A method and system for balancing the supply of heat to end users

Abstract

The present application relates to the technical field of heat supply balance management, in particular to a heat supply balance management method and system for end user heat supply. The method comprises the following steps: S1, a heat supply global data acquisition model is built, heat supply pipeline data is collected, and an end user list is obtained according to the heat supply pipeline data; outdoor temperature and historical heat supply temperature of each end user are synchronously obtained; independent temperature periods are divided according to the outdoor temperature value; the same heat supply scheme is adopted for classification and elimination of regulation and control interference; temperature differences of different users are compared under the same heat supply condition; exclusive heat loss coefficients of each household are quantitatively generated; heat loss differences caused by floors, orientations and wall heat preservation can be accurately distinguished; user target heat supply temperature is dynamically corrected according to the heat loss coefficient; the temperature correction range of users with large heat loss is increased, and the correction range of users with excellent heat preservation performance is reduced; the uneven heating and cooling problem caused by inconsistent heat dissipation of different houses is solved from the heat loss level of the user itself.

Description

Technical Field

[0001] This invention relates to the field of heat supply balance management technology, and more specifically, to a heat supply balance management method and system for end-user heating. Background Technology

[0002] With the continuous development of the urban centralized heating industry, centralized heating has become the main heating method for civil buildings in northern cities; Most existing heating data collection methods collect single data from heat exchange stations, without establishing a three-tiered integrated data collection architecture that includes the main pipeline network, building branch lines, and household access pipelines. This results in fragmented data collection, making it impossible to form a unified and complete heating dataset with consistent timelines, and making it difficult to accurately trace the actual heating status of end users. Meanwhile, existing technologies do not classify outdoor temperatures into different ranges, ignore the differentiated heat dissipation characteristics of users under different outdoor temperature conditions, and mostly adopt uniform heating standards. They cannot quantify the differences in heat loss caused by each house, house type, and insulation structure, resulting in long-term insufficient heating for top floors, corner units, and old houses, while middle units with better insulation performance may overheat and open windows. The imbalance between cold and heat between units is serious. Therefore, a heating balance management method and system for end-user heating is proposed. Summary of the Invention

[0003] The purpose of this invention is to provide a heating balance management method and system for end-user heating, so as to solve the problems mentioned in the background art.

[0004] To address the aforementioned technical problems, one objective of this invention is to provide a heat balance management method for end-user heating, comprising the following steps: S1. Build a heating-wide data acquisition model to collect heating pipeline data, and obtain a list of end users based on the heating pipeline data, while simultaneously obtaining the outdoor temperature and the historical heating temperature of each end user. S2. Divide the outdoor temperature into temperature periods based on the numerical changes, match the divided temperature periods with the corresponding historical heating temperatures, classify the temperature periods according to the heating scheme of the historical heating temperatures, compare the historical heating temperatures matched with the different classified temperature periods, and set a unique heat loss coefficient for each end user based on the comparison results. S3. Combine the list of end users with the heating pipeline data to allocate building heating branches. At the same time, extract the target heating temperature of each end user in the heating plan, combine the target heating temperature with the historical heating temperature to perform deviation analysis, and substitute the deviation results into the building heating branches. Set exclusive flow compensation coefficients for end users according to distance. S4. Obtain the real-time heating temperature and target heating temperature of the end users, match the heat loss coefficient of the end users based on the real-time outdoor temperature and heat loss coefficient, and update the target heating temperature of the end users based on the heat loss coefficient. S5. Combine the target heating temperature updated in S4 with the real-time heating temperature to generate the latest heating plan, and adjust the flow rate by combining the latest heating plan with the flow compensation coefficient. Then, manage the heating for end users based on the adjusted latest heating plan.

[0005] As a further improvement to this technical solution, in S1, an integrated data acquisition architecture covering the entire heating network, building branch pipelines, and terminal household pipelines is built as a heating-wide data acquisition model to uniformly collect operational data throughout the entire heating process. The heating pipeline network full-domain data acquisition model collects all pipeline operation data reflecting the heating operation status of the heating pipeline network, which is used as heating pipeline data. Based on the heating coverage area and pipeline connection relationship of each section of heating pipeline in the heating pipeline data, the final heating direction of each end of the heating pipeline is extracted, and each final heating direction is regarded as the end user of the heating, and a list of end users is established. During the same time period when collecting data on the heating pipeline, the real-time temperature of the external environment is simultaneously collected as the outdoor temperature. At the same time, it retrieves the historical heating temperature of each end user during past heating cycles.

[0006] As a further improvement to this technical solution, in step S2, the complete heating cycle is divided into multiple independent temperature periods with different temperatures based on the high and low values ​​of the outdoor temperature and the temperature fluctuation range. For each independently divided temperature period, bind the historical heating temperature of each end user within that period to establish a corresponding relationship between the temperature period and the user's historical heating temperature.

[0007] As a further improvement to this technical solution, in step S2, the heating scheme corresponding to each historical heating temperature is extracted, each different heating scheme is taken as a standard type, and then the historical heating temperatures of the heating schemes with the same standard type are classified and summarized by the same scheme, so that the same temperature period is grouped into the same type. Among all end users, the historical heating temperatures of different temperature periods under the same type are compared horizontally, and the difference in the impact of different outdoor temperatures on heating temperature under the same heating scheme is obtained based on the comparison results. Extract the impact differences corresponding to different outdoor temperatures and configure a unique heat loss coefficient for each end user.

[0008] As a further improvement to this technical solution, in step S3, based on the pipeline layout data and branch connection data of the heating pipeline data, the completed list of end users is divided and bound to the corresponding building heating branch, and the building heating branch to which each end user belongs is located. Extract the target heating scheme for the end user and obtain the target heating temperature for each heating scheme for the end user. The target heating temperature is the indoor temperature originally planned to be achieved by the heating scheme.

[0009] As a further improvement to this technical solution, in step S3, the heating temperature corresponding to each heating scheme for each time period is extracted from the historical heating temperature, and the deviation analysis between the extracted heating temperature corresponding to each heating scheme for each time period and the target heating temperature is performed to obtain the deviation heating temperature corresponding to each heating scheme. At the same time, flow compensation analysis is performed based on the deviation heating temperature and the target heating temperature. By simulating the flow compensation, flow compensation schemes for each heating scheme to eliminate the deviation heating temperature through simulated flow compensation are obtained. The temperature deviation results and compensation scheme are combined with the pipeline layout of the building's heating branch. Starting from the heating source, the flow compensation coefficient is set for each end user according to the order of the end user's distance from the heating source, matching the pipeline distance of the end user. Among them, the flow compensation coefficient corresponds to the flow compensation scheme for different heating schemes.

[0010] As a further improvement to this technical solution, in step S4, the real-time heating temperature and target heating temperature of the end user are obtained in real time through the heating full-domain data acquisition model, and the outdoor temperature corresponding to the end user is also obtained in real time. For each end user, the real-time outdoor temperature is matched with the heat loss coefficient to obtain the heat loss coefficient corresponding to the real-time outdoor temperature of each end user. By utilizing the matched heat loss coefficient, the target heating temperature originally set by the end user is corrected, thus updating the target heating temperature. Among them, the greater the difference in the influence of the heat loss coefficient, the greater the correction range of the target heating temperature; The smaller the difference in the influence of the heat loss coefficient, the smaller the correction range for the target heating temperature.

[0011] As a further improvement to this technical solution, in S5, the target heating temperature updated in S4 is comprehensively analyzed with the real-time heating temperature collected by the end user, and the latest heating plan is generated by combining the temperature difference between the target heating temperature and the real-time heating temperature. The latest heating plan is matched with the flow compensation coefficient to obtain the flow compensation coefficient corresponding to the latest heating plan. The flow rate of the latest heating plan is adjusted according to the flow compensation coefficient, thereby completing the update and adjustment of the latest heating plan. Then, based on the updated and adjusted heating plan, the heating pipelines are controlled to manage the heating balance for each end user.

[0012] The second objective of this invention is to provide a heat balance management system for end-user heating, including any one of the above-mentioned heat balance management methods for end-user heating, comprising a data acquisition module, a coefficient setting module, and a heating management module. The data acquisition module is used to build a heating-wide data acquisition model, collect heating pipeline data, obtain a list of end users based on the heating pipeline data, and simultaneously obtain the outdoor temperature and the historical heating temperature of each end user. The coefficient setting module is used to divide the outdoor temperature into temperature periods according to the numerical changes, match the divided temperature periods with the corresponding historical heating temperatures, classify the temperature periods according to the heating scheme of the historical heating temperatures, compare the historical heating temperatures matched with the different temperature periods after classification, and set a unique heat loss coefficient for each end user based on the comparison results. The list of end users is combined with the heating pipeline data to allocate heating branches for buildings. At the same time, the target heating temperature for each end user is extracted from the heating plan. The target heating temperature is combined with the historical heating temperature to perform deviation analysis. The deviation results are then substituted into the heating branches of the buildings, and a dedicated flow compensation coefficient is set for each end user according to distance. The heating management module is used to obtain the real-time heating temperature and target heating temperature of end users, match the heat loss coefficient of end users based on the real-time outdoor temperature and heat loss coefficient, update the target heating temperature of end users based on the heat loss coefficient, generate the latest heating plan by combining the updated target heating temperature with the real-time heating temperature, adjust the flow rate of the latest heating plan with the flow compensation coefficient, and perform heating management of end users based on the adjusted latest heating plan.

[0013] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. A heating balance management method and system for end-user heating, which divides independent temperature periods by outdoor temperature values, classifies and eliminates control interference by using the same heating scheme, compares the temperature differences of different users under the same heating conditions, and quantifies and generates a unique heat loss coefficient for each household. It can accurately distinguish the heat dissipation differences caused by floor, orientation, and wall insulation, and dynamically adjust the target heating temperature of users based on the heat loss coefficient. The temperature adjustment range is increased for users with large heat loss and decreased for users with good insulation performance. It solves the problem of uneven heating caused by inconsistent heat dissipation of different houses from the perspective of users' own heat loss, and greatly improves the uniformity of indoor temperature of end users. 2. A heating balance management method and system for end-user heating, which combines the deviation results of historical heating temperature and target temperature, sets a dedicated flow compensation coefficient according to the gradient of the distance between the user and the heating source. A smaller flow compensation coefficient is set for near-end users to achieve flow restriction and throttling, suppressing water grabbing at the near end; a larger compensation coefficient is configured for far-end users to compensate for the pressure and flow loss caused by long-distance pipeline transportation. By setting the compensation coefficient through the dual dimensions of temperature deviation and pipeline distance, the flow of branches and households is accurately corrected, the flow distribution structure of the entire network is optimized, and the industry pain point of overheating at the near end and insufficient heating at the far end, which is inherent in traditional heating systems, is solved, and heating balance at the hydraulic level of the pipeline network is achieved. Attached Figure Description

[0014] Figure 1 This is a schematic flowchart of a heat balance management method for end-user heating according to the present invention. Detailed Implementation

[0015] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0016] like Figure 1 As shown, one of the objectives of this invention is to provide a heat balance management method for end-user heating, comprising the following steps: S1. Build a heating-wide data acquisition model to collect heating pipeline data, and obtain a list of end users based on the heating pipeline data, while simultaneously obtaining the outdoor temperature and the historical heating temperature of each end user. In S1, an integrated data acquisition architecture covering the entire heating network, building branch pipelines, and terminal household pipelines is built as a heating-wide data acquisition model to uniformly collect operational data throughout the entire heating process. Determine the heating data collection hierarchy, which includes: the main heating network, building branch pipelines, and user inlet pipelines; All three layers of pipelines are incorporated into the same data acquisition and monitoring system, and an integrated data acquisition architecture is built. This integrated architecture is defined as a heating-wide data acquisition model. Then, the model acquisition ports are set to connect to the pipeline pressure sensor, temperature sensor, flow acquisition module, and indoor temperature measurement terminal, so as to realize unified reception, unified storage, and unified processing of heating-related operational data. The heating pipeline network full-domain data acquisition model collects all pipeline operation data reflecting the heating operation status of the heating pipeline network, which is used as heating pipeline data. The heating network data acquisition model is activated, and the internal operating parameters of the heating network are continuously collected at a fixed sampling frequency. The collected data includes supply water temperature, return water temperature, pipeline pressure, supply water flow, valve opening, pipeline resistance, and circulating pump operating parameters. All collected parameters that can truly reflect the operating status of the heating network are summarized and organized, and uniformly named as heating pipeline data. Based on the heating coverage area and pipeline connection relationship of each section of heating pipeline in the heating pipeline data, the final heating direction of each end of the heating pipeline is extracted, and each final heating direction is regarded as the end user of the heating, and a list of end users is established. Retrieve the collected heating pipeline data and extract the attribute information of each independent pipeline segment. The attribute information includes: pipeline laying location, pipeline heating coverage area, pipeline upstream and downstream connection relationship, and pipeline branch direction. Based on the pipeline connection relationship, trace the fluid transport path segment by segment, determine the transport endpoint of each pipeline segment, and identify the location of the pipeline endpoint that directly faces the heating user as the heating end user. During the same time period when collecting data on the heating pipeline, the real-time temperature of the external environment is simultaneously collected as the outdoor temperature. At the same time as each data collection of the heating pipeline, an outdoor temperature collection command is triggered. The outdoor weather collection terminal captures the ambient air temperature in real time, removes abnormal temperature values ​​caused by wind, rain, and instantaneous airflow interference, and records the filtered stable ambient temperature as the outdoor temperature. At the same time, it retrieves the historical heating temperature of each end user during past heating cycles.

[0017] Access the heating history database and retrieve the corresponding historical temperature measurement records one-to-one based on the user number in the end-user list.

[0018] S2. Divide the outdoor temperature into temperature periods based on the numerical changes, match the divided temperature periods with the corresponding historical heating temperatures, classify the temperature periods according to the heating scheme of the historical heating temperatures, compare the historical heating temperatures matched with the different classified temperature periods, and set a unique heat loss coefficient for each end user based on the comparison results. In S2, the outdoor temperature is used as the dividing line, and the complete heating cycle is divided into multiple independent temperature periods with different temperatures according to the temperature fluctuation range. Collect continuously changing outdoor temperature data throughout the entire heating cycle, and use the high or low outdoor temperature value as the sole criterion for classification. Multiple temperature ranges are defined based on the fluctuation range of outdoor temperature. The complete heating cycle is divided according to the boundary points of the temperature ranges, thereby splitting the continuous heating time into several independent temperature periods with different temperature values ​​and varying durations. For each independently divided temperature period, bind the historical heating temperature of each end user within that period to establish a corresponding relationship between the temperature period and the user's historical heating temperature.

[0019] Retrieve each independent temperature period that has been divided, filter the historical heating temperature of each end user within the time range of each temperature period, and bind each single time period with the historical temperature of all users in that time period to establish a mapping relationship between temperature period, end user, and historical heating temperature. In S2, the heating schemes corresponding to each historical heating temperature are extracted, and each different heating scheme is treated as a standard type. Then, the historical heating temperatures of heating schemes with the same standard type are classified and summarized by the same scheme, so that the same temperature periods are grouped into the same type. For each group of bound historical heating temperatures, trace the source, extract the heating scheme executed at the time the temperature was generated, and mark heating schemes with different control parameters, different operating logic, and different heating commands separately. Each heating scheme is defined as a standard scheme type. Compare the heating schemes corresponding to each temperature period, determine whether the current heating scheme is consistent with the standard scheme type, and collect and summarize different temperature periods with the same heating scheme parameters and consistent control logic into the same type of dataset. Among all end users, the historical heating temperatures of different temperature periods under the same type are compared horizontally, and the difference in the impact of different outdoor temperatures on heating temperature under the same heating scheme is obtained based on the comparison results. Lock the dataset under the same scheme type, filter all independent temperature periods included in the type, and for each end user, compare their own historical heating temperature in different temperature periods. At the same time, under the premise of excluding the intervention of the heating scheme, simply compare the indoor temperature fluctuation caused by the change of outdoor temperature. Based on the horizontal comparison results, the difference in indoor temperature generated by the same user under different outdoor temperatures was statistically analyzed. At the same time, the difference was excluded from interference from human control and scheme modification, and was only caused by changes in the outside temperature and differences in heat dissipation of the house. This difference was defined as the difference in the impact of outdoor temperature on heating temperature. Extract the impact differences corresponding to different outdoor temperatures, and configure a unique heat loss coefficient for each end user, as shown in the following formula: ; in, A heat loss coefficient specific to end users. The reference loss constant is used. Adjust the weights for the differences. The difference in user temperature affects the coefficient of heat loss; the greater the temperature difference, the greater the heat loss coefficient.

[0020] S3. Combine the list of end users with the heating pipeline data to allocate building heating branches. At the same time, extract the target heating temperature of each end user in the heating plan, combine the target heating temperature with the historical heating temperature to perform deviation analysis, and substitute the deviation results into the building heating branches. Set exclusive flow compensation coefficients for end users according to distance. In S3, based on the pipeline layout data and branch connection data of the heating pipeline data, the completed list of end users is divided and bound to the corresponding building heating branch, and the building heating branch to which each end user belongs is located. Retrieve pipeline layout data and branch topology connection data from the heating pipeline data, taking the heat exchange station as the heat source of the entire heating system, and sort out the connection relationship and pipeline route of the main pipeline, building branch pipeline, and household branch pipeline step by step; The completed list of end users is divided and bound to the corresponding building heating branch according to the building to which the user belongs and the connection of the branch pipe to the household, so as to accurately locate the heating branch to which each end user belongs and clarify the user's position in the heating network. Extract the target heating scheme for the end user and obtain the target heating temperature for each heating scheme for the end user. The target heating temperature is the indoor temperature originally planned to be achieved by the heating scheme.

[0021] In S3, the heating temperature corresponding to each heating scheme for each time period is extracted from the historical heating temperature, and the deviation analysis between the extracted heating temperature corresponding to each heating scheme for each time period and the target heating temperature is performed to obtain the deviation heating temperature corresponding to each heating scheme. From the historical heating temperature dataset of end users, filter and match the indoor heating temperature data during the actual execution period of each heating scheme. At the same time, calculate the difference between the user's actual historical heating temperature and the corresponding target heating temperature under the same heating scheme to obtain the temperature deviation value, which is the deviation heating temperature. The deviation value is used to determine whether the user's heating is insufficient, the temperature is up to standard, or it is too hot; At the same time, flow compensation analysis is performed based on the deviation heating temperature and the target heating temperature. By simulating the flow compensation, flow compensation schemes for each heating scheme to eliminate the deviation heating temperature through simulated flow compensation are obtained. Based on the calculated deviation heating temperature and the target heating temperature compliance requirement, a heating flow compensation simulation is performed. For users with insufficient heating and low temperatures, the system simulates increasing the flow rate of heat entering the home to raise the indoor temperature. For users experiencing overheating or temperatures exceeding the standard, the system simulates reducing the incoming heat flow to lower the indoor temperature. Through simulation calculations, the optimal flow regulation parameters that can completely eliminate temperature deviations are obtained, forming a flow compensation scheme that matches the corresponding heating scheme.

[0022] The temperature deviation results and compensation scheme are combined with the pipeline layout of the building's heating branch. Starting from the heating source, the flow compensation coefficient is set for each end user according to the order of the end user's distance from the heating source, matching the pipeline distance of the end user. Among them, the flow compensation coefficient corresponds to the flow compensation scheme for different heating schemes.

[0023] The temperature deviation calculation results and flow compensation scheme are combined with the pipeline layout of the building's heating branch. According to the order of the distance between the end users and the heating source, parameters are assigned to each end user in turn. A unique flow compensation coefficient is set that matches its own pipeline distance, temperature deviation, and heating scheme. The flow compensation coefficient corresponds to the flow compensation scheme of the corresponding heating scheme. ; in, This is a traffic compensation coefficient specifically for end users. As the baseline flow coefficient, The distance of the pipeline from the user to the heating source. Average pipe distance for building users; The farther the user is, the greater the traffic compensation coefficient; the closer the user is, the smaller the coefficient.

[0024] S4. Obtain the real-time heating temperature and target heating temperature of the end users, match the heat loss coefficient of the end users based on the real-time outdoor temperature and heat loss coefficient, and update the target heating temperature of the end users based on the heat loss coefficient. In S4, the real-time heating temperature and target heating temperature of end users are obtained in real time through the heating full-area data acquisition model, and the corresponding outdoor temperature of end users is also obtained in real time. The heating system's full-area data acquisition model is activated, and the real-time indoor heating temperature of end users is collected synchronously according to a fixed sampling period. The preset original target heating temperature for users is retrieved. Simultaneously, the real-time outdoor temperature of the user's area is collected, ensuring that the timestamps of the three types of data collection are completely consistent, and obtaining full real-time data of the current heating conditions.

[0025] For each end user, the real-time outdoor temperature is matched with the heat loss coefficient to obtain the heat loss coefficient corresponding to the real-time outdoor temperature of each end user. By utilizing the matched heat loss coefficient, the target heating temperature originally set by the end user is corrected, offsetting the heating deviation caused by the user's own heat loss differences, and the target heating temperature is updated. Among them, the greater the difference in the impact of the heat loss coefficient, the faster the heat loss in the user's house is and the greater the impact of the outdoor low temperature, and the greater the correction range of the target heating temperature. The smaller the difference in the heat loss coefficient, the better the insulation performance of the user's house and the slower the heat loss. Therefore, the smaller the correction range for the target heating temperature, as shown in the formula below: ; in, To correct the updated final target heating temperature, The target heating temperature originally set by the user; ; in, The correction range for the target heating temperature.

[0026] S5. Combine the target heating temperature updated in S4 with the real-time heating temperature to generate the latest heating plan, and adjust the flow rate by combining the latest heating plan with the flow compensation coefficient. Then, manage the heating for end users based on the adjusted latest heating plan.

[0027] In S5, the target heating temperature updated in S4 is comprehensively analyzed with the real-time heating temperature collected by end users, and the latest heating plan is generated by combining the temperature difference between the target heating temperature and the real-time heating temperature. Extract the updated target heating temperature after the correction in step S4, and simultaneously retrieve the real-time indoor heating temperature of the end user collected by the model. Compare and analyze the two sets of temperature data to determine whether the current indoor temperature has reached the updated standard temperature, and assess whether the user is currently in one of three heating states: overheating, underheating, or temperature meeting the standard. The temperature difference between the real-time heating temperature and the updated target heating temperature is calculated to eliminate the temperature difference and achieve the temperature target. At the same time, combined with the outdoor ambient temperature, the heat loss characteristics of users, and the working conditions of the branch pipeline network, a personalized heating control strategy adapted to the current time and the current user is developed, and the control strategy is defined as the latest heating scheme. The latest heating plan is matched with the flow compensation coefficient to obtain the flow compensation coefficient corresponding to the latest heating plan. The flow rate of the latest heating plan is adjusted according to the flow compensation coefficient, thereby completing the update and adjustment of the latest heating plan. Based on the user's pipeline distance and historical temperature deviation, the optimal flow compensation coefficient suitable for this heating solution is selected. For nearby users, a smaller coefficient is used to limit flow, while for distant users, a larger coefficient is used to compensate for flow. This eliminates the hydraulic imbalance in the pipe network, where heating occurs near the source and cooling occurs far away, and completes the flow rate update and adjustment for the latest heating scheme. The formula is as follows: ; in, This is the adjusted final heating flow rate. Set the initial flow rate for the latest heating plan; Then, based on the updated and adjusted heating plan, the heating pipelines are controlled to manage the heating balance for each end user.

[0028] The second objective of this invention is to provide a heat balance management system for end-user heating, including any one of the above-mentioned heat balance management methods for end-user heating, comprising a data acquisition module, a coefficient setting module, and a heating management module. The data acquisition module is used to build a data acquisition model for the entire heating area, collect data from the heating pipeline, obtain a list of end users based on the heating pipeline data, and simultaneously obtain the outdoor temperature and the historical heating temperature of each end user. The coefficient setting module is used to divide the outdoor temperature into temperature periods based on the numerical changes, match the divided temperature periods with the corresponding historical heating temperatures, classify the temperature periods according to the heating scheme of the historical heating temperatures, compare the historical heating temperatures matched with the different temperature periods after classification, and set a unique heat loss coefficient for each end user based on the comparison results. The list of end users is combined with the heating pipeline data to allocate heating branches for buildings. At the same time, the target heating temperature for each end user is extracted from the heating plan. The target heating temperature is combined with the historical heating temperature to perform deviation analysis. The deviation results are then substituted into the heating branches of the buildings, and a dedicated flow compensation coefficient is set for each end user according to distance. The heating management module is used to obtain the real-time heating temperature and target heating temperature of end users, match the heat loss coefficient of end users based on the real-time outdoor temperature and heat loss coefficient, update the target heating temperature of end users based on the heat loss coefficient, generate the latest heating plan by combining the updated target heating temperature with the real-time heating temperature, adjust the flow rate of the latest heating plan with the flow compensation coefficient, and perform heating management of end users based on the adjusted latest heating plan.

[0029] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for managing heat balance in end-user heating systems, characterized in that: Includes the following steps: S1. Build a heating-wide data acquisition model to collect heating pipeline data, and obtain a list of end users based on the heating pipeline data, while simultaneously obtaining the outdoor temperature and the historical heating temperature of each end user. S2. Divide the outdoor temperature into temperature periods based on the numerical changes, match the divided temperature periods with the corresponding historical heating temperatures, classify the temperature periods according to the heating scheme of the historical heating temperatures, compare the historical heating temperatures matched with the different classified temperature periods, and set a unique heat loss coefficient for each end user based on the comparison results. S3. Combine the list of end users with the heating pipeline data to allocate building heating branches. At the same time, extract the target heating temperature of each end user in the heating plan, combine the target heating temperature with the historical heating temperature to perform deviation analysis, and substitute the deviation results into the building heating branches. Set exclusive flow compensation coefficients for end users according to distance. S4. Obtain the real-time heating temperature and target heating temperature of the end users, match the heat loss coefficient of the end users based on the real-time outdoor temperature and heat loss coefficient, and update the target heating temperature of the end users based on the heat loss coefficient. S5. Combine the target heating temperature updated in S4 with the real-time heating temperature to generate the latest heating plan, and adjust the flow rate by combining the latest heating plan with the flow compensation coefficient. Then, manage the heating for end users based on the adjusted latest heating plan.

2. The heat balance management method for end-user heating according to claim 1, characterized in that: In S1, an integrated data acquisition architecture covering the entire heating network, building branch pipelines, and terminal household pipelines is built as a heating-wide data acquisition model to uniformly collect operational data throughout the entire heating process. The heating pipeline network full-domain data acquisition model collects all pipeline operation data reflecting the heating operation status of the heating pipeline network, which is used as heating pipeline data. Based on the heating coverage area and pipeline connection relationship of each section of heating pipeline in the heating pipeline data, the final heating direction of each end of the heating pipeline is extracted, and each final heating direction is regarded as the end user of the heating, and a list of end users is established. During the same time period when collecting data on the heating pipeline, the real-time temperature of the external environment is simultaneously collected as the outdoor temperature. At the same time, it retrieves the historical heating temperature of each end user during past heating cycles.

3. A heat balance management method for end-user heating according to claim 1, characterized in that: In S2, the complete heating cycle is divided into multiple independent temperature periods with different temperatures based on the outdoor temperature range. For each independently divided temperature period, bind the historical heating temperature of each end user within that period to establish a corresponding relationship between the temperature period and the user's historical heating temperature.

4. A heat balance management method for end-user heating according to claim 1, characterized in that: In S2, the heating schemes corresponding to each historical heating temperature are extracted, and each different heating scheme is taken as a standard type. Then, the historical heating temperatures of the heating schemes with the same standard type are classified and summarized by the same scheme, so that the same temperature period is grouped into the same type. Among all end users, the historical heating temperatures of different temperature periods under the same type are compared horizontally, and the difference in the impact of different outdoor temperatures on heating temperature under the same heating scheme is obtained based on the comparison results. Extract the impact differences corresponding to different outdoor temperatures and configure a unique heat loss coefficient for each end user.

5. A heat balance management method for end-user heating according to claim 1, characterized in that: In S3, based on the pipeline layout data and branch connection data of the heating pipeline data, the completed list of end users is divided and bound to the corresponding building heating branch, and the building heating branch to which each end user belongs is located. Extract the target heating scheme for the end user and obtain the target heating temperature for each heating scheme for the end user. The target heating temperature is the indoor temperature originally planned to be achieved by the heating scheme.

6. A heat balance management method for end-user heating according to claim 1, characterized in that: In step S3, the heating temperature corresponding to each heating scheme for each time period is extracted from the historical heating temperature, and the deviation analysis between the extracted heating temperature corresponding to each heating scheme for each time period and the target heating temperature is performed to obtain the deviation heating temperature corresponding to each heating scheme. At the same time, flow compensation analysis is performed based on the deviation heating temperature and the target heating temperature. By simulating the flow compensation, flow compensation schemes for each heating scheme to eliminate the deviation heating temperature through simulated flow compensation are obtained. The temperature deviation results and compensation scheme are combined with the pipeline layout of the building's heating branch. Starting from the heating source, the flow compensation coefficient is set for each end user according to the order of the end user's distance from the heating source, matching the pipeline distance of the end user. Among them, the flow compensation coefficient corresponds to the flow compensation scheme for different heating schemes.

7. A heat balance management method for end-user heating according to claim 1, characterized in that: In S4, the real-time heating temperature and target heating temperature of the end users are obtained in real time through the heating full-area data acquisition model, and the outdoor temperature corresponding to the end users is also obtained in real time. For each end user, the real-time outdoor temperature is matched with the heat loss coefficient to obtain the heat loss coefficient corresponding to the real-time outdoor temperature of each end user. By utilizing the matched heat loss coefficient, the target heating temperature originally set by the end user is corrected, thus updating the target heating temperature. Among them, the greater the difference in the influence of the heat loss coefficient, the greater the correction range of the target heating temperature; The smaller the difference in the influence of the heat loss coefficient, the smaller the correction range for the target heating temperature.

8. A heat balance management method for end-user heating according to claim 1, characterized in that: In S5, the target heating temperature updated in S4 is comprehensively analyzed with the real-time heating temperature collected by the end user, and the latest heating plan is generated by combining the temperature difference between the target heating temperature and the real-time heating temperature. The latest heating plan is matched with the flow compensation coefficient to obtain the flow compensation coefficient corresponding to the latest heating plan. The flow rate of the latest heating plan is adjusted according to the flow compensation coefficient, thereby completing the update and adjustment of the latest heating plan. Then, based on the updated and adjusted heating plan, the heating pipelines are controlled to manage the heating balance for each end user.

9. A heat balance management system for end-user heating, used to implement the heat balance management method for end-user heating as described in any one of claims 1-8, characterized in that: It includes a data acquisition module, a coefficient setting module, and a heating management module; The data acquisition module is used to build a heating-wide data acquisition model, collect heating pipeline data, obtain a list of end users based on the heating pipeline data, and simultaneously obtain the outdoor temperature and the historical heating temperature of each end user. The coefficient setting module is used to divide the outdoor temperature into temperature periods according to the numerical changes, match the divided temperature periods with the corresponding historical heating temperatures, classify the temperature periods according to the heating scheme of the historical heating temperatures, compare the historical heating temperatures matched with the different temperature periods after classification, and set a unique heat loss coefficient for each end user based on the comparison results. The list of end users is combined with the heating pipeline data to allocate heating branches for buildings. At the same time, the target heating temperature for each end user is extracted from the heating plan. The target heating temperature is combined with the historical heating temperature to perform deviation analysis. The deviation results are then substituted into the heating branches of the buildings, and a dedicated flow compensation coefficient is set for each end user according to distance. The heating management module is used to obtain the real-time heating temperature and target heating temperature of end users, match the heat loss coefficient of end users based on the real-time outdoor temperature and heat loss coefficient, update the target heating temperature of end users based on the heat loss coefficient, generate the latest heating plan by combining the updated target heating temperature with the real-time heating temperature, adjust the flow rate of the latest heating plan with the flow compensation coefficient, and perform heating management of end users based on the adjusted latest heating plan.

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