64, a solution module 66, and a substitution module 68. The temperature control device will be described below.

[0093] The acquisition module 62 is used to acquire the bus voltage-temperature relationship, wherein the bus voltage-temperature relationship represents the relationship between the DC bus voltage and the regulated temperature of the heat pump motor. The DC bus is used to supply power to the heat pump motor, and the heat pump motor is used to regulate the temperature of the target space.

[0094] The determination module 64, connected to the acquisition module 62, is used to determine the target function of bus voltage drop based on the bus voltage-temperature relationship. The target function of bus voltage drop includes curve parameter variables, which are obtained from the temperature transition curve to be determined.

[0095] Solving module 66, connected to the determining module 64, is used to solve the bus voltage drop objective function according to the particle swarm algorithm to obtain the optimal parameter values ​​corresponding to the curve parameter variables. When the curve parameter variables take the optimal parameter values, the bus voltage drop objective function obtains the minimum value.

[0096] Substitution module 68, connected to the above-mentioned solution module 66, is used to substitute the optimal parameter values ​​into the temperature transition curve to obtain the target temperature transition curve, wherein the target temperature transition curve represents the control strategy of the heat pump motor.

[0097] It should be noted that the acquisition module 62, determination module 64, solution module 66, and substitution module 68 mentioned above correspond to steps S202 to S208 in the embodiments. The four modules and their corresponding steps implement the same instances and application scenarios, but are not limited to the content disclosed in the above embodiments. It should also be noted that the above modules, as part of the device, can run on the computer terminal 10 provided in the embodiments.

[0098] The temperature control device includes a processor and a memory. The aforementioned acquisition module 62, determination module 64, solution module 66, and substitution module 68 are all stored as program units in the memory. The processor executes the aforementioned program units stored in the memory to realize the corresponding functions.

[0099] The processor contains a kernel, which retrieves the corresponding program unit from memory. One or more kernels can be configured, and the temperature control method described above can be implemented by adjusting the kernel parameters.

[0100] The memory may include non-permanent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0101] This invention provides a computer-readable storage medium storing a program that, when executed by a processor, implements a temperature control method.

[0102] This invention provides a processor for running a program, wherein the program executes a temperature control method during runtime.

[0103] like Figure 7 As shown, this embodiment of the invention provides an electronic device. The device includes a processor, a memory, and a program stored in the memory and executable on the processor. The memory can be used to store software programs and modules, such as the program instructions / modules corresponding to the temperature control method and device in this embodiment. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, thereby implementing the aforementioned temperature control method. The memory may include high-speed random access memory (RAM) and non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memories. In some instances, the memory may further include memory remotely located relative to the processor, which can be connected to a computer terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks (LANs), mobile communication networks, and combinations thereof.

[0104] When the processor executes the program, it performs the following steps: First, it obtains the bus voltage-temperature relationship, which represents the relationship between the DC bus voltage and the regulated temperature of the heat pump motor. The DC bus supplies power to the heat pump motor, which regulates the temperature of the target space. Second, based on the bus voltage-temperature relationship, it determines the bus voltage drop objective function, which includes curve parameter variables obtained from the temperature transition curve to be determined. Third, it solves the bus voltage drop objective function using the particle swarm optimization algorithm to obtain the optimal parameter values ​​corresponding to the curve parameter variables. The bus voltage drop objective function reaches its minimum value when the curve parameter variables reach their optimal values. Fourth, it substitutes the optimal parameter values ​​into the temperature transition curve to obtain the target temperature transition curve, which represents the control strategy for the heat pump motor.

[0105] Optionally, the processor may also perform the following steps when executing the program: solving the objective function of bus voltage drop using the particle swarm optimization algorithm to obtain the optimal parameter values ​​corresponding to the curve parameter variables, including: generating multiple particles for the particle swarm optimization algorithm based on the curve parameter variables, and generating the fitness function for the particle swarm optimization algorithm based on the objective function of bus voltage drop; iteratively solving the problem using multiple particles and the fitness function in the following manner: in each stage of iteratively updating multiple particles using the particle swarm optimization algorithm, the multiple particles are divided into a regular subgroup, a random chaotic subgroup, and a directed chaotic subgroup; a random chaotic variable is added to the position coordinates of the particles in the random chaotic subgroup; the velocity variable in the position iteration formula of the particles in the directed chaotic subgroup is replaced with a chaotic velocity variable; iterative updates are performed using the regular subgroup, the updated random chaotic subgroup, and the updated directed chaotic subgroup to obtain multiple particles for the next iteration stage; the above iterative process is repeated until the optimal parameter values ​​are obtained.

[0106] Optionally, when the processor executes the program, it may also implement the following steps: adding a random chaotic variable to the position coordinates of the particles in the random chaotic subgroup, including: determining the initial value of the chaotic variable corresponding to the current iteration stage based on the current iteration number, the maximum iteration number, and the neighborhood radius; determining the random chaotic variable based on the initial value of the chaotic variable corresponding to the current iteration stage, the current position coordinates of the particles in the random chaotic subgroup, and the random coefficient of the chaotic variable; and adding a random chaotic variable to the current position coordinates of the particles in the random chaotic subgroup.

[0107] Optionally, the processor may also perform the following steps when executing the program: replacing the velocity variable in the position iteration formula of the particles in the directed chaotic subgroup with a chaotic velocity variable, including: determining the initial value of the chaotic variable corresponding to the current iteration stage based on the current iteration number, the maximum iteration number, and the neighborhood radius; determining the chaotic velocity variable based on the initial value of the chaotic variable corresponding to the current iteration stage and the velocity variable in the position iteration formula of the particles in the directed chaotic subgroup; and replacing the velocity variable in the position iteration formula of the particles in the directed chaotic subgroup with a chaotic velocity variable.

[0108] Optionally, the processor may also perform the following steps when executing the program: determining the objective function for bus voltage drop based on the bus voltage-temperature relationship, including: obtaining the transition curve of the unknown temperature; representing the transition curve of the unknown temperature using Chebyshev polynomials to obtain a seven-segment polynomial; eliminating parameters from the seven-segment polynomial based on the thermal boundary conditions of the initial temperature, the final temperature, and the rate of change of the initial and final temperatures to obtain the curve parameter variables in the transition curve of the unknown temperature; and constructing the objective function for target voltage drop based on the bus voltage-temperature relationship and the curve parameter variables.

[0109] Optionally, the processor may also perform the following steps when executing the program: determining the temperature adjustment time based on human comfort constraints, the initial temperature, and the final temperature, wherein the temperature adjustment time is one of the constraints of the objective function of the target voltage drop.

[0110] Optionally, the processor may also perform the following steps when executing the program: obtaining the bus voltage-temperature relationship, including: calculating the output power-temperature relationship of the heat pump motor based on the heat conduction parameters of the building window and wall structure and the heat parameters of the heating network; calculating the bus voltage-temperature relationship based on the equivalent circuit and operating efficiency of the heat pump motor; and determining the bus voltage-temperature relationship of the heat pump motor based on the output power-temperature relationship and the bus voltage-temperature relationship.

[0111] The devices mentioned in this article can be servers, PCs, tablets, mobile phones, etc.

[0112] This application also provides a computer program product, which, when executed on a data processing device, is suitable for executing an initialization program with the following method steps: obtaining a bus voltage-temperature relationship, wherein the bus voltage-temperature relationship represents the relationship between the voltage of the DC bus and the regulated temperature of the heat pump motor, the DC bus being used to supply power to the heat pump motor, and the heat pump motor being used to regulate the temperature of the target space; determining a bus voltage drop objective function based on the bus voltage-temperature relationship, wherein the bus voltage drop objective function includes curve parameter variables, which are obtained from the temperature transition curve to be determined; solving the bus voltage drop objective function using a particle swarm optimization algorithm to obtain the optimal parameter values ​​corresponding to the curve parameter variables, wherein the bus voltage drop objective function reaches its minimum value when the curve parameter variables take the optimal parameter values; substituting the optimal parameter values ​​into the temperature transition curve to obtain the target temperature transition curve, wherein the target temperature transition curve represents the control strategy for the heat pump motor.

[0113] This application also provides a computer program product, which, when executed on a data processing device, is suitable for executing an initialization program with the following method steps: solving the objective function of bus voltage drop according to the particle swarm optimization algorithm to obtain the optimal parameter values ​​corresponding to the curve parameter variables, including: generating multiple particles for the particle swarm optimization algorithm based on the curve parameter variables, and generating the fitness function of the particle swarm optimization algorithm based on the objective function of bus voltage drop; iteratively solving the problem based on the multiple particles and the fitness function in the following manner: in each stage of iteratively updating multiple particles through the particle swarm optimization algorithm, the multiple particles are divided into a regular subgroup, a random chaotic subgroup, and a directed chaotic subgroup; a random chaotic variable is added to the position coordinates of the particles in the random chaotic subgroup; the velocity variable in the position iteration formula of the particles in the directed chaotic subgroup is replaced with a chaotic velocity variable; iterative updates are performed with the regular subgroup, the updated random chaotic subgroup, and the updated directed chaotic subgroup to obtain multiple particles for the next iteration stage; the above iterative process is repeated until the optimal parameter values ​​are obtained.

[0114] This application also provides a computer program product, which, when executed on a data processing device, is suitable for executing an initialization program with the following method steps: adding a random chaotic variable to the position coordinates of particles in a random chaotic subgroup, including: determining the initial value of the chaotic variable corresponding to the current iteration stage based on the current iteration number, the maximum iteration number, and the neighborhood radius; determining the random chaotic variable based on the initial value of the chaotic variable corresponding to the current iteration stage, the current position coordinates of the particles in the random chaotic subgroup, and the random coefficients of the chaotic variable; and adding a random chaotic variable to the current position coordinates of the particles in the random chaotic subgroup.

[0115] This application also provides a computer program product, which, when executed on a data processing device, is suitable for executing an initialization program having the following method steps: replacing the velocity variable in the position iteration formula of a particle in a directional chaotic subgroup with a chaotic velocity variable, including: determining the initial value of the chaotic variable corresponding to the current iteration stage based on the current iteration number, the maximum iteration number, and the neighborhood radius; determining the chaotic velocity variable based on the initial value of the chaotic variable corresponding to the current iteration stage and the velocity variable in the position iteration formula of a particle in a directional chaotic subgroup; and replacing the velocity variable in the position iteration formula of a particle in a directional chaotic subgroup with the chaotic velocity variable.

[0116] This application also provides a computer program product, which, when executed on a data processing device, is suitable for executing an initialization program with the following method steps: determining the target function for bus voltage drop based on the bus voltage-temperature relationship, including: obtaining a transition curve for a given temperature; representing the transition curve for a given temperature using a Chebyshev polynomial to obtain a seven-segment polynomial; eliminating parameters from the seven-segment polynomial based on the thermal boundary conditions of the initial temperature, the final temperature, and the rate of change of the initial and final temperatures to obtain the curve parameter variables in the transition curve for a given temperature; and constructing the target voltage drop target function based on the bus voltage-temperature relationship and the curve parameter variables.

[0117] This application also provides a computer program product that, when executed on a data processing device, is suitable for executing an initialization program with the following method steps: determining a temperature adjustment time based on human comfort constraints, a starting temperature, and an ending temperature, wherein the temperature adjustment time is one of the constraints of the objective function for the target voltage drop.

[0118] This application also provides a computer program product, which, when executed on a data processing device, is suitable for executing an initialization program having the following method steps: obtaining the bus voltage-temperature relationship, including: calculating the output power-temperature relationship of the heat pump motor based on the heat conduction parameters of the building window and wall structure and the heat parameters of the heating network; calculating the bus voltage-temperature relationship based on the equivalent circuit and operating efficiency of the heat pump motor; and determining the bus voltage-temperature relationship of the heat pump motor based on the output power-temperature relationship and the bus voltage-temperature relationship.

[0119] 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.

[0120] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. 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... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0121] 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.

[0122] 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.

[0123] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0124] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0125] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using 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 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.

[0126] 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 process, method, article, or apparatus. Unless otherwise specified, 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 that element.

[0127] 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.

[0128] The above are merely embodiments of this application and are 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 temperature control method, characterized in that, include: Obtain the bus voltage-temperature relationship, wherein the bus voltage-temperature relationship represents the relationship between the voltage of the DC bus and the regulating temperature of the heat pump motor, the DC bus is used to supply power to the heat pump motor, and the heat pump motor is used to regulate the temperature of the target space; Based on the bus voltage-temperature relationship, a target function for bus voltage sag is determined, wherein the target function for bus voltage sag includes curve parameter variables, which are obtained from the temperature transition curve to be determined; The objective function for the bus voltage drop is solved using the particle swarm optimization algorithm to obtain the optimal parameter values ​​corresponding to the curve parameter variables. When the curve parameter variables take the optimal parameter values, the objective function for the bus voltage drop reaches its minimum value. Substituting the optimal parameter values ​​into the temperature transition curve to be determined yields the target temperature transition curve, wherein the target temperature transition curve represents the control strategy for the heat pump motor. The step of solving the objective function of the bus voltage drop using the particle swarm optimization (PSO) algorithm to obtain the optimal parameter values ​​corresponding to the curve parameter variables includes: generating multiple particles for the PSO algorithm based on the curve parameter variables, and generating the fitness function for the PSO algorithm based on the objective function of the bus voltage drop; iteratively solving the problem using the multiple particles and the fitness function in the following manner: in each stage of iteratively updating multiple particles using the PSO algorithm, the multiple particles are divided into a regular subgroup, a random chaotic subgroup, and a directed chaotic subgroup; a random chaotic variable is added to the position coordinates of the particles in the random chaotic subgroup; the velocity variable in the position iteration formula of the particles in the directed chaotic subgroup is replaced with a chaotic velocity variable; iterative updates are performed using the regular subgroup, the updated random chaotic subgroup, and the updated directed chaotic subgroup to obtain multiple particles for the next iteration stage; the above iterative process is repeated until the optimal parameter values ​​are obtained.

2. The method according to claim 1, characterized in that, Add a random chaotic variable to the position coordinates of each particle in the random chaotic subgroup, including: Determine the initial values ​​of the chaotic variables corresponding to the current iteration stage based on the current iteration number, the maximum iteration number, and the neighborhood radius; The random chaotic variable is determined based on the initial value of the chaotic variable corresponding to the current iteration stage, the current position coordinates of the particles in the random chaotic subgroup, and the random coefficient of the chaotic variable. Add a random chaotic variable to the current position coordinates of the particles in the random chaotic subgroup.

3. The method according to claim 1, characterized in that, Replace the velocity variable in the position iteration formula of a particle in a directional chaotic subgroup with a chaotic velocity variable, including: Determine the initial values ​​of the chaotic variables corresponding to the current iteration stage based on the current iteration number, the maximum iteration number, and the neighborhood radius; The chaotic velocity variable is determined based on the initial value of the chaotic variable corresponding to the current iteration stage and the velocity variable in the position iteration formula of the particles in the directional chaotic subgroup. Replace the velocity variable in the position iteration formula of a particle in a directional chaotic subgroup with a chaotic velocity variable.

4. The method according to claim 1, characterized in that, Based on the aforementioned bus voltage-temperature relationship, the objective function for bus voltage sag is determined, including: Obtain the transition curve of the temperature to be determined; The undetermined temperature transition curve is represented by Chebyshev polynomials, resulting in a seven-segment polynomial. Based on the thermal boundary conditions of the initial temperature, the final temperature, and the rate of change of the initial and final temperatures, the seven-segment polynomial is eliminated to obtain the curve parameter variables in the transition curve of the undetermined temperature. Based on the bus voltage-temperature relationship and the curve parameter variables, the target voltage drop objective function is constructed.

5. The method according to claim 1, characterized in that, Also includes: The temperature adjustment time is determined based on human comfort constraints, the initial temperature, and the final temperature, wherein the temperature adjustment time is one of the constraints of the target voltage drop objective function.

6. The method according to any one of claims 1 to 5, characterized in that, Obtain the bus voltage-temperature relationship, including: The output power-temperature relationship of the heat pump motor is calculated based on the heat conduction parameters of the building window and wall structure and the heat parameters of the heating network. Based on the equivalent circuit and operating efficiency of the heat pump motor, the bus voltage-temperature relationship is calculated. The bus voltage-temperature relationship of the heat pump motor is determined based on the output power-temperature relationship and the bus voltage-temperature relationship of the heat pump motor.

7. A temperature control device, characterized in that, include: The acquisition module is used to acquire the bus voltage-temperature relationship, wherein the bus voltage-temperature relationship represents the relationship between the DC bus voltage and the regulating temperature of the heat pump motor, the DC bus is used to supply power to the heat pump motor, and the heat pump motor is used to regulate the temperature of the target space; The determination module is used to determine the bus voltage drop objective function based on the bus voltage-temperature relationship, wherein the bus voltage drop objective function includes curve parameter variables, which are obtained from the temperature transition curve to be determined; The solution module is used to solve the objective function of the bus voltage drop according to the particle swarm optimization algorithm, and obtain the optimal parameter value corresponding to the curve parameter variable. When the curve parameter variable takes the optimal parameter value, the objective function of the bus voltage drop takes the minimum value. The substitution module is used to substitute the optimal parameter values ​​into the temperature transition curve to obtain the target temperature transition curve, wherein the target temperature transition curve represents the control strategy for the heat pump motor. The solution module is further configured to generate multiple particles for the particle swarm optimization algorithm based on the curve parameter variables, and to generate the fitness function for the particle swarm optimization algorithm based on the bus voltage drop objective function. Based on the multiple particles and the fitness function, iterative solution is performed as follows: In each stage where the particle swarm optimization algorithm iteratively updates multiple particles, the multiple particles are divided into a regular subgroup, a random chaotic subgroup, and a directed chaotic subgroup; a random chaotic variable is added to the position coordinates of each particle in the random chaotic subgroup; the velocity variable in the position iteration formula of the particles in the directed chaotic subgroup is replaced with a chaotic velocity variable; iterative updates are performed using the regular subgroup, the updated random chaotic subgroup, and the updated directed chaotic subgroup to obtain multiple particles for the next iteration stage; the above iterative process is repeated until the optimal parameter value is obtained.

8. A non-volatile storage medium, characterized in that, When the instructions in the non-volatile storage medium are executed by the processor of the electronic device, the electronic device is able to perform the temperature control method as described in any one of claims 1 to 6.

9. An electronic device, characterized in that, It includes one or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement the temperature control method according to any one of claims 1 to 6.

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