Adaptive Scheduling Optimization Method for Courtyard Heat Network Combining Secondary Network and Building Pipe Network

Abstract

The present invention relates to the technical field of optimized scheduling, and particularly to an adaptive scheduling optimization method for a courtyard heat network combining a secondary network and a building network. The adaptive scheduling optimization method for a courtyard heat network combining a secondary network and a building network includes the following steps: S1: Obtain relevant location data, ambient temperature data, and historical heat consumption data of personalized users, and adjust the valve opening of personalized users using the opening adjustment formula; S2: Obtain the community capacity, historical active users, and personalized users within the target area, and use the total heat load calculation formula to obtain the total heat demand of the area; S3: Regulate the heat delivery of the secondary network based on the total heat demand of the area. Through the adjustment method based on personalized user needs and real-time data, the present invention performs refined control on the actual needs of each user, enabling the heat network system to flexibly adjust the heat distribution according to the actual needs and real-time status of each user.

Description

Technical Field

[0001] The present invention relates to the technical field of optimized scheduling, and particularly to an adaptive scheduling optimization method for a courtyard heat network combining a secondary network and a building pipe network. Background Art

[0002] With the advancement of urbanization, centralized heating systems have been widely applied in many cities. However, traditional centralized heating systems and heat network scheduling methods have certain limitations, especially facing many challenges in aspects such as heat distribution, energy management, and user comfort. The problems of the existing technologies are mainly reflected in the following aspects: low efficiency of heat network scheduling and energy waste. Traditional heat network scheduling methods usually adopt simple scheduling strategies based on average heat load or heat demand calculation for the whole region, ignoring the differences in personalized user needs; ignoring the changes in external environment and personalized factors. Currently, most heat network scheduling systems respond slowly to changes in external environmental temperature and user personalized needs. Existing technologies mainly rely on preset temperature compensation mechanisms or weighted methods based on simple rules to adjust heat delivery, and these methods often lack flexibility and real-time performance; it is difficult to finely control user comfort. In traditional heat network systems, the adjustment of heat supply is mostly based on the overall load calculation of the region, ignoring the individual comfort temperature and actual demand differences of users. Summary of the Invention

[0003] In order to overcome the shortcoming that traditional heat network systems ignore the individual comfort temperature and actual demand differences of users, the present invention provides an adaptive scheduling optimization method for a courtyard heat network combining a secondary network and a building pipe network.

[0004] Technical Solution: An adaptive scheduling optimization method for a courtyard heat network combining a secondary network and a building pipe network includes the following steps:

[0005] S1: Obtain relevant location data, environmental temperature data, and historical heat consumption data of personalized users, and adjust the valve opening of personalized users using the opening adjustment formula;

[0006] S2: Obtain the community capacity, historical active users, and personalized users within the target area, and use the total heat load calculation formula to obtain the total heat demand of the area;

[0007] S3: Regulate the heat delivery of the secondary network based on the total heat demand of the area.

[0008] Preferably, before obtaining the relevant location data, environmental temperature data, and historical heat consumption data of the personalized user and adjusting the valve opening of the personalized user using the opening adjustment formula, it includes: obtaining the historical location data of the personalized user, determining the maximum opening distance for valve opening adjustment according to the historical location data, and obtaining the midpoint of the opening adjustment formula using the dynamic intermediate distance formula based on the average travel speed of the personalized user and the estimated room heating time data; wherein the relevant location data includes the historical location data and the actual location data of the personalized user.

[0009] Preferably, the determining the maximum opening distance for valve opening adjustment according to the historical location data includes: based on the actual location data of the personalized user, when the distance between the actual location data of the personalized user and the area is greater than or equal to the preset distance, after weighting and adjusting the preset distance, it is used as the maximum opening distance for valve opening adjustment; when the actual location data of the personalized user is less than the preset distance, the preset distance is used as the maximum opening distance for valve opening adjustment.

[0010] Preferably, the obtaining the midpoint of the opening adjustment formula using the dynamic intermediate distance formula based on the average travel speed of the personalized user and the estimated room heating time data includes: wherein the dynamic intermediate distance formula is:

[0011] ;

[0012] In the formula, is the midpoint of the opening adjustment formula; is the adjustment coefficient; is the preset proportional coefficient; is the maximum opening distance; is the estimated room heating time; is the average travel speed of the personalized user; is a function set according to user preferences.

[0013] Preferably, the obtaining the relevant location data, environmental temperature data, and historical heat consumption data of the personalized user and adjusting the valve opening of the personalized user using the opening adjustment formula includes: obtaining the actual location data of the personalized user, the historical comfortable temperature of the personalized user, and the environmental temperature of the area, obtaining the distance of the personalized user from home according to the actual location data; obtaining the coefficient adjusted based on the external temperature according to the environmental temperature of the area; using the opening adjustment formula to obtain the valve opening data of the personalized user.

[0014] Preferably, the using the opening adjustment formula to obtain the valve opening data of the personalized user includes: wherein the opening adjustment formula is:

[0015] ;

[0016] In the formula, is the valve opening adjustment result for the personalized user; is the minimum valve opening corresponding to the historical comfortable temperature of the personalized user; is the maximum valve opening of the valve; is the distance of the personalized user from home; is the midpoint of the opening adjustment formula; is the coefficient based on the external temperature .

[0017] Preferably, obtaining the coefficient adjusted based on the external temperature according to the environmental temperature of the region includes: obtaining a reference external temperature, a minimum expected external temperature, and an external environmental temperature based on the environmental temperature of the region, and using a temperature coefficient formula to obtain the coefficient adjusted based on the external temperature, where the temperature coefficient formula is:

[0018] ;

[0019] In the formula, is the reference slope; is the reference external temperature; is the external environmental temperature; is the minimum expected external temperature.

[0020] Preferably, obtaining the cell capacity, historical active users, and personalized users in the target region, and using a total heat load calculation formula to obtain the total heat demand of the region includes: obtaining ordinary inactive users based on the cell capacity and the historical active users; obtaining ordinary users based on the historical active users and the personalized users; and obtaining the total heat demand of the region based on the ordinary inactive users, ordinary users, and personalized users using a total heat load calculation formula.

[0021] Preferably, obtaining the total heat demand of the region based on the ordinary inactive users, ordinary users, and personalized users using a total heat load calculation formula includes: where the total heat load calculation formula is:

[0022] ;

[0023] In the formula, is the total heat demand of the region; is the weight adjustment coefficient; is the number of ordinary inactive users; is the average heat demand of each ordinary inactive user; is the number of ordinary users; is the average heat demand of each ordinary user; is the number of personalized users; is the average heat demand for each personalized user.

[0024] Preferably, the regulation of the heat transmission in the secondary network based on the total heat demand of the region includes: using the opening adjustment formula for the region at preset intervals to obtain the number of personalized users, and based on the number of personalized users, as well as the number of ordinary inactive users and ordinary users, using the total heat load calculation formula to re-obtain the total heat demand of the region, and regulating the heat transmission in the secondary network according to the loss rate regulation formula from the secondary heat network to the region, where the loss rate regulation formula is:

[0025] ;

[0026] In the formula, is the heat entering the secondary heat network; is the total heat demand reaching the region; is the dynamically calculated loss rate.

[0027] Beneficial effects:

[0028] 1. Through the adjustment method based on personalized user needs and real-time data, the heat network system can flexibly adjust the heat distribution according to the actual needs and real-time status of each user. This personalized adjustment method affects the adjustment of the valve opening in real time according to the user's location, ambient temperature, and historical heat consumption data, ensuring that the heat demand of each user can be met at the most appropriate time. This precise adjustment method enables the heat network system to adapt to various dynamic changes, improving the accuracy of heat network scheduling;

[0029] 2. By dynamically calculating and adjusting the heat consumption demand of personalized users, combined with the dynamic adjustment of external ambient temperature and historical data, the system can respond in a timely manner and adjust the heat output in the case of alternating cold and hot seasons and drastic temperature changes, further reducing energy waste;

[0030] 3. Carry out refined control according to the actual needs of each user. While ensuring heat supply, fully consider the comfort of users, combine the user's walking speed and the estimated heating time of the room, accurately predict the heat demand when the user arrives home, and provide a more comfortable living environment;

[0031] 4. Comprehensively consider the needs of different types of users, namely ordinary inactive users, ordinary users, and personalized users, accurately evaluate the total heat demand in the region, and achieve efficient heat distribution. The accurate heat distribution can not only avoid local overheating or overcooling, but also reduce the burden on the heat network system and improve the overall operation efficiency of the system. Description of the Drawings

[0032] Figure 1It is a flowchart of the adaptive scheduling optimization method for the courtyard heat network combining the secondary network and the building pipe network of the present invention;

[0033] Figure 2 It is a judgment schematic diagram for setting the maximum opening distance of the present invention. Specific embodiments

[0034] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0035] Embodiment 1: An adaptive scheduling optimization method for the courtyard heat network combining the secondary network and the building pipe network, as Figure 1 and Figure 2 shown, includes the following steps:

[0036] S1: Obtain the relevant location data, environmental temperature data, and historical heat consumption data of personalized users, and adjust the valve opening of personalized users using the opening adjustment formula;

[0037] Obtain the historical location data of personalized users, determine the maximum opening distance for valve opening adjustment according to the historical location data, and obtain the midpoint of the opening adjustment formula using the dynamic intermediate distance formula based on the average traveling speed of personalized users and the estimated heating time data of the room; wherein, the relevant location data includes the historical location data and actual location data of personalized users.

[0038] It should be explained that before adjusting the valve opening of personalized users, first obtain the historical location data of the user. For example, obtain the location at home, workplace, and frequently visited staying locations of the user through an intelligent device authorized by the user; after obtaining the historical location data of the user, the system needs to further calculate the maximum opening distance for valve opening adjustment. The determination method of this maximum opening distance is related to the historical behavior pattern of the user, the common activity range of the user, and the temperature requirement. For example, if the user usually moves within a certain range from home, the system infers the activity range of the user based on the historical location data and sets a reasonable maximum opening distance to reduce energy waste or cause unnecessary overheating.

[0039] Based on the actual location data of personalized users, when the distance between the actual location data of personalized users and the area is greater than or equal to the preset distance, after weighting and adjusting the preset distance, use it as the maximum opening distance for valve opening adjustment; when the actual location data of personalized users is less than the preset distance, then use the preset distance as the maximum opening distance for valve opening adjustment.

[0040] It should be explained that the actual location data of the user is obtained through a smart device or a positioning system via GPS. Based on a preset distance value set by the system, the preset distance value is set based on the range of the user's daily activities. For example, if the user is usually far from home or often goes to different areas, the preset distance value is increased. The response speed and heating range of the heat network system are flexibly adjusted according to the actual distance of the user, so as to ensure the accuracy and effectiveness of heat network scheduling; if the actual location data of the user is less than the preset distance, it means that the user is close to the heat network area or at home, and the system does not need to overly adjust the valve opening in advance.

[0041] The dynamic intermediate distance formula is as follows:

[0042] ;

[0043] In the formula, is the midpoint of the opening adjustment formula; is the adjustment coefficient; is the preset proportional coefficient; is the maximum opening distance; is the estimated heating time of the room; is the average traveling speed of the personalized user; is a function set according to the user's preference.

[0044] It should be explained that after determining the maximum opening distance of the valve opening adjustment, the midpoint of the opening adjustment formula is calculated using the average traveling speed of the user and the estimated heating time of the room. The average traveling speed of the personalized user is obtained through historical usage data or through modern positioning devices. The time required for the room to reach the set temperature from the current temperature is estimated based on the type, size, wall material of the room, and external temperature, and the estimated heating time of the room is obtained. In the formula, is the midpoint of the opening adjustment formula, which is used as a reference point when adjusting the valve opening, affects the distribution of heating time and the accuracy of temperature adjustment, and is the position where the valve opening starts to change significantly. The valve opening is adjusted according to the maximum opening distance of the personalized user, the estimated heating time of the room, the average traveling speed of the personalized user, and the function set according to the user's preference, so that the valve opening of the personalized user's room is reasonable. For example, when the estimated heating time of the personalized user's room becomes longer due to environmental factors or secondary network heating factors, the midpoint of the opening adjustment formula will be adjusted to a position where the distance of the personalized user from home becomes smaller, so that the time for the valve opening to enter the rapid closing state is reduced, and thus more heat is available to heat the room; is the time required for the room to rise from the current temperature to the required target temperature. The heating time of the room is related to the size, insulation performance of the room, and external environmental temperature. When the room is large or the external temperature is low, The value will increase. Based on the estimated heating time, the heating intensity and duration are determined to precisely adjust the valve opening; The function set according to user preferences is adjusted according to the needs of different users. For example, when some users prefer a low temperature, the calculated value of the midpoint is adjusted by adjusting this function, thereby reducing energy consumption.

[0045] Obtain the actual location data of the personalized user, the historical comfortable temperature of the personalized user, and the ambient temperature of the area. Obtain the distance of the personalized user from home based on the actual location data; obtain the coefficient adjusted based on the external temperature according to the ambient temperature of the area; use the opening adjustment formula to obtain the valve opening data of the personalized user.

[0046] It should be explained that the indoor temperature preferred by the user is obtained through the collection system and used as a reference value for future adjustment of the valve opening. The historical comfortable temperature of the user has the ability to be continuously updated and optimized during long-term use to ensure that the adjustment of the system is consistent with the needs of the user; for the ambient temperature of the area, the ambient temperature of the area is obtained through meteorological equipment, sensors or weather forecasts in the area, and the ambient temperature has a direct impact on the heating demand.

[0047] Among them, the opening adjustment formula is:

[0048] ;

[0049] In the formula, is the adjustment result of the valve opening for the personalized user; is the minimum valve opening corresponding to the historical comfortable temperature of the personalized user; is the maximum valve opening of the valve; is the distance of the personalized user from home; is the midpoint of the opening adjustment formula; is based on the external temperature coefficient.

[0050] It should be explained that is the minimum valve opening corresponding to the historical comfortable temperature of the personalized user. The minimum valve opening is set based on the comfortable temperature of the user. When the user is within the comfortable range, the valve is not fully opened and serves as the minimum valve opening; is the maximum valve opening of the valve. When the heat supply network needs to provide the maximum amount of heat, the valve reaches the maximum opening; is the distance of the personalized user from home; is the midpoint of the opening adjustment formula. The opening of the valve is precisely adjusted through the setting of the midpoint; by using the opening adjustment formula, the distance from home and the external ambient temperature are comprehensively considered to dynamically adjust the opening of the valve, so that the indoor temperature reaches the comfortable temperature of the personalized user when the user arrives home.

[0051] Based on the ambient temperature of the said area, obtain the reference external temperature, the lowest expected external temperature, and the external ambient temperature, and use the temperature coefficient formula to obtain the coefficient adjusted based on the external temperature, where the temperature coefficient formula is:

[0052] ;

[0053] In the formula, is the reference slope; is the reference external temperature; is the external ambient temperature; is the lowest expected external temperature.

[0054] It should be explained that is the coefficient based on the external temperature and is used to dynamically adjust the valve opening according to the change of the external ambient temperature; is the reference slope and is used to control the change rate of the temperature coefficient; is the reference external temperature and serves as the ideal external temperature; is the lowest expected external temperature, which is the coldest temperature that the system can handle. By adjusting the system has the ability to dynamically adjust the heat supply according to the temperature of the external environment.

[0055] S2: Obtain the cell capacity, historical active users, and personalized users in the target area, and use the total heat load calculation formula to obtain the total heat demand of the area;

[0056] Based on the cell capacity and the historical active users, obtain the ordinary inactive users; based on the historical active users and the personalized users, obtain the ordinary users; based on the ordinary inactive users, ordinary users, and personalized users, use the total heat load calculation formula to obtain the total heat demand of the area.

[0057] It should be explained that obtaining the data in the target area: the cell capacity is the maximum number of users that the entire cell can carry; the historical active users are the users who have user activities and heat consumption records within a preset time period; the personalized users are the users who adjust the heat supply according to personalized needs; by subtracting the number of historical active users from the cell capacity, obtain the number of ordinary inactive users; by subtracting the number of personalized users from the number of historical active users, obtain the number of ordinary users; through the ordinary inactive users, ordinary users, and personalized users, use the total heat load calculation formula to obtain the total heat demand of the area.

[0058] where the total heat load calculation formula is:

[0059] ;

[0060] In the formula, is the total heat demand of the area; is the weight adjustment coefficient; is the number of ordinary inactive users; is the average heat demand of each ordinary inactive user; is the number of ordinary users; is the average heat demand of each ordinary user; is the number of personalized users; is the average heat demand of each personalized user.

[0061] It should be explained that by performing a weighted sum of the heat demands of ordinary inactive users, ordinary users, and personalized users, the total heat demand of the area is comprehensively obtained; in the formula is the weight adjustment coefficient, which is used to adjust the influence weight of different types of users on the total heat demand, that is, the influence degrees of ordinary inactive users, ordinary users, and personalized users on the heat demand are different, and are adjusted through the weight coefficient; is the number of ordinary inactive users, and the heat demand of the ordinary inactive users is usually fixed; is the number of ordinary users, and the heat demand of ordinary users is estimated based on their historical activity data; is the number of personalized users.

[0062] S3: Regulate the heat delivery of the secondary network based on the total heat demand of the area.

[0063] At every preset time, use the opening adjustment formula for the area to obtain the number of personalized users, and based on the number of personalized users, as well as the number of ordinary inactive users and the number of ordinary users, use the total heat load calculation formula to re-obtain the total heat demand of the area, and regulate the heat delivery of the secondary network according to the loss rate regulation formula from the secondary heat network to the area, where the loss rate regulation formula is:

[0064] ;

[0065] In the formula, is the heat entering the secondary heat network; is the total heat demand reaching the area; is the dynamically calculated loss rate.

[0066] It should be noted that the loss rate refers to the actual heat loss caused by the heat loss of the pipe network during the heat transfer process from the secondary network to the area. The dynamic loss rate is obtained through the operating state of the pipe network, the external environmental temperature, the flow rate, and the pipe material; use SVM to obtain the dynamic loss rate of this area; by introducing the dynamic loss rate regulation formula, the heat delivery is adjusted more precisely, ensuring the efficient operation of the heat network.

[0067] The above embodiments are only preferred embodiments of the present invention and are not intended to limit the scope of implementation of the present invention. Therefore, all equivalent changes made based on the content described in the claims of the present invention should be included within the scope of the claims of the present invention.

Claims

1. The adaptive scheduling optimization method of courtyard heat network combining secondary network and building pipe network is characterized by: The following steps are involved: S1: Obtain relevant location data, ambient temperature data and historical heat usage data of personalized users, and use the opening adjustment formula to adjust the valve opening of personalized users; S2: Obtain the cell capacity, historical active users, and personalized users in the target area, and use the total heat load calculation formula to obtain the total heat demand of the area; S3: regulating heat delivery of the secondary network based on the total heat demand of the region; Before obtaining the relevant location data, ambient temperature data and historical heat consumption data of the personalized user and using the opening adjustment formula to adjust the valve opening of the personalized user, the method includes: obtaining the historical location data of the personalized user, determining the maximum opening distance of the valve opening adjustment according to the historical location data, and obtaining the middle point of the opening adjustment formula using the dynamic middle distance formula based on the average travel speed of the personalized user and the estimated heating time data of the room; wherein the relevant location data includes the historical location data and actual location data of the personalized user; The determining of the maximum opening distance for adjusting the valve opening according to the historical location data includes: based on the actual location data of the personalized user, when the distance between the actual location data of the personalized user and the area is greater than or equal to the preset distance, the preset distance is weighted and used as the maximum opening distance for adjusting the valve opening; when the actual location data of the personalized user is less than the preset distance, the preset distance is used as the maximum opening distance for adjusting the valve opening; The method of obtaining the middle point of the opening adjustment formula using the dynamic middle distance formula based on the average travel speed of the personalized user and the estimated heating time data of the room includes: wherein the dynamic middle distance formula is: ； In the formula, It is the middle point of the opening adjustment formula; is the adjustment coefficient; is the preset proportional coefficient; is the maximum opening distance; Estimate heating time for the room; The average travel speed of personalized users; is a function set according to user preferences.

2. The adaptive scheduling optimization method for courtyard heating network combining secondary network and building pipe network as claimed in claim 1 is characterized in that: The method of obtaining relevant location data, ambient temperature data and historical heat usage data of a personalized user, and using an opening adjustment formula to adjust the valve opening of the personalized user includes: obtaining the actual location data of the personalized user, the historical comfort temperature of the personalized user and the ambient temperature of the area, and obtaining the distance of the personalized user from home according to the actual location data; obtaining a coefficient based on external temperature adjustment according to the ambient temperature of the area; and obtaining the valve opening data of the personalized user using the opening adjustment formula.

3. The adaptive scheduling optimization method for courtyard heating network combining secondary network and building pipe network as claimed in claim 2 is characterized in that: The use of the opening adjustment formula to obtain the valve opening data of the personalized user includes: wherein the opening adjustment formula is: ； In the formula, To personalize the valve opening adjustment results for users; The minimum valve opening corresponding to the personalized user's historical comfort temperature; is the maximum valve opening of the valve; To personalize the user's distance from home; It is the middle point of the opening adjustment formula; Based on the external temperature The coefficient of .

4. The adaptive scheduling optimization method for courtyard heating network combining secondary network and building pipe network as claimed in claim 3 is characterized in that: The step of obtaining a coefficient based on an external temperature adjustment according to the ambient temperature of the area includes: obtaining a reference external temperature, a minimum expected external temperature, and an external ambient temperature based on the ambient temperature of the area, and obtaining a coefficient based on an external temperature adjustment using a temperature coefficient formula, wherein the temperature coefficient formula is: ； In the formula, is the base slope; is the reference external temperature; is the external ambient temperature; is the lowest expected outside temperature.

5. The adaptive scheduling optimization method for courtyard heating network combining secondary network and building pipe network as claimed in claim 1 is characterized in that: The obtaining of the cell capacity, historical active users and personalized users in the target area, and the use of a total heat load calculation formula to obtain the total heat demand of the area include: obtaining ordinary inactive users based on the cell capacity and the historical active users; obtaining ordinary users based on the historical active users and personalized users; and obtaining the total heat demand of the area based on the ordinary inactive users, ordinary users and personalized users using a total heat load calculation formula.

6. The adaptive scheduling optimization method for courtyard heating network combining secondary network and building pipe network as claimed in claim 5 is characterized in that: The total heat demand of the region is obtained by using a total heat load calculation formula based on the common inactive users, common users and personalized users, including: wherein the total heat load calculation formula is: ； In the formula, is the total heat demand of the region; is the weight adjustment coefficient; is the number of ordinary inactive users; is the average heat demand of each normal inactive user; is the number of ordinary users; is the average heat demand of each common user; The number of personalized users; Average heat demand for each individual user.

7. The adaptive scheduling optimization method for courtyard heating network combining secondary network and building pipe network as claimed in claim 1 is characterized in that: The heat transfer of the secondary network is regulated based on the total heat demand of the region, including: using the opening adjustment formula for the region at preset intervals to obtain the number of personalized users, and using the total heat load calculation formula based on the number of personalized users, the number of ordinary inactive users and the number of ordinary users to re-obtain the total heat demand of the region, and regulating the heat transfer of the secondary network according to the loss rate control formula from the secondary heat network to the region, wherein the loss rate control formula is: ； In the formula, is the heat entering the secondary heating network; is the total heat demand of the arrival area; is the loss rate calculated dynamically.

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