Remote control method for heating system

Abstract

The invention relates to the technical field of heating systems, in particular to a remote control method for a heating system, and the method comprises the steps: taking any region covered by the heating system as a target region, and obtaining the outdoor temperature and hot water flow value of the target region at the current moment in real time; using a bat algorithm to obtain all solutions of the hot water flow value of the target area at the current moment obtained by each iteration, and determining the fitness value of each solution of the hot water flow value of the target area at the current moment obtained by each iteration; determining the demand degree for the next iteration after the current iteration is completed after the bat algorithm completes the preset number of iterations; determining whether the next iteration is carried out according to the demand degree to obtain a target value of the hot water flow value at the current moment; and remote control of the heating system is realized. According to the method, the iteration times of the bat algorithm are adaptively adjusted by calculating the demand degree, so that the calculation efficiency of the algorithm and the output accuracy of the globally optimal solution are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of heating systems, and more particularly to a remote control method for a heating system. Background Art

[0002] With the intensification of global climate change and the continued rise in energy consumption, improving energy efficiency has become a global challenge, particularly in the area of ​​intelligent energy management for heating systems. In recent years, the concept of energy conservation and emission reduction has continued to advance in urban construction, and improving building energy efficiency has become an industry consensus. Remote control technology for heating systems, through intelligent thermal management, can precisely avoid energy waste, providing key support for environmental protection and sustainable development goals.

[0003] The Bat Algorithm (BA), an emerging heuristic optimization algorithm, draws its core concept from bat echolocation and simulates the spatial search behavior of living organisms to solve complex optimization problems. When applied to heating systems, this algorithm can precisely control hot water flow parameters, optimize system energy flow paths, effectively avoid energy loss caused by excessive heating, and ultimately achieve optimal allocation of thermal resources and energy savings.

[0004] However, traditional bat algorithms typically use a fixed threshold for the maximum number of iterations during iterative optimization as the output condition for the global optimal solution. This setting directly affects the algorithm's ability to avoid local optimal solutions. If the maximum number of iterations is set too low, the bat algorithm may terminate prematurely before fully exploring the solution space, causing the optimization results to stagnate at the local optimal solution and making it difficult to achieve ideal accuracy. If the maximum number of iterations is set too high, while it can expand the solution space exploration dimension and theoretically improve the probability of finding the optimal solution, it will significantly increase computational time and resource consumption, ultimately affecting the accuracy and efficiency of heating system control. Summary of the Invention

[0005] In order to solve the problem that the traditional bat algorithm usually adopts a fixed threshold maximum number of iterations as the output condition of the global optimal solution during the iterative optimization process, and the setting of the maximum number of iterations directly affects the ability of the algorithm to avoid the local optimal solution: if the maximum number of iterations is set too small, the bat algorithm may terminate prematurely before the solution space is fully explored, causing the optimization result to stagnate at the local optimal solution and it is difficult to achieve the ideal accuracy; if the maximum number of iterations is set too large, although it can expand the exploration dimension of the solution space and theoretically improve the probability of finding the optimal solution, it will significantly increase the computing time and resource consumption, and ultimately affect the accuracy and efficiency of the heating system control. The present invention proposes a remote control method for a heating system, which includes the following steps: For any area covered by the heating system, the outdoor temperature and hot water flow value at the current moment are obtained in real time, and the target value of the hot water flow value at the current moment is obtained by using the bat algorithm, which is used to remotely control the heating system. The target value is the optimal solution of the hot water flow value at the current moment when the bat algorithm iteration terminates; in each iteration of the bat algorithm, each solution of the hot water flow value at the current moment obtained by the current iteration, the average of all hot water flow values ​​at the same historical moments as the current outdoor temperature, the average of all hot water flow values ​​at the same historical moments with the same time sequence as the current moment, and the hot water flow at the current moment obtained by the current iteration are used. The heating cost corresponding to each solution of the value is used to determine the fitness value of each solution of the hot water flow value at the current moment obtained in the current iteration; in each iteration process after the bat algorithm completes the preset number of iterations, according to the variance of the fitness values ​​of all solutions of the hot water flow value at the current moment obtained in the current iteration, the number of iterations after the bat algorithm completes the preset number of iterations and the maximum and minimum values ​​of the fitness values ​​of all solutions of the hot water flow value at the current moment obtained in the current iteration and the previous iterations, the demand for the next iteration after the current iteration is completed is determined, and whether to perform the next iteration is determined according to the size of the demand.

[0006] The beneficial effects are: by comprehensively considering the fitness of the current solution, the historical average of hot water flow with the same outdoor temperature and time series, and the heating cost in each iteration, the fitness function is made more comprehensive and accurate, and the algorithm's ability to discern the solution space is enhanced, thereby effectively reducing the risk of falling into local optimality; dynamic monitoring of fitness variance, number of iterations, and historical maximum and minimum fitness values ​​is introduced, and the necessity of the next iteration is intelligently judged based on the convergence state of the algorithm, rather than simply relying on a fixed maximum number of iterations, effectively taking into account both the saving of computing resources and the improvement of optimization effects; while ensuring optimization accuracy, unnecessary redundant iterations are effectively avoided, computing time and resource consumption are reduced, the real-time control capability of the system is improved, and the operation of the heating system is more efficient; through the improved bat algorithm optimization process, the heating system can autonomously adjust the optimization strategy, respond quickly to environmental changes, realize intelligent remote management, and promote the development of energy conservation, emission reduction and green heating.

[0007] Furthermore, the outdoor temperature and hot water flow rate values ​​are filtered and denoised outdoor temperature and hot water flow rate values.

[0008] Furthermore, the filtering and denoising adopts a median filtering algorithm.

[0009] Furthermore, the fitness value satisfies: Where, For the The hot water flow value at the current moment obtained by the iteration The fitness value of a solution, For the The hot water flow value at the current moment obtained by the iteration A solution, is the mean of the hot water flow rate values ​​at all historical moments with the same outdoor temperature as the current moment, is the mean of the hot water flow rate values ​​at all historical moments with the same time series as the current moment, For the The hot water flow value at the current moment obtained by the iteration The corresponding heating cost of each solution is, is the standard normalization function.

[0010] The beneficial effects are as follows: the fitness function simultaneously considers the deviation of the hot water flow solution from the historical mean and time series mean of the same outdoor temperature, and multiplies it with the heating cost after standard normalization, which can fully reflect the rationality and economy of the solution, thereby making the optimization process more targeted and practical; the heating cost is directly incorporated into the fitness function, so that the optimization results not only pursue the reasonable matching of hot water flow, but also take into account economic benefits, which is conducive to achieving low-energy operation of the heating system, reducing energy waste, and contributing to the achievement of green environmental protection goals; the parameters are processed through the standard normalization function, eliminating the influence of different dimensions and numerical scales, making the contribution of each factor balanced and fair, avoiding a certain indicator dominating the optimization results, and helping to improve the convergence speed and global search ability of the algorithm.

[0011] Furthermore, the average of all hot water flow values ​​at the same historical moments as the current outdoor temperature is: the average of all hot water flow values ​​at the same historical moments as the current outdoor temperature and the same water temperature as the current water heat flow value.

[0012] Furthermore, the average of all hot water flow values ​​at the same historical moments as the current moment is: the average of all hot water flow values ​​at the same historical moments as the current moment and with the same water temperature as the water heat flow value at the current moment.

[0013] Furthermore, the heating cost is: the total cost of heating for each solution of the hot water flow value at the current moment within a preset time period.

[0014] Furthermore, the requirement degree satisfies: Where, The number of iterations after the bat algorithm completes the preset number of iterations The demand for the next iteration after the first iteration is completed, The number of iterations after the bat algorithm completes the preset number of iterations The variance of the fitness values ​​of all solutions of the hot water flow value at the current moment obtained by iteration, To terminate the first The number of iterations, and They are respectively the first The maximum and minimum values ​​of the fitness values ​​of all solutions of the hot water flow value at the current moment obtained by the iteration and the previous iterations, is the natural exponential function, is the standard normalization function.

[0015] The beneficial effects are: the demand function scientifically quantifies the need for continued iteration by combining the variance of the fitness value, the current number of iterations, and the difference between the current and historical maximum and minimum fitness values, which helps to dynamically adjust the termination conditions of the algorithm and avoid the disadvantages brought by the traditional fixed maximum number of iterations; by utilizing the attenuation effect of the exponential function on the fitness variance, combined with the fitness extreme value difference, it can identify the convergence state and diversity of the current solution group, avoid the algorithm from staying in the local optimum too early or meaningless continuous iteration, and improve the ability to fully explore the solution space; through the normalized demand measurement, the heating system optimization control can intelligently decide whether to proceed to the next iteration, reasonably allocate computing resources, reduce unnecessary computing overhead, ensure system response speed and real-time performance, and is suitable for remote and online control scenarios.

[0016] Furthermore, determining whether to perform the next iteration based on the demand degree includes: in response to the demand degree being greater than a preset iteration threshold, determining that the iteration of the bat algorithm is incomplete, and performing the next iteration; otherwise, the iteration is terminated.

[0017] The beneficial effect is that by setting a dynamic threshold based on demand to determine whether to continue iteration, the inflexibility of the traditional fixed number of iterations is avoided, allowing the algorithm to intelligently determine the timing of iteration termination based on the actual optimization effect, thereby improving the adaptability and efficiency of the algorithm.

[0018] Furthermore, the method for remotely controlling the heating system includes: adjusting the hot water flow value at the current moment to the target value, thereby completing the remote control method for the heating system.

[0019] The beneficial effects are: by adjusting the hot water flow value in the target area to the optimal solution calculated by the algorithm, the optimal match between the heating parameters and the environment and demand is effectively guaranteed, thereby improving the accuracy and comfort of the heating effect; by utilizing the optimal solution obtained by dynamic optimization of the bat algorithm, remote control can quickly respond to environmental changes and user needs, realize real-time dynamic adjustment, and enhance the flexibility and adaptability of the system; by accurately adjusting the hot water flow, excessive or insufficient heating is avoided, energy waste is reduced, heating costs are reduced, and economical and energy-saving operation is achieved, in line with the green energy-saving concept.

[0020] The present invention has the following beneficial effects: (1) Abandoning the limitation of the traditional fixed maximum number of iterations, the algorithm dynamically evaluates whether to continue iteration through "demand degree". After the preset number of iterations, the algorithm combines the fitness variance of the solution, historical iteration data and other factors to adaptively judge the necessity of iteration. This not only avoids falling into the local optimum due to insufficient iterations, but also prevents the waste of computing resources caused by excessive iterations, significantly improving the algorithm's optimization efficiency and solution accuracy in heating system control.

[0021] (2) The fitness value calculation integrates multi-dimensional data such as the current solution, the mean of historical temperature and time series corresponding flow, and heating cost, so that the algorithm is more suitable for the actual operation scenario of the heating system; for example, considering the mean of historical data can reduce the interference of random fluctuations, and incorporating heating costs is directly related to the energy-saving goal, guiding the algorithm to converge to the optimal solution of economical and efficient hot water flow, and optimizing the heat energy distribution strategy.

[0022] (3) Real-time acquisition of outdoor temperature and hot water flow data, combined with the dynamically adjusted bat algorithm, can quickly respond to environmental changes and optimize hot water flow in a timely manner; at the same time, through precise iterative control, algorithm oscillation or premature stagnation can be avoided, ensuring that the heating system can operate stably and efficiently under different working conditions, reducing energy waste and helping to achieve energy conservation and emission reduction goals. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 The present invention is a flowchart of a remote control method for a heating system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0024] The following is a clear and complete description of the technical solutions in the embodiments of the present invention. The described embodiments are part of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative efforts are within the scope of protection of the present invention.

[0025] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0026] See also Figure 1, which shows a flowchart of a remote control method for a heating system provided by one embodiment of the present invention, the method comprising the following steps: S01: Taking any area covered by the heating system as the target area, obtain the outdoor temperature and hot water flow value of the target area at the current moment in real time.

[0027] Implementers can set the collection frequency according to specific implementation circumstances, for example, 5 minutes per time.

[0028] Specifically, the outdoor temperature (obtained by a temperature sensor) and the hot water flow rate value (obtained by a flow rate sensor) are the outdoor temperature and the hot water flow rate value after filtering and denoising.

[0029] Specifically, the filtering and denoising adopts a median filtering algorithm.

[0030] S02: using the bat algorithm to obtain all solutions of the hot water flow value of the target area at the current moment obtained in each iteration, and determining the fitness value of each solution of the hot water flow value of the target area at the current moment obtained in each iteration.

[0031] The implementer can set the various parameters of the bat algorithm according to the specific implementation situation. For example, the number of randomly generated solutions in the solution space for each iteration is preset to an empirical value of 50, and the solution space is preset to an empirical value of , the speed solution space range is preset to the empirical value , the search frequency range is preset to , the initial loudness is preset to the empirical value 1, the loudness attenuation coefficient is preset to the empirical value 0.95, the pulse rate is preset to the empirical value 0.6, and the pulse enhancement coefficient is preset to the empirical value 0.08.

[0032] It should be noted that in order to facilitate the dynamic adaptation of the maximum number of iterations of the bat algorithm when the global optimal solution is output at the current moment, this step needs to construct a calculation method for the fitness value of each solution of the bat algorithm in each iteration process. The logic based on the specific construction is: if the cost of the hot water flow value corresponding to a solution is greater within a certain period of time, it means that the preference of this solution is lower and the corresponding fitness value is larger; if the hot water flow value corresponding to a solution is smaller than the mean difference of the hot water flow value at the historical moment with the same time sequence as the target area at the current moment (24-hour system, for example, the current time is 12 o'clock, and the same time sequence is 12 o'clock on each day of historical heating), it means that this solution is more in line with the heating demand of the target area at the current moment, which means that the greater its preference, the smaller its fitness value (analyzed from this perspective). , we can consider the characteristic that the hot water flow rate value of the heating system changes with time, so that the optimal solution is more in line with the actual needs at the current moment); if the hot water flow rate value corresponding to a solution is smaller than the mean difference between the hot water flow rate value of the historical moments at the same outdoor temperature of the target area at the current moment, it means that this solution is more in line with the heating demand of the target area at the current outdoor temperature, indicating that its preference is greater and its fitness value is smaller (from this perspective, we can consider the characteristic that the hot water flow rate of the heating system changes with the outdoor temperature, so that the optimal solution is more in line with the actual needs at the current moment).

[0033] In each iterative process of obtaining the optimal solution using the bat algorithm, the fitness value of each solution of the hot water flow value of the target area at the current moment of the current iteration is determined based on each solution of the hot water flow value of the target area at the current moment, the average of the hot water flow values ​​of the target area at all historical moments with the same outdoor temperature as the current moment, the average of the hot water flow values ​​of the target area at all historical moments with the same time sequence as the current moment, and the heating cost corresponding to each solution of the hot water flow value of the target area at the current moment of the current iteration.

[0034] Specifically, the average of the hot water flow rate values ​​of the target area at all historical moments with the same outdoor temperature as the current moment is: The average of the hot water flow rate values ​​of the target area at all historical times with the same outdoor temperature and the same water temperature as the current time.

[0035] Specifically, the average value of the hot water flow rate of the target area at all historical moments with the same time sequence as the current moment is: The average of the hot water flow rate values ​​of the target area at all historical moments with the same time series and the same water temperature as the current moment.

[0036] Specifically, the heating cost is: The total cost of heating the target area within a preset time period for each solution of the hot water flow value at the current moment.

[0037] Implementers can set the time period according to specific implementation circumstances, for example, 5 minutes.

[0038] Specifically, the fitness value satisfies: ; Where, For the The target area of ​​the iteration is the hot water flow value at the current moment. The fitness value of a solution, For the The target area of ​​the iteration is the hot water flow value at the current moment. A solution, is the average of the hot water flow rate values ​​of the target area at all historical moments with the same outdoor temperature as the current moment, is the mean of the hot water flow rate values ​​of the target area at all historical moments with the same time series as the current moment, For the The target area of ​​the iteration is the hot water flow value at the current moment. The corresponding heating cost of each solution is, is the standard normalization function.

[0039] in, The smaller it is, the more closely this solution fits the heating demand of the target area at the current outdoor temperature. The greater its preference, the smaller its fitness value; and vice versa. The smaller it is, the more this solution fits the heating demand of the target area at the current moment, the greater its preference, and the smaller the fitness value; vice versa. The larger it is, the greater the cost corresponding to this solution, the smaller its preference, and the greater the fitness value; vice versa.

[0040] S03: Determine the demand for the next iteration after the bat algorithm completes the preset number of iterations.

[0041] It should be noted that to ensure the algorithm has sufficient iterations to search for the global optimal solution in the initial stages, the algorithm is required to dynamically determine whether further iterations are needed after completing the initial iterations (a preset number of iterations, illustratively, 20). Based on this, this step analyzes the diversity of all solutions and the changing characteristics of the optimal solution in each iteration to determine the algorithm's need for the next iteration after each iteration following the initial iteration. The logic used to calculate the algorithm's need for the next iteration after each iteration following the initial iteration is as follows: if the diversity of fitness values ​​corresponding to all solutions after the initial iteration is lower, the algorithm needs to continue iterative search and search the solution space to prevent it from falling into a local optimum. Therefore, the need for the next iteration after this iteration is greater. If the number of completed iterations after the initial iteration is lower, the algorithm is more likely to be in the early stage after the current iteration. To more accurately output the optimal solution, the algorithm needs to search more extensively, and the need for the next iteration after this iteration is greater. However, it cannot be ruled out that the algorithm will converge to the global optimal solution with fewer iterations when the current global optimal solution is output. Therefore, we can continue to analyze the changing characteristics of the fitness value corresponding to the optimal solution in multiple iterations. If an iteration after the initial iteration is completed, the fitness value before it is The fitness value corresponding to the optimal solution in the iterations does not show a significant downward trend (that is, the quality of the optimal solution in multiple iterations has not been significantly improved), indicating that the algorithm is more likely to be close to saturation. In this case, the iteration can be stopped in advance to avoid wasting computing resources. Therefore, after this iteration is completed, the demand for the next iteration will be smaller. is the preset number of iterations. In this embodiment, .

[0042] During each iteration after the bat algorithm completes a preset number of iterations, the requirement for the next iteration after the bat algorithm completes the preset number of iterations is determined based on the variance of the fitness values ​​of all solutions of the hot water flow value of the target area of ​​the current iteration at the current moment, the number of iterations ending at the current iteration after completing the preset number of iterations, and the maximum and minimum values ​​of the fitness values ​​of all solutions of the hot water flow value of the target area at the current moment in the previous several iterations with the same number of iterations as the current iteration.

[0043] Specifically, the requirements are met: ; Where, The number of iterations after the bat algorithm completes the preset number of iterations The demand of the next iteration for the next iteration, The number of iterations after the bat algorithm completes the preset number of iterations The variance of the fitness values ​​of all solutions of the hot water flow value in the target area of ​​the iteration at the current moment, To terminate the first The number of iterations, and They are respectively the first iterations, the maximum and minimum values ​​of the fitness values ​​of all solutions of the hot water flow value of the target area at the current moment in the previous iterations with the same number of preset iterations, is the natural exponential function, is the standard normalization function.

[0044] in, The smaller it is, the better the algorithm is after the initial iteration. After the first iteration, the lower the diversity of fitness values ​​corresponding to all solutions, the more likely it is that the algorithm needs to continue iterative search and continue searching in the solution space to prevent the algorithm from falling into a local optimum. After the first iteration is completed, the demand for the next iteration will be greater. The smaller the value, the smaller the number of completed iterations of the algorithm after the initial iteration. After the first iteration is completed, the greater the possibility that the algorithm is in the early stage, in order to output the optimal solution more accurately, the algorithm needs to search more widely, so the first After the first iteration is completed, the demand for the next iteration will be greater. The smaller it is, the better the algorithm is after the initial iteration. After the first iteration is completed, if the fitness value of the optimal solution in the previous iterations does not show a significant downward trend (that is, the quality of the optimal solution in multiple iterations has not been significantly improved), the greater the possibility that the algorithm is close to saturation, the iteration can be stopped in advance to avoid wasting computing resources. Therefore, the first iteration is After the first iteration is completed, the demand for the next iteration will be smaller.

[0045] S04: Determine whether to perform the next iteration based on the demand level, and obtain the target value of the hot water flow value at the current moment.

[0046] Specifically, the iteration termination includes: In response to the demand being greater than a preset iteration threshold, it is determined that the iteration of the bat algorithm is incomplete, and the next iteration is performed; otherwise, the iteration is terminated.

[0047] Implementers can set the iteration threshold according to specific implementation circumstances, for example, 0.7.

[0048] S05: Realize remote control of heating system.

[0049] The bat algorithm is used to obtain the target value of the hot water flow rate at the current moment, which is used to remotely control the heating system.

[0050] Specifically, the method for realizing remote control of a heating system includes: The hot water flow value of the target area at the current moment is adjusted to the hot water flow value corresponding to the optimal solution, thereby completing the remote control method for the heating system.

[0051] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A remote control method for a heating system, characterized in that: include: For any area covered by the heating system, the current outdoor temperature and hot water flow value are obtained in real time. The target value of the hot water flow value at the current moment is obtained using the bat algorithm for remote control of the heating system. The target value is the optimal solution for the hot water flow value at the current moment when the bat algorithm iteration terminates. In each iteration of the bat algorithm, the fitness value of each solution of the hot water flow value at the current moment obtained in the current iteration is determined based on each solution of the hot water flow value at the current moment obtained in the current iteration, the average of all hot water flow values ​​at the same historical moments with the same outdoor temperature as the current moment, the average of all hot water flow values ​​at the same historical moments with the same time sequence as the current moment, and the heating cost corresponding to each solution of the hot water flow value at the current moment obtained in the current iteration; During each iteration after the bat algorithm completes a preset number of iterations, the demand for the next iteration after the current iteration is completed is determined based on the variance of the fitness values ​​of all solutions of the hot water flow value at the current moment obtained from the current iteration, the number of iterations up to the current iteration after the bat algorithm completes the preset number of iterations, and the maximum and minimum values ​​of the fitness values ​​of all solutions of the hot water flow value at the current moment obtained from the current iteration and several previous iterations. Whether to perform the next iteration is determined based on the size of the demand.

2. A remote control method for a heating system according to claim 1, characterized in that: The outdoor temperature and hot water flow rate values ​​are the outdoor temperature and hot water flow rate values ​​after filtering and denoising.

3. A remote control method for a heating system according to claim 2, characterized in that: The filtering and denoising adopts a median filtering algorithm.

4. A remote control method for a heating system according to claim 1, characterized in that: The fitness value satisfies: ； Where, For the The hot water flow value at the current moment obtained by the iteration The fitness value of a solution, For the The hot water flow value at the current moment obtained by the iteration A solution, is the mean of the hot water flow rate values ​​at all historical moments with the same outdoor temperature as the current moment, is the mean of the hot water flow rate values ​​at all historical moments with the same time series as the current moment, For the The hot water flow value at the current moment obtained by the iteration The corresponding heating cost of each solution is, is the standard normalization function.

5. A remote control method for a heating system according to claim 1 or 4, characterized in that: The average of all hot water flow rates at historical times with the same outdoor temperature as the current time is: The average of all hot water flow rates at historical times that have the same outdoor temperature as the current time and the same water temperature as the current water heat flow rate.

6. A remote control method for a heating system according to claim 1 or 4, characterized in that: The average value of the hot water flow rate at all historical moments with the same time series as the current moment is: The mean of all hot water flow values ​​at the same time series as the current moment and the same water temperature as the current moment's water heat flow value.

7. A remote control method for a heating system according to claim 1 or 4, characterized in that: The heating cost is: The total cost of heating for each solution of the hot water flow value at the current moment within the preset time period.

8. A remote control method for a heating system according to claim 1, characterized in that: The requirements are met: ； Where, The number of iterations after the bat algorithm completes the preset number of iterations The demand for the next iteration after the first iteration is completed, The number of iterations after the bat algorithm completes the preset number of iterations The variance of the fitness values ​​of all solutions of the hot water flow value at the current moment obtained by iteration, To terminate the first The number of iterations, and They are respectively the first The maximum and minimum values ​​of the fitness values ​​of all solutions of the hot water flow value at the current moment obtained by the iteration and the previous iterations, is the natural exponential function, is the standard normalization function.

9. A remote control method for a heating system according to claim 1, characterized in that: The step of determining whether to perform the next iteration according to the demand degree includes: In response to the demand being greater than a preset iteration threshold, it is determined that the iteration of the bat algorithm is incomplete, and the next iteration is performed; otherwise, the iteration is terminated.

10. A remote control method for a heating system according to claim 1, characterized in that: The heating system for remote control comprises: The hot water flow value at the current moment is adjusted to the target value to complete the remote control method for the heating system.

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