Heating load prediction method and system based on unmanned aerial vehicle
Through the drone-based heating load prediction method, infrared temperature measurement and deviation coefficient evaluation are used to solve the problems of temperature inhomogeneity and energy waste in traditional heating systems, and more accurate heating load prediction is achieved.
Patent Information
- Application Number
- CN202411982547.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-31
- Publication Date
- 2025-04-25
AI Technical Summary
There are temperature unevenness and room temperature fluctuations in traditional heating systems, resulting in waste of energy and a decline in user experience. The existing temperature sensors are costly to install and have limited coverage, making it difficult to obtain the overall temperature distribution.
The heating load prediction method based on drones is adopted, by determining the heating unit consumption data of different building rooms, determining the heating unit consumption abnormal type, determining the optimal temperature measurement route based on infrared temperature measurement time and temperature measurement route, and using drones to perform infrared temperature measurement, and calculating the deviation coefficient to predict the heating load.
The determination of the abnormality of heating unit consumption during the building's historical heating process is achieved based on the heating unit consumption data of different rooms, which improves the accuracy of the prediction results of the heating load, and avoids the inaccurate temperature measurement problem caused by deviations in the temperature measurement result.
Smart Images
Figure CN119886446A_ABST
Abstract
Description
Technical Field
[0001] The present invention belongs to the field of temperature measurement technology, and in particular relates to a heating load prediction method and system based on unmanned aerial vehicle. Background Art
[0002] In traditional heating systems, temperature non-uniformity and room temperature fluctuations are common problems, resulting in energy waste and reduced user experience. Therefore, how to accurately measure the user's indoor temperature to reduce the occurrence of indoor oversupply or short supply has become a technical problem that needs to be solved urgently.
[0003] At present, the room temperature data is usually obtained by installing temperature sensors inside the building. However, this method has problems such as high installation cost, limited coverage and difficulty in obtaining the overall temperature distribution.
[0004] In order to solve the above technical problems, the present application provides a heating load prediction method and system based on drones. Summary of the invention
[0005] A heating load prediction method based on drone, specifically comprising: S1 determines the heating unit consumption data of rooms in different buildings within the community, and determines the abnormal heating unit consumption types of different buildings based on the heating unit consumption data of different rooms in different buildings, and determines the temperature measurement time of infrared temperature measurement of different buildings based on the abnormal heating unit consumption types; S2 determines the infrared temperature measurement time of different buildings in the community based on the infrared temperature measurement time and the distribution data of different buildings, and determines the optimal temperature measurement route among the temperature measurement routes through the infrared temperature measurement time and the distribution data of the obstacle buildings in the temperature measurement route; S3, based on the optimal temperature measurement route, uses a drone to perform infrared temperature measurement of different rooms in the building along the fire climbing surface of the building to obtain temperature measurement results, and determines the deviation coefficient of the temperature measurement results of different rooms based on the deviation of the temperature measurement results of different rooms and the corresponding reference rooms; S4 determines whether the building needs to be remeasured based on the deviation coefficient of the temperature measurement results of different rooms inside the building and the abnormal type of heating unit consumption. When remeasurement is not required, the heating load of the building in the community is predicted according to the temperature measurement results.
[0006] A further technical solution is that the heating unit consumption data of the room is determined based on the analysis results of the historical heating data of the room.
[0007] A further technical solution is that the method for determining the abnormal type of heating unit consumption of the building is: Determining the heating unit consumption of different rooms on different dates based on the heating unit consumption data of different rooms in the building; Based on the heating unit consumption on different dates, the proportion of the number of dates when the heating unit consumption of different rooms is greater than the preset unit consumption is determined, and the unit consumption abnormality coefficient of different rooms is determined by the proportion of the number of dates when the heating unit consumption is greater than the preset unit consumption; The abnormal unit consumption rooms of the building are determined based on the abnormal unit consumption coefficients of different rooms of the building, and the abnormal unit consumption type of heating of the building is determined by the proportion of the number of the abnormal unit consumption rooms.
[0008] A further technical solution is to determine the abnormal type of heating unit consumption of the building according to the proportion of the number of rooms with abnormal unit consumption, which specifically includes: Determine a preset abnormality type corresponding to the building based on the proportion of the number of rooms with abnormal unit consumption; The abnormal type of heating unit consumption of the building is determined based on the preset abnormal type corresponding to the building.
[0009] A further technical solution is that the abnormal unit consumption room of the building is a room whose abnormal unit consumption coefficient is greater than a preset abnormal coefficient threshold.
[0010] A further technical solution is that the deviation reference room is a reference room whose deviation of the temperature measurement result is not within a preset temperature measurement deviation range.
[0011] A further technical solution is to determine whether the building needs to be remeasured, specifically including: Determining a room with deviation temperature measurement in the building based on deviation coefficients of temperature measurement results of different rooms in the building; Based on the abnormal type of heating unit consumption, determining a threshold value of the number of rooms with deviation temperature measurement under the abnormal type of heating unit consumption; Whether the building needs to be remeasured is determined based on the number of rooms with deviation temperature measurement in the building and the room number threshold.
[0012] A further technical solution is that when the number of rooms with deviation temperature measurement inside the building is greater than the room number threshold, it is determined that the building needs to be remeasured.
[0013] On the other hand, an embodiment of the present application provides a heating load prediction system based on a drone, which adopts the above-mentioned heating load prediction method based on a drone, and specifically includes: Temperature measurement time determination module, temperature measurement route determination module, deviation coefficient evaluation module, heating load prediction module; The temperature measurement time determination module is responsible for determining the heating unit consumption data of the rooms of different buildings within the community, and determining the abnormal heating unit consumption types of different buildings based on the heating unit consumption data of different rooms in different buildings, and determining the temperature measurement time of infrared temperature measurement of different buildings based on the abnormal heating unit consumption types; The temperature measurement route determination module is responsible for determining the infrared temperature measurement time of different buildings in the community based on the temperature measurement time, and combining the distribution data of different buildings to determine the infrared temperature measurement time of different temperature measurement routes adopted by the drone, and determining the optimal temperature measurement route among the temperature measurement routes through the infrared temperature measurement time and the distribution data of the obstacle buildings in the temperature measurement route; The deviation coefficient evaluation module is responsible for using the optimal temperature measurement route as a basis, along the fire climbing surface of the building, using the drone to perform infrared temperature measurement of different rooms in the building to obtain temperature measurement results, and determining the deviation coefficients of the temperature measurement results of different rooms through the deviations of the temperature measurement results of different rooms and the corresponding reference rooms; The heating load prediction module is responsible for determining whether the building needs to be remeasured based on the deviation coefficient of the temperature measurement results of different rooms inside the building and the abnormal type of heating unit consumption. When remeasurement is not required, the heating load of the building in the community is predicted based on the temperature measurement results.
[0014] The beneficial effects of the present invention are: The temperature measurement time of infrared temperature measurement in different buildings is determined based on the heating unit consumption data of different rooms in different buildings, thereby realizing the determination of the abnormal degree of heating unit consumption in the historical heating process of the building based on the heating unit consumption data of different rooms, ensuring the accuracy of infrared temperature measurement of buildings with a high abnormal degree of heating unit consumption in the historical heating process, and also laying the foundation for differentiated heating load prediction, thereby improving the accuracy of heating load prediction results.
[0015] Whether the building needs to be remeasured is determined based on the deviation coefficient of the temperature measurement results of different rooms inside the building and the type of abnormal heating unit consumption, thereby avoiding the technical problem of inaccurate temperature measurement results inside the building due to deviations in the temperature measurement results of the rooms. At the same time, by further combining the type of abnormal heating unit consumption, the accuracy of temperature measurement results inside buildings with higher abnormalities in heating unit consumption is also improved. BRIEF DESCRIPTION OF THE DRAWINGS
[0016] The above and other features and advantages of the present invention will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings.
[0017] Figure 1 It is a flow chart of a heating load forecasting method based on drone.
[0018] Figure 2 It is a flow chart of a method for determining abnormal types of heating unit consumption of a building.
[0019] Figure 3 It is a flow chart of a method for determining the time taken for infrared temperature measurement of a temperature measurement route. DETAILED DESCRIPTION
[0020] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of ways and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that the present invention will be comprehensive and complete and fully convey the concepts of the example embodiments to those skilled in the art. The same reference numerals in the figures represent the same or similar structures, and thus their detailed description will be omitted.
[0021] The terms "a", "an", "the", and "said" are used to indicate the presence of one or more elements / components / etc.; the terms "comprising" and "having" are used to express an open-ended inclusive meaning and mean that additional elements / components / etc. may be present in addition to the listed elements / components / etc.
[0022] Example 1 To solve the above problems, according to one aspect of the present invention, Figure 1 As shown, a heating load prediction method based on drone is provided, which specifically includes: S1 determines the heating unit consumption data of rooms in different buildings within the community, and determines the abnormal heating unit consumption types of different buildings based on the heating unit consumption data of different rooms in different buildings, and determines the temperature measurement time of infrared temperature measurement of different buildings based on the abnormal heating unit consumption types; Furthermore, the heating unit consumption data of the room is determined based on the analysis results of the historical heating data of the room.
[0023] Specifically, Figure 2 As shown, the method for determining the abnormal type of heating unit consumption of the building is: Determining the heating unit consumption of different rooms on different dates based on the heating unit consumption data of different rooms in the building; Based on the heating unit consumption on different dates, the proportion of the number of dates when the heating unit consumption of different rooms is greater than the preset unit consumption is determined, and the unit consumption abnormality coefficient of different rooms is determined by the proportion of the number of dates when the heating unit consumption is greater than the preset unit consumption; The abnormal unit consumption rooms of the building are determined based on the abnormal unit consumption coefficients of different rooms of the building, and the abnormal unit consumption type of heating of the building is determined by the proportion of the number of the abnormal unit consumption rooms.
[0024] Furthermore, the abnormal type of heating unit consumption of the building is determined according to the proportion of the number of rooms with abnormal unit consumption, specifically including: Determine a preset abnormality type corresponding to the building based on the proportion of the number of rooms with abnormal unit consumption; The abnormal type of heating unit consumption of the building is determined based on the preset abnormal type corresponding to the building.
[0025] Specifically, the abnormal unit consumption room of the building is a room whose abnormal unit consumption coefficient is greater than a preset abnormal coefficient threshold.
[0026] Optionally, the method for determining the abnormal type of heating unit consumption of the building is: Based on the heating unit consumption data of different rooms in the building, the heating unit consumption of different rooms on different dates is determined, and when there is no room with a heating unit consumption greater than a preset unit consumption, the abnormal heating unit consumption type of the building is determined based on a preset type; When there is a room with heating unit consumption greater than the preset unit consumption: The rooms with heating unit consumption greater than the preset unit consumption are regarded as abnormal rooms, and the dates with abnormal rooms are regarded as abnormal dates. When the number of abnormal dates does not meet the requirements, the abnormal type of heating unit consumption of the building is determined according to the specified abnormal type; When the number and proportion of abnormal dates meet the requirements: Determine the date abnormality coefficients of different abnormal dates according to the proportion of the number of abnormal rooms on different abnormal dates; when the number of abnormal dates with date abnormality coefficients greater than a preset date coefficient threshold does not meet the requirement, determine the abnormal type of heating unit consumption of the building according to the specified abnormal type; When the number of abnormal dates whose date abnormal coefficient is greater than the preset date coefficient threshold meets the requirement: Based on the heating unit consumption on different dates, the proportion of the number of dates when the heating unit consumption of different rooms is greater than the preset unit consumption is determined, and the unit consumption abnormality coefficients of different rooms are determined by the proportion of the number of dates when the heating unit consumption is greater than the preset unit consumption and the heating unit consumption on different dates; when the number of rooms whose unit consumption abnormality coefficients do not meet the requirements is greater than the preset number of rooms, the heating unit consumption abnormality type of the building is determined according to the specified abnormality type; When the number of rooms whose unit consumption abnormal coefficient does not meet the requirements is not greater than the preset number of rooms: The abnormal unit consumption coefficient of the building is determined based on the abnormal unit consumption coefficients of different rooms in the building, and the abnormal type of heating unit consumption of the building is determined through the abnormal unit consumption coefficient of the building.
[0027] S2 determines the infrared temperature measurement time of different buildings in the community based on the infrared temperature measurement time and the distribution data of different buildings, and determines the optimal temperature measurement route among the temperature measurement routes through the infrared temperature measurement time and the distribution data of the obstacle buildings in the temperature measurement route; Furthermore, the temperature measurement time of the infrared temperature measurement of the building is determined according to the abnormal type of heating unit consumption of the building, and specifically determined according to the preset temperature measurement time corresponding to the abnormal type of heating unit consumption of the building.
[0028] Specifically, Figure 3 As shown, the method for determining the infrared temperature measurement time of the temperature measurement route is: Based on the distribution data of different buildings, determine the flight time of different temperature measurement routes; The infrared temperature measurement time for different temperature measurement routes is determined according to the flight time for different temperature measurement routes and the infrared temperature measurement time for different buildings.
[0029] Specifically, the method for determining the optimal temperature measurement route is: Determine the number of obstacle buildings in the temperature measurement route according to the distribution data of the obstacle buildings in the temperature measurement route, determine the height of the obstacle buildings according to the types of the obstacle buildings in the temperature measurement route, and determine the obstacle buildings with a height greater than a preset height based on the heights of different obstacle buildings, and take them as buildings of concern; The optimal temperature measurement route among the temperature measurement routes is determined based on the number of the buildings of interest and the time taken for infrared temperature measurement.
[0030] Further, the optimal temperature measurement route among the temperature measurement routes is determined based on the number of the buildings of interest and the infrared temperature measurement time, specifically including: Based on the infrared temperature measurement time, a temperature measurement route whose infrared temperature measurement time is within a preset time interval is selected, and the route is used as a screening temperature measurement route; The optimal temperature measurement route among the screened temperature measurement routes is determined based on minimizing the number of buildings of concern.
[0031] Optionally, the flight interference coefficient of the obstacle buildings within the altitude range on the UAV is determined according to a preset interference coefficient corresponding to the number of obstacle buildings within the altitude range.
[0032] It should be noted that the route deviation coefficient of the temperature measurement route is determined according to the average value of the flight interference coefficient of the obstacle buildings in different height intervals of the temperature measurement route on the drone.
[0033] Optionally, the method for determining the optimal temperature measurement route includes steps S21-S23, specifically: S21 determines the number of obstacle buildings in the temperature measurement route according to the distribution data of the obstacle buildings in the temperature measurement route, determines the height of the obstacle buildings according to the types of the obstacle buildings in the temperature measurement route, and determines the number of obstacle buildings in different height intervals based on the heights of different obstacle buildings; S22 determines the flight interference coefficient of the obstacle buildings in different height intervals to the UAV according to the number of obstacle buildings in different height intervals, and determines the route deviation coefficient of the temperature measurement route based on the flight interference coefficient of the obstacle buildings in different height intervals to the UAV; S23 determines the flight route adaptation coefficient of the temperature measurement route based on the route deviation coefficient and the infrared temperature measurement time, and determines the optimal temperature measurement route among the temperature measurement routes through the flight route adaptation coefficient.
[0034] Before entering step S21, it is also necessary to determine whether the infrared temperature measurement time of the temperature measurement route is within the preset time interval. When the infrared temperature measurement time of the temperature measurement route is within the preset time interval, enter step S21. When the infrared temperature measurement time of the temperature measurement route is not within the preset time interval, the temperature measurement route is determined to be the optimal temperature measurement route.
[0035] Optionally, the above step S21 includes steps S211-S213, which are specifically: S211 determines the number of the obstacle buildings in the temperature measurement route according to the distribution data of the obstacle buildings in the temperature measurement route. When the number of the obstacle buildings in the temperature measurement route is greater than the preset number of buildings, the process proceeds to step S212. When the number of the obstacle buildings in the temperature measurement route is not greater than the preset number of buildings, the process proceeds to step S213. S212 determines the height of the obstacle building according to the type of the obstacle building in the temperature measurement route. When the average height of the obstacle buildings in the temperature measurement route is greater than the preset height threshold, it is determined that the temperature measurement route does not belong to the optimal temperature measurement route. When the average height of the obstacle buildings in the temperature measurement route is not greater than the preset height threshold, the process proceeds to step S213. S213 determines the number of obstacle buildings in different height intervals based on the heights of different obstacle buildings. When the number of obstacle buildings in the preset height interval is greater than the preset number of buildings, it is determined that the temperature measurement route does not belong to the optimal temperature measurement route. When the number of obstacle buildings in the preset height interval is not greater than the preset number of buildings, proceed to step S22.
[0036] Optionally, the above step S22 includes steps S221-S223, specifically: S221 determines the flight interference coefficient of the obstacle buildings in different height intervals to the UAV by the number of obstacle buildings in different height intervals, and when there is a height interval with a flight interference coefficient greater than a preset interference coefficient, proceeds to step S222; when there is no height interval with a flight interference coefficient greater than the preset interference coefficient, proceeds to step S223; S222: When the number of altitude intervals where the flight interference coefficient is greater than the preset interference coefficient does not meet the requirement, it is determined that the temperature measurement route does not belong to the optimal temperature measurement route; when the number of altitude intervals where the flight interference coefficient is greater than the preset interference coefficient meets the requirement, the process proceeds to step S223; S223 determines the route deviation coefficient of the temperature measurement route based on the flight interference coefficient of the obstacle buildings in different height ranges on the drone. When the route deviation coefficient of the temperature measurement route does not meet the requirements, it is determined that the temperature measurement route does not belong to the optimal temperature measurement route. When the route deviation coefficient of the temperature measurement route meets the requirements, proceed to step S23.
[0037] Furthermore, the reference room is located at a position similar to the position of the room in the building, and is specifically determined based on the floor of the room and adjacent room data.
[0038] S3, based on the optimal temperature measurement route, uses a drone to perform infrared temperature measurement of different rooms in the building along the fire climbing surface of the building to obtain temperature measurement results, and determines the deviation coefficient of the temperature measurement results of different rooms based on the deviation of the temperature measurement results of different rooms and the corresponding reference rooms; Optionally, the temperature measurement result of the room is determined based on weather data and temperature measurement data of infrared temperature measurement.
[0039] It should be noted that the method for determining the temperature measurement result of the room is: Based on the weather data, determine the weather temperature, weather humidity and wind speed; Based on the weather temperature, weather humidity and wind speed, determine a preset temperature measurement compensation coefficient corresponding to the weather data; The temperature measurement result of the room is determined according to the product of the preset temperature measurement compensation coefficient and the temperature measurement data.
[0040] S4 determines whether the building needs to be remeasured based on the deviation coefficient of the temperature measurement results of different rooms inside the building and the abnormal type of heating unit consumption. When remeasurement is not required, the heating load of the building in the community is predicted according to the temperature measurement results.
[0041] Specifically, the method for determining the deviation coefficient of the temperature measurement result of the room is: Determine the deviation amount of the temperature measurement result between the room and the corresponding reference room based on the deviation of the temperature measurement result between the room and the corresponding reference room; Determining a deviation reference room among the reference rooms based on deviations from temperature measurement results of different reference rooms; The deviation coefficient of the temperature measurement result of the room is determined by the proportion of the deviation reference rooms in the reference rooms.
[0042] Furthermore, the deviation reference room is a reference room whose deviation of the temperature measurement result is not within a preset temperature measurement deviation range.
[0043] Optionally, determining whether the building needs to be remeasured specifically includes: Determining a room with deviation temperature measurement in the building based on deviation coefficients of temperature measurement results of different rooms in the building; Based on the abnormal type of heating unit consumption, determining a threshold value of the number of rooms with deviation temperature measurement under the abnormal type of heating unit consumption; Whether the building needs to be remeasured is determined based on the number of rooms with deviation temperature measurement in the building and the room number threshold.
[0044] Furthermore, when the number of rooms with deviation temperature measurement inside the building is greater than the room number threshold, it is determined that the building needs to be remeasured.
[0045] Optionally, determining whether the building needs to be remeasured includes steps S41-S43, specifically: S41: determining a temperature measurement deviation coefficient inside the building based on deviation coefficients of temperature measurement results of different rooms inside the building; S42 determines a deviation coefficient threshold value under the abnormal type of heating unit consumption based on the abnormal type of heating unit consumption; S43 determines whether the building needs to be remeasured based on the temperature measurement deviation coefficient inside the building and the deviation coefficient threshold.
[0046] Optionally, before entering step S41, the deviation coefficient of the temperature measurement results of different rooms inside the building is used to determine whether there is a deviated temperature measurement room inside the building. When there is a deviated temperature measurement room inside the building, proceed to step S41. When there is no deviated temperature measurement room inside the building, it is determined that the building does not need to be remeasured.
[0047] Optionally, the above step S41 includes steps S411-S413, which are specifically: S411 determines the deviation temperature measurement rooms in the building based on the deviation coefficients of the temperature measurement results of different rooms in the building. When the number of deviation temperature measurement rooms in the building does not meet the requirement, it is determined that the building needs to be remeasured. When the number of deviation temperature measurement rooms in the building meets the requirement, the process proceeds to step S412. S412: Determine the screening deviation coefficient inside the building based on the number of deviation temperature measurement rooms inside the building and the deviation coefficients of the temperature measurement results of different deviation temperature measurement rooms. When the screening deviation coefficient inside the building does not meet the requirements, it is determined that the building needs to be remeasured. When the screening deviation coefficient inside the building meets the requirements, proceed to step S413. S413 determines the temperature measurement deviation coefficient inside the building based on the deviation coefficient of the temperature measurement results of different rooms inside the building. When the temperature measurement deviation coefficient inside the building is greater than the preset temperature measurement deviation coefficient threshold, it is determined that the building needs to be remeasured. When the temperature measurement deviation coefficient inside the building is not greater than the preset temperature measurement deviation coefficient threshold, proceed to step S42.
[0048] Example 2 On the other hand, an embodiment of the present application provides a heating load prediction system based on a drone, which adopts the above-mentioned heating load prediction method based on a drone, and specifically includes: Temperature measurement time determination module, temperature measurement route determination module, deviation coefficient evaluation module, heating load prediction module; The temperature measurement time determination module is responsible for determining the heating unit consumption data of the rooms of different buildings within the community, and determining the abnormal heating unit consumption types of different buildings based on the heating unit consumption data of different rooms in different buildings, and determining the temperature measurement time of infrared temperature measurement of different buildings based on the abnormal heating unit consumption types; The temperature measurement route determination module is responsible for determining the infrared temperature measurement time of different buildings in the community based on the temperature measurement time, and combining the distribution data of different buildings to determine the infrared temperature measurement time of different temperature measurement routes adopted by the drone, and determining the optimal temperature measurement route among the temperature measurement routes through the infrared temperature measurement time and the distribution data of the obstacle buildings in the temperature measurement route; The deviation coefficient evaluation module is responsible for using the optimal temperature measurement route as a basis, along the fire climbing surface of the building, using the drone to perform infrared temperature measurement of different rooms in the building to obtain temperature measurement results, and determining the deviation coefficients of the temperature measurement results of different rooms through the deviations of the temperature measurement results of different rooms and the corresponding reference rooms; The heating load prediction module is responsible for determining whether the building needs to be remeasured based on the deviation coefficient of the temperature measurement results of different rooms inside the building and the abnormal type of heating unit consumption. When remeasurement is not required, the heating load of the building in the community is predicted based on the temperature measurement results.
[0049] In the embodiments of the present invention, the term "plurality" refers to two or more than two, unless otherwise clearly defined. The terms "installation", "connection", "fixation" and the like should be understood in a broad sense. For example, "connection" can be a fixed connection, a detachable connection, or an integral connection. For those skilled in the art, the specific meanings of the above terms in the embodiments of the present invention can be understood according to the specific circumstances.
[0050] In the description of the embodiments of the present invention, it needs to be understood that the directions or positional relationships indicated by the terms "upper" and "lower" are based on the directions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the embodiments of the present invention and simplifying the description, rather than indicating or implying that the device or unit referred to must have a specific direction, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the embodiments of the present invention.
[0051] In the description of this specification, the description of the terms "one embodiment", "a preferred embodiment", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the embodiment of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.
[0052] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and changes. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims
1. A heating load prediction method based on drones, characterized in that: Specifically include: Determine the heating unit consumption data of rooms in different buildings within the community, and determine the abnormal heating unit consumption types of different buildings based on the heating unit consumption data of different rooms in different buildings, and determine the temperature measurement time of infrared temperature measurement of different buildings based on the abnormal heating unit consumption types; Based on the infrared temperature measurement time of different buildings in the community, and in combination with the distribution data of different buildings, the infrared temperature measurement time of the drone using different temperature measurement routes is determined, and the optimal temperature measurement route among the temperature measurement routes is determined through the infrared temperature measurement time and the distribution data of the obstacle buildings in the temperature measurement route; Based on the optimal temperature measurement route, along the fire climbing surface of the building, the infrared temperature measurement of different rooms in the building is performed by using a drone to obtain the temperature measurement results, and the deviation coefficient of the temperature measurement results of different rooms is determined by the deviation of the temperature measurement results of different rooms and the corresponding reference rooms; Based on the deviation coefficient of the temperature measurement results of different rooms inside the building and the abnormal type of heating unit consumption, it is determined whether the building needs to be remeasured. When remeasurement is not required, the heating load of the building in the community is predicted based on the temperature measurement results.
2. The heating load prediction method based on drone according to claim 1, characterized in that: The heating unit consumption data of the room is determined according to the analysis result of the historical heating data of the room.
3. The heating load prediction method based on drone according to claim 1, characterized in that: The method for determining the abnormal type of heating unit consumption of the building is: Determining the heating unit consumption of different rooms on different dates based on the heating unit consumption data of different rooms in the building; Based on the heating unit consumption on different dates, the proportion of the number of dates when the heating unit consumption of different rooms is greater than the preset unit consumption is determined, and the unit consumption abnormality coefficient of different rooms is determined by the proportion of the number of dates when the heating unit consumption is greater than the preset unit consumption; The abnormal unit consumption rooms of the building are determined based on the abnormal unit consumption coefficients of different rooms of the building, and the abnormal unit consumption type of heating of the building is determined by the proportion of the number of the abnormal unit consumption rooms.
4. The method for predicting heating load based on drone according to claim 3, characterized in that: The abnormal type of heating unit consumption of the building is determined by the proportion of the number of rooms with abnormal unit consumption, specifically including: Determine a preset abnormality type corresponding to the building based on the proportion of the number of rooms with abnormal unit consumption; The abnormal type of heating unit consumption of the building is determined based on the preset abnormal type corresponding to the building.
5. The method for predicting heating load based on drone according to claim 4, characterized in that: The abnormal unit consumption room of the building is a room whose abnormal unit consumption coefficient is greater than a preset abnormal coefficient threshold.
6. The method for predicting heating load based on drone according to claim 1, characterized in that: The method for determining the abnormal type of heating unit consumption of the building is: Based on the heating unit consumption data of different rooms in the building, the heating unit consumption of different rooms on different dates is determined, and when there is no room with a heating unit consumption greater than a preset unit consumption, the abnormal heating unit consumption type of the building is determined based on a preset type; When there is a room with heating unit consumption greater than the preset unit consumption: The rooms with heating unit consumption greater than the preset unit consumption are regarded as abnormal rooms, and the dates with abnormal rooms are regarded as abnormal dates. When the number of abnormal dates does not meet the requirements, the abnormal type of heating unit consumption of the building is determined according to the specified abnormal type; When the number and proportion of abnormal dates meet the requirements: Determine the date abnormality coefficients of different abnormal dates according to the proportion of the number of abnormal rooms on different abnormal dates; when the number of abnormal dates with date abnormality coefficients greater than a preset date coefficient threshold does not meet the requirement, determine the abnormal type of heating unit consumption of the building according to the specified abnormal type; When the number of abnormal dates whose date abnormal coefficient is greater than the preset date coefficient threshold meets the requirement: Based on the heating unit consumption on different dates, the proportion of the number of dates when the heating unit consumption of different rooms is greater than the preset unit consumption is determined, and the unit consumption abnormality coefficients of different rooms are determined by the proportion of the number of dates when the heating unit consumption is greater than the preset unit consumption and the heating unit consumption on different dates; when the number of rooms whose unit consumption abnormality coefficients do not meet the requirements is greater than the preset number of rooms, the heating unit consumption abnormality type of the building is determined according to the specified abnormality type; When the number of rooms whose unit consumption abnormal coefficient does not meet the requirements is not greater than the preset number of rooms: The abnormal unit consumption coefficient of the building is determined based on the abnormal unit consumption coefficients of different rooms in the building, and the abnormal type of heating unit consumption of the building is determined through the abnormal unit consumption coefficient of the building.
7. The heating load prediction method based on drone according to claim 1, characterized in that: The infrared temperature measurement time of the building is determined according to the abnormal heating unit consumption type of the building, and specifically determined according to the preset temperature measurement time corresponding to the abnormal heating unit consumption type of the building.
8. The heating load prediction method based on drone according to claim 1, characterized in that: The method for determining the infrared temperature measurement time of the temperature measurement route is: Based on the distribution data of different buildings, determine the flight time of different temperature measurement routes; The infrared temperature measurement time for different temperature measurement routes is determined according to the flight time for different temperature measurement routes and the infrared temperature measurement time for different buildings.
9. The method for predicting heating load based on drone according to claim 1, characterized in that: Determine whether the building in question needs to be remeasured, specifically: Determining a temperature measurement deviation coefficient inside the building based on deviation coefficients of temperature measurement results of different rooms inside the building; Based on the abnormal type of heating unit consumption, determining a deviation coefficient threshold value under the abnormal type of heating unit consumption; Whether the building needs to be remeasured is determined by the temperature measurement deviation coefficient inside the building and the deviation coefficient threshold.
10. A heating load prediction system based on drones, using a heating load prediction method based on drones as claimed in any one of claims 1 to 9, characterized in that: Specifically include: Temperature measurement time determination module, temperature measurement route determination module, deviation coefficient evaluation module, heating load prediction module; The temperature measurement time determination module is responsible for determining the heating unit consumption data of the rooms of different buildings within the community, and determining the abnormal heating unit consumption types of different buildings based on the heating unit consumption data of different rooms in different buildings, and determining the temperature measurement time of infrared temperature measurement of different buildings based on the abnormal heating unit consumption types; The temperature measurement route determination module is responsible for determining the infrared temperature measurement time of different buildings in the community based on the temperature measurement time, and combining the distribution data of different buildings to determine the infrared temperature measurement time of different temperature measurement routes adopted by the drone, and determining the optimal temperature measurement route among the temperature measurement routes through the infrared temperature measurement time and the distribution data of the obstacle buildings in the temperature measurement route; The deviation coefficient evaluation module is responsible for using the optimal temperature measurement route as a basis, along the fire climbing surface of the building, using the drone to perform infrared temperature measurement of different rooms in the building to obtain temperature measurement results, and determining the deviation coefficients of the temperature measurement results of different rooms through the deviations of the temperature measurement results of different rooms and the corresponding reference rooms; The heating load prediction module is responsible for determining whether the building needs to be remeasured based on the deviation coefficient of the temperature measurement results of different rooms inside the building and the abnormal type of heating unit consumption. When remeasurement is not required, the heating load of the building in the community is predicted based on the temperature measurement results.