Heat supply balance control method, system and medium based on intelligent heat source supply

Abstract

The present application provides a heat balance control method, system and medium based on intelligent heat source supply. The method includes: obtaining the real-time heating data of the thermal power station and the average temperature data of the thermal power station in multiple preset time periods and processing them, respectively obtaining the heating capacity evaluation index and the heating fluctuation factor, and processing them according to the heating fluctuation factor and the heating capacity evaluation index to obtain a stable heating capacity evaluation index, obtaining the heat demand impact data within the preset range of the thermal power station and processing them to obtain the heat demand evaluation index, comparing the stable heating capacity evaluation index and the heat demand evaluation index to obtain the heat demand deviation rate, and then performing a threshold comparison, and adjusting the heat source heating according to the comparison result; the present application analyzes the heating capacity in combination with the heating fluctuation factor, and obtains the heat demand deviation rate in combination with the user's heat demand, thereby realizing the intelligent operation and maintenance and refined control of the thermal power station, ensuring sufficient heating while reducing energy consumption.

Description

Technical Field

[0001] The present application relates to the field of heating technology, and more specifically, to a method, system and medium for heat balance control based on intelligent heat source supply. Background Art

[0002] In a centralized heating system, heat balance is an important issue. Traditional heating methods often find it difficult to achieve accurate heating for different areas and users, and are prone to uneven heating, resulting in overheating in some areas and overcooling in others. At the same time, traditional heating methods also have shortcomings in energy efficiency, and are unable to intelligently adjust heat supply according to actual needs, resulting in energy waste.

[0003] In view of the above problems, effective technical solutions are urgently needed. Summary of the invention

[0004] The purpose of this application is to provide a heat balance control method, system and medium based on the intelligent supply of heat sources. The heating capacity can be analyzed by combining the heating fluctuation factor, and the heat demand deviation rate can be obtained by combining the user's heat demand, thereby realizing the intelligent operation and maintenance and refined control of the thermal power station, ensuring sufficient heating while reducing energy consumption.

[0005] The present application also provides a heat supply balance control method based on intelligent heat source supply, comprising the following steps:

[0006] Obtain real-time heating data of the heating station and average temperature data of the heating station in multiple preset time periods;

[0007] Processing the real-time heating data to obtain a heating capacity evaluation index, processing the mean temperature data to obtain a heating fluctuation factor, and processing the heating capacity evaluation index combined with the heating fluctuation factor to obtain a stable heating capacity evaluation index;

[0008] Obtain and process the heat demand impact data within the preset range of the heat station to obtain the heat demand assessment index;

[0009] The stable heating capacity evaluation index and the heat demand evaluation index are compared and processed, and the heat supply of the heat source is adjusted according to the comparison result.

[0010] Optionally, in the heat supply balance control method based on intelligent heat source supply described in the present application, the real-time heat supply data is processed to obtain a heat supply capacity evaluation index, the mean temperature data is processed to obtain a heat supply fluctuation factor, and the heat supply capacity evaluation index is processed in combination with the heat supply fluctuation factor to obtain a stable heat supply capacity evaluation index, including:

[0011] The real-time heating data includes real-time heating temperature data, heat exchange efficiency and heat exchanger capacity;

[0012] Inputting the real-time heating temperature data, heat exchange efficiency and heat exchanger capacity into a preset heating capacity evaluation model for processing to obtain a heating capacity evaluation index;

[0013] Processing the temperature average data corresponding to multiple preset time periods to obtain a heating fluctuation factor;

[0014] The heating fluctuation factor and the heating capacity evaluation index are input into a preset stable heating capacity evaluation model for processing to obtain a stable heating capacity evaluation index.

[0015] Optionally, in the heat supply balance control method based on intelligent heat source supply described in the present application, the heat demand impact data within a preset range of the thermal power station is obtained and processed to obtain a heat demand evaluation index, including:

[0016] The heat demand impact data include basic personnel data, radiator heat dissipation efficiency, building area data and thermal insulation performance data;

[0017] The basic personnel data include the aging rate and the number of permanent personnel;

[0018] The aging rate, number of permanent residents, heat dissipation efficiency of radiators, construction area data and thermal insulation performance data are input into a preset heat demand assessment model for processing to obtain a heat demand assessment index.

[0019] Optionally, in the heat supply balance control method based on heat source intelligent supply described in the present application, the comparison processing of the stable heating capacity evaluation index and the heat demand evaluation index, and adjusting the heat supply of the heat source according to the comparison result, includes:

[0020] Compare and process the stable heating capacity evaluation index and the heat demand evaluation index to obtain a heat demand deviation rate;

[0021] Comparing the heat demand deviation rate with a preset standard heat demand deviation rate threshold to obtain a relative heat demand deviation value;

[0022] Performing a threshold comparison of the thermal demand deviation relative value with a preset thermal demand deviation relative threshold;

[0023] If it is less than or equal to the relative threshold of heat demand deviation, the heat supply of the heat source will not be adjusted;

[0024] If it is greater than the relative threshold of heat demand deviation, the heat supply of the heat source is adjusted.

[0025] Optionally, the heat supply balance control method based on intelligent heat source supply described in the present application further includes:

[0026] Obtain the heating flow difference and heating pressure difference of the heating network within the preset range of the heating station;

[0027] The heat supply flow difference and the heat supply pressure difference are processed in combination with the preset heat supply flow loss average and the heat supply pressure loss average to obtain a heat supply network damage index;

[0028] Comparing the heat pipe network damage index with a preset heat pipe network damage threshold to obtain a heat pipe network damage level, wherein the preset heat pipe network damage threshold includes a first preset heat pipe network damage threshold and a second preset heat pipe network damage threshold, and the first preset heat pipe network damage threshold is less than the second preset heat pipe network damage threshold;

[0029] If it is less than or equal to the first preset heat pipe network damage threshold, it corresponds to level one damage;

[0030] If it is greater than the first preset heat pipe network damage threshold and less than or equal to the second preset heat pipe network damage threshold, it corresponds to level 2 damage;

[0031] If it is greater than the second preset heat pipe network damage threshold, it corresponds to level 3 damage;

[0032] Initiate corresponding preset plans according to the damage level of the thermal pipeline network.

[0033] Optionally, the heat supply balance control method based on intelligent heat source supply described in the present application further includes:

[0034] Obtaining real-time temperature averages of multiple preset ranges in a heating building and corresponding first control node information;

[0035] Comparing the real-time temperature average with the preset standard temperature to obtain the actual temperature deviation rate;

[0036] Performing a threshold comparison between the actual temperature deviation rate and a preset actual temperature deviation rate threshold;

[0037] If it is less than or equal to the preset actual temperature deviation rate threshold, no adjustment will be made;

[0038] If it is greater than the preset actual temperature deviation rate threshold, and the real-time temperature average is greater than the preset standard temperature, the heating amount is reduced according to the first control node information;

[0039] If it is greater than the preset actual temperature deviation rate threshold and the real-time temperature average is less than the preset standard temperature, the heating amount is increased according to the first control node information.

[0040] In a second aspect, the present application provides a heat supply balance control system based on heat source intelligent supply, the system comprising: a memory and a processor, the memory comprising a program of a heat supply balance control method based on heat source intelligent supply, the program of the heat supply balance control method based on heat source intelligent supply when executed by the processor implements the following steps:

[0041] Obtain real-time heating data of the heating station and average temperature data of the heating station in multiple preset time periods;

[0042] Processing the real-time heating data to obtain a heating capacity evaluation index, processing the mean temperature data to obtain a heating fluctuation factor, and processing the heating capacity evaluation index combined with the heating fluctuation factor to obtain a stable heating capacity evaluation index;

[0043] Obtain and process the heat demand impact data within the preset range of the heat station to obtain the heat demand assessment index;

[0044] The stable heating capacity evaluation index and the heat demand evaluation index are compared and processed, and the heat supply of the heat source is adjusted according to the comparison result.

[0045] Optionally, in the heat supply balance control system based on intelligent heat source supply described in the present application, the real-time heat supply data is processed to obtain a heat supply capacity evaluation index, the mean temperature data is processed to obtain a heat supply fluctuation factor, and the heat supply capacity evaluation index is processed in combination with the heat supply fluctuation factor to obtain a stable heat supply capacity evaluation index, including:

[0046] The real-time heating data includes real-time heating temperature data, heat exchange efficiency and heat exchanger capacity;

[0047] Inputting the real-time heating temperature data, heat exchange efficiency and heat exchanger capacity into a preset heating capacity evaluation model for processing to obtain a heating capacity evaluation index;

[0048] Processing the temperature average data corresponding to multiple preset time periods to obtain a heating fluctuation factor;

[0049] The heating fluctuation factor and the heating capacity evaluation index are input into a preset stable heating capacity evaluation model for processing to obtain a stable heating capacity evaluation index.

[0050] Optionally, in the heat supply balance control system based on intelligent heat source supply described in the present application, the heat demand impact data within a preset range of the heat station is obtained and processed to obtain the heat demand evaluation index, including:

[0051] The heat demand impact data include basic personnel data, radiator heat dissipation efficiency, building area data and thermal insulation performance data;

[0052] The basic personnel data include the aging rate and the number of permanent personnel;

[0053] The aging rate, number of permanent residents, heat dissipation efficiency of radiators, construction area data and thermal insulation performance data are input into a preset heat demand assessment model for processing to obtain a heat demand assessment index.

[0054] In the third aspect, the present application also provides a computer-readable storage medium, which stores a program for a heat supply balance control method based on intelligent supply of heat sources. When the program for the heat supply balance control method based on intelligent supply of heat sources is executed by a processor, the steps of the heat supply balance control method based on intelligent supply of heat sources as described in any one of the above items are implemented.

[0055] From the above, it can be seen that the heat balance control method, system and medium based on intelligent supply of heat source provided in the present application analyze the heating capacity by combining the heating fluctuation factor, and obtain the heat demand deviation rate by combining the user's heat demand, thereby realizing the intelligent operation and maintenance and refined control of the thermal power station, ensuring sufficient heating while reducing energy consumption.

[0056] Other features and advantages of the present application will be described in the following description, and partly become apparent from the description, or understood by practicing the embodiments of the present application. The purpose and other advantages of the present application can be realized and obtained by the structures specifically pointed out in the written description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0058] Figure 1 A flow chart of a heat supply balance control method based on intelligent heat source supply provided in an embodiment of the present application;

[0059] Figure 2 A flow chart of obtaining a stable heating capacity evaluation index for a heat supply balance control method based on intelligent heat source supply provided in an embodiment of the present application;

[0060] Figure 3 A flow chart of obtaining a heat demand assessment index for a heat supply balance control method based on intelligent heat source supply provided in an embodiment of the present application. DETAILED DESCRIPTION

[0061] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0062] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0063] Please refer to Figure 1 , Figure 1 The flowchart of the heat supply balance control method based on the intelligent supply of heat source in some embodiments of the present application. The heat supply balance control method based on the intelligent supply of heat source is used in terminal devices, such as computers, mobile phone terminals, etc. The heat supply balance control method based on the intelligent supply of heat source includes the following steps:

[0064] S11, obtaining real-time heating data of the heating station and average temperature data of the heating station in multiple preset time periods;

[0065] S12, processing the real-time heating data to obtain a heating capacity evaluation index, processing the mean temperature data to obtain a heating fluctuation factor, and processing the heating capacity evaluation index combined with the heating fluctuation factor to obtain a stable heating capacity evaluation index;

[0066] S13, obtaining and processing heat demand impact data within a preset range of the heat station to obtain a heat demand assessment index;

[0067] S14, comparing the stable heating capacity evaluation index and the heat demand evaluation index, and adjusting the heating amount of the heat source according to the comparison result.

[0068] It should be noted that in order to achieve precise control of the heat supply of the heat source, first obtain the real-time heating data of the heat station including real-time heating temperature data, heat exchange efficiency and heat exchanger capacity, and process to obtain the heating capacity evaluation index for evaluating the overall heating capacity of the heat station; the heating temperature of the heat source will change when it is heated through the thermal network. By obtaining the average temperature data of the heat station in multiple preset time periods, the heating fluctuation factor is obtained and the heating stability performance of the heat source is evaluated. In this embodiment, the preset time period is set to 1 day; the heating capacity evaluation index is comprehensively analyzed in combination with the influence of the heating fluctuation factor to obtain a stable heating capacity evaluation index; different buildings and different users within the radiation range of the thermal station have different heat demands. Only by understanding the user's demand for heating can we ensure that heating is reduced while reducing energy consumption. Therefore, the heat demand impact data including basic personnel data, radiator heat dissipation efficiency, construction area data and insulation performance data are obtained, and the heat demand evaluation index is obtained; the stable heating capacity evaluation index and the heat demand evaluation index are compared and processed to obtain the heat demand deviation rate, and then the threshold value is compared, and it is determined whether to adjust the heat supply of the heat source according to the comparison result.

[0069] Please refer to Figure 2 , Figure 2 The present invention is a flow chart of obtaining a stable heating capacity evaluation index of a heat balance control method based on intelligent heat source supply in some embodiments of the present application. According to an embodiment of the present invention, the real-time heating data is processed to obtain a heating capacity evaluation index, the temperature mean data is processed to obtain a heating fluctuation factor, and the heating capacity evaluation index is processed in combination with the heating fluctuation factor to obtain a stable heating capacity evaluation index, including:

[0070] S21, the real-time heating data includes real-time heating temperature data, heat exchange efficiency and heat exchanger capacity;

[0071] S22, inputting the real-time heating temperature data, heat exchange efficiency and heat exchanger capacity into a preset heating capacity evaluation model for processing to obtain a heating capacity evaluation index;

[0072] S23, processing the temperature mean data corresponding to multiple preset time periods in combination with the preset historical average temperature mean data to obtain a heat supply fluctuation factor;

[0073] S24, inputting the heating fluctuation factor and the heating capacity evaluation index into a preset stable heating capacity evaluation model for processing to obtain a stable heating capacity evaluation index.

[0074] It should be noted that the heating capacity of the heating station is an important part of ensuring the heating demand of users, so it needs to be evaluated first. By obtaining real-time heating temperature data, heat exchange efficiency and heat exchanger capacity, inputting them into the preset heating capacity evaluation model for processing, the heating capacity evaluation index is obtained;

[0075] The calculation formula of the heating capacity evaluation index in the heating capacity evaluation model is:

[0076] ;

[0077] in, is the heating capacity assessment index, , , They are real-time heating temperature data, heat exchange efficiency and heat exchanger capacity. , is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the heat balance control platform);

[0078] At the same time, the hot water or hot air delivered by the heat source to the thermal power station through the thermal network will change the output temperature due to the unstable performance of the thermal power station equipment, which will lead to fluctuations in the heat supply. The fluctuation directly affects the continuous heating effect. Therefore, it needs to be evaluated and analyzed as an important influencing factor. By obtaining the temperature mean data corresponding to multiple preset time periods, and then combining the preset historical average temperature mean data for processing, the heat supply fluctuation factor is obtained. The temperature mean data is obtained by collecting multiple real-time temperatures within the preset time period and averaging them. In this embodiment, the preset time period is set to 1 day, which can be adjusted according to the specific application scenario;

[0079] The calculation formula of the heating fluctuation factor is:

[0080] ;

[0081] in, is the heating fluctuation factor, , , , They are respectively the mean temperature data corresponding to the i-th preset time period, the mean temperature data corresponding to the i+1-th preset time period, the number of preset time periods and the historical average temperature data. is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the heat balance control platform);

[0082] Heating stability is an important factor to ensure user comfort. Therefore, the heating capacity evaluation index is combined with the heating fluctuation factor to obtain a stable heating capacity evaluation index.

[0083] The calculation formula of the stable heating capacity evaluation index in the stable heating capacity evaluation model is:

[0084] ;

[0085] in, To stabilize the heating capacity assessment index, , are heating fluctuation factor and heating capacity assessment index, respectively. It is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the heat balance control platform).

[0086] Please refer to Figure 3 , Figure 3 The flowchart of obtaining the heat demand evaluation index of the heat balance control method based on the intelligent supply of heat source in some embodiments of the present application is as follows. According to an embodiment of the present invention, the heat demand impact data within the preset range of the thermal power station is obtained and processed to obtain the heat demand evaluation index, including:

[0087] S31, the heat demand impact data includes basic personnel data, radiator heat dissipation efficiency, building area data and thermal insulation performance data;

[0088] S32. The basic personnel data include the aging rate and the number of permanent personnel;

[0089] S33, inputting the elderly rate, number of permanent residents, heat dissipation efficiency of radiators, construction area data and thermal insulation performance data into a preset heat demand assessment model for processing to obtain a heat demand assessment index.

[0090] It should be noted that different groups and buildings of different sizes have different demands for heating. For example, the elderly have a higher demand for heating than young people. The thermal insulation performance and radiator heat dissipation efficiency of different buildings also have a greater impact on the heating effect. Therefore, in order to evaluate the demand for heating, first obtain the heat demand impact data within the preset range of the thermal power station, including the elderly rate, the number of permanent residents, the heat dissipation efficiency of the radiator, the construction area data and the thermal insulation performance data. Among them, the elderly rate is the proportion of the elderly in the permanent population. The heat demand impact data is input into the preset heat demand assessment model for processing to obtain the heat demand assessment index.

[0091] The heat demand assessment index calculation formula in the heat demand assessment model is:

[0092] ;

[0093] in, is the heat demand assessment index, , , , , They are the elderly rate, number of permanent residents, radiator heat dissipation efficiency, construction area data and thermal insulation performance data. , It is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the heat balance control platform).

[0094] According to an embodiment of the present invention, the step of comparing the stable heating capacity evaluation index with the heat demand evaluation index and adjusting the heat supply of the heat source according to the comparison result includes:

[0095] Compare and process the stable heating capacity evaluation index and the heat demand evaluation index to obtain a heat demand deviation rate;

[0096] Comparing the heat demand deviation rate with a preset standard heat demand deviation rate threshold to obtain a relative heat demand deviation value;

[0097] Performing a threshold comparison of the thermal demand deviation relative value with a preset thermal demand deviation relative threshold;

[0098] If it is less than or equal to the relative threshold of heat demand deviation, the heat supply of the heat source will not be adjusted;

[0099] If it is greater than the relative threshold of heat demand deviation, the heat supply of the heat source is adjusted.

[0100] It should be noted that, according to the comparison processing of the stable heating capacity evaluation index and the heat demand evaluation index, the heat demand deviation rate is obtained, and the deviation rate is compared with the preset standard heat demand deviation rate threshold to obtain the relative value of the heat demand deviation. For example, if the heat demand deviation rate is 0.2 and the preset standard heat demand deviation rate threshold is 1, 0.2 / 1=0.2 is the relative value of the heat demand deviation. In this embodiment, the relative thresholds of the heat demand deviation are set to (0, 0.3] and (0.3, 1], corresponding to no adjustment of the heat source heating supply and adjustment of the heat source heating supply, respectively. For example, if the relative value of the heat demand deviation is 0.2, the heat source heating supply is not adjusted.

[0101] According to an embodiment of the present invention, it also includes:

[0102] Obtain the heating flow difference and heating pressure difference of the heating network within the preset range of the heating station;

[0103] Obtaining a preset heating flow loss average value and a preset heating pressure loss average value;

[0104] The heating flow difference and the heating pressure difference are combined with the preset heating flow loss average and the preset heating pressure loss average, and input into a preset heat pipe network damage assessment model for processing to obtain a heat pipe network damage index;

[0105] Comparing the heat pipe network damage index with a preset heat pipe network damage threshold to obtain a heat pipe network damage level, wherein the preset heat pipe network damage threshold includes a first preset heat pipe network damage threshold and a second preset heat pipe network damage threshold, and the first preset heat pipe network damage threshold is less than the second preset heat pipe network damage threshold;

[0106] If it is less than or equal to the first preset heat pipe network damage threshold, it corresponds to level one damage;

[0107] If it is greater than the first preset heat pipe network damage threshold and less than or equal to the second preset heat pipe network damage threshold, it corresponds to level 2 damage;

[0108] If it is greater than the second preset heat pipe network damage threshold, it corresponds to level 3 damage;

[0109] Initiate corresponding preset plans according to the damage level of the thermal pipeline network.

[0110] It should be noted that the integrity of the thermal network is very important for the heating efficiency. By collecting the heating flow difference and heating pressure difference of the thermal network within the preset range of the thermal station, combined with the inevitable heating flow loss mean and heating pressure loss mean, the data are input into the preset thermal network damage assessment model for processing to obtain the thermal network damage index; wherein the thermal network damage assessment model is obtained by obtaining a large number of historical samples of heating flow difference, heating pressure difference, preset heating flow loss mean and preset heating pressure loss mean and corresponding thermal network damage index training; the obtained thermal network damage index is compared with the preset thermal network damage threshold. In this embodiment, the thermal network damage threshold is set to (0, 0.2], (0.2, 0.4], (0.4, 1], corresponding to the first level damage, the second level damage, and the third level damage, wherein the third level damage is the most serious. For example, if the obtained thermal network damage index is 0.1, it corresponds to the first level damage, and the corresponding preset plan is activated, including but not limited to shutdown maintenance.

[0111] According to an embodiment of the present invention, it also includes:

[0112] Obtaining real-time temperature averages of multiple preset ranges in a heating building and corresponding first control node information;

[0113] Comparing the real-time temperature average with the preset standard temperature to obtain the actual temperature deviation rate;

[0114] Performing a threshold comparison between the actual temperature deviation rate and a preset actual temperature deviation rate threshold;

[0115] If it is less than or equal to the preset actual temperature deviation rate threshold, no adjustment will be made;

[0116] If it is greater than the preset actual temperature deviation rate threshold, and the real-time temperature average is greater than the preset standard temperature, the heating amount is reduced according to the first control node information;

[0117] If it is greater than the preset actual temperature deviation rate threshold and the real-time temperature average is less than the preset standard temperature, the heating amount is increased according to the first control node information.

[0118] It should be noted that the current heating adopts a unified flow rate and a unified temperature. The real-time temperatures in different areas are different. Some temperatures are too high, and some are insufficient. By obtaining the real-time temperature averages of multiple preset ranges in the heating building and the corresponding first control node information, it is possible to understand whether the real-time temperature of the area meets the demand. The first control node information refers to the position information of the heating valve that controls the area. The real-time temperature average is compared with the preset standard temperature to obtain the actual temperature deviation rate.

[0119] The actual temperature deviation rate calculation formula is:

[0120] ;

[0121] in, is the actual temperature deviation rate, , They are real-time temperature average and preset standard temperature respectively;

[0122] The obtained actual temperature deviation rate is compared with the preset actual temperature deviation rate threshold. In this embodiment, the actual temperature deviation rate threshold is set to (0, 0.1], (0.1, 1]. For example, if the real-time temperature average is 19°C and the preset standard temperature is 18°C, the actual temperature deviation rate is 0.06, which is less than the preset actual temperature deviation rate threshold, and no adjustment is made; if the real-time temperature average is 15°C and the preset standard temperature is 18°C, the actual temperature deviation rate is 0.17, which is greater than the preset actual temperature deviation rate threshold, and the real-time temperature average is less than the preset standard temperature, then the heating supply is increased according to the first control node information; if the real-time temperature average is 23°C and the preset standard temperature is 18°C, the actual temperature deviation rate is 0.28, which is greater than the preset actual temperature deviation rate threshold, and the real-time temperature average is greater than the preset standard temperature, then the heating supply is reduced according to the first control node information.

[0123] It is worth mentioning that according to an embodiment of the present invention, it also includes:

[0124] Obtain weather forecast information within the preset range of the thermal power station and extract weather change data, including temperature difference extremes, wind speed extremes, and rain and snow data;

[0125] Processing the data on the temperature difference extreme value, wind speed extreme value and rainfall and snow volume to obtain a heat demand influencing factor;

[0126] The heat demand assessment index is corrected according to the heat demand impact factor to obtain a predicted heat demand assessment index.

[0127] It should be noted that weather changes have a great impact on heating demand. Weather forecast information is obtained according to the location of the heating station, and weather change data, including temperature difference extremes, wind speed extremes, and rainfall and snow data, are extracted. The weather change data is processed to obtain the heat demand influencing factors;

[0128] The heat demand influencing factor calculation formula is:

[0129] ;

[0130] in, is the heat demand influencing factor, , , They are the temperature difference extreme value, wind speed extreme value and rain and snow amount data respectively. , is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the heat balance control platform);

[0131] The heat demand assessment index is corrected according to the obtained heat demand impact factor to obtain the predicted heat demand assessment index;

[0132] The calculation formula of the predicted heat demand assessment index is:

[0133] ;

[0134] in, To predict the heat demand assessment index, , are heat demand impact factor and heat demand assessment index, respectively. It is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the heat balance control platform).

[0135] It is worth mentioning that according to an embodiment of the present invention, it also includes:

[0136] Obtain the heat pipe network distribution data, power pipe network distribution data, water supply pipe network distribution data and road planning data within the preset range of the heat station;

[0137] Obtaining public line data based on the heat pipe network distribution data, power pipe network distribution data, water supply pipe network distribution data and road planning data;

[0138] Get heating time data;

[0139] Obtain construction planning data, including construction route data and construction time data;

[0140] Matching the construction route data with the heat pipe network distribution data to obtain route matching status data, including route matching or route mismatching;

[0141] Matching the construction time data with the heating time data to obtain time matching status data, including time matching or time mismatching;

[0142] If the route matching status data is a route matching, and the time matching status data is a time matching, the construction planning data is sent to the thermal power department, and the thermal power department initiates the emergency plan according to the construction planning data.

[0143] It should be noted that the current distribution overlap of water, electricity, heating pipelines and roads is relatively high, and there is a problem of poor communication among departments. During the heating period, if the water, electricity or roads that overlap with the thermal pipeline are repaired, it may affect the heating. Therefore, the thermal pipeline distribution data, power pipeline distribution data, water supply pipeline distribution data and road planning data within the preset range of the thermal station are first obtained, and the public line data is processed to obtain the heating time data. Finally, route matching and time matching are performed respectively according to the construction planning data. According to the matching results, it is determined whether the construction route and construction time overlap with the thermal pipeline and heating time. If they overlap, the construction planning data will be sent to the thermal management department, and the thermal management department will initiate the emergency plan based on the construction planning data.

[0144] It is worth mentioning that according to an embodiment of the present invention, it also includes:

[0145] Get the building type information within the preset range of the thermal power station;

[0146] Acquire corresponding heating demand time information and second control node information according to the building type information;

[0147] The valve opening of the control node is adaptively adjusted according to the control node information combined with the heating demand time information, including fully open valve, half open valve or fully closed valve.

[0148] It should be noted that the thermal power station radiates heat for different types of buildings, such as residential areas, office buildings or schools. Different types of heating buildings have different demands for continuous heating. For example, residential areas require 24-hour heating, office buildings are heated according to working hours and keep low temperature operation after working hours, and schools stop heating after the winter vacation. Therefore, the corresponding heating demand time information and the second control node information are obtained according to the building type information within the preset range of the thermal power station. The second control node information refers to the position information of the heating valve that controls the building. The control node valve opening is adaptively adjusted according to the control node information and the heating demand time information, including the valve being fully open, the valve being half open or the valve being fully closed.

[0149] The present invention also discloses a heat supply balance control system based on heat source intelligent supply, comprising a memory and a processor, wherein the memory comprises a heat supply balance control method program based on heat source intelligent supply, and when the heat supply balance control method program based on heat source intelligent supply is executed by the processor, the following steps are implemented:

[0150] Obtain real-time heating data of the heating station and average temperature data of the heating station in multiple preset time periods;

[0151] Processing the real-time heating data to obtain a heating capacity evaluation index, processing the mean temperature data to obtain a heating fluctuation factor, and processing the heating capacity evaluation index combined with the heating fluctuation factor to obtain a stable heating capacity evaluation index;

[0152] Obtain and process the heat demand impact data within the preset range of the heat station to obtain the heat demand assessment index;

[0153] The stable heating capacity evaluation index and the heat demand evaluation index are compared and processed, and the heat supply of the heat source is adjusted according to the comparison result.

[0154] It should be noted that in order to achieve precise control of the heat supply of the heat source, first obtain the real-time heating data of the heat station including real-time heating temperature data, heat exchange efficiency and heat exchanger capacity, and process to obtain the heating capacity evaluation index for evaluating the overall heating capacity of the heat station; the heating temperature of the heat source will change when it is heated through the thermal network. By obtaining the average temperature data of the heat station in multiple preset time periods, the heating fluctuation factor is obtained and the heating stability performance of the heat source is evaluated. In this embodiment, the preset time period is set to 1 day; the heating capacity evaluation index is comprehensively analyzed in combination with the influence of the heating fluctuation factor to obtain a stable heating capacity evaluation index; different buildings and different users within the radiation range of the thermal station have different heat demands. Only by understanding the user's demand for heating can we ensure that heating is reduced while reducing energy consumption. Therefore, the heat demand impact data including basic personnel data, radiator heat dissipation efficiency, construction area data and insulation performance data are obtained, and the heat demand evaluation index is obtained; the stable heating capacity evaluation index and the heat demand evaluation index are compared and processed to obtain the heat demand deviation rate, and then the threshold value is compared, and it is determined whether to adjust the heat supply of the heat source according to the comparison result.

[0155] According to an embodiment of the present invention, the real-time heating data is processed to obtain a heating capacity evaluation index, the mean temperature data is processed to obtain a heating fluctuation factor, and the heating capacity evaluation index is processed in combination with the heating fluctuation factor to obtain a stable heating capacity evaluation index, including:

[0156] The real-time heating data includes real-time heating temperature data, heat exchange efficiency and heat exchanger capacity;

[0157] Inputting the real-time heating temperature data, heat exchange efficiency and heat exchanger capacity into a preset heating capacity evaluation model for processing to obtain a heating capacity evaluation index;

[0158] The heating fluctuation factor is obtained by processing the temperature mean value data corresponding to multiple preset time periods in combination with the preset historical average temperature mean value data;

[0159] The heating fluctuation factor and the heating capacity evaluation index are input into a preset stable heating capacity evaluation model for processing to obtain a stable heating capacity evaluation index.

[0160] It should be noted that the heating capacity of the heating station is an important part of ensuring the heating demand of users, so it needs to be evaluated first. By obtaining real-time heating temperature data, heat exchange efficiency and heat exchanger capacity, inputting them into the preset heating capacity evaluation model for processing, the heating capacity evaluation index is obtained;

[0161] The calculation formula of the heating capacity evaluation index in the heating capacity evaluation model is:

[0162] ;

[0163] in, is the heating capacity assessment index, , , They are real-time heating temperature data, heat exchange efficiency and heat exchanger capacity. , is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the heat balance control platform);

[0164] At the same time, the hot water or hot air delivered by the heat source to the thermal power station through the thermal network will change the output temperature due to the unstable performance of the thermal power station equipment, which will lead to fluctuations in the heat supply. The fluctuation directly affects the continuous heating effect. Therefore, it needs to be evaluated and analyzed as an important influencing factor. By obtaining the temperature mean data corresponding to multiple preset time periods, and then combining the preset historical average temperature mean data for processing, the heat supply fluctuation factor is obtained. The temperature mean data is obtained by collecting multiple real-time temperatures within the preset time period and averaging them. In this embodiment, the preset time period is set to 1 day, which can be adjusted according to the specific application scenario;

[0165] The calculation formula of the heating fluctuation factor is:

[0166] ;

[0167] in, is the heating fluctuation factor, , , , They are respectively the mean temperature data corresponding to the i-th preset time period, the mean temperature data corresponding to the i+1-th preset time period, the number of preset time periods and the historical average temperature data. is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the heat balance control platform);

[0168] Heating stability is an important factor to ensure user comfort. Therefore, the heating capacity evaluation index is combined with the heating fluctuation factor to obtain a stable heating capacity evaluation index.

[0169] The calculation formula of the stable heating capacity evaluation index in the stable heating capacity evaluation model is:

[0170] ;

[0171] in, To stabilize the heating capacity assessment index, , are heating fluctuation factor and heating capacity assessment index, respectively. It is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the heat balance control platform).

[0172] According to an embodiment of the present invention, the step of obtaining and processing the heat demand impact data within a preset range of the thermal power station to obtain the heat demand assessment index includes:

[0173] The heat demand impact data include basic personnel data, radiator heat dissipation efficiency, building area data and thermal insulation performance data;

[0174] The basic personnel data include the aging rate and the number of permanent personnel;

[0175] The aging rate, number of permanent residents, heat dissipation efficiency of radiators, construction area data and thermal insulation performance data are input into a preset heat demand assessment model for processing to obtain a heat demand assessment index.

[0176] It should be noted that different groups and buildings of different sizes have different demands for heating. For example, the elderly have a higher demand for heating than young people. The thermal insulation performance and radiator heat dissipation efficiency of different buildings also have a greater impact on the heating effect. Therefore, in order to evaluate the demand for heating, first obtain the heat demand impact data within the preset range of the thermal power station, including the elderly rate, the number of permanent residents, the heat dissipation efficiency of the radiator, the construction area data and the thermal insulation performance data. Among them, the elderly rate is the proportion of the elderly in the permanent population. The heat demand impact data is input into the preset heat demand assessment model for processing to obtain the heat demand assessment index.

[0177] The heat demand assessment index calculation formula in the heat demand assessment model is:

[0178] ;

[0179] in, is the heat demand assessment index, , , , , They are the elderly rate, number of permanent residents, radiator heat dissipation efficiency, construction area data and thermal insulation performance data. , It is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the heat balance control platform).

[0180] According to an embodiment of the present invention, the step of comparing the stable heating capacity evaluation index with the heat demand evaluation index and adjusting the heat supply of the heat source according to the comparison result includes:

[0181] Compare and process the stable heating capacity evaluation index and the heat demand evaluation index to obtain a heat demand deviation rate;

[0182] Comparing the heat demand deviation rate with a preset standard heat demand deviation rate threshold to obtain a relative heat demand deviation value;

[0183] Performing a threshold comparison of the thermal demand deviation relative value with a preset thermal demand deviation relative threshold;

[0184] If it is less than or equal to the relative threshold of heat demand deviation, the heat supply of the heat source will not be adjusted;

[0185] If it is greater than the relative threshold of heat demand deviation, the heat supply of the heat source is adjusted.

[0186] It should be noted that, according to the comparison processing of the stable heating capacity evaluation index and the heat demand evaluation index, the heat demand deviation rate is obtained, and the deviation rate is compared with the preset standard heat demand deviation rate threshold to obtain the relative value of the heat demand deviation. For example, if the heat demand deviation rate is 0.2 and the preset standard heat demand deviation rate threshold is 1, 0.2 / 1=0.2 is the relative value of the heat demand deviation. In this embodiment, the relative thresholds of the heat demand deviation are set to (0, 0.3] and (0.3, 1], corresponding to no adjustment of the heat source heating supply and adjustment of the heat source heating supply, respectively. For example, if the relative value of the heat demand deviation is 0.2, the heat source heating supply is not adjusted.

[0187] According to an embodiment of the present invention, it also includes:

[0188] Obtain the heating flow difference and heating pressure difference of the heating network within the preset range of the heating station;

[0189] Obtaining a preset heating flow loss average value and a preset heating pressure loss average value;

[0190] The heating flow difference and the heating pressure difference are combined with the preset heating flow loss average and the preset heating pressure loss average, and input into a preset heat pipe network damage assessment model for processing to obtain a heat pipe network damage index;

[0191] Comparing the heat pipe network damage index with a preset heat pipe network damage threshold to obtain a heat pipe network damage level, wherein the preset heat pipe network damage threshold includes a first preset heat pipe network damage threshold and a second preset heat pipe network damage threshold, and the first preset heat pipe network damage threshold is less than the second preset heat pipe network damage threshold;

[0192] If it is less than or equal to the first preset heat pipe network damage threshold, it corresponds to level one damage;

[0193] If it is greater than the first preset heat pipe network damage threshold and less than or equal to the second preset heat pipe network damage threshold, it corresponds to level 2 damage;

[0194] If it is greater than the second preset heat pipe network damage threshold, it corresponds to level 3 damage;

[0195] Initiate corresponding preset plans according to the damage level of the thermal pipeline network.

[0196] It should be noted that the integrity of the thermal network is very important for the heating efficiency. By collecting the heating flow difference and heating pressure difference of the thermal network within the preset range of the thermal station, combined with the inevitable heating flow loss mean and heating pressure loss mean, the data are input into the preset thermal network damage assessment model for processing to obtain the thermal network damage index; wherein the thermal network damage assessment model is obtained by obtaining a large number of historical samples of heating flow difference, heating pressure difference, preset heating flow loss mean and preset heating pressure loss mean and corresponding thermal network damage index training; the obtained thermal network damage index is compared with the preset thermal network damage threshold. In this embodiment, the thermal network damage threshold is set to (0, 0.2], (0.2, 0.4], (0.4, 1], corresponding to the first level damage, the second level damage, and the third level damage, wherein the third level damage is the most serious. For example, if the obtained thermal network damage index is 0.1, it corresponds to the first level damage, and the corresponding preset plan is activated, including but not limited to shutdown maintenance.

[0197] According to an embodiment of the present invention, it also includes:

[0198] Obtaining real-time temperature averages of multiple preset ranges in a heating building and corresponding first control node information;

[0199] Comparing the real-time temperature average with the preset standard temperature to obtain the actual temperature deviation rate;

[0200] Performing a threshold comparison between the actual temperature deviation rate and a preset actual temperature deviation rate threshold;

[0201] If it is less than or equal to the preset actual temperature deviation rate threshold, no adjustment will be made;

[0202] If it is greater than the preset actual temperature deviation rate threshold, and the real-time temperature average is greater than the preset standard temperature, the heating amount is reduced according to the first control node information;

[0203] If it is greater than the preset actual temperature deviation rate threshold and the real-time temperature average is less than the preset standard temperature, the heating amount is increased according to the first control node information.

[0204] It should be noted that the current heating adopts a unified flow rate and a unified temperature. The real-time temperatures in different areas are different. Some temperatures are too high, and some are insufficient. By obtaining the real-time temperature averages of multiple preset ranges in the heating building and the corresponding first control node information, it is possible to understand whether the real-time temperature of the area meets the demand. The first control node information refers to the position information of the heating valve that controls the area. The real-time temperature average is compared with the preset standard temperature to obtain the actual temperature deviation rate.

[0205] The actual temperature deviation rate calculation formula is:

[0206] ;

[0207] in, is the actual temperature deviation rate, , They are real-time temperature average and preset standard temperature respectively;

[0208] The obtained actual temperature deviation rate is compared with the preset actual temperature deviation rate threshold. In this embodiment, the actual temperature deviation rate threshold is set to (0, 0.1], (0.1, 1]. For example, if the real-time temperature average is 19°C and the preset standard temperature is 18°C, the actual temperature deviation rate is 0.06, which is less than the preset actual temperature deviation rate threshold, and no adjustment is made; if the real-time temperature average is 15°C and the preset standard temperature is 18°C, the actual temperature deviation rate is 0.17, which is greater than the preset actual temperature deviation rate threshold, and the real-time temperature average is less than the preset standard temperature, then the heating supply is increased according to the first control node information; if the real-time temperature average is 23°C and the preset standard temperature is 18°C, the actual temperature deviation rate is 0.28, which is greater than the preset actual temperature deviation rate threshold, and the real-time temperature average is greater than the preset standard temperature, then the heating supply is reduced according to the first control node information.

[0209] It is worth mentioning that according to an embodiment of the present invention, it also includes:

[0210] Obtain weather forecast information within the preset range of the thermal power station and extract weather change data, including temperature difference extremes, wind speed extremes, and rain and snow data;

[0211] Processing the data on the temperature difference extreme value, wind speed extreme value and rainfall and snow volume to obtain a heat demand influencing factor;

[0212] The heat demand assessment index is corrected according to the heat demand impact factor to obtain a predicted heat demand assessment index.

[0213] It should be noted that weather changes have a great impact on heating demand. Weather forecast information is obtained according to the location of the heating station, and weather change data, including temperature difference extremes, wind speed extremes, and rainfall and snow data, are extracted. The weather change data is processed to obtain the heat demand influencing factors;

[0214] The heat demand influencing factor calculation formula is:

[0215] ;

[0216] in, is the heat demand influencing factor, , , They are the temperature difference extreme value, wind speed extreme value and rain and snow amount data respectively. , is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the heat balance control platform);

[0217] The heat demand assessment index is corrected according to the obtained heat demand impact factor to obtain the predicted heat demand assessment index;

[0218] The calculation formula of the predicted heat demand assessment index is:

[0219] ;

[0220] in, To predict the heat demand assessment index, , are heat demand impact factor and heat demand assessment index, respectively. It is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the heat balance control platform).

[0221] It is worth mentioning that according to an embodiment of the present invention, it also includes:

[0222] Obtain the heat pipe network distribution data, power pipe network distribution data, water supply pipe network distribution data and road planning data within the preset range of the heat station;

[0223] Obtaining public line data based on the heat pipe network distribution data, power pipe network distribution data, water supply pipe network distribution data and road planning data;

[0224] Get heating time data;

[0225] Obtain construction planning data, including construction route data and construction time data;

[0226] Matching the construction route data with the heat pipe network distribution data to obtain route matching status data, including route matching or route mismatching;

[0227] Matching the construction time data with the heating time data to obtain time matching status data, including time matching or time mismatching;

[0228] If the route matching status data is a route matching, and the time matching status data is a time matching, the construction planning data is sent to the thermal power department, and the thermal power department initiates the emergency plan according to the construction planning data.

[0229] It should be noted that the current distribution overlap of water, electricity, heating pipelines and roads is relatively high, and there is a problem of poor communication among departments. During the heating period, if the water, electricity or roads that overlap with the thermal pipeline are repaired, it may affect the heating. Therefore, the thermal pipeline distribution data, power pipeline distribution data, water supply pipeline distribution data and road planning data within the preset range of the thermal station are first obtained, and the public line data is processed to obtain the heating time data. Finally, route matching and time matching are performed respectively according to the construction planning data. According to the matching results, it is determined whether the construction route and construction time overlap with the thermal pipeline and heating time. If they overlap, the construction planning data will be sent to the thermal management department, and the thermal management department will initiate the emergency plan based on the construction planning data.

[0230] It is worth mentioning that according to an embodiment of the present invention, it also includes:

[0231] Get the building type information within the preset range of the thermal power station;

[0232] Acquire corresponding heating demand time information and second control node information according to the building type information;

[0233] The valve opening of the control node is adaptively adjusted according to the control node information combined with the heating demand time information, including fully open valve, half open valve or fully closed valve.

[0234] It should be noted that the thermal power station radiates heat for different types of buildings, such as residential areas, office buildings or schools. Different types of heating buildings have different demands for continuous heating. For example, residential areas require 24-hour heating, office buildings are heated according to working hours and keep low temperature operation after working hours, and schools stop heating after the winter vacation. Therefore, the corresponding heating demand time information and the second control node information are obtained according to the building type information within the preset range of the thermal power station. The second control node information refers to the position information of the heating valve that controls the building. The control node valve opening is adaptively adjusted according to the control node information and the heating demand time information, including the valve being fully open, the valve being half open or the valve being fully closed.

[0235] A third aspect of the present invention provides a readable storage medium, which stores a program for a heat supply balance control method based on intelligent supply of heat sources. When the program for the heat supply balance control method based on intelligent supply of heat sources is executed by a processor, the steps of the heat supply balance control method based on intelligent supply of heat sources as described in any one of the above items are implemented.

[0236] The heat balance control method, system and medium based on the intelligent supply of heat sources disclosed in the present invention analyze the heating capacity in combination with the heating fluctuation factor, and obtain the heat demand deviation rate in combination with the user's heat demand, thereby realizing the intelligent operation and maintenance and refined control of the thermal power station, ensuring sufficient heating while reducing energy consumption.

[0237] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0238] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0239] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0240] Those skilled in the art can understand that: all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions, the aforementioned program can be stored in a readable storage medium, and when the program is executed, it executes the steps of the above method embodiments; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), disks or optical disks, and other media that can store program codes.

[0241] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

Claims

1. A heat supply balance control method based on intelligent heat source supply, characterized in that: The following steps are involved: Obtain real-time heating data of the heating station and average temperature data of the heating station in multiple preset time periods; Processing the real-time heating data to obtain a heating capacity evaluation index, processing the mean temperature data to obtain a heating fluctuation factor, and processing the heating capacity evaluation index combined with the heating fluctuation factor to obtain a stable heating capacity evaluation index; Obtain and process the heat demand impact data within the preset range of the heat station to obtain the predicted heat demand assessment index; Compare and process the stable heating capacity evaluation index and the predicted heat demand evaluation index, and adjust the heat supply of the heat source according to the comparison result; The processing according to the real-time heating data to obtain the heating capacity evaluation index, the processing according to the mean temperature data to obtain the heating fluctuation factor, and the processing according to the heating capacity evaluation index combined with the heating fluctuation factor to obtain the stable heating capacity evaluation index, including: The real-time heating data includes real-time heating temperature data, heat exchange efficiency and heat exchanger capacity; Inputting the real-time heating temperature data, heat exchange efficiency and heat exchanger capacity into a preset heating capacity evaluation model for processing to obtain a heating capacity evaluation index; Processing the temperature average data corresponding to multiple preset time periods to obtain a heating fluctuation factor; Inputting the heating fluctuation factor and the heating capacity evaluation index into a preset stable heating capacity evaluation model for processing to obtain a stable heating capacity evaluation index; The calculation formula of the heating capacity evaluation index in the heating capacity evaluation model is: ； in, is the heating capacity assessment index, , , They are real-time heating temperature data, heat exchange efficiency and heat exchanger capacity. , is the preset characteristic coefficient; The calculation formula of the heating fluctuation factor is: ； in, is the heating fluctuation factor, , , , They are respectively the mean temperature data corresponding to the i-th preset time period, the mean temperature data corresponding to the i+1-th preset time period, the number of preset time periods and the historical average temperature data. is the preset characteristic coefficient; The calculation formula of the stable heating capacity evaluation index in the stable heating capacity evaluation model is: ； in, To stabilize the heating capacity assessment index, , are heating fluctuation factor and heating capacity assessment index, respectively. is the preset characteristic coefficient; The heat demand impact data within the preset range of the heat station is obtained and processed to obtain the predicted heat demand evaluation index, including: The heat demand impact data include basic personnel data, radiator heat dissipation efficiency, building area data and thermal insulation performance data; The basic personnel data include the aging rate and the number of permanent personnel; The aging rate, number of permanent residents, heat dissipation efficiency of radiators, construction area data and thermal insulation performance data are input into a preset heat demand assessment model for processing to obtain a heat demand assessment index; The heat demand assessment index calculation formula in the heat demand assessment model is: ； in, is the heat demand assessment index, , , , , They are the elderly rate, number of permanent residents, radiator heat dissipation efficiency, construction area data and thermal insulation performance data. , is the preset characteristic coefficient; Also includes: Obtain weather forecast information within the preset range of the thermal power station and extract weather change data, including temperature difference extremes, wind speed extremes, and rain and snow data; Processing the data on the temperature difference extreme value, wind speed extreme value and rainfall and snow volume to obtain a heat demand influencing factor; Correcting the heat demand assessment index according to the heat demand influencing factor to obtain a predicted heat demand assessment index; The heat demand influencing factor calculation formula is: ； in, is the heat demand influencing factor, , , They are the temperature difference extreme value, wind speed extreme value and rain and snow amount data respectively. , is the preset characteristic coefficient; The calculation formula of the predicted heat demand assessment index is: ； in, To predict the heat demand assessment index, , are heat demand impact factor and heat demand assessment index, respectively. is the preset characteristic coefficient.

2. The heat supply balance control method based on heat source intelligent supply according to claim 1 is characterized in that: Comparing the stable heating capacity evaluation index with the predicted heat demand evaluation index, and adjusting the heat supply of the heat source according to the comparison result, including: Comparing the stable heating capacity evaluation index with the predicted heat demand evaluation index to obtain a heat demand deviation rate; Comparing the heat demand deviation rate with a preset standard heat demand deviation rate threshold to obtain a relative heat demand deviation value; Performing a threshold comparison of the thermal demand deviation relative value with a preset thermal demand deviation relative threshold; If it is less than or equal to the relative threshold of heat demand deviation, the heat supply of the heat source will not be adjusted; If it is greater than the relative threshold of heat demand deviation, the heat supply of the heat source is adjusted.

3. The heat supply balance control method based on heat source intelligent supply according to claim 2 is characterized in that: Also includes: Obtain the heating flow difference and heating pressure difference of the heating network within the preset range of the heating station; The heat supply flow difference and the heat supply pressure difference are processed in combination with the preset heat supply flow loss average and the heat supply pressure loss average to obtain a heat supply network damage index; Comparing the heat pipe network damage index with a preset heat pipe network damage threshold to obtain a heat pipe network damage level, wherein the preset heat pipe network damage threshold includes a first preset heat pipe network damage threshold and a second preset heat pipe network damage threshold, and the first preset heat pipe network damage threshold is less than the second preset heat pipe network damage threshold; If it is less than or equal to the first preset heat pipe network damage threshold, it corresponds to level one damage; If it is greater than the first preset heat pipe network damage threshold and less than or equal to the second preset heat pipe network damage threshold, it corresponds to level 2 damage; If it is greater than the second preset heat pipe network damage threshold, it corresponds to level 3 damage; Initiate corresponding preset plans according to the damage level of the thermal pipeline network.

4. The heat supply balance control method based on heat source intelligent supply according to claim 3 is characterized in that: Also includes: Obtaining real-time temperature averages of multiple preset ranges in a heating building and corresponding first control node information; Comparing the real-time temperature average with the preset standard temperature to obtain the actual temperature deviation rate; Performing a threshold comparison between the actual temperature deviation rate and a preset actual temperature deviation rate threshold; If it is less than or equal to the preset actual temperature deviation rate threshold, no adjustment will be made; If it is greater than the preset actual temperature deviation rate threshold, and the real-time temperature average is greater than the preset standard temperature, the heating amount is reduced according to the first control node information; If it is greater than the preset actual temperature deviation rate threshold and the real-time temperature average is less than the preset standard temperature, the heating amount is increased according to the first control node information.

5. A heat supply balance control system based on intelligent heat source supply, characterized in that: The invention comprises a memory and a processor, wherein the memory comprises a program of a heat supply balance control method based on heat source intelligent supply, and the method program of heat supply balance control based on heat source intelligent supply is executed by the processor to implement the following steps: Obtain real-time heating data of the heating station and average temperature data of the heating station in multiple preset time periods; Processing the real-time heating data to obtain a heating capacity evaluation index, processing the mean temperature data to obtain a heating fluctuation factor, and processing the heating capacity evaluation index combined with the heating fluctuation factor to obtain a stable heating capacity evaluation index; Obtain and process the heat demand impact data within the preset range of the heat station to obtain the predicted heat demand assessment index; Compare and process the stable heating capacity evaluation index and the predicted heat demand evaluation index, and adjust the heat supply of the heat source according to the comparison result; The processing according to the real-time heating data to obtain the heating capacity evaluation index, the processing according to the mean temperature data to obtain the heating fluctuation factor, and the processing according to the heating capacity evaluation index combined with the heating fluctuation factor to obtain the stable heating capacity evaluation index, including: The real-time heating data includes real-time heating temperature data, heat exchange efficiency and heat exchanger capacity; Inputting the real-time heating temperature data, heat exchange efficiency and heat exchanger capacity into a preset heating capacity evaluation model for processing to obtain a heating capacity evaluation index; Processing the temperature average data corresponding to multiple preset time periods to obtain a heating fluctuation factor; Inputting the heating fluctuation factor and the heating capacity evaluation index into a preset stable heating capacity evaluation model for processing to obtain a stable heating capacity evaluation index; The calculation formula of the heating capacity evaluation index in the heating capacity evaluation model is: ； in, is the heating capacity assessment index, , , They are real-time heating temperature data, heat exchange efficiency and heat exchanger capacity. , is the preset characteristic coefficient; The calculation formula of the heating fluctuation factor is: ； in, is the heating fluctuation factor, , , , They are respectively the mean temperature data corresponding to the i-th preset time period, the mean temperature data corresponding to the i+1-th preset time period, the number of preset time periods and the historical average temperature data. is the preset characteristic coefficient; The calculation formula of the stable heating capacity evaluation index in the stable heating capacity evaluation model is: ； in, To stabilize the heating capacity assessment index, , are heating fluctuation factor and heating capacity assessment index, respectively. is the preset characteristic coefficient; The heat demand impact data within the preset range of the heat station is obtained and processed to obtain the predicted heat demand evaluation index, including: The heat demand impact data include basic personnel data, radiator heat dissipation efficiency, building area data and thermal insulation performance data; The basic personnel data include the aging rate and the number of permanent personnel; The aging rate, number of permanent residents, heat dissipation efficiency of radiators, construction area data and thermal insulation performance data are input into a preset heat demand assessment model for processing to obtain a heat demand assessment index; The heat demand assessment index calculation formula in the heat demand assessment model is: ； in, is the heat demand assessment index, , , , , They are the elderly rate, number of permanent residents, radiator heat dissipation efficiency, construction area data and thermal insulation performance data. , is the preset characteristic coefficient; Also includes: Obtain weather forecast information within the preset range of the thermal power station and extract weather change data, including temperature difference extremes, wind speed extremes, and rain and snow data; Processing the data on the temperature difference extreme value, wind speed extreme value and rainfall and snow volume to obtain a heat demand influencing factor; Correcting the heat demand assessment index according to the heat demand influencing factor to obtain a predicted heat demand assessment index; The heat demand influencing factor calculation formula is: ； in, is the heat demand influencing factor, , , They are the temperature difference extreme value, wind speed extreme value and rain and snow amount data respectively. , is the preset characteristic coefficient; The calculation formula of the predicted heat demand assessment index is: ； in, To predict the heat demand assessment index, , are heat demand impact factor and heat demand assessment index, respectively. is the preset characteristic coefficient.

6. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a heat supply balance control method program based on intelligent heat source supply. When the heat supply balance control method program based on intelligent heat source supply is executed by a processor, the steps of the heat supply balance control method based on intelligent heat source supply as described in any one of claims 1 to 4 are implemented.

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