Identification processing method, system and equipment for indoor temperature of heat consumer in heat supply network and medium

Abstract

The invention discloses an identification processing method, system, equipment and medium for the indoor temperature of a heat consumer in a heat supply pipe network, and belongs to the technical field of heat supply pipe networks, and the identification processing method comprises the steps that an identification target site is screened out based on the change condition of the deviation value of the return water temperature and the water supply temperature of the heat supply pipe network; dividing hot users into monitored users and unmonitored users, performing similarity matching, and determining reference monitored users for the unmonitored users; determining heat supply state deviations of associated heat users of the reference monitoring users of the unmonitored users in different heat supply flow intervals, and identifying a deviated heat supply deviation flow interval; and dynamically determining a strategy for carrying out indoor temperature identification processing on unmonitored users in different heat supply flow intervals. According to the method, a heat supply abnormal area is accurately positioned through return water temperature fluctuation analysis, and rapid screening of a problem area is achieved; and a reliable temperature estimation reference system is established through heat relevance matching, so that the problem of temperature data acquisition of unmonitored users is solved.

Description

Technical Field

[0001] This invention relates to the field of heating network technology, specifically to a method, system, equipment, and medium for identifying and processing the indoor temperature of heat users in a heating network. Background Technology

[0002] As the heating area gradually expands, the number of heat users is also increasing. However, because the terminal nodes of these users often lack temperature monitoring equipment, the heating network frequently fails to effectively identify users with abnormal heating demand during load scheduling. This leads to a mismatch between load scheduling results and user needs, resulting in frequent instances of oversupply and undersupply. The inability to effectively identify the heating status of users contributes to these frequent oversupply and undersupply situations.

[0003] To address the aforementioned technical problems, this invention proposes a method for identifying and processing the indoor temperature of heat users in a heating network. This method can accurately identify the indoor temperature of heat users, thereby providing more reliable data support for load scheduling of the heating system and minimizing the occurrence of oversupply or undersupply. Summary of the Invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by this invention is: how to accurately and efficiently identify the indoor temperature of heat users when they lack temperature monitoring equipment, and provide reliable data support for the load scheduling of the heating network, thereby effectively avoiding over-supply or under-supply caused by information loss and achieving precise heating.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a method for identifying and processing the indoor temperature of heat users in a heating network, comprising, Based on the variation of the deviation between the return water temperature and the supply water temperature of the heating network in the most recent preset time period, the target location is selected from several heating locations. Within the identified target location, heat users are divided into monitored users and unmonitored users, and reference monitored users are determined for unmonitored users based on the correlation of heat usage. When the distribution of unmonitored users and reference monitoring users meets the preset requirements, determine the heating status deviation of the associated heat users of the reference monitoring users of unmonitored users in different heating flow ranges, and identify the heating deviation flow range of the deviation. By utilizing the distribution density of users estimated by deviation within different deviation flow ranges, and the similarity of users estimated by deviation within different deviation flow ranges, a strategy for indoor temperature identification processing of unmonitored users under different heating flow ranges is determined.

[0007] As a preferred embodiment of the method for identifying indoor temperature of heat users in a heating network according to the present invention, the step of selecting target locations from several heating locations based on the variation of the deviation between the return water temperature and the supply water temperature of the heating network within the most recent preset time period includes: For different water supply temperature ranges, calculate the fluctuation of key parameters characterizing heating stability within a preset time period; The moment when the deviation exceeds the preset threshold is marked as an abnormal recovery moment, and the percentage of abnormal recovery moments in different water supply temperature ranges is counted. When the proportion of the quantities in all water supply temperature ranges exceeds the preset proportion threshold, the current heating location is determined to be the target location for indoor temperature identification.

[0008] This invention can quickly and accurately screen out target locations with abnormal return water temperature fluctuations and poor heating stability from numerous heating locations. This allows subsequent temperature identification processing to no longer blindly cover the entire area, but to achieve precise focusing from the surface to the point, thus improving the processing efficiency and targeting of the entire method.

[0009] As a preferred embodiment of the method for identifying and processing indoor temperatures of heat users in a heating network according to the present invention, the step of dividing heat users into monitored users and unmonitored users within the target location, and determining reference monitored users for unmonitored users based on heat consumption correlation, includes: Based on spatial location or historical heat consumption behavior, identify the associated heat users for monitored users and unmonitored users. The matching degree of the heating status of unmonitored users and related heat users is compared with that of each monitored user and related heat user. Based on the matching results, the monitoring user with the highest matching degree is selected for the unmonitored user and determined as the reference monitoring user for the current unmonitored user.

[0010] As a preferred embodiment of the method for identifying and processing indoor temperatures of heat users in a heating network according to the present invention, wherein: determining the heating status deviation of associated heat users of unmonitored users as reference monitored users under different heating flow ranges, and identifying the heating deviation flow ranges include, Calculate the deviation of key physical quantities characterizing the heating status between unmonitored users and reference monitored users in the same heating flow range; For each heating flow range, count the number or proportion of related heat user pairs whose key physical quantities deviate from a preset threshold among all unmonitored users and their reference monitored users. When the number or proportion of related heat users with deviations within a certain heating flow range reaches a preset condition, the current heating flow range is identified as a heating deviation flow range.

[0011] This invention discovers and defines the heating deviation flow range, enabling the system to identify under which specific operating conditions the conventional reference relationship will fail. This allows the subsequent temperature identification strategy to be dynamically adjusted based on real-time flow conditions, improving the accuracy and reliability of estimating the indoor temperature of unmonitored users.

[0012] As a preferred embodiment of the method for identifying indoor temperature of heat users in a heating network according to the present invention, the strategy for identifying indoor temperature of unmonitored users under different heating flow ranges includes: utilizing the deviation estimate of user distribution density within different deviation flow ranges, and the similarity of users within different deviation flow ranges; Based on the distribution of users with estimated deviations within several heating deviation flow ranges, assess the extent to which heating consistency is affected; Based on the distribution and the overlap of estimated deviation users within the heating flow deviation range, the identification and processing requirements values ​​for different heating flow deviation ranges are determined. Based on the identification and processing requirements, a strategy is developed to identify and process the indoor temperature of unmonitored users in the target area within the heating flow deviation range.

[0013] As a preferred embodiment of the method for identifying and processing indoor temperatures of heat users in a heating network according to the present invention, the step of assessing the degree to which heating consistency is affected based on the estimated distribution of users with deviations within several heating deviation flow intervals includes, Statistics show the percentage of unmonitored users who are users of the estimated deviation within any heating deviation flow range. The analysis examines the frequency of overlap among unmonitored users who are considered users of the deviation estimation in different heating deviation flow ranges. By combining the aforementioned quantity percentages and the frequency of overlap, an assessment result is generated to characterize the extent to which heating consistency is affected.

[0014] As a preferred embodiment of the method for identifying and processing the indoor temperature of heat users in a heating network according to the present invention, the strategy for determining the indoor temperature of unmonitored users in the target area within the heating flow deviation range includes: If the proportion of users with estimated deviations in the target area is greater than the preset proportion threshold, then indoor temperature identification processing will be performed on all unmonitored users within the corresponding heating deviation flow range. If the proportion of the number of users whose deviation is estimated in the target area is not greater than the preset proportion threshold, but the overlap frequency indicates that the unmonitored users who belong to the deviation estimation users exist in all heating deviation flow intervals, then indoor temperature identification processing is performed on all unmonitored users in the corresponding heating deviation flow interval. If the proportion of the number of users whose deviation is estimated in the target area is not greater than the preset proportion threshold, and the overlap frequency indicates that the unmonitored users who belong to the deviation estimation users do not exist in all heating deviation flow intervals, but the proportion is greater than zero, then in the corresponding heating deviation flow interval, only the unmonitored users who belong to the deviation estimation users in the current interval will be processed for indoor temperature identification. If the proportion of the number of users with estimated deviation in the target area is not greater than the preset proportion threshold, and the users with estimated deviation in the heating flow deviation interval are not all users with estimated deviation in all other heating flow deviation intervals, but the estimated demand factor of all users with estimated deviation is greater than the preset demand factor threshold, then indoor temperature identification processing is performed on all unmonitored users in the corresponding heating flow deviation interval; the estimated demand factor is the proportion of the number of users with estimated deviation in the heating flow deviation interval to the total number of users in the heating flow deviation interval. If the proportion of the number of users with deviation estimation in the target area is not greater than the preset proportion threshold, and the users with deviation estimation in the heating flow deviation interval are not all users with deviation estimation in all other heating flow deviation intervals, and the estimated demand factor of the users with deviation estimation is not greater than the preset demand factor threshold, but the identification processing demand value determined based on the estimated demand factor and the identification demand factor of each user with deviation estimation is greater than the preset demand threshold, then indoor temperature identification processing shall be performed on all unmonitored users in the corresponding heating flow deviation interval. The identification demand factor is the proportion of the number of users with deviation estimation in the identification target area, and the identification processing demand value is determined based on the average value of the estimated demand factors of each user with deviation estimation and the average value of the identification demand factor. If the number of users with deviation estimation accounts for zero of the number in the target area, then in the corresponding heating deviation flow range, indoor temperature identification processing will only be performed on unmonitored users after a preset time has elapsed since the last identification process.

[0015] This invention provides a system for identifying and processing the indoor temperature of heat users in a heating network.

[0016] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a system for identifying and processing the indoor temperature of heat users in a heating network, comprising: a screening module, a division module, an identification deviation module, and an output module; The screening module identifies target locations from several heating locations based on the variation in the deviation between the return water temperature and the supply water temperature of the heating network within the most recent preset time period. The segmentation module divides heat users into monitored users and unmonitored users within the identified target location, and determines reference monitored users for unmonitored users based on heat usage correlation. The deviation identification module determines the heating status deviation of the associated heat users of the unmonitored users under different heating flow ranges when the distribution of the unmonitored users and the reference monitoring users meet the preset requirements, and identifies the deviation heating flow range. The output module uses the distribution density of users estimated by deviation within different deviation flow ranges, and the similarity of users estimated by deviation within different deviation flow ranges, to determine the strategy for indoor temperature identification processing of unmonitored users under different heating flow ranges.

[0017] The present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, characterized in that the processor executes the computer program to implement the steps of the method for identifying and processing the indoor temperature of heat users in a heating network.

[0018] The present invention provides a computer-readable storage medium having a computer program stored thereon, characterized in that, when the computer program is executed by a processor, it implements the steps of the method for identifying and processing the indoor temperature of heat users in a heating network.

[0019] The beneficial effects of this invention are as follows: This invention accurately locates areas of abnormal heating by analyzing return water temperature fluctuations, enabling rapid screening of problem areas; furthermore, it solves the problem of obtaining temperature data for unmonitored users by establishing a reliable temperature estimation reference system through thermal correlation matching; based on this, it achieves optimized allocation of computing resources and precise control of heating quality by identifying heating deviation flow ranges and establishing dynamic processing strategies. This invention transforms heating management from an experience-driven, extensive model to a data-driven, precise model, improving the operational efficiency and service quality of the heating system. Attached Figure Description

[0020] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1This is a flowchart illustrating an overall process for identifying and processing the indoor temperature of heat users in a heating network, as provided in one embodiment of the present invention. Detailed Implementation

[0022] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0023] Example 1, referring to Figure 1 This is one embodiment of the present invention, which provides a method for identifying and processing the indoor temperature of heat users in a heating network, including: In the existing operation and management of heating networks, the lack of indoor temperature monitoring equipment at user terminals makes it difficult for heating companies to accurately and promptly grasp the actual heating status and indoor temperature of end users. This data gap results in a lack of precise data support for load scheduling, often relying on experience or limited network parameters for extensive adjustments. Consequently, the heating system frequently experiences a structural contradiction of oversupply and undersupply: some users experience excessively high indoor temperatures, leading to energy waste, while others remain in a state of chronically low temperatures, compromising heating comfort. The core pain point of this problem lies in the inability to effectively identify areas and individuals with abnormal heating among a vast number of users, and even more so in the inability to implement adaptive and precise management based on dynamically changing heating conditions.

[0024] To address the aforementioned technical bottlenecks, this invention provides a method for identifying and processing the indoor temperature of heat users in heating networks. This method constructs a hierarchical, dynamically optimized intelligent identification logic to accurately estimate the indoor temperature of users, thereby providing a reliable basis for load scheduling. This embodiment will describe the execution process of this method in detail, and its core flow mainly includes the following four steps: S1. Based on the variation of the deviation between the return water temperature and the supply water temperature of the heating network in the most recent preset time period, select and identify the target location from several heating locations. S2. Within the identified target location, heat users are divided into monitored users and unmonitored users, and reference monitored users are determined for unmonitored users based on the correlation of heat usage. S3. When the distribution of unmonitored users and reference monitoring users meets the preset requirements, determine the heating status deviation of the associated heat users of the reference monitoring users of unmonitored users in different heating flow ranges, and identify the heating deviation flow range of the deviation. S4. By utilizing the distribution density of users estimated by deviation within different deviation flow ranges, and the similarity of users estimated by deviation within different deviation flow ranges, a strategy for indoor temperature identification processing of unmonitored users under different heating flow ranges is determined.

[0025] Example 2, an embodiment of the present invention, provides a method for identifying and processing the indoor temperature of heat users in a heating network, based on the previous embodiment, including: In step S1, based on the variation of the deviation between the return water temperature and the supply water temperature of the heating network within the most recent preset time period, the target location is selected from several heating locations, including the following steps A1~A3: A1. For different water supply temperature ranges, calculate the fluctuation of key parameters characterizing heating stability within a preset time period.

[0026] A2. Mark the moment when the deviation exceeds the preset threshold as the recovery abnormal moment, and count the percentage of recovery abnormal moments in different water supply temperature ranges.

[0027] A3. When the proportion of the quantity in all water supply temperature ranges exceeds the preset proportion threshold, the current heating location is determined to be the target location for indoor temperature identification.

[0028] When the proportion of abnormal recovery times in different water supply temperature ranges is greater than 0.4, the heating location is determined as the target location for identification. Heating locations include residential areas, shopping malls, and office buildings.

[0029] When the location is not the target location, there is no need to identify the indoor temperature. It should be noted that for heating locations with stable heating, the heating status of different heat users is relatively stable. Therefore, there is no need to use the indoor temperature for load scheduling of the heating network. Thus, there is no need to identify the indoor temperature and then perform load scheduling of the heating network.

[0030] In embodiment A1 of this application, calculating the fluctuation of key parameters characterizing heating stability within a preset time period includes determining the deviation between the recovery temperature in the most recent preset time period and the average value of the recovery temperature at different times within the water supply temperature range.

[0031] With the water supply temperature and flow rate fixed, the recovery temperature is generally also fixed. Therefore, if the heating flow rate remains constant, the deviation between the recovery temperature in the most recent preset time period and the average recovery temperature at different times within the water supply temperature range is determined during the heating period corresponding to different water supply temperature ranges. The moment when the deviation is greater than the preset deviation threshold is regarded as the recovery abnormal moment. Based on the proportion of recovery abnormal moments in different water supply temperature ranges, it is determined whether the heating location is the target location for identification.

[0032] In one optional implementation, the calculation of A1 includes calculating the fluctuation of key parameters characterizing heating stability within a preset time period, such as the return water pressure variance within the corresponding time period.

[0033] Current engineering practices demonstrate that, under stable water supply parameters, abnormal fluctuations in return water pressure often indicate an imbalance in the hydraulic conditions of the pipe network. This imbalance directly impacts the heating performance for end users. By setting a threshold for pressure variance, areas of unstable heating caused by pressure fluctuations can be effectively identified, providing a crucial basis for selecting target locations.

[0034] This method, through quantitative analysis of the temperature fluctuation characteristics of the return water network under different water supply conditions, can effectively identify problem locations that exhibit continuous instability under all operating conditions. This allows for highly focused temperature identification resources, avoiding unnecessary calculations in areas with stable heating, thereby fundamentally improving the diagnostic efficiency and resource utilization of the entire system.

[0035] In step S2, within the identified target location, heat users are divided into monitored users and unmonitored users, and reference monitored users are determined for unmonitored users based on heat usage correlation; including the following steps B1~B3: B1. Based on spatial location or historical heat consumption behavior, identify the associated heat users for monitored users and unmonitored users.

[0036] Associated heat users are other heat users adjacent to the monitored user, specifically including heat users upstairs and downstairs.

[0037] B2. Compare the matching degree of the heating status of unmonitored users and related heat users with the heating status of each monitored user and related heat user.

[0038] When the heating status of the upstairs and downstairs heat users of an unmonitored user is consistent with that of the associated heat users of a monitored user, the monitored user is identified as a reference monitored user of the unmonitored user. The heating status includes heating and not heating.

[0039] B3. Based on the matching degree comparison results, select the monitoring user with the highest matching degree for the unmonitored user and determine it as the reference monitoring user for the current unmonitored user.

[0040] In the implementation method S2 of this application, thermal correlation is established by comparing the consistency of the heating status of the associated heat users of unmonitored users and monitored users to establish a reliable reference relationship.

[0041] Once a match is successful, the corresponding monitoring user is designated as a reference monitoring user. When the heating status of the upstairs and downstairs heat users of the unmonitored user is consistent with that of the associated heat user of the monitoring user, the associated heat user is designated as a reference monitoring user. The heating status includes both heating and not heating.

[0042] The number of unmonitored users without a reference user at the target location is determined. If the number of unmonitored users without a reference user exceeds a preset threshold for the number of unmonitored users (in this embodiment, if the number of unmonitored users is more than 5), then the distribution of reference users for unmonitored users at the target location is determined to be unsatisfactory. A temperature estimation is performed directly using an identification model based on heating flow rate and heating temperature to ensure that the system provides reliable indoor temperature identification results under various conditions.

[0043] It should be noted that the indoor temperature identification process, based on the heating flow and heating temperature of the unmonitored users, specifically includes: Based on monitoring the heating flow and heating temperature of users, when it is determined that the heating flow and heating temperature of the unmonitored users are consistent with those of the monitored users, the indoor temperature of the users is monitored. When the heating flow rate and heating temperature of the unmonitored user are consistent with those of the monitored user, the average indoor temperature of the monitored user is used as the identification result of the indoor temperature of the unmonitored user.

[0044] It should be noted that when identifying the indoor temperature of unmonitored users, the degree of consistency over a period of time needs to be considered. For example, if the heating flow and heating temperature of the reference heat user and the unmonitored user are basically similar within the most recent preset time period (1 hour), and the heating flow and heating temperature of the reference heat user and the related heat user of the unmonitored user are basically similar within 1 hour, then identification can be performed. This approach will be more comprehensive. "Basic similarity" is determined by the average value of the heating flow and the average value of the heating temperature in the previous hour. For example, if the deviation rate between the average values ​​is less than 5%, it is considered that they are basically similar.

[0045] In one optional implementation, the thermal correlation of S2 is based on a matching method for historical heating behavior patterns. This method establishes a digital profile of users' heating habits by analyzing their heating behavior characteristics over a preset period. Specifically, this includes: collecting behavioral data such as heating flow rate change curves, valve opening adjustment frequency, and room temperature maintenance preferences for each user during typical heating periods; and calculating the similarity of the above behavioral characteristics among different users to match the monitoring user whose heating behavior pattern is closest to that of the unmonitored user as a reference monitoring user.

[0046] In another alternative implementation, the hot correlation of S2 employs a matching method based on machine learning clustering analysis. This method uses the hot status data of monitored and unmonitored users as feature vectors, and automatically groups all users using unsupervised learning algorithms (such as K-means clustering or hierarchical clustering); monitored users in the same cluster are automatically identified as reference monitored users for unmonitored users in that cluster.

[0047] This invention achieves the technical effect of covering a massive number of unmonitored users with a limited number of monitoring points by establishing a heat consumption correlation matching mechanism between monitored and unmonitored users. When the distribution of reference users is sufficient, the system can establish a reliable temperature estimation reference for unmonitored users based on the consistency of the heat consumption status of associated heat users; when the distribution of reference users is insufficient, the system automatically activates an identification model based on heating parameters to ensure that stable temperature estimation results can be provided under any circumstances.

[0048] In step S3, when it is determined that the distribution of unmonitored users and reference monitored users meets the preset requirements, the heating status deviation of the associated heat users of the reference monitored users under different heating flow ranges is determined, and the heating deviation flow range is identified by the following steps C1~C2: C1. Calculate the deviation of key physical quantities characterizing the heating status between unmonitored users and reference monitored users in the same heating flow range. C2. For each heating flow range, count the number or proportion of related heat user pairs whose key physical quantities deviate from the preset threshold among all unmonitored users and their reference monitored users. When the number or proportion of related heat users with deviations within a certain heating flow range reaches a preset condition, the current heating flow range is identified as a heating deviation flow range.

[0049] If the deviation of the heating status of associated heat users in different heating flow ranges is determined, and the deviation of the heating flow and heating temperature of associated heat users who are not monitored in different heating flow ranges, and the deviation of the heating flow and heating temperature of associated heat users who are referenced monitoring users in different heating flow ranges are greater than a preset deviation, then the unmonitored users are considered as deviation estimation users within the heating flow range.

[0050] When the number of users with estimated deviation within the heating flow range is greater than a preset threshold for the number of users with estimated deviation, the heating flow range is determined to be a heating deviation flow range.

[0051] In the implementation method C1 of this application, the deviation of the key physical quantity characterizing the heating status is to compare the degree of difference in the core heating flow between the unmonitored user and the reference monitoring user under the same heating conditions (same flow range) to objectively assess the consistency level of the heating environment between the two, and to provide a data basis for subsequent identification of systematic deviations.

[0052] It should be noted that when there is a deviation in the heating flow rate, it is because there is a deviation in the heating pipeline of the related heat users. Therefore, the heating flow rate and heating temperature reaching the related heat users may fluctuate. Thus, it is necessary to determine the range of heating flow rate deviation based on the above data.

[0053] Specifically, the deviation is determined based on the changes in the heating flow rate and heating temperature of the associated heat users within the heating flow rate range.

[0054] In one alternative implementation, the deviation of the key physical quantity characterizing the heating status of C1 is determined by calculating the difference in return water temperature between associated heat users within the same heating flow range.

[0055] Return water temperature data for each of the unmonitored and reference monitored users' associated heat users within the corresponding flow range are collected. The absolute difference or variance of their average return water temperatures is calculated as the deviation. When this deviation exceeds a temperature threshold, it indicates a significant difference in the thermal environment of the two groups of associated heat users under specific flow conditions. In another alternative implementation, the deviation of the key physical quantity characterizing the heating status of C1 is determined by calculating the difference in return water temperature between associated heat users within the same heating flow range.

[0056] Return water temperature data for each of the unmonitored and reference monitored users' associated heat users within the corresponding flow range are collected, and the absolute difference or variance of their average return water temperature is calculated as the deviation. When this deviation exceeds the temperature threshold, it indicates that there is a difference in the thermal environment of the two groups of associated heat users under specific flow conditions.

[0057] This method enables precise location and quantitative diagnosis of abnormal operating conditions in heating systems. By identifying systematic deviations in the heating status of related user groups within specific flow ranges, this invention reveals uneven heating caused by hydraulic imbalances in the pipe network. This transforms the traditional overall control approach into precise intervention targeting problem flow ranges, avoiding the waste of resources in global processing and improving the accuracy of temperature identification and the specificity of system regulation.

[0058] In step S4, the distribution density of users estimated by deviation within different deviation flow ranges, and the similarity of users estimated by deviation within different deviation flow ranges, are used to determine the strategy for indoor temperature identification of unmonitored users under different heating flow ranges, including the following steps D1~D3: D1. Based on the distribution of estimated deviation users within several heating deviation flow ranges, assess the degree to which heating consistency is affected.

[0059] D2. Based on the distribution and the overlap of estimated deviation users within the heating flow deviation range, determine the identification and processing requirements for different heating flow deviation ranges.

[0060] When the unmonitored user belongs to the deviation estimation user in different heating flow deviation intervals, the deviation of the heating status in different heating flow deviation intervals is determined. If the deviation of the heating flow and heating temperature of the associated heat user of the unmonitored user in different heating flow intervals, and the deviation of the heating flow and heating temperature of the associated heat user of the reference monitoring user in the same heating flow interval, are not greater than a preset deviation, indoor temperature identification processing is required. That is, the average indoor temperature of the reference heat user is taken as the indoor temperature of the unmonitored user.

[0061] It should also be noted that when the unmonitored users are not all considered as deviation estimation users in different heating flow deviation intervals, the identification and processing requirements in different heating flow deviation intervals are determined based on the number of deviation estimation users in different heating flow deviation intervals and the number of overlaps with deviation estimation users in other heating flow deviation intervals. The greater the number of deviation estimation users and the greater the overlap with deviation estimation users in other heating flow deviation intervals, the greater the identification and processing requirements.

[0062] D3. A method for identifying and processing the indoor temperature of unmonitored users in the target area within the heating flow deviation range based on the identification and processing requirements value.

[0063] Obtain the number of users whose deviation is estimated within the heating flow deviation range, and determine whether the proportion of the number of users whose deviation is estimated within the heating flow deviation range in the target area is greater than a preset proportion threshold (0.1). If so, determine that within the heating flow deviation range, when the deviation of the heating flow and heating temperature of the associated heat users of the unmonitored users in different heating flow ranges, and the deviation of the heating flow and heating temperature of the associated heat users of the reference monitoring users in the heating flow range is not greater than a preset deviation, indoor temperature identification processing is required, that is, the average indoor temperature of the reference heat users is taken as the indoor temperature of the unmonitored users. If not, proceed to the next step. The proportion of users with estimated deviations within the heating flow deviation range to the number in the target area is used as the identification demand factor. It is determined whether users with estimated deviations within the heating flow deviation range are also users with estimated deviations in other heating flow deviation ranges. If so, it is determined that within the heating flow deviation range, if the deviation between the heating flow and heating temperature of the associated heat users of the unmonitored users in different heating flow ranges, and the deviation between the heating flow and heating temperature of the associated heat users of the reference monitoring users in the heating flow range, is not greater than a preset deviation, indoor temperature identification processing is required. That is, the average indoor temperature of the reference heat users is used as the indoor temperature of the unmonitored users. If not, proceed to the next step. Unmonitored heat users who belong to the estimated deviation users within the heating flow deviation range are considered as deviation heat users. Based on the proportion of the number of deviation heat users belonging to the estimated deviation user heating flow deviation range to the total number of heating flow deviation ranges, the estimated demand factor of the deviation heat users is determined. It is then determined whether the estimated demand factors of different deviation heat users are all greater than the preset demand factor threshold (0.3). If so, it is determined that within the heating flow deviation range, if the deviation of the heating flow and heating temperature of the associated heat users of the unmonitored users in different heating flow ranges, and the deviation of the heating flow and heating temperature of the associated heat users of the reference monitoring users in the heating flow range are not greater than the preset deviation, indoor temperature identification processing is required. That is, the average indoor temperature of the reference heat users is used as the indoor temperature of the unmonitored users. If not, proceed to the next step. Based on the average value of the estimated demand factors of different deviation heat users and the average value of the identified demand factors, the identification processing demand value within the heating flow deviation range is determined. When the identification processing demand value within the heating flow deviation range is greater than a preset demand threshold (0.1), it is determined that within the heating flow deviation range, if the deviation between the heating flow and heating temperature of the associated heat users of the unmonitored user in different heating flow ranges, and the deviation between the heating flow and heating temperature of the associated heat users of the reference monitored user in the heating flow range, is not greater than a preset deviation, indoor temperature identification processing is required. If the average indoor temperature of the reference heat users is used as the indoor temperature of the unmonitored users, then within the heating flow deviation range, if the deviation between the heating flow and heating temperature of the unmonitored users and the reference heat users in different heating flow ranges is not greater than a preset deviation, and the time elapsed since the last indoor temperature identification is greater than a preset time (1 hour), then indoor temperature identification processing is required, i.e., the average indoor temperature of the reference heat users is used as the indoor temperature of the unmonitored users.

[0064] It should be noted that when identifying the indoor temperature of unmonitored users, the degree of consistency over a period of time needs to be considered. For example, if the heating flow and heating temperature of the reference heat user and the unmonitored user are basically similar within the most recent preset time period (1 hour), and the heating flow and heating temperature of the reference heat user and the related heat user of the unmonitored user are basically similar within 1 hour, then identification can be performed. This approach will be more comprehensive. "Basic similarity" is determined by the average value of the heating flow and the average value of the heating temperature in the previous hour. For example, if the deviation rate between the average values ​​is less than 5%, it is considered that they are basically similar.

[0065] In the implementation of this application, the similarity of users in S4 is estimated as follows: based on the distribution data and overlap of unmonitored users in multiple heating deviation flow intervals, the proportion of unmonitored users belonging to the deviation estimation users in any heating deviation flow interval is statistically analyzed, and the frequency of overlap of unmonitored users belonging to the deviation estimation users in different heating deviation flow intervals is analyzed to comprehensively assess the degree to which heating consistency is affected.

[0066] In one alternative implementation, the estimation of user similarity in S4 is as follows: based on the consistency characteristics of the return water temperature of the heating system, the standard deviation or coefficient of variation of the return water temperature of all monitored users within a specific heating deviation flow range is calculated. When the statistic exceeds a preset threshold, it is determined that the heating consistency in that range is significantly affected.

[0067] In another optional implementation, the similarity of users in S4 is estimated as follows: based on the consistency analysis of heating parameters at the user entrance, the dispersion of heating parameter distribution is evaluated by calculating the eigenvalues ​​of the covariance matrix of heating flow and heating temperature at each user entrance within a specific heating deviation flow range. When the eigenvalues ​​exceed a preset range, it is determined that the heating consistency is systematically affected.

[0068] This method analyzes the distribution and overlap characteristics of users across multiple heating flow ranges by estimating deviations. It can automatically identify the severity of the impact on heating consistency and, based on preset multi-level quantification standards, intelligently determine the triggering conditions and execution frequency for temperature identification of unmonitored users within specific flow ranges. This allows limited computing resources to prioritize processing needs in areas with prominent problems, improving overall system efficiency while ensuring the reliability of temperature data.

[0069] Example 4 is an embodiment of the present invention. This embodiment provides a system for identifying and processing the indoor temperature of heat users in a heating network, including a screening module, a division module, an identification deviation module, and an output module.

[0070] The screening module identifies target locations from several heating locations based on the variation in the deviation between the return water temperature and the supply water temperature of the heating network within the most recent preset time period.

[0071] The segmentation module identifies heat users within the target location and divides them into monitored users and unmonitored users, and determines reference monitored users for unmonitored users based on heat usage correlation. The deviation identification module determines the heating status deviation of the associated heat users of the unmonitored users under different heating flow ranges when the distribution of unmonitored users and reference monitoring users meets preset requirements, and identifies the deviation heating flow range.

[0072] The output module uses the distribution density of users estimated by deviation within different deviation flow ranges, and the similarity of users estimated by deviation within different deviation flow ranges, to determine the strategy for indoor temperature identification processing of unmonitored users under different heating flow ranges.

[0073] This embodiment also provides an electronic device applicable to a method for identifying and processing the indoor temperature of heat users in a heating network, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the method for identifying and processing the indoor temperature of heat users in a heating network as proposed in the above embodiment.

[0074] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements a method for identifying and processing the indoor temperature of heat users in a heating network as proposed in the above embodiment.

[0075] The storage medium proposed in this embodiment and the method for identifying and processing the indoor temperature of heat users in a heating network proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0076] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0077] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for identifying and processing the indoor temperature of heat users in a heating network, characterized in that: include, Based on the variation of the deviation between the return water temperature and the supply water temperature of the heating network in the most recent preset time period, the target location is selected from several heating locations. Within the identified target location, heat users are divided into monitored users and unmonitored users, and reference monitored users are determined for unmonitored users based on the correlation of heat usage. When the distribution of unmonitored users and reference monitoring users meets the preset requirements, determine the heating status deviation of the associated heat users of the reference monitoring users of unmonitored users in different heating flow ranges, and identify the heating deviation flow range of the deviation. By utilizing the distribution density of users estimated by deviation within different deviation flow ranges, and the similarity of users estimated by deviation within different deviation flow ranges, a strategy for indoor temperature identification processing of unmonitored users under different heating flow ranges is determined.

2. The method for identifying and processing indoor temperature of heat users in a heating network as described in claim 1, characterized in that: The method of selecting target locations from several heating locations based on the variation of the deviation between the return water temperature and the supply water temperature of the heating network within the most recent preset time period includes: For different water supply temperature ranges, calculate the fluctuation of key parameters characterizing heating stability within a preset time period; The moment when the deviation exceeds the preset threshold is marked as an abnormal recovery moment, and the percentage of abnormal recovery moments in different water supply temperature ranges is counted. When the proportion of the quantities in all water supply temperature ranges exceeds the preset proportion threshold, the current heating location is determined to be the target location for indoor temperature identification.

3. The method for identifying and processing indoor temperature of heat users in a heating network as described in claim 2, characterized in that: The process of classifying heat users into monitored and unmonitored users within the identified target location, and determining reference monitored users for unmonitored users based on heat usage correlation, includes: Based on spatial location or historical heat consumption behavior, identify the associated heat users for monitored users and unmonitored users; The matching degree of the heating status of unmonitored users and related heat users is compared with that of each monitored user and related heat user. Based on the matching results, the monitoring user with the highest matching degree is selected for the unmonitored user and determined as the reference monitoring user for the current unmonitored user.

4. The method for identifying and processing indoor temperature of heat users in a heating network as described in claim 3, characterized in that: The step of determining the heating status deviation of the associated heat users of the reference monitored users who are not monitored, under different heating flow ranges, includes identifying the heating deviation flow ranges. Calculate the deviation of key physical quantities characterizing the heating status between unmonitored users and reference monitored users in the same heating flow range; For each heating flow range, count the number or proportion of related heat user pairs whose key physical quantities deviate from a preset threshold among all unmonitored users and their reference monitored users. When the number or proportion of related heat users with deviations within a certain heating flow range reaches a preset condition, the current heating flow range is identified as a heating deviation flow range.

5. The method for identifying and processing indoor temperature of heat users in a heating network as described in claim 4, characterized in that: The strategy for identifying indoor temperature for unmonitored users under different heating flow ranges includes utilizing the distribution density of users estimated by deviation within different deviation flow ranges, and the similarity of users estimated by deviation within different deviation flow ranges. Based on the distribution of users with estimated deviations within several heating deviation flow ranges, assess the extent to which heating consistency is affected; Based on the distribution and the overlap of estimated deviation users within the heating flow deviation range, the identification and processing requirements values ​​for different heating flow deviation ranges are determined. Based on the identification and processing requirements, a strategy is developed to identify and process the indoor temperature of unmonitored users in the target area within the heating flow deviation range.

6. The method for identifying and processing indoor temperature of heat users in a heating network as described in claim 5, characterized in that: The assessment of the extent to which heating consistency is affected, based on the distribution of estimated deviation users within several heating deviation flow intervals, includes: Statistics show the percentage of unmonitored users who are users of the estimated deviation within any heating deviation flow range. The analysis examines the frequency of overlap among unmonitored users who are considered users of the deviation estimation in different heating deviation flow ranges. By combining the aforementioned quantity percentages and the frequency of overlap, an assessment result is generated to characterize the extent to which heating consistency is affected.

7. The method for identifying and processing indoor temperature of heat users in a heating network as described in claim 6, characterized in that: The strategy for identifying and processing the indoor temperature of unmonitored users within the heating flow deviation range of the target area includes... If the proportion of users with estimated deviations in the target area is greater than a preset threshold, then indoor temperature identification processing will be performed on all unmonitored users within the corresponding heating deviation flow range. If the proportion of the number of users whose deviation is estimated in the target area is not greater than the preset proportion threshold, but the overlap frequency indicates that the unmonitored users who belong to the deviation estimation users exist in all heating deviation flow intervals, then indoor temperature identification processing is performed on all unmonitored users in the corresponding heating deviation flow interval. If the proportion of the number of users whose deviation is estimated in the target area is not greater than the preset proportion threshold, and the overlap frequency indicates that the unmonitored users who belong to the deviation estimation users do not exist in all heating deviation flow intervals, but the proportion is greater than zero, then in the corresponding heating deviation flow interval, only the unmonitored users who belong to the deviation estimation users in the current interval will be processed for indoor temperature identification. If the proportion of the number of users with deviation estimation in the target area is not greater than the preset proportion threshold, and the users with deviation estimation in the heating flow deviation interval are not all users with deviation estimation in all other heating flow deviation intervals, but the estimated demand factor of all users with deviation estimation is greater than the preset demand factor threshold, then indoor temperature identification processing is performed on all unmonitored users in the corresponding heating flow deviation interval. The estimated demand factor is the percentage of the number of heat flow deviation ranges belonging to the estimated deviation ranges of heat users in the total number of heat flow deviation ranges. If the proportion of the number of users with deviation estimation in the target area is not greater than the preset proportion threshold, and the users with deviation estimation in the heating flow deviation interval are not all users with deviation estimation in all other heating flow deviation intervals, and the estimated demand factor of the users with deviation estimation is not greater than the preset demand factor threshold, but the identification processing demand value determined based on the estimated demand factor and the identification demand factor of each user with deviation estimation is greater than the preset demand threshold, then indoor temperature identification processing shall be performed on all unmonitored users in the corresponding heating flow deviation interval. The identification demand factor is the proportion of the number of users with deviation estimation in the identification target area, and the identification processing demand value is determined based on the average value of the estimated demand factors of each user with deviation estimation and the average value of the identification demand factor. If the number of users with the deviation estimate accounts for zero in the target area, then in the corresponding heating deviation flow range, indoor temperature identification processing will only be performed on unmonitored users after a preset time has elapsed since the last identification process.

8. A system for identifying and processing the indoor temperature of heat users in a heating network, comprising the method for identifying and processing the indoor temperature of heat users in a heating network as described in any one of claims 1 to 7, characterized in that, include: The module includes a filtering module, a segmentation module, a deviation identification module, and an output module. The screening module identifies target locations from several heating locations based on the variation in the deviation between the return water temperature and the supply water temperature of the heating network within the most recent preset time period. The segmentation module divides heat users into monitored users and unmonitored users within the identified target location, and determines reference monitored users for unmonitored users based on heat usage correlation. The deviation identification module determines the heating status deviation of the associated heat users of the unmonitored users under different heating flow ranges when the distribution of the unmonitored users and the reference monitoring users meet the preset requirements, and identifies the deviation heating flow range. The output module uses the distribution density of users estimated by deviation within different deviation flow ranges, and the similarity of users estimated by deviation within different deviation flow ranges, to determine the strategy for indoor temperature identification processing of unmonitored users under different heating flow ranges.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method for identifying and processing the indoor temperature of heat users in a heating network as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the method for identifying and processing the indoor temperature of heat users in a heating network as described in any one of claims 1 to 7.

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