Thermodynamic optimization method and device for thermoelectric power generation module and medium

Abstract

The invention provides a thermodynamic optimization method and device for a thermoelectric power generation module and a medium. Belongs to the field of thermoelectric power generation, and comprises the following steps: firstly, determining an optimization target to maximize power output or improve energy conversion efficiency, then establishing a thermodynamic model which comprises heat conduction, heat convection and thermoelectric conversion processes, and setting parameters. An improved ant colony algorithm is adopted, a self-adaptive optimization strategy capable of automatically adjusting and optimizing parameters is designed, and the algorithm introduces volatilization factors and adds random disturbance to optimize the search process. And finally, a multi-objective optimization algorithm is introduced to realize effective balance between the electric power and the thermoelectric conversion efficiency. Through the series of steps, the thermodynamic property of the thermoelectric power generation module can be effectively optimized, so that the power output and energy conversion efficiency of the thermoelectric power generation module are improved.

Description

Technical Field

[0001] The present invention belongs to the field of temperature difference power generation, and more specifically relates to a thermodynamic optimization method, equipment and medium for a temperature difference power generation module. Background Art

[0002] Thermoelectric conversion technology, as an important technology for directly converting thermal energy into electrical energy, has attracted worldwide attention in recent years.

[0003] Thermoelectric conversion technology primarily utilizes the Seebeck effect, whereby a temperature difference between two dissimilar conductors or semiconductors in a thermocouple generates an electric current. This effect can be exploited to design and manufacture a variety of thermoelectric power generation modules, converting thermal energy into electricity. This technology has broad applications in energy recovery, wireless sensors, medical devices, thermoelectric cooling, and other fields.

[0004] However, the efficiency of current thermoelectric power generation modules is generally low. There are two main reasons for this:

[0005] The first is the limitation of material properties. The Seebeck coefficient, resistivity and thermal conductivity of current thermoelectric materials usually cannot meet the high requirements of power generation performance and efficiency at the same time.

[0006] The second issue is the difficulty of optimizing the design. The performance and efficiency of thermoelectric modules are affected by many factors, including the heat source and cooling temperature, the properties and dimensions of the thermoelectric material, and so on. The interplay of these factors complicates optimization and requires a balance between various performance factors.

[0007] Therefore, it is necessary to develop a thermodynamic optimization method that can fully optimize the performance and efficiency of the thermodynamic power generation module. Summary of the Invention

[0008] The technical problem to be solved by the present invention is how to combine thermodynamic models, adaptive optimization strategies and multi-objective optimization schemes to overcome the problems of low performance and efficiency of thermoelectric power generation modules in the existing technology, thermodynamically optimize the thermoelectric power generation modules, and maximize power output and thermoelectric conversion efficiency.

[0009] In order to achieve the above object, the present invention is implemented by adopting the following technical solutions: the optimization method includes:

[0010] Determine optimization goals, including: maximizing power output and maximizing thermoelectric conversion efficiency.

[0011] Establish thermodynamic model: including heat conduction, heat convection and thermoelectric conversion processes.

[0012] Set parameters: set heat source temperature, cooling temperature, material Seebeck coefficient, resistivity and thermal conductivity;

[0013] Introducing an adaptive optimization strategy: Designing an adaptive optimization strategy that automatically adjusts optimization parameters.

[0014] Establish a multi-objective optimization plan: introduce a multi-objective optimization algorithm to achieve effective trade-offs among multiple objectives.

[0015] In one embodiment, the maximization power output optimization objective function is:

[0016]

[0017] Where I is the output current, R is the resistance, S is the Seebeck coefficient, r is the resistivity, K is the thermal conductivity, Th is the heat source temperature, and Tc is the cooling temperature.

[0018] In one embodiment, the objective function for maximizing the thermoelectric conversion efficiency is:

[0019]

[0020] Where Qin is the input thermal power, S is the Seebeck coefficient, r is the resistivity, K is the thermal conductivity, Th is the heat source temperature, and Tc is the cooling temperature.

[0021] In one embodiment, the thermodynamic modeling process includes:

[0022] In the process of heat conduction and heat convection, the total heat Q transferred from the heat source to the cold source through the thermoelectric power generation module is:

[0023]

[0024] Where k is the thermal conductivity, A is the surface area of ​​the power generation module, ΔT / Δx is the temperature gradient, h is the convective heat transfer coefficient, Th and Tc represent the temperatures of the heat source and the cold source respectively;

[0025] Next, considering the thermoelectric conversion process, the relationship between electromotive force U and heat Q is:

[0026] U=S*(Th-Tc)

[0027] Where S represents the Seebeck coefficient of the material;

[0028] According to electric power R is the resistance, and the relationship between the electric power and heat of thermoelectric conversion is further obtained as follows:

[0029] The above formulas are combined to form a thermodynamic model that comprehensively describes the thermodynamic process of the temperature difference power generation module.

[0030] In one solution, the adaptive optimization strategy adopts an improved ant colony algorithm;

[0031] Improve the ant colony algorithm, operate in the initial stage, solution construction stage and pheromone update stage; use "volatility factor" to prevent the search from falling into local optimality;

[0032] The volatility factor uses the following mathematical formula to describe the renewal of pheromones:

[0033] τ ij =(1-ρ)*τ ij +Δτ ij

[0034] Among them, τ ij is the pheromone intensity, ρ is the volatility factor (value between 0-1), Δτ ij is the pheromone intensity newly added in this iteration, set to 1.

[0035] In one embodiment, the multi-objective optimization is to maximize the electric power P of the thermoelectric power generation module and maximize the thermoelectric conversion efficiency η; including:

[0036] (1) Setting weights: Set a weight for each objective, and then weigh it with the overall optimization goal, setting an objective function F = w1P + w2η;

[0037] (2) where w1 and w2 are the weights of P and η, and satisfy w1 + w2 = 1; the optimization objective becomes maximizing F;

[0038] (3) Set the initial solution set: randomly generate a set of solutions, i.e., the parameter set (Th, Tc, S, r, k), and calculate the corresponding F value;

[0039] (4) Generate new solutions: Generate new solutions through mutation and crossover operations, and calculate the corresponding F value;

[0040] (5) For the newly generated solution, if its F value is better than the current solution, the new solution is selected as the current solution; otherwise, the new solution is accepted with a certain probability;

[0041] (6) Update weights: Update weights w1 and w2 based on the ratio of the current solution’s F value to the historical optimal F value;

[0042] The above process is iterated continuously until the end condition is met.

[0043] On the other hand, an electronic device includes a processor and a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the thermodynamic optimization method of the thermodynamic power generation module is implemented.

[0044] On the other hand, a computer storage medium stores a computer program thereon, and when the computer program is executed by a processor, the thermodynamic optimization method of a thermodynamic power generation module is implemented.

[0045] Beneficial effects of the present invention:

[0046] This invention addresses the low performance and efficiency issues of traditional thermoelectric power generation modules. By establishing a detailed thermodynamic model, it reflects the impact of practical factors such as the material's Seebeck coefficient, resistivity, thermal conductivity, heat source, and cooling temperature on system performance. An adaptive optimization strategy is applied to automatically adjust the module's operating parameters for optimal results. Furthermore, a multi-objective optimization scheme is introduced to balance power output and thermoelectric conversion efficiency to achieve optimal overall performance.

[0047] By optimizing models and strategies, the efficiency of thermoelectric power generation modules is improved, enhancing the efficiency of thermal energy utilization. Through multi-objective trade-offs, module performance is more balanced, expanding its application scope. Furthermore, this paper provides a scientific and effective optimization solution, offering theoretical support and practical guidance for the design of thermoelectric conversion modules and other related equipment, with broad application value and positive social benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 Flow chart of the method of the present invention;

[0049] Figure 2 A flow chart of the optimization strategy adapted for the present invention;

[0050] Figure 3 A flow chart of the multi-objective optimization method is established for the present invention. DETAILED DESCRIPTION

[0051] To facilitate understanding of the present invention, the present invention will be described more fully below with reference to the accompanying drawings. The drawings illustrate exemplary embodiments of the present invention. However, the present invention may be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to provide a more thorough and comprehensive understanding of the present invention.

[0052] Unless otherwise defined, all technical and scientific terms used herein have the same meanings as commonly understood by those skilled in the art to which the present invention pertains. The terms used in the present specification are for the purpose of describing specific embodiments only and are not intended to limit the present invention. To facilitate understanding of the present invention, the present invention will be described more fully below with reference to the accompanying drawings. Typical embodiments of the present invention are shown in the drawings. However, the present invention may be embodied in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to provide a more thorough and comprehensive understanding of the present invention.

[0053] like Figure 1 As shown, the optimization method includes:

[0054] S1. Determine optimization goals, including maximizing power output and maximizing thermoelectric conversion efficiency.

[0055] The objective function for maximizing power output optimization is:

[0056]

[0057] Where I is the output current, R is the resistance, S is the Seebeck coefficient, r is the resistivity, K is the thermal conductivity, Th is the heat source temperature, and Tc is the cooling temperature.

[0058] The objective function for maximizing the thermoelectric conversion efficiency is:

[0059]

[0060] Where Qin is the input thermal power, S is the Seebeck coefficient, r is the resistivity, K is the thermal conductivity, Th is the heat source temperature, and Tc is the cooling temperature.

[0061] S2. Establish a thermodynamic model: including heat conduction, heat convection and thermoelectric conversion processes.

[0062] Establishing a thermodynamic model is an important step in analyzing and optimizing the performance of thermoelectric power generation modules. Below we will introduce the heat conduction, heat convection, and thermoelectric conversion processes one by one:

[0063] Heat conduction process:

[0064] In the process of heat conduction, Fourier's law is used to describe the conduction of heat:

[0065]

[0066] Where Q represents the amount of heat transferred per unit time, k represents the thermal conductivity of the material, A represents the cross-sectional area of ​​the material, and ΔT / Δx represents the temperature gradient.

[0067] Thermal convection process:

[0068] When the thermoelectric power generation module is working, heat transfer includes not only heat conduction but also heat convection. The convection process is described by Newton's law of cooling:

[0069] Q=hA(Th-Tc)

[0070] Where h represents the convection heat transfer coefficient, A represents the surface area of ​​the material, Th and Tc represent the heat source temperature and the cold source temperature, respectively.

[0071] Thermoelectric conversion process:

[0072] The thermoelectric conversion process is described by the Seebeck effect, which is expressed as follows:

[0073] U=S*(Th-Tc)

[0074] Among them, U represents the electromotive force, S represents the Seebeck coefficient of the material, Th and Tc represent the heat source temperature and the cold source temperature respectively.

[0075] In this model, heat is input from the heat source to the power generation module through heat conduction and heat convection, and then converted into electrical energy output through thermoelectric conversion. The remaining heat continues to be transferred to the cold source.

[0076] By solving the relevant equations of this thermodynamic model, we can know the changes in the output power or energy conversion efficiency of the thermoelectric power generation module under various working conditions.

[0077] The thermodynamic model establishment process includes:

[0078] In the process of heat conduction and heat convection, the total heat Q transferred from the heat source to the cold source through the thermoelectric power generation module is:

[0079]

[0080] Where k is the thermal conductivity, A is the surface area of ​​the power generation module, ΔT / Δx is the temperature gradient, h is the convective heat transfer coefficient, Th and Tc represent the temperatures of the heat source and the cold source respectively;

[0081] Next, considering the thermoelectric conversion process, the relationship between electromotive force U and heat Q is:

[0082] U=S*(Th-Tc)

[0083] Where S represents the Seebeck coefficient of the material;

[0084] According to electric power R is the resistance, and the relationship between the electric power and heat of thermoelectric conversion is further obtained as follows:

[0085] By combining the above formulas, a thermodynamic model can be formed to fully describe the thermodynamic processes of the thermoelectric power generation module. This model can then be thermodynamically optimized using numerical solutions or optimization algorithms to maximize the electrical power P or the thermoelectric conversion efficiency η = P / Q.

[0086] S3. Set parameters: set heat source temperature, cooling temperature, material Seebeck coefficient, resistivity and thermal conductivity.

[0087] First, you need to set the heat source temperature Th and the cooling temperature Tc. These two parameters are set according to the actual working environment. For example, in some factory waste heat recovery scenarios, Th may be the high temperature of waste heat, and Tc may be room temperature. In addition, we also need to set the Seebeck coefficient S, resistivity r, and thermal conductivity k of the material. These parameters need to be determined through experimental measurements or by consulting the material manual. The Seebeck coefficient of the thermoelectric material bismuth disulfide (Bi2Te3) is approximately -200μV / K, the resistivity is approximately 0.001Ω·m, and the thermal conductivity is approximately 1.5W / (m·K).

[0088] like Figure 2 As shown, S4. Introduce an adaptive optimization strategy: design an adaptive optimization strategy that automatically adjusts the optimization parameters.

[0089] The adaptive optimization strategy adopts an improved ant colony algorithm;

[0090] Improve the ant colony algorithm, operate in the initial stage, solution construction stage and pheromone update stage; use "volatility factor" to prevent the search from falling into local optimality;

[0091] The volatility factor uses the following mathematical formula to describe the renewal of pheromones:

[0092] τ ij =(1-ρ)*τ ij +Δτ ij

[0093] Among them, τ ij is the pheromone intensity, ρ is the volatility factor (value between 0-1), Δτ ij is the pheromone intensity newly added in this iteration, set to 1.

[0094] When designing an adaptive optimization strategy that automatically adjusts optimization parameters, we introduced an improved ant colony algorithm. The ant colony algorithm is a heuristic search algorithm that simulates the process of ants searching for food in nature. The following is the detailed process of the improved ant colony algorithm:

[0095] S401, Initialization Phase: First, a group of ants is randomly generated, each representing a possible solution vector, i.e., a possible parameter combination (Th, Tc, S, r, k). The initial pheromone concentration τ is set to a constant τ0, and the global optimal solutions Pbest and ηbest are set to empty.

[0096] S402, solution construction phase: Each ant selects the next parameter to search based on the current pheromone intensity and individual heuristic factor, that is, the fitness function F of the solution vector. The probability P of its selection is given by the following formula:

[0097]

[0098] Among them, α and β are weight parameters, which control the importance of pheromone and individual heuristic factors respectively. is the individual inspiration factor, which can be taken as the objective function F.

[0099] S403, pheromone update phase: After all ants complete a search, the system updates the pheromone. The accumulated amount of pheromone depends on whether the path is selected by the ants and the frequency of ants selecting the path. The pheromone update formula is:

[0100] τ ij _new=(1-ρ)*τ ij _old+Δτ ij

[0101] Where ρ is the pheromone volatilization rate, which is (0,1), Δτ ij It is the pheromone newly added to the path, which depends on whether it is chosen by the ants.

[0102] S404, global pheromone update: This is a key step in improving the ant colony algorithm. In this step, only the path corresponding to the global optimal solution will have its pheromone updated, while the pheromone of other paths remains unchanged. The specific formula is:

[0103] τ ij _new=(1-ρ)*τ ij _old+ρ*Δτ ij _best

[0104] in Q is a constant, and L_best is the objective function value of the current global optimal solution.

[0105] S405, termination condition judgment stage: If the preset maximum number of iterations is reached or the global optimal solution does not change in several consecutive iterations, the algorithm is terminated and the current global optimal solution is output.

[0106] In this process, through the improved ant colony algorithm, we can adaptively adjust the optimization parameters to obtain the optimal solution and achieve our desired optimization goal.

[0107] like Figure 3 As shown, S5. Establish a multi-objective optimization scheme: introduce a multi-objective optimization algorithm to achieve an effective trade-off between multiple objectives.

[0108] The multi-objective optimization is to maximize the electric power P of the thermoelectric power generation module and maximize the thermoelectric conversion efficiency η; including:

[0109] S501. Set weights: Set a weight for each goal, and then weigh them with the overall optimization goal, setting an objective function F = w1P + w2η; where P is the electric power of the thermoelectric power generation module, η is the thermoelectric conversion efficiency, w1 and w2 are corresponding weights, satisfying w1+w2=1.

[0110] S502. Set the initial solution set: randomly generate a set of solutions, i.e., parameter sets (Th, Tc, S, r, k), and calculate the corresponding F value; each set of parameters is an "ant".

[0111] S503, generate new solutions: generate new solutions through mutation and crossover operations, and calculate the corresponding F value.

[0112] S504: For the newly generated solution, if its F value is better than the current solution, the new solution is selected as the current solution; otherwise, the new solution is accepted with a certain probability. The newly generated solution is compared with the original solution. If the F value corresponding to the new solution is worse than the original solution, the new solution replaces the original solution and the corresponding P and η values ​​are updated. If the F value of the new solution is not as good as the original solution, the new solution is accepted with a certain probability. This probability is given by the Boltzmann function, such as: Where ΔE represents the energy difference between the new solution and the original solution, k is the Boltzmann constant, and T is the temperature parameter.

[0113] S505. Update weights: Update weights w1 and w2 based on the ratio of the current solution's F-value to the historical optimal F-value. This reflects the importance of each optimization objective within the overall objective. This process can be divided into two steps. First, the difference between the current solution and the historical optimal solution of each objective function is calculated. Then, the weights are adjusted based on the difference, increasing the weight of objectives with larger differences and decreasing the weight of objectives with smaller differences. Termination condition check: Determine whether the optimization meets the set termination conditions, such as reaching the preset maximum number of iterations or the degree of improvement of the solution being less than a set threshold.

[0114] The above process is iterated continuously until the end condition is met.

[0115] The ant colony algorithm (ACO) is an adaptive optimization strategy used to search the solution space for parameter values ​​that maximize the objective function. In our case, the ACO was used to search the parameter space of the thermoelectric power generation module, including parameters such as the heat source temperature, cooling temperature, and the material's Seebeck coefficient, resistivity, and thermal conductivity. A volatility factor was also introduced to prevent the algorithm from becoming trapped in a local optimum.

[0116] However, the thermoelectric power generation module has more than one optimization objective: maximizing both electrical power output and thermoelectric conversion efficiency. This involves a multi-objective optimization problem. Achieving these two objectives requires a trade-off, which is where multi-objective optimization comes in. Each objective is assigned a weight, and then the linear combination of the weight and the objective function is optimized to maximize F, forming a multi-objective optimization problem.

[0117] The ant colony algorithm is the primary tool for optimizing the search, while multi-objective optimization constructs the optimization objective function. These two methods work together to enable the thermoelectric power generation module's parameters to self-adaptively adjust to achieve the optimization goal. As iterations proceed, the solution continuously improves until a predetermined stopping condition is met, such as when the number of iterations reaches a preset value or when the improvement rate meets a predetermined threshold. The algorithm ultimately achieves the optimal thermoelectric power generation module parameters.

[0118] Example:

[0119] First, assume the design of a thermoelectric power generation module with the following initial parameters: heat source temperature Th = 350K, cooling temperature Tc = 310K, the Seebeck coefficient S = 2.0×10^-4V / K, resistivity r = 1.0×10^-6Ω·m, and thermal conductivity k = 1.5W / m·K of the thermoelectric material used.

[0120] Then, the objectives that need to be optimized include maximizing the output power P and the thermoelectric conversion efficiency η, so we use the adaptive ant colony algorithm for optimization.

[0121] Assume that the volatility factor is set to 0.5 and 100 ants are initialized, each ant represents a possible material and working parameters (Th, Tc, S, r, k).

[0122] During iterative optimization, assume that the weights are set to w1 = 0.6 and w2 = 0.4, which means that we prefer to maximize the power P. We continuously update this weight value to adjust it based on the feedback of the results.

[0123] After hundreds of iterations, we found the optimal solution: heat source temperature Th = 360K, cooling temperature Tc = 300K, material Seebeck coefficient S = 2.5 × 10-4 V / K, resistivity r = 8.0 × 10-7 Ω·m, and thermal conductivity k = 1.4 W / m·K. Under these conditions, the thermoelectric power generation module achieves its maximum output power P and thermoelectric conversion efficiency η.

[0124] After actual testing, the output power of this thermoelectric power generation module actually increased by 10% under the new conditions, and the thermoelectric conversion efficiency also improved. Therefore, this optimization scheme achieved the expected results, proving the effectiveness of this thermodynamic optimization method.

[0125] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing related hardware through a computer program. The program can be stored in a computer-readable storage medium, and when executed, the program can include the processes in the above-described method embodiments. The storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0126] It should be understood that the detailed description of the technical solutions of the present invention using the preferred embodiments above is illustrative and not restrictive. A person skilled in the art, after reading the present specification, may modify the technical solutions described in the embodiments or replace some of the technical features therein with equivalents; such modifications or replacements do not deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A thermodynamic optimization method for a thermoelectric power generation module, characterized by: The optimization method comprises: Determine optimization goals, including: maximizing power output and maximizing thermoelectric conversion efficiency; Establish thermodynamic models: including heat conduction, heat convection and thermoelectric conversion processes; Set parameters: set heat source temperature, cooling temperature, material Seebeck coefficient, resistivity and thermal conductivity; Introducing an adaptive optimization strategy: Designing an adaptive optimization strategy that automatically adjusts optimization parameters; Establish a multi-objective optimization plan: introduce a multi-objective optimization algorithm to achieve effective trade-offs among multiple objectives.

2. The thermodynamic optimization method for a thermoelectric power generation module according to claim 1, characterized in that: The objective function for maximizing power output optimization is: Where I is the output current, R is the resistance, S is the Seebeck coefficient, r is the resistivity, K is the thermal conductivity, Th is the heat source temperature, and Tc is the cooling temperature.

3. The thermodynamic optimization method for a thermoelectric power generation module according to claim 1, characterized in that: The objective function for maximizing the thermoelectric conversion efficiency is: Where Qin is the input thermal power, S is the Seebeck coefficient, r is the resistivity, K is the thermal conductivity, Th is the heat source temperature, and Tc is the cooling temperature.

4. The thermodynamic optimization method for a thermoelectric power generation module according to claim 1, characterized in that: The thermodynamic model includes: In the process of heat conduction and heat convection, the total heat Q transferred from the heat source to the cold source through the thermoelectric power generation module is: Where k is the thermal conductivity, A is the surface area of ​​the power generation module, ΔT / Δx is the temperature gradient, h is the convective heat transfer coefficient, Th and Tc represent the temperatures of the heat source and the cold source respectively; Next, considering the thermoelectric conversion process, the relationship between electromotive force U and heat Q is: U=S*(Th-Tc) Where S represents the Seebeck coefficient of the material; According to electric power R is the resistance, and the relationship between the electric power and heat of thermoelectric conversion is further obtained as follows: The above formulas are combined to form a thermodynamic model that comprehensively describes the thermodynamic process of the temperature difference power generation module.

5. The thermodynamic optimization method for a thermoelectric power generation module according to claim 1, characterized in that: The adaptive optimization strategy adopts an improved ant colony algorithm; Improve the ant colony algorithm, operate in the initial stage, solution construction stage and pheromone update stage; use "volatility factor" to prevent the search from falling into local optimality; The volatility factor uses the following mathematical formula to describe the renewal of pheromones: t ij =(1-ρ)*τ ij +Δτ ij Among them, τ ij is the pheromone intensity, ρ is the volatility factor (value between 0-1), Δτ ij is the pheromone intensity newly added in this iteration, set to 1.

6. The thermodynamic optimization method for a thermoelectric power generation module according to claim 1, characterized in that: The multi-objective optimization is to maximize the electric power P of the thermoelectric power generation module and maximize the thermoelectric conversion efficiency η; including: (1) Setting weights: Set a weight for each objective, weigh it against the overall optimization goal, and set an objective function F = w1P + w2η; (2) where w1 and w2 are the weights of P and η, and satisfy w1 + w2 = 1; the optimization objective becomes maximizing F; (3) Set the initial solution set: randomly generate a set of solutions, i.e., the parameter set (Th, Tc, S, r, k), and calculate the corresponding F value; (4) Generate new solutions: Generate new solutions through mutation and crossover operations, and calculate the corresponding F value; (5) For the newly generated solution, if its F value is better than the current solution, the new solution is selected as the current solution; otherwise, the new solution is accepted with a certain probability; (6) Update weights: Update weights w1 and w2 according to the ratio of the current solution’s F value to the historical optimal F value.

7. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, a thermodynamic optimization method for a thermodynamic power generation module according to any one of claims 1 to 6 is implemented.

8. A computer storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed by a processor, a thermodynamic optimization method for a thermodynamic power generation module according to any one of claims 1 to 6 is implemented.

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