Intelligent adjusting system and method for regulating and controlling heat supply temperature and energy distribution in real time

Abstract

The invention relates to the technical field of energy distribution, in particular to an intelligent adjusting system and method for regulating and controlling heat supply temperature and energy distribution in real time. The system comprises a data collection unit used for collecting real-time environment data of heat supply areas and obtaining user temperature preferences of different heat supply areas and priorities between the heat supply areas; the control optimization unit is used for adjusting heat supply operation parameters according to the real-time environment data and distributing energy according to user temperature preferences of different heat supply areas and priorities between the heat supply areas. According to the intelligent adjusting system and method for regulating and controlling the heat supply temperature and the energy distribution in real time, the dynamic characteristic and the environment friendliness of the heat supply system can be more comprehensively considered, the flexibility and adaptability of the system are improved, the user satisfaction degree and the sustainability of the system are enhanced, and meanwhile the user experience is improved while the user requirement is met. And efficient utilization of energy and stable operation of the system can be realized.

Description

Technical Field

[0001] The present invention relates to the technical field of energy distribution, and in particular to an intelligent regulation system and method for real-time regulation of heating temperature and energy distribution. Background Art

[0002] Under the background of increasingly tight energy resources and increasing environmental protection requirements, regulating heating temperature and energy distribution has become an important issue in modern urban management. Traditional heating methods often have problems such as serious waste of heat energy and extensive temperature control, and cannot meet the personalized needs of different user groups. Therefore, by introducing intelligent technical means, such as the Internet of Things, big data analysis and automatic control systems, the refined management of the heating system can be achieved, ensuring that while providing a stable and comfortable thermal environment, the goal of energy conservation and emission reduction is achieved, and the sustainable development of the social economy is promoted; due to different uses, population density, building insulation performance and other factors, different regions also have different needs and urgency for heating. For example, public service facilities such as hospitals, schools, and nursing homes usually need to give priority to ensuring heating to ensure the quality of life and safety of special groups of people; while some non-critical commercial or industrial areas can appropriately lower the heating standards when energy supply is tight. The existing heating system lacks the ability to dynamically respond to the actual needs of different regions. As a result, in some areas (such as commercial areas during non-peak hours), if the heating cannot be dynamically adjusted according to actual demand, it may lead to excessive heating and energy waste. Therefore, an intelligent adjustment system and method for real-time regulation of heating temperature and energy distribution are provided. Summary of the invention

[0003] The purpose of the present invention is to provide an intelligent adjustment system and method for real-time control of heating temperature and energy distribution, so as to solve the problem that the existing heating system proposed in the above background technology lacks the ability to dynamically respond to the actual needs of different regions, resulting in excessive heating and energy waste in some areas (such as commercial areas during non-peak hours) if the heating cannot be dynamically adjusted according to actual needs.

[0004] To achieve the above objectives, on the one hand, the present invention aims to provide an intelligent regulation system for real-time regulation of heating temperature and energy distribution, comprising:

[0005] A data collection unit, the data collection unit is used to collect real-time environmental data of the heating area and obtain user temperature preferences of different heating areas and the priority between each heating area; wherein the data collection unit includes a multi-area monitoring module and a response setting module;

[0006] A control optimization unit, which is used to adjust heating operation parameters according to real-time environmental data and allocate energy according to user temperature preferences in different heating areas and the priority between each heating area; wherein the control optimization unit includes a load balancing module and an energy management module.

[0007] As a further improvement of the present technical solution, in the data collection unit, the multi-zone monitoring module 11 installs sensors in each heating area to obtain real-time environmental data of each heating area; the response setting module is used to collect the temperature setting preferences of users in different heating areas, and set priorities according to the functions of different heating areas and the importance of users, and introduces time factors and environmental impact factors in the process of setting priorities for optimization.

[0008] As a further improvement of the present technical solution, each heating area in the multi-area monitoring module is equipped with an independent control module, and the control modules can individually adjust the heating flow and temperature of each corresponding heating area.

[0009] As a further improvement of the technical solution, the response setting module sets priorities according to the functions of different heating areas and the importance of users, as follows:

[0010] P i =w F ·F i +w U ·U i +w E ·E i +w C ·C i +w R ·R i ;

[0011] Among them, P i is the priority score of the i-th heating area; F i Score the functional importance of the i-th heating area; w F The weight of the functional importance score of the i-th heating area; U i is the importance score of users in the i-th heating area; w U is the weight of the user importance score of the i-th heating area; E i is the energy efficiency score of the i-th heating area; w E is the weight of the energy efficiency score of the i-th heating area; C i is the emergency demand score of the i-th heating area; w C is the weight of the emergency demand score of the i-th heating area; R i is the user feedback score of the i-th heating area; w Ris the weight of the user feedback score for the i-th heating area.

[0012] As a further improvement of the technical solution, in the response setting module, the time factor and the environmental impact factor are introduced in the process of setting the priority for optimization, and the optimization is as follows:

[0013] P i ′(t)=w F (t)·F i +w U (t)·U i +w E (t)·E i +w C (t)·C i +w R (t)·R i +w V (t)·V i ;

[0014] Among them, P i '(t) is the priority score of the optimized i-th heating area at time t; w V (t) is the weight of the environmental impact factor score of the ith heating area at time t; V i is the environmental impact factor score of the i-th heating area; w F (t) is the weight of the functional importance score of the i-th heating area at time t; w U (t) is the weight of the user importance score of the i-th heating area at time t; w E (t) is the weight of the energy efficiency score of the i-th heating area at time t; w C (t) is the weight of the emergency demand score of the i-th heating area at time t; w R (t) is the weight of the user feedback score of the i-th heating area at time t.

[0015] As a further improvement of the present technical solution, in the control optimization unit, the load balancing module is used to use historical data and weather forecasts to predict the future heating demand of different heating areas, and adjust the heating ratio of each heating area based on the predicted future heating demand of different heating areas and real-time monitored environmental data. In the process of adjusting the heating ratio of each heating area, user behavior patterns are introduced for optimization; the energy management module is used to divide the heating area into multiple sub-areas, and each sub-area can be adjusted and controlled according to its own situation.

[0016] As a further improvement of the technical solution, the load balancing module has the following specific steps:

[0017] Use the time series analysis model to predict future heating demand for each heating zone:

[0018] D i,t =f(D i,t-1 ,D i,t-2 ,…,W t );

[0019] Among them, D i,t is the predicted heating demand of the ith heating area at time t; D i,t-1 is the actual heating demand of the ith heating area at time t-1; D i,t-2 is the actual heating demand of the ith heating area at time t-2; W t is the weather forecast data at time t;

[0020] Calculate the predicted demand ratio P for each heating area i,t :

[0021]

[0022] Among them, β i is the priority factor of the ith heating area; b min is the minimum value of the priority factor; b mid is the middle value of the priority factor; b max is the maximum value of the priority factor; threshold1 is the threshold set for implementation;

[0023]

[0024] Among them, P i,t is the heating proportion of the ith heating area at time t; H i,t is the actual heating supply of the ith heating area at time t; α i is the user temperature preference factor of the i-th heating area; γ i is the environmental factor of the i-th heating area; N is the number of heating areas; i = 1, 2, ..., N; S t is the total heating capacity of the system at time t;

[0025] H i,t ′=S t ·P i,t ·A i,t ;

[0026] Among them, H i,t ' is the actual heating supply of the ith heating area at time t after adjustment; A i,t is the adjustment factor of the i-th heating area at time t.

[0027] As a further improvement of the technical solution, in the load balancing module, the user behavior pattern is introduced to optimize the process of adjusting the heating ratio of each heating area, and the optimization is as follows:

[0028] D i,t ′=f(D i,t-1 ,D i,t-2 ,…,W t ,B i,t );

[0029] Among them, D i,t ' is the predicted heating demand of the optimized i-th heating area at time t; B i,t is the user behavior pattern data of the i-th heating area at time t;

[0030] Calculate the predicted demand ratio P for each heating area i,t :

[0031]

[0032] Among them, P i,t ' is the optimized heating ratio of the i-th heating area at time t;

[0033] H i,t =S t ·P i,t '·A i,t ·U i,t ;

[0034] Among them, H i,t ” is the actual heating supply of the ith heating area at time t after optimization and adjustment; U i,t is the user behavior pattern adjustment factor of the i-th heating area at time t.

[0035] As a further improvement of the technical solution, in the energy management module, the principle of dividing the heating area into multiple sub-areas is based on the functional similarity of the heating areas, the proximity of geographical locations, and the similarity of user needs.

[0036] On the other hand, the present invention provides an intelligent regulation method for real-time regulation of heating temperature and energy distribution, based on the above-mentioned intelligent regulation system for real-time regulation of heating temperature and energy distribution, comprising the following steps:

[0037] S10.1. Install sensors in each heating zone to collect real-time environmental data and user temperature preferences, and set priorities between each heating zone based on the function of the heating zone and the importance of the user;

[0038] S10.2. Use historical data and weather forecasts to predict future heating demand in different heating areas, and divide the heating areas into multiple sub-areas;

[0039] S10.3. Adjust the heating proportion of each heating area based on the predicted future heating demand of different heating areas, real-time collected environmental data, user preferences for temperature, and the priority between each heating area.

[0040] Compared with the prior art, the present invention has the following beneficial effects:

[0041] 1. The intelligent regulation system and method for real-time control of heating temperature and energy distribution can more comprehensively consider the dynamic characteristics and environmental friendliness of the heating system by introducing time factors and environmental impact factors. At the same time, the introduction of environmental impact factors enables the system to reduce the negative impact on the environment while meeting user needs, promote the development of green heating, and not only improve the flexibility and adaptability of the system, but also enhance user satisfaction and the sustainability of the system.

[0042] 2. The intelligent regulation system and method for real-time control of heating temperature and energy distribution combines historical heating data, weather forecasts and user behavior patterns, which can more accurately predict the future heating demand of each heating area. At the same time, by combining the user behavior pattern adjustment factor, the actual heating amount of each heating area can be dynamically adjusted to ensure that while meeting user needs, efficient energy utilization and stable operation of the system are achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is the overall flow chart of the present invention;

[0044] Figure 2 is a flow chart of the method of the present invention;

[0045] The meaning of each number in the figure is:

[0046] 1. Data collection unit; 11. Multi-zone monitoring module; 12. Response setting module; 2. Control optimization unit; 21. Load balancing module; 22. Energy management module. DETAILED DESCRIPTION

[0047] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0048] Embodiment 1:

[0049] See also Figure 1 As shown, an intelligent regulation system for real-time control of heating temperature and energy distribution is provided, including a data collection unit 1 and a control optimization unit 2; wherein the data collection unit 1 is used to collect real-time environmental data of the heating area and obtain user temperature preferences of different heating areas and the priority between each heating area; the data collection unit 1 includes a multi-area monitoring module 11 and a response setting module 12;

[0050] In data collection unit 1:

[0051] The multi-zone monitoring module 11 installs sensors such as temperature and humidity sensors in each heating area to obtain real-time environmental data of each heating area; each heating area is equipped with an independent control valve or variable frequency pump, so that the heating flow and temperature of the heating area can be adjusted individually.

[0052] Each heating area in the multi-area monitoring module 11 is equipped with an independent control module, and the control module can individually adjust the heating flow and temperature of each corresponding heating area.

[0053] The response setting module 12 is used to collect the temperature setting preferences of users in different heating areas, and set priorities according to the functions of different heating areas and the importance of users, and introduce time factors and environmental impact factors for optimization in the process of setting priorities;

[0054] The heating areas are classified according to their functions, including residential areas, office areas and public facilities. Among them, residential areas are living areas for residents, which have higher requirements for temperature comfort. Office areas include office buildings, commercial complexes, etc., which have more concentrated temperature demands during working hours. Public facilities include hospitals, schools, nursing homes, etc., which have higher requirements for temperature stability, and some facilities (such as hospitals) are more sensitive to temperature.

[0055] In the response setting module 12, priorities are set according to the functions of different heating areas and the importance of users, as follows:

[0056] P i =w F ·F i +w U ·U i +w E ·E i +w C ·C i +w R ·R i ;

[0057] Among them, P i is the priority score of the i-th heating area; F i Score the functional importance of the i-th heating area; wF The weight of the functional importance score of the i-th heating area; U i is the importance score of users in the i-th heating area; w U is the weight of the user importance score of the i-th heating area; E i is the energy efficiency score of the i-th heating area; w E is the weight of the energy efficiency score of the i-th heating area; C i is the emergency demand score of the i-th heating area; w C is the weight of the emergency demand score of the i-th heating area; R i is the user feedback score of the i-th heating area; w R are the weights of the user feedback scores for the i-th heating area; these weight coefficients can be adjusted according to actual conditions to reflect the relative importance of each factor in the decision-making.

[0058] Among them, functional importance is to assign different weight values ​​according to the main use of the heating area (such as hospitals, schools, commercial areas, residential areas, etc.). For example, hospitals and schools may have higher weights because their service objects are public health and education, which are part of social infrastructure; user importance will consider the nature of specific users in the area, such as the elderly, children, patients and other groups that need special attention, and give higher scores; energy efficiency is to evaluate factors such as building insulation performance and equipment energy efficiency in each area, and give higher scores to areas with high energy efficiency to encourage energy conservation and emission reduction; emergency needs are to temporarily prioritize certain areas according to real-time conditions (such as extreme weather, emergencies, etc.); user feedback is to collect user feedback on the current heating quality, and appropriately increase the score of areas with poor feedback to improve user experience.

[0059] The specific steps are as follows:

[0060] Define the specific numerical range of each factor and determine the weight coefficient: for example, functional importance can be scored from 1 (lowest) to 5 (highest); user importance can also be scored from 1 to 5; energy efficiency can be based on the energy efficiency level of the building, from 0 (worst) to 1 (best); emergency demand is a binary variable, which is 1 when there is an emergency and 0 otherwise; user feedback can be measured through a satisfaction survey, from -2 (very dissatisfied) to +2 (very satisfied);

[0061] Then, collect relevant data of each heating area regularly or in real time, including but not limited to weather data, user behavior data, equipment operation status, etc.; substitute the collected data into the above formula to calculate the priority score of each area; finally, adjust the scoring criteria and weight coefficients in a timely manner according to the actual operation effect and social feedback, and continuously optimize the performance of the heating system;

[0062] In this way, more refined and intelligent heating management and resource allocation can be achieved, improving the efficiency and service quality of the overall heating system.

[0063] In the response setting module 12, the time factor and the environmental impact factor are introduced in the process of setting the priority for optimization, and the optimization is as follows:

[0064] P i ′(t)=w F (t)·F i +w U (t)·U i +w E (t)·E i +w C (t)·C i +w R (t)·R i +w V (t)·V i ;

[0065] Among them, P i '(t) is the priority score of the optimized i-th heating area at time t; w V (t) is the weight of the environmental impact factor score of the ith heating area at time t; V i is the environmental impact factor score of the i-th heating area; w F (t) is the weight of the functional importance score of the i-th heating area at time t; w U (t) is the weight of the user importance score of the i-th heating area at time t; w E (t) is the weight of the energy efficiency score of the i-th heating area at time t; w C (t) is the weight of the emergency demand score of the i-th heating area at time t; w R (t) is the weight of the user feedback score of the i-th heating area at time t;

[0066] By introducing time factors and environmental impact factors, the dynamic characteristics and environmental friendliness of the heating system can be considered more comprehensively. At the same time, the introduction of environmental impact factors enables the system to reduce the negative impact on the environment while meeting user needs, thereby promoting the development of green heating. This optimization not only improves the flexibility and adaptability of the system, but also enhances user satisfaction and the sustainability of the system.

[0067] The control optimization unit 2 is used to adjust the heating operation parameters according to the real-time environmental data, such as the output power of the heat source, the speed of the water pump, the opening degree of the valve, etc., and allocate energy according to the temperature preferences of users in different heating areas and the priority between each heating area, so as to avoid the situation where some heating areas are overheated while others are insufficient. During peak hours or when energy is tight, the incentive mechanism is used to encourage users to reduce unnecessary energy consumption or delay the use of high-energy-consuming appliances;

[0068] Wherein, the control optimization unit 2 includes a load balancing module 21 and an energy management module 22;

[0069] In control optimization unit 2:

[0070] The load balancing module 21 is used to predict the future heating demand of different heating areas by using historical data and weather forecasts, and adjust the heating ratio of each heating area based on the predicted future heating demand of different heating areas and the real-time monitored environmental data. For example, when the demand of some heating areas decreases, the excess heat energy can be transferred to the heating areas with higher demand. In the process of adjusting the heating ratio of each heating area, the user behavior pattern is introduced for optimization;

[0071] The specific steps of the load balancing module 21 are as follows:

[0072] Use the time series analysis model to predict future heating demand for each heating zone:

[0073] D i,t =f(D i,t-1 ,D i,t-2 ,…,W t );

[0074] Among them, D i,t is the predicted heating demand of the ith heating area at time t; D i,t-1 is the actual heating demand of the ith heating area at time t-1; D i,t-2 is the actual heating demand of the ith heating area at time t-2; W t is the weather forecast data at time t;

[0075] Calculate the predicted demand ratio P for each heating area i,t :

[0076]

[0077] Among them, β i is the priority factor of the ith heating area, reflecting the priority of the area in energy allocation. For example, β i The value will be higher; b min is the minimum value of the priority factor; b midis the middle value of the priority factor; b max is the maximum value of the priority factor; threshold1 is the threshold set for implementation, which is used to divide different priority intervals; since the priority score has different influences in certain intervals, a piecewise linear conversion is used.

[0078]

[0079] Among them, P i,t is the heating proportion of the ith heating area at time t; H i,t is the actual heating supply of the ith heating area at time t; α i is the user temperature preference factor of the i-th heating area, which reflects the user's preference for temperature. For example, if the user wants a higher indoor temperature, then α i The value will be larger; γ i is the environmental factor of the ith heating area, reflecting the impact of environmental conditions (such as outdoor temperature, wind speed, etc.) on heating demand. For example, in cold weather, γ i The value will increase; N is the number of heating areas; i = 1, 2, ..., N; S t is the total heating capacity of the system at time t; It represents the sum of actual heating supply of all heating areas at time t;

[0080] This formula takes two aspects into account: first, the proportion of actual heating supply relative to the forecast demand, and second, the proportion of actual heating supply of all heating areas relative to the total heating capacity of the system; in this way, a reasonable allocation of heating resources can be ensured, taking into account the overall heating capacity of the system.

[0081] H i,t ′=S t ·P i,t ·A i,t ;

[0082] Among them, H i,t ' is the actual heating supply of the ith heating area at time t after adjustment; A i,t is the adjustment factor of the i-th heating area at time t, which can be dynamically adjusted according to the real-time monitored indoor and outdoor temperatures, user feedback, etc.; the heating ratio H is dynamically adjusted according to the real-time monitored environmental data and user feedback. i,t , ensuring efficient operation of the heating system.

[0083] The load balancing module 21 uses historical data, weather forecasts and real-time environmental data in combination with user behavior patterns to accurately predict and dynamically adjust the heating demand and heating ratio of each heating area. Specifically, the module first uses a time series analysis model to predict the heating demand of each heating area in the next few days, and then calculates the priority factor of each heating area through a piecewise linear conversion method to ensure that the heating demand of important places is met first. Then, based on the predicted demand, user temperature preference, environmental factors and the total heating capacity of the system, the heating ratio of each heating area is calculated to ensure the reasonable allocation of heating resources. Finally, by dynamically adjusting the factors, the heating ratio is further optimized according to the real-time monitored indoor and outdoor temperatures and user feedback to ensure the efficient operation of the heating system. This process not only improves energy utilization efficiency, but also improves user comfort and satisfaction, and realizes intelligent and refined energy management.

[0084] In the load balancing module 21, the user behavior pattern is introduced to optimize the process of adjusting the heating ratio of each heating area. The optimization is as follows:

[0085] D i,t ′=f(D i,t-1 ,D i,t-2 ,…,W t ,B i,t );

[0086] Among them, D i,t ' is the predicted heating demand of the optimized i-th heating area at time t; B i,t is the user behavior pattern data of the i-th heating area at time t;

[0087] Calculate the predicted demand ratio P for each heating area i,t :

[0088]

[0089] Among them, P i,t ' is the optimized heating ratio of the i-th heating area at time t;

[0090] H i,t =S t ·P i,t '·A i,t ·U i,t ;

[0091] Among them, H i,t ” is the actual heating supply of the ith heating area at time t after optimization and adjustment; U i,t is the user behavior pattern adjustment factor of the i-th heating area at time t;

[0092] U i,t=g(B i,t ,T i,t );

[0093] Among them, T i,t is the actual temperature of the ith heating area at time t; g is a function that can adjust the heating ratio according to the user behavior pattern and the actual temperature;

[0094] The optimized load balancing module 21 introduces user behavior pattern data B i,t and user behavior pattern adjustment factor U i,t , significantly improving the accuracy of heating demand forecasting and the rationality of heating resource allocation. Specifically, the optimized formula combines historical heating data, weather forecasts, and user behavior patterns to more accurately predict future heating demand in each heating area. The calculated predicted demand ratio P i,t 'Not only the ratio of actual heat supply to predicted demand is taken into account, but also the total heating capacity of the system is taken into account, ensuring the rational allocation of heating resources. Ultimately, the actual heat supply of each heating area can be dynamically adjusted by adjusting the formula combined with the user behavior pattern adjustment factor, ensuring that while meeting user needs, energy efficiency and system stability are achieved, which not only improves the flexibility and response speed of the system, but also enhances user satisfaction and system sustainability.

[0095] The energy management module 22 is used to divide the heating area into multiple sub-areas, and each sub-area can be adjusted and controlled according to its own situation;

[0096] In the energy management module 22, the division principle of the heating area into multiple sub-areas is based on the functional similarity of the heating areas, the proximity of geographical locations, and the similarity of user needs;

[0097] The functional similarity of heating areas is to divide areas with similar functions into the same sub-areas, such as office areas, residential areas, etc.; the geographical proximity is to consider the geographical proximity to facilitate centralized management and optimization; the similarity of user needs refers to the division based on the similarity of user needs, such as elderly residential areas, student activity areas, etc.

[0098] The energy management module 22 achieves more sophisticated and efficient energy management by dividing the heating area into multiple sub-areas based on functional similarity, geographical proximity and similarity of user needs. This division allows each sub-area to independently control heating according to its own actual situation, so as to better adapt to changes in the local environment and user needs. In the intelligent regulation system that controls the heating temperature and energy distribution in real time, not only the energy utilization efficiency is improved and energy waste is reduced, but also the user's comfort and satisfaction are improved, ensuring the stability and reliability of the heating system.

[0099] Embodiment 2:

[0100] See also Figure 2 As shown, an intelligent adjustment method for real-time regulation of heating temperature and energy distribution is provided. Based on the above-mentioned intelligent adjustment system for real-time regulation of heating temperature and energy distribution, the method comprises the following steps:

[0101] S10.1. Install sensors in each heating zone to collect real-time environmental data and user temperature preferences, and set priorities between each heating zone based on the function of the heating zone and the importance of the user;

[0102] S10.2. Use historical data and weather forecasts to predict future heating demand in different heating areas, and divide the heating areas into multiple sub-areas;

[0103] S10.3. Adjust the heating proportion of each heating area based on the predicted future heating demand of different heating areas, real-time collected environmental data, user preferences for temperature, and the priority between each heating area.

[0104] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments, and the above embodiments and descriptions are only preferred examples of the present invention, and are not intended to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements all fall within the scope of the present invention to be protected.

Claims

1. An intelligent regulation system for real-time control of heating temperature and energy distribution, characterized in that: include: A data collection unit (1), the data collection unit (1) is used to collect real-time environmental data of heating areas and obtain user temperature preferences of different heating areas and the priority between each heating area; wherein the data collection unit (1) includes a multi-area monitoring module (11) and a response setting module (12); A control optimization unit (2), the control optimization unit (2) is used to adjust heating operation parameters according to real-time environmental data, and allocate energy according to user temperature preferences in different heating areas and the priority between each heating area; wherein the control optimization unit (2) includes a load balancing module (21) and an energy management module (22).

2. The intelligent regulation system for real-time control of heating temperature and energy distribution according to claim 1 is characterized in that: In the data collection unit (1), the multi-area monitoring module (11) installs a sensor in each heating area to obtain real-time environmental data of each heating area; the response setting module (12) is used to collect the temperature setting preferences of users in different heating areas, and set priorities according to the functions of different heating areas and the importance of users, and introduces time factors and environmental impact factors in the process of setting priorities for optimization.

3. The intelligent regulation system for real-time control of heating temperature and energy distribution according to claim 2 is characterized in that: Each heating area in the multi-area monitoring module (11) is equipped with an independent control module, and the control module can independently adjust the heating flow and temperature of the corresponding heating area.

4. The intelligent regulation system for real-time control of heating temperature and energy distribution according to claim 3 is characterized by: The response setting module (12) sets priorities according to the functions of different heating areas and the importance of users, as follows: P i =w F ·F i +w U ·U i +w E ·E i +w C ·C i +w R ·R i ; Among them, P i is the priority score of the i-th heating area; F i Score the functional importance of the i-th heating area; w F The weight of the functional importance score of the i-th heating area; U i is the importance score of users in the i-th heating area; w U is the weight of the user importance score of the i-th heating area; E i is the energy efficiency score of the i-th heating area; w E is the weight of the energy efficiency score of the i-th heating area; C i is the emergency demand score of the i-th heating area; w C is the weight of the emergency demand score of the i-th heating area; R i is the user feedback score of the i-th heating area; w R is the weight of the user feedback score for the i-th heating area.

5. The intelligent regulation system for real-time control of heating temperature and energy distribution according to claim 4 is characterized in that: In the response setting module (12), the time factor and the environmental impact factor are introduced to optimize the priority setting process, and the optimization is as follows: P i ′(t)=w F (t)·F i +w U (t)·U i +w E (t)·E i +w C (t)·C i +w R (t)·R i +w V (t)·V i ; Among them, P i '(t) is the priority score of the optimized i-th heating area at time t; w V (t) is the weight of the environmental impact factor score of the ith heating area at time t; V i is the environmental impact factor score of the i-th heating area; w F (t) is the weight of the functional importance score of the i-th heating area at time t; w U (t) is the weight of the user importance score of the i-th heating area at time t; w E (t) is the weight of the energy efficiency score of the i-th heating area at time t; w C (t) is the weight of the emergency demand score of the i-th heating area at time t; w R (t) is the weight of the user feedback score of the i-th heating area at time t.

6. The intelligent regulation system for real-time control of heating temperature and energy distribution according to claim 5 is characterized in that: In the control optimization unit (2), the load balancing module (21) is used to use historical data and weather forecasts to predict the future heating demand of different heating areas, and adjust the heating ratio of each heating area based on the predicted future heating demand of different heating areas and real-time monitored environmental data. In the process of adjusting the heating ratio of each heating area, user behavior patterns are introduced for optimization; the energy management module (22) is used to divide the heating area into a plurality of sub-areas, and each sub-area can be adjusted and controlled according to its own situation.

7. The intelligent regulation system for real-time control of heating temperature and energy distribution according to claim 6 is characterized in that: The load balancing module (21) has the following specific steps: Use the time series analysis model to predict future heating demand for each heating zone: D i,t =f(D i,t-1 ,D i,t-2 ,…,W t ); Among them, D i,t is the predicted heating demand of the ith heating area at time t; D i,t-1 is the actual heating demand of the ith heating area at time t-1; D i,t-2 is the actual heating demand of the ith heating area at time t-2; W t is the weather forecast data at time t; Calculate the predicted demand ratio P for each heating area i,t : Among them, β i is the priority factor of the ith heating area; b min is the minimum value of the priority factor; b mid is the middle value of the priority factor; b max is the maximum value of the priority factor; threshold1 is the threshold set for implementation; Among them, P i,t is the heating proportion of the ith heating area at time t; H i,t is the actual heating supply of the ith heating area at time t; α i is the user temperature preference factor of the i-th heating area; γ i is the environmental factor of the i-th heating area; N is the number of heating areas; i = 1, 2, ..., N; S t is the total heating capacity of the system at time t; H i,t ′=S t ·P i,t ·A i,t ; Among them, H i,t ' is the actual heating supply of the ith heating area at time t after adjustment; A i,t is the adjustment factor of the i-th heating area at time t.

8. The intelligent regulation system for real-time control of heating temperature and energy distribution according to claim 7 is characterized in that: In the load balancing module (21), the user behavior pattern is introduced to optimize the process of adjusting the heating ratio of each heating area, and the optimization is as follows: D i,t ′=f(D i,t-1 ,D i,t-2 ,…,W t ,B i,t ); Among them, D i,t ' is the predicted heating demand of the optimized i-th heating area at time t; B i,t is the user behavior pattern data of the i-th heating area at time t; Calculate the predicted demand ratio P for each heating area i,t : Among them, P i,t ' is the optimized heating ratio of the i-th heating area at time t; H i,t ″=S t ·P i,t ′·A i,t ·U i,t ; Among them, H i,t ” is the actual heating supply of the ith heating area at time t after optimization and adjustment; U i,t is the user behavior pattern adjustment factor of the i-th heating area at time t.

9. The intelligent regulation system for real-time control of heating temperature and energy distribution according to claim 8 is characterized in that: In the energy management module (22), the principle of dividing the heating area into a plurality of sub-areas is based on the functional similarity, geographical proximity and user demand similarity of the heating areas.

10. An intelligent regulation method for real-time regulation of heating temperature and energy distribution, based on the intelligent regulation system for real-time regulation of heating temperature and energy distribution as claimed in any one of claims 1 to 9, characterized in that: The steps include: S10.

1. Install sensors in each heating zone to collect real-time environmental data and user temperature preferences, and set priorities between each heating zone based on the function of the heating zone and the importance of the user; S10.

2. Use historical data and weather forecasts to predict future heating demand in different heating areas, and divide the heating areas into multiple sub-areas; S10.

3. Adjust the heating proportion of each heating area based on the predicted future heating demand of different heating areas, real-time collected environmental data, user preferences for temperature, and the priority between each heating area.

Images