Heat supply control system based on big data and AI technology

Abstract

The invention relates to the technical field of heat supply regulation and control, in particular to a heat supply control system based on big data and AI technology, which comprises a temperature estimation module, a data analysis module, a heat storage unit and a heat supply quantity regulation and control unit, and is characterized in that the temperature estimation module estimates the outdoor temperature of a heat supply area; the data analysis module determines the required heat supply amount in the large heat supply period according to the historical heat supply condition and the estimated temperature, and periodically and repeatedly determines the required heat supply amount of the remaining time limit; the heat storage unit stores heat supply; and the heat supply regulation and control unit allocates, supplements or allocates heat of the heat storage unit according to the data analysis result. Through cooperative work of the temperature estimation module and the data analysis module, the heat supply demand can be accurately predicted, heat allocation can be planned in advance, insufficient or excessive heat supply is avoided, the energy utilization efficiency is improved, and the heat supply cost is reduced.

Description

Technical Field

[0001] The present invention relates to the field of heating control technology, and in particular to a heating control system based on big data and AI technology. Background Art

[0002] In the traditional heating field, the heating system often lacks a precise heat control mechanism. In the past, fixed heating plans were usually adopted, without fully considering factors such as outdoor temperature changes, differences in heat demand in different regions, and dynamic changes in the heating process. This may lead to insufficient heating in cold weather, affecting the living comfort of residents; and in periods of high temperature, it may cause heat waste, increasing energy consumption and heating costs.

[0003] With the acceleration of urbanization and the improvement of residents' requirements for heating quality, the disadvantages of traditional heating methods have become increasingly prominent. On the one hand, it is impossible to effectively use historical heating data to guide subsequent heating decisions, making it difficult to achieve optimal allocation of heating resources; on the other hand, there is a lack of scientific management of heat storage and allocation during the heating process, and it is impossible to flexibly adjust heat storage and heating strategies according to actual heat usage.

[0004] Chinese Patent Publication No.: CN115962509A discloses a heating control method, including obtaining historical operation data of a heat exchange station and historical operation data of a user side, performing data cleaning, and establishing a sample database; establishing a pressure difference calculation model based on the sample database, the pressure difference calculation model is used to represent the corresponding relationship between the actual supply and return water pressure difference of the user and the outdoor temperature; determining the most unfavorable user, calculating the actual supply and return water pressure difference of the most unfavorable user based on the pressure difference calculation model, adjusting the variable frequency operation of the water pump based on the actual supply and return water pressure difference of the most unfavorable user and the supply and return water pressure difference set for the most unfavorable user; and applying a machine learning algorithm based on the sample database to establish a water supply temperature calculation model to adjust the secondary side water supply temperature.

[0005] Currently, there is a lack of refined division and analysis methods in the management of heating areas. Personalized heating is provided in areas according to users' heating habits, and the overall efficiency of the heating system is insufficient. Summary of the invention

[0006] To this end, the present invention provides a heating control system based on big data and AI technology to overcome the problems in the existing technology of lack of refined division and analysis methods in heating area management, personalized heating in areas according to users' heating habits, and insufficient overall efficiency of the heating system.

[0007] To achieve the above objectives, the present invention provides a heating control system based on big data and AI technology, comprising:

[0008] The temperature estimation module is used to estimate the outdoor temperature of the environment in the heating area;

[0009] A data analysis module is used to determine the required heating amount within a large heating cycle based on the historical heating situation and the data estimated by the temperature estimation module, and periodically repeatedly determine the required heating amount within the remaining time limit within the large heating cycle by the length of the remaining time limit and the re-estimated outdoor temperature;

[0010] A heat storage unit, used to store the heat required in the heating area;

[0011] The heat supply control unit is used to allocate the heat supply required within a large heating cycle to the heat storage unit in advance according to the analysis results of the data analysis module, or, according to the periodic analysis results of the data analysis module, to determine the heat stored in the heat storage unit so as to supplement or remove the heat in the heat storage unit.

[0012] The heating control system based on big data and AI technology also includes:

[0013] A data storage module, used for storing historical heating data;

[0014] External temperature collection module, used to collect outdoor temperature;

[0015] Room temperature collection modules, which are provided in several numbers and are used to collect indoor temperature;

[0016] A plurality of heat output units are provided, and correspond one to one with the room temperature collection modules, and are used for heat supply;

[0017] A heating control module, which is connected to each of the heating output units and is used to manually adjust the working mode of the heating output unit;

[0018] The heating area division module is used to divide the heating range into areas;

[0019] The data analysis module divides the heating period into sections according to the heating demand, and records them as a large heating period and a small heating period.

[0020] Furthermore, the data analysis module determines the periodic heating consumption of a large heating cycle in a historical stage of any heating area based on the heating output of each of the heating output units and the data collected by the external temperature collection module.

[0021] Furthermore, the data analysis module performs data analysis on any heating small cycle of the heating output unit in the historical stage, obtains the indoor temperature change data collected by the corresponding room temperature collection module and the outdoor temperature change data collected by the indoor and outdoor temperature collection module at the same time node, and determines the amount of heat consumed by the corresponding heating output unit in the analyzed heating small cycle according to the outdoor temperature change data and the indoor temperature change data;

[0022] Perform data analysis on all small heating cycles contained in a large heating cycle of any heating output unit to determine the amount of heat consumed in the large heating cycle;

[0023] The heat consumption of all heat output units in any heating area during the large heating cycle is summarized to determine the periodic heating consumption of the heating area.

[0024] Furthermore, the data analysis module determines the heating data curve according to the determined periodic heating consumption and the outdoor temperature stored in the data storage module, and determines the initial heat demand by matching the outdoor temperature of the next large heating cycle estimated by the temperature estimation module with the heating data curve, and determines the heat delivery value of the next large heating cycle according to the initial heat demand. If the heat delivery value is greater than the initial heat demand, the heat supply control unit delivers the heat corresponding to the calculated heat delivery value to the heat storage unit before the start of the next large heating cycle.

[0025] Furthermore, the data analysis module determines the ratio relationship between the heat delivery value and the initial heat demand by means of the value of the initial heat demand.

[0026] Furthermore, the temperature estimation module estimates the outdoor temperature of each heating small cycle within the next heating large cycle;

[0027] The data analysis module determines the heat consumption of the current large heating cycle after each small heating cycle. The heat supply control unit determines whether the remaining stored heat in the heat storage unit meets the remaining heat demand of the current large heating cycle based on the determination result, and determines whether the remaining heat is excessive.

[0028] Furthermore, the heat supply control unit controls and supplements the heat to the heat storage unit when the remaining heat does not meet the heating demand;

[0029] When there is excess remaining heat, the heat supply control unit calls the excess heat in the heat storage unit to other areas.

[0030] Further, the data analysis module is provided with a residual heat evaluation value for determining the residual heat situation, including a first residual heat evaluation value and a second residual heat evaluation value;

[0031] If the residual heat is less than or equal to the first residual heat evaluation value, the data analysis module determines that the residual heat does not meet the heating demand;

[0032] If the residual heat is greater than the second residual heat evaluation value, the data analysis module determines that the residual heat is excessive.

[0033] Furthermore, the numerical value of each residual heat evaluation value is related to the outdoor temperature and the number of residual heating small cycles.

[0034] Furthermore, after each heating mini-cycle, the data analysis module obtains the actual outdoor temperature of the heating mini-cycle and compares it with the estimated outdoor temperature. Based on the comparison result, the outdoor temperature of the remaining heating mini-cycles is re-estimated.

[0035] Furthermore, based on the estimated outdoor temperature, the basic heating demand for the remaining heating cycle is determined, and the determined basic heating demand is compared with the preset heat evaluation value, and the initial heat supplement parameters of the heating demand are determined based on the comparison result, so as to calculate the theoretical remaining heating demand. The calculation method of the theoretical remaining heating demand is the same as the calculation method of the heat delivery value.

[0036] Furthermore, the first residual heat evaluation value and the second residual heat evaluation value are calculated by using the theoretical residual heat demand and the number of residual heating small cycles.

[0037] Compared with the prior art, the beneficial effect of the present invention is that, through the collaborative work of the temperature estimation and data analysis modules, the heating demand can be accurately predicted, the heat allocation can be planned in advance, the insufficient or excessive heat supply can be avoided, the energy utilization efficiency can be improved, and the heating cost can be reduced. The setting of the heat storage unit enhances the stability of the system, and the heat can be replenished or transferred in time when the heating demand fluctuates, ensuring the indoor temperature is stable, improving the user's comfort, reducing equipment loss and energy waste caused by temperature fluctuations, and realizing efficient, economical and stable operation of the heating system.

[0038] Furthermore, the data storage module provides historical data support for the system, which facilitates the analysis of rules and optimization strategies. Multi-modules collect temperature information to ensure that the data is comprehensive and accurate, providing a reliable basis for heat supply control. The division and numbering of heating areas facilitates refined management, optimizes heating according to the characteristics of different areas, and improves the overall heating quality. The heating control module provides a manual adjustment function, which enhances the flexibility of the system, can respond to emergencies or special needs, and effectively improves the adaptability of the heating system and user satisfaction.

[0039] Furthermore, for any heating area, the data analysis module analyzes the number of its heating output unit. The historical heating data of the heating output unit is obtained, the heating cycle and small cycle are divided, the indoor and outdoor temperature changes are analyzed to determine the heating amount, and the periodic heating consumption of the heating area is summarized. On the one hand, it can accurately grasp the heating characteristics and demand change trends of each area, provide a data basis for personalized heating, make heating distribution more reasonable, and reduce energy waste in the transmission and distribution process. On the other hand, through in-depth mining and analysis of historical data, the heating plan is continuously optimized, the intelligence level of the heating system is improved, and the operation and maintenance costs are reduced. At the same time, the heating effect of each area is guaranteed, the user experience is improved, and the sustainable development of heating services is promoted.

[0040] Furthermore, reasonable division of heating cycles is convenient for data statistical analysis and timely response to short-term fluctuations in heating demand. By accurately estimating heating demand and delivery value, it is ensured that the heat storage unit reserves an appropriate amount of heat before heating to avoid excessive or insufficient reserves. This not only improves the scientific nature of energy reserves and reduces idle energy waste, but also ensures a stable supply in the early stage of heating, allowing the indoor temperature to quickly reach a comfortable range, improving the timeliness and reliability of the heating system, and enhancing users' trust in heating services.

[0041] Furthermore, before the next large heating cycle begins, the temperature estimation module estimates the outdoor temperature of the small cycle. After the large heating cycle begins, after each small heating cycle, the data analysis module determines the consumed heat, and the heat supply control unit determines the heat situation of the heat storage unit and controls it. Whether to adjust is determined by the residual heat evaluation value, which is related to the outdoor temperature and the number of remaining small heating cycles. Continuous temperature estimation and heat consumption monitoring enable the system to track changes in heating demand in real time and adjust the heat of the heat storage unit in time. Accurate judgment based on the residual heat evaluation value can avoid unnecessary heat adjustment operations and reduce equipment loss and energy consumption. Flexible adjustment based on the outdoor temperature and the remaining heating cycle ensures stable heating under different working conditions, effectively responds to sudden weather changes and other situations, improves the resilience and energy utilization efficiency of the heating system, and provides users with a continuous and comfortable heating environment.

[0042] Furthermore, by comparing and re-estimating the temperature, the heat demand forecast is continuously revised to improve the forecast accuracy and make the heat supply control more in line with actual needs. The theoretical remaining heat demand and the remaining heat evaluation value are accurately calculated to provide a scientific decision-making basis for the heat supply control unit, ensuring that the heat of the heat storage unit is always within a reasonable range. This refined management and control method effectively avoids insufficient or overheating of heat supply, improves the rationality of energy utilization, reduces heating costs, and ensures the consistency and stability of heating quality.

[0043] Furthermore, the value of the evaluation value of the remaining number of small heating cycles is determined based on the number of small heating cycles remaining, which can fully take into account the characteristics of different stages of the heating process. In the early stage of a large heating cycle, the number of small heating cycles remaining is large, and the remaining stored heat is also large. Therefore, the set value has a smaller impact on the evaluation value of the remaining heat, and the sensitivity adjustment range to the change of the remaining heat is appropriately reduced, which not only ensures that the system can respond reasonably to the change of heat, but also avoids too frequent or excessive regulation. In the later stage of a large heating cycle, the remaining stored heat is small. In order to ensure the timeliness of the heat meter, a larger value is set to have a larger impact on the evaluation value of the remaining heat, ensuring the pertinence of the data calculation.

[0044] Furthermore, when the theoretical remaining heat demand is large, the value of the supplementary parameter for calculating the remaining heat evaluation value is set to a small value, because the heat storage unit needs to have a certain heat redundancy to ensure sufficient heat supply and prevent waste caused by excessive heat storage. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 This is a schematic diagram of the structure of a heating control system based on big data and AI technology in an embodiment;

[0046] Figure 2 A flow chart for determining the periodic heating consumption of a heating area in an embodiment;

[0047] Figure 3 This is a flow chart of residual heat determination in the embodiment;

[0048] Figure 4 This is a flow chart for determining the residual heat evaluation value in the embodiment. DETAILED DESCRIPTION

[0049] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0050] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0051] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside" and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the drawings. This is merely for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0052] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0053] See also Figure 1 As shown, Figure 1 This is a schematic diagram of the structure of a heating control system based on big data and AI technology in an embodiment; Figure 2 A flow chart for determining the periodic heating consumption of a heating area in an embodiment; Figure 3 This is a flow chart of residual heat determination in the embodiment; Figure 4 This is a flow chart for determining the residual heat evaluation value in the embodiment.

[0054] The present invention provides a heating control system based on big data and AI technology, comprising:

[0055] The temperature estimation module is used to estimate the outdoor temperature of the environment in the heating area;

[0056] A data analysis module is used to determine the required heating amount within a large heating cycle based on the historical heating situation and the data estimated by the temperature estimation module, and periodically repeatedly determine the required heating amount within the remaining time limit within the large heating cycle by the length of the remaining time limit and the re-estimated outdoor temperature;

[0057] A heat storage unit, used to store the heat required in the heating area;

[0058] The heat supply control unit is used to allocate the heat supply required within a large heating cycle to the heat storage unit in advance according to the analysis results of the data analysis module, or, according to the periodic analysis results of the data analysis module, to determine the heat stored in the heat storage unit so as to supplement or remove the heat in the heat storage unit.

[0059] Through the collaborative work of temperature estimation and data analysis modules, heating demand can be accurately predicted, heat allocation can be planned in advance, insufficient or excessive heat supply can be avoided, energy efficiency can be improved, and heating costs can be reduced. The setting of the heat storage unit enhances the stability of the system, and heat can be replenished or transferred in time when heating demand fluctuates, ensuring stable indoor temperature, improving user comfort, reducing equipment loss and energy waste caused by temperature fluctuations, and achieving efficient, economical and stable operation of the heating system.

[0060] Specifically, the heating control system based on big data and AI technology also includes:

[0061] A data storage module, used for storing historical heating data;

[0062] External temperature collection module, used to collect outdoor temperature;

[0063] Room temperature collection modules, which are provided in several numbers and are used to collect indoor temperature;

[0064] A plurality of heat output units are provided, and correspond one to one with the room temperature collection modules, and are used for heat supply;

[0065] A heating control module, which is connected to each of the heating output units and is used to manually adjust the working mode of the heating output unit;

[0066] The heating area division module is used to divide the heating range into areas;

[0067] The data analysis module divides the heating period into sections according to the heating demand, and records them as a large heating period and a small heating period.

[0068] Specifically, the data storage module provides historical data support for the system, which facilitates the analysis of rules and optimization strategies. Multiple modules collect temperature information to ensure that the data is comprehensive and accurate, providing a reliable basis for heat supply control. The division and numbering of heating areas facilitates refined management, optimizes heating according to the characteristics of different areas, and improves the overall heating quality. The heating control module provides a manual adjustment function, which enhances the flexibility of the system, can respond to emergencies or special needs, and effectively improves the adaptability of the heating system and user satisfaction.

[0069] The heating area division module divides the heating range into areas, and for any heating area, the data analysis module numbers and analyzes the heating output units contained therein.

[0070] Specifically, for any heat output unit, the data analysis module obtains its historical heat data including:

[0071] According to the heating demand, the heating period is divided into sections, which are recorded as large heating period and small heating period;

[0072] Perform data analysis on any heating short cycle of the heating output unit in the historical stage, and obtain the indoor temperature change data collected by the corresponding room temperature collection module and the outdoor temperature change data collected by the indoor and outdoor temperature collection module at the same time node;

[0073] Determine the amount of heat consumed by the corresponding heating output unit in the analyzed heating short cycle according to the outdoor temperature change data and the indoor temperature change data;

[0074] Data analysis is performed on all small heating cycles contained in a large heating cycle of any heating output unit to determine the amount of heat consumed in the large heating cycle.

[0075] The heat consumption of all heat output units in any heating area during the large heating cycle is summarized to determine the periodic heating consumption of the heating area.

[0076] On the one hand, it can accurately grasp the heating characteristics and demand change trends of each region, provide a data basis for personalized heating, make heating distribution more reasonable, and reduce energy waste in the transmission and distribution process. On the other hand, through in-depth mining and analysis of historical data, it continuously optimizes heating plans, improves the intelligence level of heating systems, reduces operation and maintenance costs, and at the same time ensures the heating effect of each region, improves user experience, and promotes the sustainable development of heating services.

[0077] In this embodiment, the large heating cycle is set to one day, and the small heating cycle is set to three hours.

[0078] Specifically, the heating demand for the next large heating cycle in the heating area is preliminarily determined based on the obtained periodic heating consumption.

[0079] Specifically, when determining the heating demand for the next large heating cycle, obtain the historical outdoor temperature and the heating data curve consumed in each large cycle, and estimate the outdoor temperature of the next large heating cycle. According to the estimated outdoor temperature, match it with the obtained heating data curve to determine the initial heat of the heating demand.

[0080] Reasonable division of heating cycles is convenient for data statistical analysis and timely response to short-term fluctuations in heating demand. By accurately estimating heating demand and delivery value, it is ensured that the heat storage unit reserves an appropriate amount of heat before heating to avoid excessive or insufficient reserves. This not only improves the scientific nature of energy reserves and reduces idle energy waste, but also ensures stable supply in the early stage of heating, allowing indoor temperature to quickly reach a comfortable range, improving the timeliness and reliability of the heating system, and enhancing users' trust in heating services.

[0081] Specifically, based on the heat range in which the initial heat of the heating demand is determined, and based on the heat range in which the supplementary parameters of the initial heat of the heating demand are determined, the heat delivery value of the next large heating cycle is determined by multiplying the initial heat of the heating demand by the determined supplementary parameters.

[0082] Specifically, in this implementation, the initial heat supplement parameter of the first heating demand is set to 1.05, the initial heat supplement parameter of the second heating demand is set to 1.08, and the initial heat supplement parameter of the third heating demand is set to 1.1. The heat interval is divided by the first heat evaluation value Q1 and the second heat evaluation value Q2, wherein, Q1=1×10 7 Kilojoule, Q2 = 1.8 × 10 7 Kilojoules.

[0083] If the initial heat quantity of the heating demand is less than or equal to the first heat evaluation value, selecting the first initial heat quantity supplementary parameter of the heating demand as the supplementary parameter of the initial heat quantity of the heating demand;

[0084] If the initial heat quantity of the heating demand is greater than the first heat evaluation value and less than or equal to the second heat evaluation value, then selecting the second initial heat quantity supplementary parameter of the heating demand as the supplementary parameter of the initial heat quantity of the heating demand;

[0085] If the initial heat quantity of the heating demand is greater than the second heat evaluation value, the third initial heat quantity supplementary parameter of the heating demand is selected as the supplementary parameter of the initial heat quantity of the heating demand.

[0086] For example, if the estimated initial heat demand is 1.35×10 7 kJ, then the heat delivery value is determined as

[0087] 1.35×10 7 ×1.08=1.458×10 7 (kilojoules).

[0088] Specifically, the heat supply control unit transmits heat corresponding to the calculated heat supply value to the heat storage unit before the next large heating cycle begins.

[0089] Before the next large heating cycle begins, the temperature estimation module estimates the outdoor temperature of each small heating cycle within the next large heating cycle.

[0090] Before the next large heating cycle begins, the temperature estimation module estimates the outdoor temperature of the small cycle. After the large heating cycle begins, after each small heating cycle, the data analysis module determines the heat consumption, and the heat supply control unit determines the heat situation of the heat storage unit and controls it. Whether to adjust is determined by the residual heat evaluation value, which is related to the outdoor temperature and the number of remaining small heating cycles. Continuous temperature estimation and heat consumption monitoring enable the system to track changes in heating demand in real time and adjust the heat of the heat storage unit in time. Accurate judgment based on the residual heat evaluation value avoids unnecessary heat adjustment operations, reduces equipment loss and energy consumption. Flexible adjustment based on outdoor temperature and remaining heating cycles ensures stable heating under different working conditions, effectively responds to sudden weather changes and other situations, improves the resilience and energy utilization efficiency of the heating system, and provides users with a continuous and comfortable heating environment.

[0091] Specifically, after the start of each large heating cycle, after each small heating cycle, the data analysis module determines the heat consumption of the current large heating cycle, and the heat supply control unit determines whether the remaining stored heat in the heat storage unit meets the remaining heat demand of the current large heating cycle based on the determination result, and determines whether the remaining heat is excessive.

[0092] If the remaining heat does not meet the heating demand, the heat supply control unit controls the heat to the heat storage unit;

[0093] In the case of excess remaining heat, the heat supply control unit calls the excess heat in the heat storage unit to other areas.

[0094] The residual heat condition is determined by a residual heat evaluation value, including a first residual heat evaluation value and a second residual heat evaluation value;

[0095] If the residual heat is less than or equal to the first residual heat evaluation value, the data analysis module determines that the residual heat does not meet the heating demand;

[0096] If the residual heat is greater than the second residual heat evaluation value, the data analysis module determines that the residual heat is excessive.

[0097] Specifically, for the residual heat between the first residual heat evaluation value and the second residual heat evaluation value, the heat supply control unit does not adjust the heat in the heat storage unit until the next heating small cycle passes and the residual heat is determined again.

[0098] Specifically, the residual heat evaluation value is related to the outdoor temperature and the number of residual heating mini-cycles.

[0099] After each heating cycle, the data analysis module obtains the actual outdoor temperature of the heating cycle and compares it with the estimated outdoor temperature. Based on the comparison results, the outdoor temperature of the remaining heating cycles is re-estimated.

[0100] Specifically, based on the estimated outdoor temperature, the basic heating demand for the remaining heating cycle is determined, and the determined basic heating demand is compared with the preset heat assessment value. The initial heat supplement parameters of the heating demand are determined based on the comparison results to calculate the theoretical remaining heating demand. The calculation method of the theoretical remaining heating demand is the same as the calculation method of the heat delivery value.

[0101] By comparing and re-estimating the temperature, the heat demand forecast is continuously revised to improve the forecast accuracy and make the heat supply control more in line with the actual demand. The theoretical remaining heat demand and the remaining heat evaluation value are accurately calculated to provide a scientific decision-making basis for the heat supply control unit, ensuring that the heat of the heat storage unit is always within a reasonable range. This refined management and control method effectively avoids insufficient or overheating of heat supply, improves the rationality of energy utilization, reduces heating costs, and ensures the consistency and stability of heating quality.

[0102] The first residual heat evaluation value and the second residual heat evaluation value are calculated by the theoretical residual heat demand and the number of residual heating small cycles.

[0103] Set the first residual heat evaluation value to A1, the second residual heat evaluation value to A2, the residual heating small cycle number evaluation value to P0, the theoretical residual heating demand to W, the first residual heat evaluation value calculation supplementary parameter to C1, the second residual heat evaluation value calculation supplementary parameter to C2, P0 = P1 or P2, then

[0104] A1=W×C1×P1; A2=W×C2×P2;

[0105] Specifically, the value of the remaining heating small cycle number evaluation value P0 is related to the remaining heating small cycle number.

[0106] If the number of remaining heating small cycles is less than or equal to 2, then P2=1.1, P1=0.9.

[0107] If the number of remaining heating small cycles is greater than 2 and less than or equal to 5, then P2=1.05 and P1=0.95.

[0108] If the number of remaining heating small cycles is greater than 5, then P2=P1=1.

[0109] Determining the value of the remaining number of small heating cycles evaluation value based on the remaining number of small heating cycles can fully take into account the characteristics of different stages of the heating process. In the early stage of a large heating cycle, the remaining number of small heating cycles is large, and the remaining stored heat is also large. Therefore, the set value has a smaller impact on the residual heat evaluation value, and appropriately reduces the sensitivity adjustment range to the residual heat change, which not only ensures that the system can respond reasonably to heat changes, but also avoids overly frequent or excessive regulation. In the later stage of a large heating cycle, the remaining stored heat is small. In order to ensure the timeliness of the heat meter, a larger value is set to have a larger impact on the residual heat evaluation value, ensuring the pertinence of the data calculation.

[0110] Specifically, the values ​​of the first residual heat evaluation value calculation supplementary parameter C1 and the second residual heat evaluation value calculation supplementary parameter C2 are related to the theoretical residual heat demand W.

[0111] W>1.6×10 7 Kilojoules, C1=0.97, C2=1.02.

[0112] 1.3×10 7 KJ<W≤1.6×10 7 Kilojoule, C1=0.95, C2=1.05.

[0113] 1.0×10 7 KJ<W≤1.3×10 7 Kilojoules, C1=0.92, C2=1.06.

[0114] W≤1.0×10 7 Kilojoules, C1=0.9, C2=1.06.

[0115] When the theoretical remaining heat demand is large, the value of the supplementary parameter for calculating the remaining heat evaluation value is set to a small value, because the heat storage unit needs to have a certain amount of heat redundancy to ensure sufficient heat supply and prevent waste caused by excessive heat storage.

[0116] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

Claims

1. A heating control system based on big data and AI technology, characterized in that: include, The temperature estimation module is used to estimate the outdoor temperature of the environment in the heating area; A data analysis module is used to determine the required heating amount within a large heating cycle based on the historical heating situation and the data estimated by the temperature estimation module, and periodically repeatedly determine the required heating amount within the remaining time limit within the large heating cycle by the length of the remaining time limit and the re-estimated outdoor temperature; A heat storage unit, used to store the heat required in the heating area; The heat supply control unit is used to allocate the heat supply required within a large heating cycle to the heat storage unit in advance according to the analysis results of the data analysis module, or, according to the periodic analysis results of the data analysis module, to determine the heat stored in the heat storage unit so as to supplement or remove the heat in the heat storage unit.

2. The heating control system based on big data and AI technology according to claim 1 is characterized in that: Also includes, A data storage module, used for storing historical heating data; External temperature collection module, used to collect outdoor temperature; Room temperature collection modules, which are provided in several numbers and are used to collect indoor temperature; A plurality of heat output units are provided, and correspond one to one with the room temperature collection modules, and are used for heat supply; A heating control module, which is connected to each of the heating output units and is used to manually adjust the working mode of the heating output unit; The heating area division module is used to divide the heating range into areas; The data analysis module divides the heating period into sections according to the heating demand, and records them as a large heating period and a small heating period.

3. The heating control system based on big data and AI technology according to claim 2 is characterized in that: The data analysis module determines the periodic heating consumption of a large heating cycle in a historical stage of any heating area based on the heating output of each of the heating output units and the data collected by the external temperature collection module.

4. The heating control system based on big data and AI technology according to claim 3 is characterized in that: The data analysis module performs data analysis on any heating small cycle of the heating output unit in the historical stage, obtains the indoor temperature change data collected by the corresponding room temperature collection module and the outdoor temperature change data collected by the indoor and outdoor temperature collection module at the same time node, and determines the amount of heat consumed by the corresponding heating output unit in the analyzed heating small cycle according to the outdoor temperature change data and the indoor temperature change data; Perform data analysis on all small heating cycles contained in a large heating cycle of any heating output unit to determine the amount of heat consumed in the large heating cycle; The heat consumption of all heat output units in any heating area during the large heating cycle is summarized to determine the periodic heating consumption of the heating area.

5. The heating control system based on big data and AI technology according to claim 3 is characterized in that: The data analysis module determines the heating data curve according to the determined periodic heating consumption and the outdoor temperature stored in the data storage module, and determines the initial heating demand heat through the matching of the outdoor temperature of the next large heating cycle estimated by the temperature estimation module and the heating data curve, and determines the heating delivery value of the next large heating cycle according to the initial heating demand heat. If the heat delivery value is greater than the initial heating demand heat, the heat supply control unit delivers the heat corresponding to the calculated heating delivery value to the heat storage unit before the next large heating cycle starts.

6. The heating control system based on big data and AI technology according to claim 5 is characterized in that: The data analysis module determines the ratio between the heat delivery value and the initial heat demand by using the value of the initial heat demand.

7. The heating control system based on big data and AI technology according to claim 2 is characterized in that: The temperature estimation module estimates the outdoor temperature of each small heating cycle within the next large heating cycle; The data analysis module determines the heat consumption of the current large heating cycle after each small heating cycle. The heat supply control unit determines whether the remaining stored heat in the heat storage unit meets the remaining heat demand of the current large heating cycle based on the determination result, and determines whether the remaining heat is excessive.

8. The heating control system based on big data and AI technology according to claim 7 is characterized in that: The heat supply control unit controls and supplements the heat to the heat storage unit when the remaining heat does not meet the heating demand; When there is excess remaining heat, the heat supply control unit calls the excess heat in the heat storage unit to other areas.

9. The heating control system based on big data and AI technology according to claim 7, characterized in that: The data analysis module is provided with a residual heat evaluation value for determining the residual heat situation, including a first residual heat evaluation value and a second residual heat evaluation value; If the residual heat is less than or equal to the first residual heat evaluation value, the data analysis module determines that the residual heat does not meet the heating demand; If the residual heat is greater than the second residual heat evaluation value, the data analysis module determines that the residual heat is excessive.

10. The heating control system based on big data and AI technology according to claim 9, characterized in that: The numerical value of each residual heat evaluation value is related to the outdoor temperature and the number of residual heating small cycles.

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