Regional heat supply balance state visual image maintenance system, method and device and cloud server

Abstract

The invention relates to a regional heat supply balance state visual image maintenance system, method and device and a cloud server, and belongs to the technical field of heat supply management.The system is characterized in that an on-off controller and a room temperature controller are installed on a heat distribution pipeline of each user in a region, and the on-off controller is electrically connected with the room temperature controller; a heat meter and an electric adjusting valve are further installed at an inlet of the heat distribution pipeline of the building in each area, a data collector is further arranged in each area, and a data collection module on the data collector is electrically connected with on-off controllers of a plurality of users and the heat meters; the system further comprises a balance adjusting module electrically connected with the electric adjusting valve, and the balance adjusting module is arranged in the data collector or arranged in the electric adjusting valve. The data collector communicates with the cloud server; or the data collector further comprises a positioning module; and the cloud server generates an adjusting signal sent to the balance adjusting module and also generates and displays a regional heat supply balance state visual image, so that the working efficiency is improved.

Description

Technical Field

[0001] This application relates to the technical field of heating management, and in particular to a system, method, apparatus and cloud server for maintaining a visual image of the regional heating balance status. Background Technology

[0002] Heating networks are a core component of urban centralized heating systems and a vital infrastructure for ensuring residential heating and industrial heat supply. To facilitate unified management, heat maps are created based on the existing infrastructure to display the balance of heating data for the region.

[0003] Currently, in the process of creating heat maps based on existing infrastructure, it is necessary to manually plot the data from each electric regulating valve and data acquisition device in each region onto the corresponding location on the map and display them in different colors to represent different levels of heating balance in the pipe network. If a community or unit does not require heating, the data at the corresponding location on the map needs to be manually removed; if heating is added, the relevant data also needs to be manually displayed at the corresponding location on the map.

[0004] Therefore, the current heating balance status display diagram relies heavily on manual maintenance, resulting in low work efficiency. Summary of the Invention

[0005] To improve work efficiency, this application provides a regional heating balance status visualization image maintenance system, method, apparatus, and cloud server.

[0006] In the first aspect, this application provides a regional heating balance status visualization image maintenance system, in which an on / off controller and a room temperature controller are installed on the heating pipe of each user in each region. The on / off controller and the room temperature controller are electrically connected. A heat meter and an electric regulating valve are also installed at the inlet of the building heating pipe in each region. At least one data acquisition device is also set in each region. The data acquisition device includes a data collection module. The data collection module is electrically connected to the on / off controllers of multiple users and is used to collect user room temperature data. The data collection module is electrically connected to the heat meter and is used to collect building heat data; The system also includes a balance adjustment module, which is located in the data acquisition unit or built into the electric regulating valve. The balance adjustment module is electrically connected to the electric regulating valve and is used to send adjustment signals to the electric regulating valve to control the building's heating pipes. The system also includes a cloud server, and the data acquisition unit communicates with the cloud server. Alternatively, the data collector may further include a positioning module for acquiring location data of the data collector. The cloud server generates the adjustment signal based on the room temperature data, building heat data, communication quality data, or location data sent by the data acquisition device and sends it to the balance adjustment module. It also generates and displays a visualization image of the regional heating balance status.

[0007] By adopting the above technical solution, the room temperature data and building heat data of each user can be automatically collected by the data acquisition device set in each building. The data is then packaged together with the location data or communication quality data of the data acquisition device and sent to the cloud server. After obtaining the room temperature data and building heat data, the cloud server combines the relevant data of other buildings to generate the heating network adjustment information of the current building. In turn, the balance adjustment module adjusts the opening of the electric regulating valve on the heating pipe at the entrance of the corresponding building to achieve heating balance in each area of ​​the building.

[0008] Furthermore, the cloud server generates a visual image of the regional heating balance status, displays image points on the map of the visual image based on location data, or displays image points after determining the location data based on communication quality data, and determines the display characteristics of the image points based on the magnitude of room temperature data and building heat data.

[0009] When a new building starts heating, the data acquisition device in that building automatically receives the uploaded data and generates image points. Conversely, when a building stops heating, the image points on the regional heating balance visualization image automatically disappear as the building's data acquisition device shuts down. This achieves automated visual management of the regional heating balance, eliminating the need for manual data input and significantly improving work efficiency compared to manually entering heating network diagrams or distribution maps.

[0010] Secondly, this application provides a method for maintaining a visual image of regional heating balance, using a visual image maintenance system for regional heating balance as described in the first aspect. The method is executed by the cloud server and adopts the following technical solution: The data acquisition device acquires the packaged heating status data sent by the data acquisition device. The heating status data includes the user's room temperature data, the building's heat data, communication quality data, or location data. If the heating status data includes communication quality data, then the location data of the data acquisition device is determined based on the communication quality data through triangulation. A visualization image of the regional heating balance is established. Based on the location data, the corresponding image point in the visualization image of the regional heating balance is determined. The display characteristics of the image point are determined based on the user room temperature data and the building heat data.

[0011] By adopting the above technical solution, multi-dimensional heating status data such as user room temperature and building heat can be obtained at once, avoiding the tediousness of scattered collection, improving data acquisition efficiency, and supplementing location information through triangulation based on communication quality data when no direct location data is available. No additional upgrade of the positioning module is required. Furthermore, a regional heating balance visualization image can be constructed. By combining location data to locate image points and setting display characteristics based on room temperature and building heat data, the differences in heating distribution within the region can be presented intuitively, helping to quickly identify areas of heating imbalance. There is no need for manual input of heating-related data, thus improving the efficiency of regional heating balance management.

[0012] Furthermore, the communication quality data includes location data of at least three cellular base stations, as well as the communication signal strength and communication signal delay between the data collector and each cellular base station. The step of determining the location data of the data collector based on the communication quality data through triangulation includes: Based on the location data of the cellular base station, retrieve the operating frequency, transmit power and antenna gain of the cellular base station from the enterprise database; The sum of the transmit power and antenna gain of each cellular base station is calculated, and the sum is subtracted from the communication signal strength between the data collector and the corresponding cellular base station to calculate the path loss of the communication signal between the data collector and each cellular base station. Based on the path loss and the calculation formula for the distance between the data collector and the cellular base station, the first distance between the data collector and each of the cellular base stations is calculated; The second distance between the data collector and each cellular base station is calculated based on the product of the signal propagation speed and the communication signal delay between the data collector and each cellular base station. The first distance and the second distance between the data collector and each cellular base station are calculated according to a preset weight to obtain the corresponding distance; Based on the coordinate data of each cellular base station and the distance between the data collector and each cellular base station, the location data of the data collector is determined through triangulation.

[0013] By adopting the above technical solution, multi-dimensional data supports distance calculation. The first distance is obtained by combining the path loss with the base station parameters, and the second distance is obtained by using the signal speed and delay. This improves the accuracy of distance data. The two distances are fused according to preset weights, and the location of the data collector is determined by triangulation. This solves the problem of distance calculation error from single data and provides accurate location basis for subsequent heating visualization.

[0014] Furthermore, before calculating the corresponding distance by assigning preset weights to the first distance and the second distance between the data collector and each cellular base station, the method further includes: Determine whether the difference between the first distance and the second distance is greater than a preset value; If so, then execute: Acquire multiple sets of new communication quality data and calculate multiple sets of new first distance and new second distance; The variance of the first distance is calculated based on multiple sets of the new first distances, and the variance of the second distance is calculated based on multiple sets of the new second distances; Calculate the ratio of the variance of the first distance to the variance of the second distance; Obtain the original preset weights corresponding to the first distance and the second distance; The corrected ratio is obtained by taking the reciprocal of the ratio. The updated preset weight is obtained by taking the original preset weight and the corrected ratio. The average of multiple sets of new first distances is taken as the updated first distance, and the average of multiple sets of new second distances is taken as the updated second distance.

[0015] By adopting the above technical solution, it is first determined whether the difference between the first and second distances calculated in the initial calculation exceeds the preset value. If the difference exceeds the limit, multiple sets of data are collected. The weights are adjusted by variance calculation and correction ratio to avoid the influence of single data error. The distance is updated with the mean of the new data to improve the accuracy of distance calculation and provide more reliable data for the position of the subsequent triangulation acquisition device, ensuring the accuracy of position matching in heating visualization.

[0016] Further, the step of determining the location data of the data collector through triangulation based on the coordinate data of each cellular base station and the distance between the data collector and each cellular base station includes: The data collector and any two adjacent cellular base stations are considered as a computing group; For each of the calculation groups, the base station distance d between the two cellular base stations is calculated based on the coordinate data of the two cellular base stations; Based on the distance between the base stations, and the distances a and b between the data collector and each of the cellular base stations, the cosine and sine values ​​of any included angle in the triangle formed between the data collector and two of the cellular base stations are calculated using the law of cosines: , ; Determine the direction angle α of the line connecting the two cellular base stations based on their coordinate data; Based on the coordinate data (x1, y1) of the cellular base station where any of the included angles are located, the orientation angle α, the sine value, and the cosine value, two candidate coordinate data in the calculation group are calculated:

[0017] The overlapping candidate coordinate data in each of the calculation groups are determined as the location data of the data acquisition device.

[0018] By adopting the above technical solution, the data acquisition device is grouped according to adjacent base stations. The angle between the acquisition device and the base station is calculated using the cosine theorem. Combined with the direction angle of the line connecting the base stations, two candidate coordinates for each group are derived. The logic is rigorous. The overlapping candidate coordinates of each group are taken as the position of the data acquisition device, invalid coordinates are eliminated, and the error of multiple solutions in trigonometric calculation is avoided. The accuracy of the location is greatly improved, and precise coordinate support is provided for the visualization of heating.

[0019] Furthermore, if no overlapping candidate coordinate data exists, the method further includes: Determine the fourth distance between each candidate coordinate data in each calculation group and each candidate coordinate data in other calculation groups; Delete the candidate coordinate data corresponding to the largest fourth distance in each calculation group; The center coordinates of the candidate coordinate data of each calculation group are used as the location data of the data acquisition device.

[0020] By adopting the above technical solution, when there are no overlapping candidate coordinates, the fourth distance between each group of candidate coordinates is calculated first, the candidate coordinates corresponding to the largest fourth distance in each group are deleted, abnormal data with high deviation are eliminated, and the center coordinates of the remaining candidate coordinates are taken as the position of the data collector. This avoids positioning failure when there are no overlapping coordinates, ensures the continuity and accuracy of positioning, and provides reliable location data for heating visualization.

[0021] Furthermore, the regional heating balance visualization image includes a regional map, and determining the corresponding image point in the regional heating balance visualization image based on the location data includes: Determine the corresponding location of the location data on the regional map; Determine whether an image point already exists at the corresponding location; If no image point exists at the corresponding location, then an image point is determined on the map of the area. If an image point already exists at the corresponding location, then it includes: If there are overlapping candidate coordinate data, the environmental information between the corresponding location and the cellular base station is determined on the area map. The environmental information includes building height and number of buildings. The environmental complexity is determined based on the building height and number of buildings. Based on the environmental complexity, the communication signal strength and communication signal delay between the data acquisition device and each cellular base station are adjusted, and the step of determining the location data of the data acquisition device through triangulation is repeated until an image point is determined on the regional map. If there are no overlapping candidate coordinate data, then the candidate locations corresponding to each candidate coordinate data are determined on the area map. Candidate regions are determined based on each of the candidate locations; In the candidate area, identify the building closest to the corresponding location, and on the area map, determine the image point at the location of the corresponding building.

[0022] By adopting the above technical solution, we first determine whether there are image points at the location corresponding to the location data. If not, we directly determine the location to avoid duplication. If there are, we handle them according to different situations: if there are overlapping candidate coordinates, we correct the signal data and recalculate the position according to the environmental complexity; if not, we determine the candidate area and find the nearest building to set the point to ensure the accuracy of the image points, provide reliable location mapping for heating visualization, and help with heating balance analysis.

[0023] Furthermore, before correcting the communication signal strength and communication signal delay between the data acquisition device and each cellular base station according to the environmental complexity, the process includes: Multiple environmental complexity ranges are defined based on environmental information; Acquire multiple sets of sample data, which include multiple sample environmental complexities located in each environmental complexity range, as well as the standard communication signal strength, actual communication signal strength, standard communication signal delay, and actual communication signal delay corresponding to each environmental complexity. Based on the actual communication signal strength, the environmental complexity, and the standard communication signal strength, a linear relationship model for communication signal strength is established. Based on the actual communication signal delay, the environmental complexity, and the standard communication signal delay, a linear relationship model for communication signal delay is established. Using the sample data, the parameters of the linear relationship model of the communication signal strength and the linear relationship model of the communication signal delay are fitted by regression analysis, so as to obtain the correct communication signal strength based on the environmental complexity and the communication signal strength, and / or, to obtain the correct communication signal delay based on the environmental complexity and the communication signal delay.

[0024] By adopting the above technical solution, and by dividing the environmental complexity range, and by establishing and fitting a linear model of signal strength and time delay based on sample data, the impact of the environment on the signal can be accurately quantified, making the corrected signal parameters more reliable, providing accurate data support for subsequent position calculations, and improving system accuracy.

[0025] Thirdly, this application provides a visual image maintenance device for the regional heating balance status. The following technical solution is adopted: The data acquisition module is used to acquire the heating status data sent by the data collector after being packaged. The heating status data includes the user room temperature data, the building heat data, and the communication quality data. The location data calculation module is used to determine the location data of the data collector based on the communication quality data through triangulation. The regional heating balance status visualization image establishment module is used to establish a regional heating balance status visualization image, determine the corresponding image point in the regional heating balance status visualization image based on the location data, and determine the display characteristics of the image point based on the user room temperature data and the building heat data.

[0026] By adopting the above technical solutions, the data acquisition module can acquire multi-dimensional heating status data such as user room temperature and building heat at once, avoiding the tediousness of scattered collection and improving data acquisition efficiency. When there is no direct location data, the location data calculation module can complete the location information through triangulation based on communication quality data, without the need for additional upgrades to the positioning module. The regional heating balance status visualization image establishment module further constructs a regional heating balance visualization image, combines location data to locate image points, and sets display characteristics based on room temperature and building heat data to intuitively present the differences in heating distribution within the region, helping to quickly identify areas of heating imbalance without the need for manual input of heating-related data, thus improving the efficiency of regional heating balance management.

[0027] Fourthly, this application provides a cloud server, which adopts the following technical solution: At least one processor; At least one memory; At least one computer program, wherein the at least one computer program is stored in the memory and configured to be executed by the at least one processor, the at least one computer program being configured to: perform the regional heating balance state visualization image maintenance method as described in any one of the second aspects.

[0028] By adopting the above technical solution, the processor executes the computer program in the memory to acquire multi-dimensional heating status data such as user room temperature and building heat at once, avoiding the tediousness of scattered collection, improving data acquisition efficiency. When there is no direct location data, the location information can be supplemented by triangulation based on communication quality data, without the need for additional upgrades to the positioning module. Furthermore, a regional heating balance visualization image is constructed. By combining location data to locate image points and setting display characteristics based on room temperature and building heat data, the differences in heating distribution within the region are presented intuitively, helping to quickly identify areas of heating imbalance. There is no need for manual input of heating-related data, thus improving the efficiency of regional heating balance management.

[0029] In summary, this application includes at least one of the following beneficial technical effects: 1. The regional heating balance status visualization image maintenance system can automatically collect room temperature data and building heat data from each user through data acquisition devices set up in each building. Combined with the location data or communication quality data of the data acquisition devices, the data is packaged and sent to the cloud server. After obtaining the room temperature data and building heat data, the cloud server combines the relevant data from other buildings to generate heating network adjustment information for the current building. This information then enables the balance adjustment module to adjust the opening of the electric regulating valve on the corresponding building's inlet heating pipe, so as to achieve heating balance in each area. 2. The cloud server generates a visual image of the regional heating balance status. Based on location data, image points are displayed on the map of the visual image. The display characteristics of the image points are determined based on the magnitude of room temperature data and building heat data, thus completing the automatic visual management of the regional heating balance status. No manual input of relevant data is required, which can improve work efficiency compared to manually entering heating network maps or distribution maps. Attached Figure Description

[0030] Figure 1 This is a structural diagram of a regional heating balance status visualization image maintenance system, one of the embodiments of this application.

[0031] Figure 2 This is a flowchart illustrating the method for maintaining the visualization image of the regional heating balance status in an embodiment of this application.

[0032] Figure 3 This is a schematic diagram showing overlapping candidate coordinate data when determining candidate coordinate data in an embodiment of this application.

[0033] Figure 4 This is a schematic diagram showing that there are no overlapping candidate coordinate data when determining candidate coordinate data in the embodiments of this application.

[0034] Figure 5 This is a structural block diagram of the regional heating balance status visualization image maintenance device in the embodiments of this application.

[0035] Figure 6 This is a structural block diagram of the electronic device in the embodiments of this application. Detailed Implementation

[0036] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0037] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article, unless otherwise specified, generally indicates that the preceding and following related objects have an "or" relationship.

[0038] This application discloses a regional heating balance status visualization image maintenance system, which can monitor the heating status of each region and visualize the regional heating status.

[0039] Reference Figure 1 In each area, each user's heating pipe is equipped with an on / off controller and a room temperature controller. The room temperature controller is installed in the user's room to measure, display, and set the room temperature, and can control the on / off controller via communication. The on / off controller and the room temperature controller can be connected wirelessly, for example, using NearLink wireless communication.

[0040] Each building's heating pipe inlet is also equipped with a heat meter and an electric regulating valve. The heat meter is used to measure and calculate the building's total heating demand.

[0041] At least one data acquisition unit is also installed in each area. The data acquisition unit includes a heat data collection module. The data collection module is connected to the on / off controller of each user in the building via RS485 communication and can collect room temperature data. The data collection module is also connected to the heat meter via RS485 communication and can collect building heat data. Therefore, the data acquisition unit obtains user room temperature data and building heat data from the heat data collection module.

[0042] The system also includes a cloud server. The data collector connects to the cloud server via 4G / 5G communication and sends user room temperature data and building heat data to the cloud server.

[0043] To ensure balanced heating across different areas, the cloud server includes an on / off time-area method management platform. This platform regulates room temperature via on / off controllers and distributes the heat measured by the building's heat meters to heat users based on the cumulative valve opening time and building area. Furthermore, the platform connects to an enterprise database for data communication, storing heating data and operational information.

[0044] To facilitate the adjustment of the building's heating pipe opening for heating control, the cloud server is also equipped with a pipe network balance management system. An electric regulating valve is installed at the inlet of the building's heating pipe. The electric regulating valve can control the opening of the heating pipe. The electric regulating valve is connected to the data acquisition device via RS485 communication. Data communication is carried out between the on / off time area method management platform and the pipe network balance management system. The pipe network balance management system generates adjustment signals for the electric regulating valve according to the building's heating demand.

[0045] The system also includes a balance adjustment module, which can be installed in the data acquisition unit or in the electrically adjustable valve. For example... Figure 1 The image shown is a visualization image maintenance system for the regional heating balance status when the balance adjustment module is set in the data acquisition unit.

[0046] When the balance adjustment module is set in the data acquisition unit, it can be programmed into the same processor as the calculation program corresponding to the data collection module, or it can be set in the data acquisition unit as a separate programmed hardware module and connected to the original processor of the data acquisition unit.

[0047] The electrically adjustable valve and the data acquisition unit are connected via RS485 and AIO, enabling data exchange and control command transmission through analog signals. The balancing module is electrically connected to the electrically adjustable valve. Based on the adjustment signals from the building's heating pipelines sent by the pipeline balancing management system, the balancing module controls the opening of the electrically adjustable valve to control the amount of heating supplied to the building and achieve a heating balance.

[0048] In one implementation, to facilitate the location of heating data and replace manual data input, a positioning module is set in the data acquisition device. The positioning module can acquire the location data of the positioning acquisition device and package it together with the user's room temperature data and the building's heat data and send it to the cloud server. This allows the pipeline balance management system to determine the display location on the map corresponding to the visualization image based on the location data, and then generate image point display features at the display location based on the user's room temperature data and the building's heat data.

[0049] Reference Figure 1 In another possible implementation, there is no need to install a positioning module in the data collector. The data collector acquires the communication quality data of various surrounding cellular base stations, packages the communication quality data together with the user's room temperature data and the building's heat data, and sends them to the cloud server. The pipeline balance management system calculates the location data based on the communication quality data, and then determines the display location on the map corresponding to the visualization image, thereby generating image points.

[0050] Therefore, the regional heating balance status visualization image maintenance system of this application can automatically collect the room temperature data and building heat data of each user through the data acquisition device set in each building. Combined with the location data or communication quality data of the data acquisition device, the data is packaged and sent to the cloud server. After obtaining the room temperature data and building heat data, the cloud server combines the relevant data of other buildings to generate the heating network adjustment information of the current building. In turn, the balance adjustment module adjusts the opening of the electric regulating valve on the heating pipe at the entrance of the corresponding building to achieve heating balance in each area.

[0051] Furthermore, the cloud server generates a visual image of the district's heating balance status. Image points are displayed on the map of this visual image based on location data, or after determining location data using communication quality data. The display characteristics of the image points are determined based on the magnitude of room temperature data and building heat data. When a new building starts heating, the data collector in that building automatically receives the uploaded data and generates image points; conversely, when a building stops heating, the image points on the district heating balance visualization image automatically disappear as the building's data collector shuts down. This achieves automated visual management of the district heating balance status, eliminating the need for manual data input and improving work efficiency.

[0052] This application discloses a method for maintaining a visualized image of regional heating balance. (Refer to...) Figure 2 This is executed by the cloud server, including (steps S101 to S103): Step S101: Obtain the heating status data sent by the data acquisition device after packaging. The heating status data includes user room temperature data, building heat data, communication quality data, or location data.

[0053] Specifically, if the data acquisition device is equipped with a positioning module, such as a GPS module, the heating status data sent by the data acquisition device will include the location data of the data acquisition device; if the data acquisition device is not equipped with a positioning module, the heating status data will include communication quality data.

[0054] The communication quality data includes the location data of at least three cellular base stations, as well as the communication signal strength and communication signal delay between the data collector and each cellular base station.

[0055] When communicating with various cellular base stations, the data collector can obtain base station location data sent to the data collector by the operator through the operator's authorized IoT private network.

[0056] If the heating status data includes communication quality data, then proceed to step S102: Based on the communication quality data, determine the location data of the data acquisition device through triangulation, including (steps S1021 to S1025): Step S1021: Based on the location data of the cellular base station, retrieve the corresponding operating frequency, transmission power, and antenna gain of the cellular base station from the enterprise database.

[0057] Specifically, the cloud server has pre-set location data, operating frequency, transmission power, and antenna gain of each cellular base station within the heating area and its surrounding area, stored in the enterprise database for easy access. Therefore, once the cloud server obtains the location data of the cellular base stations, it can directly retrieve the relevant base station data from the enterprise database.

[0058] Step S1021: Calculate the sum of the transmit power and antenna gain of each cellular base station, subtract the sum from the communication signal strength between the data collector and the corresponding cellular base station, and calculate the path loss of the communication signal between the data collector and each cellular base station.

[0059] Specifically, since the power of communication signals decreases with distance, it is easier to calculate the propagation distance.

[0060] In an open, unobstructed environment, the signal propagates in a straight line. Without reflection interference, the path loss is: , For cellular base station transmission power, For antenna gain, The signal strength is represented by the value PL. Substituting these values ​​into the path loss calculation formula, the path loss PL is calculated.

[0061] Step S1022: Based on the calculation formula of path loss and distance between data collector and cellular base station, calculate the first distance between data collector and each cellular base station.

[0062] Specifically, in an unobstructed environment, path loss follows a logarithmic relationship with propagation distance and signal frequency: , Where is the distance between the data collector and the cellular base station, f is the operating frequency of the cellular base station (e.g., 1800 / 2600 MHz for 4G and 3500 / 2600 MHz for 5G), and a is a constant derived from the speed of light, with a value of 147.55.

[0063] However, heating systems are installed in urban environments, where communication signals experience additional attenuation due to obstacles and reflections. Therefore, the above path loss formula needs to be modified: ,in, This is the shadow attenuation value, measured in dB. It describes the additional attenuation caused by signal reflection or scattering due to the environment. The typical value ranges from 8 to 12 dB, with larger values ​​in densely populated urban areas and smaller values ​​in suburban areas. Therefore, it should be preset according to the actual situation of the area.

[0064] By working backward, the first distance between the data collector and the cellular base station can be calculated. .

[0065] Step S1023: Calculate the second distance between the data collector and each cellular base station based on the product of the signal propagation speed and the communication signal delay between the data collector and each cellular base station.

[0066] Specifically, the signal propagation speed is adopted as the speed of light c≈3× m / s, the round-trip time T of the signal between the data acquisition unit and the cellular base station is measured, and the one-way propagation time is calculated. Then the second distance is calculated. .

[0067] Step S1024: Calculate the corresponding distance by assigning a first distance and a second distance between the data collector and each cellular base station according to a preset weight.

[0068] Specifically, the environment has different effects on the distance calculation based on the strength and delay of the communication signal, resulting in a difference between the first distance and the second distance. Therefore, the corresponding distance is calculated using a preset weight.

[0069] Based on actual experiments, the preset weights of the first distance and the second distance are determined. If the distance measurement deviation through communication signal delay is small, the weight of the second distance is higher. If the distance measurement deviation through communication signal strength is large, the weight of the first distance is determined to be larger. The distance is then calculated.

[0070] However, if the environment is complex and the difference between the first and second distances is too large, the weighted calculation result will also have a large deviation. In this case, the first and second distances should be recalculated to avoid large deviations as much as possible.

[0071] Before executing step S1024, the following steps (steps S11 to S16) are executed: Step S11: Determine whether the difference between the first distance and the second distance is greater than a preset value. If yes, proceed to steps S12 to S16; otherwise, proceed to step S1024.

[0072] Step S12: Obtain multiple sets of new communication quality data and calculate multiple sets of new first distance and new second distance.

[0073] Step S13: Calculate the variance of the first distance based on multiple sets of new first distances, and calculate the variance of the second distance based on multiple sets of new second distances.

[0074] Step S14: Calculate the ratio of the variance of the first distance to the variance of the second distance.

[0075] Step S15: Obtain the original preset weights corresponding to the first distance and the second distance.

[0076] Step S16: Obtain the corrected ratio based on the reciprocal of the ratio; obtain the updated preset weights based on the preset weights and the corrected ratios; and use the average of multiple sets of new first distances as the updated first distance, and the average of multiple sets of new second distances as the updated second distance. Specifically, the cloud server repeats the process of acquiring communication quality data multiple times, obtaining multiple sets of new communication quality data sent by the data collector, and calculating multiple sets of new first distances and new second distances. The magnitude of the variance reflects the stability of the data. In the ratio of the variance of the first distance to the variance of the second distance, the larger the variance, the larger the corresponding ratio, indicating poorer stability. Therefore, a smaller weight is needed, and a larger ratio corresponds to a smaller correction ratio.

[0077] For example, if the original preset weight is 2:3 and the correction ratio is 1:2, then the updated preset weight is 3:5. In the updated preset weight, the weight of the more unstable first distance is reduced, thus reducing the deviation of the final distance.

[0078] Step S1025: Based on the coordinate data of each cellular base station and the distance between the data collector and each cellular base station, determine the location data of the data collector through triangulation, including (steps S21 to S26): Step S21: Assume a computing group between the data collector and any two adjacent cellular base stations.

[0079] Specifically, by using the distance between the data collector and two cellular base stations, and in the case of unknown directions, triangulation can be performed to calculate the possible locations of the two data collectors. To determine a unique location, the distances between the data collector and at least three cellular base stations are required. Therefore, a triangle is formed between the data collector and any two adjacent cellular base stations, and this triangle is used as a calculation group for the calculation.

[0080] For example Figure 3 The data acquisition device can be grouped into one computing group with base station A and base station B, another can be grouped into one computing group with base station B and base station C, and yet another can be grouped into one computing group with base station A and base station C.

[0081] Step S22: For each calculation group, calculate the base station distance d between the two cellular base stations based on the coordinate data of the two cellular base stations.

[0082] Specifically, the coordinate data of a cellular base station can be latitude and longitude, and the distance between two cellular base stations can be calculated using the Haversine formula.

[0083] Step S23: Based on the base station distance d, and the distances a and b between the data collector and each cellular base station, calculate the cosine and sine values ​​of any angle formed by the triangle between the data collector and the two cellular base stations using the law of cosines: , .

[0084] Step S24: Determine the direction angle α of the line connecting the two cellular base stations based on their coordinate data.

[0085] Step S25: Based on the coordinate data (x1, y1), orientation angle α, sine value, and cosine value of the cellular base station where any included angle is located, calculate the two candidate coordinate data in the calculation group:

[0086] Step S26: Determine the overlapping candidate coordinate data in each calculation group as the location data of the data acquisition device.

[0087] Specifically, once there are results from at least two sets of calculations, the overlapping candidate coordinate data can be identified as the location data of the data acquisition device.

[0088] For example Figure 3 Candidate coordinates 1 and 2 are the results of the first calculation group, candidate coordinates 3 and 4 are the results of the second calculation group, and candidate coordinates 5 and 6 are the results of the third calculation group. Since candidate coordinates 2 and 3 coincide, their coordinate data are determined as position data.

[0089] Furthermore, due to discrepancies in distance calculations, there is a possibility that candidate coordinate data may not overlap within each calculation group. (Refer to...) Figure 4 If there are no overlapping candidate coordinate data, then the following steps (steps S31 to S33) are performed: Step S31: Determine the fourth distance between each candidate coordinate data in each calculation group and each candidate coordinate data in other calculation groups.

[0090] Reference Figure 4For the first calculation group, determine the fourth distance between candidate coordinate 1 and candidate coordinates 3, 4, 5, and 6 respectively; determine the fourth distance between candidate coordinate 2 and candidate coordinates 3, 4, 5, and 6 respectively.

[0091] Step S32: Delete the candidate coordinate data corresponding to the largest fourth distance in each calculation group.

[0092] Specifically, refer to Figure 4 For the first calculation group, candidate coordinate 1 has the largest fourth distance compared to candidate coordinate 2, so candidate coordinate 1 is deleted. Similarly, candidate coordinate 4 and candidate coordinate 6 are also deleted.

[0093] Step S33: Determine the center coordinate data of the candidate coordinate data of each calculation group as the location data of the data acquisition device.

[0094] Specifically, refer to Figure 4 The center coordinates of candidate coordinates 2, 3, and 5 are determined as the location data of the data acquisition device.

[0095] Step S103: Establish a visualization image of the regional heating balance status, determine the corresponding image points in the visualization image of the regional heating balance status based on location data, and determine the display characteristics of the image points based on user room temperature data and building heat data.

[0096] Specifically, the cloud server creates a regional map based on the layout of buildings in the area. The regional map can display image points of the heating balance status of the buildings, thus forming a visual image.

[0097] When the cloud server determines the corresponding image point in the visualization image of the regional heating balance status based on the location data, it includes (steps S41 to S49): Step S41: Determine the corresponding location of the location data on the regional map.

[0098] Specifically, the cloud server maps the coordinates of the regional map to the actual locations one by one, so that each actual location can be uniquely located on the regional map.

[0099] Step S42: Determine whether an image point already exists at the corresponding location.

[0100] If no image point exists at the corresponding location, proceed to step S43: determine the image point on the area map.

[0101] Specifically, if no image point exists at the corresponding location, the data from the newly added data collector is the first time it has been added, and the image point is then determined on the regional map.

[0102] If an image point already exists at the corresponding location, then it includes: Specifically, if an image point already exists at the corresponding location, it may be that the currently determined location deviates significantly from the actual location, thus locating the existing data acquisition device. Therefore, further correction of the location data is required.

[0103] Step S44: If there are overlapping candidate coordinate data, determine the environmental information between the corresponding location and the cellular base station on the regional map. The environmental information includes building height and number of buildings.

[0104] First, if the location data is obtained from overlapping candidate coordinate data when determining the location data, then the environmental information between the corresponding location and the cellular base station is determined on the regional map, and the candidate coordinate data is corrected based on the environmental information so that the candidate coordinate data can be re-determined.

[0105] The cloud server pre-stores the height of each building in the regional map in the enterprise database, which enables it to quickly determine the number of buildings between a location and a cellular base station.

[0106] Step S45: Determine the environmental complexity based on the building height and number of buildings.

[0107] Specifically, the cloud server uses the average height of each building as the environmental complexity. The higher the building, the more significant the impact on communication signals, meaning the greater the environmental complexity.

[0108] Step S46: Adjust the communication signal strength and communication signal delay between the data collector and each cellular base station according to the environmental complexity, and repeat the step of determining the location data of the data collector through triangulation until the image point is determined on the regional map.

[0109] Specifically, before the cloud server adjusts the communication signal strength and latency between the data collector and each cellular base station based on environmental complexity, it determines the impact model of environmental complexity on communication signal strength and latency. This facilitates the reconstruction of relevant standard data based on environmental complexity, including (steps Sa to Se): Step Sa: Divide the environment into multiple complexity ranges based on environmental information.

[0110] For example, corresponding environmental complexity ranges are set for suburban areas, general urban areas, and densely populated urban areas.

[0111] Step Sb: Obtain multiple sets of sample data. The sample data includes multiple sample environmental complexities located in each environmental complexity range, as well as the standard communication signal strength, actual communication signal strength, standard communication signal delay, and actual communication signal delay corresponding to each environmental complexity.

[0112] Step Sc: Based on the actual communication signal strength, environmental complexity, and standard communication signal strength, establish a linear relationship model for communication signal strength.

[0113] Specifically, the actual communication signal strength is the result of the standard signal strength being attenuated or enhanced by environmental complexity, resulting in a linear relationship model for the communication signal strength: ,in, This represents the actual communication signal strength. For standard communication signal strength, Due to environmental complexity, This is a constant term, corresponding to the system's default minimum signal threshold in scenarios with no signal and no interference. , and This is the coefficient representing the impact of environmental complexity on communication signal strength. This is the random error term.

[0114] Step Sd: Based on the actual communication signal delay, environmental complexity, and standard communication signal delay, establish a linear relationship model for communication signal delay.

[0115] Specifically, the actual communication signal delay is the sum of the standard communication signal delay and the additional delay introduced by complexity, resulting in a linear relationship model for the communication signal delay: Y represents the actual communication signal delay. For standard communication signal delay, Due to environmental complexity, It is a constant term. , and This is the coefficient representing the impact of environmental complexity on communication signal delay. This is the random error term.

[0116] Step Se: Apply sample data and fit the parameters of the linear relationship model of communication signal strength and the linear relationship model of communication signal delay through regression analysis, so as to obtain the correct communication signal strength based on environmental complexity and communication signal strength, and / or, the correct communication signal delay based on environmental complexity and communication signal delay.

[0117] Specifically, the cloud server uses sample data to calculate the coefficients of each model through regression analysis such as least squares, thereby obtaining a linear relationship model. After obtaining the environmental complexity and the actual communication signal strength, the standard communication signal strength is calculated in reverse. Alternatively, the standard communication signal delay is calculated in reverse using the environmental complexity and the actual communication signal delay.

[0118] This process then corrects the communication signal strength and latency between the data acquisition unit and each cellular base station based on environmental complexity. The corrected communication signal strength and latency reduce the impact of the environment on communication. The location data obtained after triangulation is more accurate until image points are determined on the regional map.

[0119] Step S47: If there are no overlapping candidate coordinate data, determine the candidate locations corresponding to each candidate coordinate data on the regional map.

[0120] Step S48: Determine candidate regions based on each candidate location.

[0121] Specifically, the cloud server determines the common circle of candidate locations, and then determines the candidate region concentric with the common circle. The shortest distance between the edge of the candidate region and the edge of the common circle is a preset value.

[0122] Step S49: Determine the building closest to the corresponding location in the candidate area, and determine the image point at the location of the corresponding building on the area map.

[0123] If all candidate locations are located near the community or workplace, then the data collector is most likely located in the community or workplace, and an image point is determined at the corresponding location.

[0124] After determining the image points, the cloud server determines the display characteristics of the image points based on the user's room temperature data and the building's heat data. The display characteristics can be colors. For example, building heat image points can be displayed simultaneously. The color of the image points is determined based on the building's heat data, dividing the image points into multiple temperature levels. Each temperature level corresponds to a color, with temperatures ranging from low to high as blue, yellow, green, orange, and red. Green represents normal temperature, blue represents the lowest temperature, and red represents the highest temperature.

[0125] Furthermore, the user's room temperature image point is hidden at the location of the building's heat image point. For example, the user's room temperature image point will only be displayed after the user clicks on the building's heat image point, which helps to make the visualization image of the regional heating balance status neat and beautiful.

[0126] To ensure accurate positioning, the cloud server generates prompts after determining the location of image points based on location data, allowing for manual verification of accuracy and improving work efficiency while ensuring accuracy.

[0127] To better implement the above method, this application also provides a regional heating balance status visualization image maintenance device, referring to... Figure 5 The district heating balance status visualization image maintenance device 200 includes: The data acquisition module 201 is used to acquire the heating status data sent by the data collector after packaging. The heating status data includes user room temperature data, building heat data and communication quality data. The location data calculation module 202 is used to determine the location data of the data acquisition device based on communication quality data and through triangulation calculation. The regional heating balance status visualization image establishment module 203 is used to establish a regional heating balance status visualization image, determine the corresponding image points in the regional heating balance status visualization image based on location data, and determine the display characteristics of the image points based on user room temperature data and building heat data.

[0128] The regional heating balance status visualization image creation module 203, when determining the location data of the data acquisition device through triangulation based on communication quality data, is specifically used for: Based on the location data of cellular base stations, retrieve the corresponding operating frequency, transmit power, and antenna gain of the cellular base stations from the enterprise database; The sum of the transmit power and antenna gain of each cellular base station is calculated. This sum is then subtracted from the communication signal strength between the data collector and the corresponding cellular base station to calculate the path loss of the communication signal between the data collector and each cellular base station. Based on the formula for calculating path loss and the distance between the data collector and the cellular base station, the first distance between the data collector and each cellular base station is calculated. The second distance between the data collector and each cellular base station is calculated based on the product of the signal propagation speed and the communication signal delay between the data collector and each cellular base station. The first and second distances between the data collector and each cellular base station are calculated according to preset weights to obtain the corresponding distances; Based on the coordinates of each cellular base station and the distance between the data collector and each cellular base station, the location data of the data collector is determined through triangulation.

[0129] Furthermore, the district heating balance status visualization image maintenance device 200 also includes: The difference judgment module is used to determine whether the difference between the first distance and the second distance is greater than a preset value; If so, then execute: The new data acquisition module is used to acquire multiple sets of new communication quality data and calculate multiple sets of new first distance and new second distance. The variance calculation module is used to calculate the variance of the first distance based on multiple sets of new first distances, and to calculate the variance of the second distance based on multiple sets of new second distances. The ratio calculation module is used to calculate the ratio of the variance of the first distance to the variance of the second distance. The original preset weight acquisition module is used to acquire the original preset weights corresponding to the first distance and the second distance. The weight correction module is used to obtain the correction ratio based on the reciprocal of the ratio, obtain the updated preset weight based on the original preset weight and the correction ratio, and use the average of multiple new first distances as the updated first distance and the average of multiple new second distances as the updated second distance.

[0130] The regional heating balance status visualization image creation module 203, when determining the location data of the data collector through triangulation based on the coordinate data of each cellular base station and the distance between the data collector and each cellular base station, is specifically used for: The data acquisition unit and any two adjacent cellular base stations are treated as a computing group; For each calculation group, the distance d between the two cellular base stations is calculated based on the coordinate data of the two cellular base stations. Based on the distance between the base stations, and the distances a and b between the data collector and each cellular base station, the cosine and sine values ​​of any included angle in the triangle formed between the data collector and the two cellular base stations are calculated using the law of cosines: , ; Determine the direction angle α of the line connecting the two cellular base stations based on their coordinate data; Based on the coordinate data (x1, y1), orientation angle α, sine value, and cosine value of the cellular base station where any included angle is located, two candidate coordinate data in the calculation group are calculated:

[0131] The overlapping candidate coordinate data in each calculation group are determined as the location data of the data acquisition device.

[0132] Furthermore, the district heating balance status visualization image maintenance device 200 also includes the following functionality when there is no overlapping candidate coordinate data: The fourth distance determination module is used to determine the fourth distance between each candidate coordinate data in each calculation group and each candidate coordinate data in other calculation groups. The candidate coordinate data pruning module is used to delete the candidate coordinate data corresponding to the largest fourth distance in each calculation group; The location data determination module is used to determine the center coordinate data of the candidate coordinate data of each calculation group as the location data of the data acquisition device.

[0133] The district heating balance status visualization image creation module 203, when determining the corresponding image points in the district heating balance status visualization image based on location data, is specifically used for: Determine the corresponding location of the location data on the regional map; Determine whether an image point already exists at the corresponding location; If no image point exists at the corresponding location, then an image point is determined on the regional map; If an image point already exists at the corresponding location, then it includes: If there are overlapping candidate coordinate data, the environmental information between the corresponding location and the cellular base station is determined on the regional map. The environmental information includes building height and number of buildings. The complexity of the environment is determined by the height and number of buildings. Based on the environmental complexity, the communication signal strength and communication signal delay between the data acquisition unit and each cellular base station are adjusted. The steps of determining the location data of the data acquisition unit through triangulation are repeated until the image point is determined on the regional map. If there are no overlapping candidate coordinate data, then determine the candidate locations corresponding to each candidate coordinate data on the regional map; Candidate areas were determined based on each candidate location; Identify the building closest to the corresponding location within the candidate area, and then locate the image point at the corresponding building's position on the area map.

[0134] Before adjusting the communication signal strength and latency between the data acquisition unit and each cellular base station based on environmental complexity, the following steps are included: Multiple environmental complexity ranges are defined based on environmental information; Acquire multiple sets of sample data, including multiple sample environmental complexities located in each environmental complexity range, as well as the standard communication signal strength, actual communication signal strength, standard communication signal delay, and actual communication signal delay corresponding to each environmental complexity. A linear relationship model for communication signal strength is established based on actual communication signal strength, environmental complexity, and standard communication signal strength. A linear relationship model for communication signal delay is established based on actual communication signal delay, environmental complexity, and standard communication signal delay. By applying sample data, regression analysis is used to fit the parameters of linear relationship models for communication signal strength and communication signal delay, so as to obtain the correct communication signal strength based on environmental complexity and communication signal strength, and / or, the correct communication signal delay based on environmental complexity and communication signal delay.

[0135] The various variations and specific examples of the methods in the foregoing embodiments are also applicable to the regional heating balance status visualization image maintenance device of this embodiment. Through the foregoing detailed description of the regional heating balance status visualization image maintenance method, those skilled in the art can clearly understand the implementation method of the regional heating balance status visualization image maintenance device of this embodiment. Therefore, for the sake of brevity, it will not be described in detail here.

[0136] To better implement the above methods, this application provides a cloud server, as shown in the embodiments below. Figure 6 The cloud server 300 includes a processor 301, a memory 303, and a display screen 305. The memory 303 and the display screen 305 are both connected to the processor 301, such as via a bus 302. Optionally, the cloud server 300 may also include a transceiver 304. It should be noted that in practical applications, the transceiver 304 is not limited to one unit, and the structure of this cloud server 300 does not constitute a limitation on the embodiments of this application.

[0137] Processor 301 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 301 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0138] Bus 302 may include a pathway for transmitting information between the aforementioned components. Bus 302 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Bus 302 may be divided into address bus, data bus, control bus, etc.

[0139] The memory 303 may be a ROM (Read Only Memory) or other type of static storage device capable of storing static information and instructions, RAM (Random Access Memory) or other type of dynamic storage device capable of storing information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), CD-ROM (Compact Disc Read Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto.

[0140] The memory 303 is used to store application code that executes the solution of this application, and its execution is controlled by the processor 301. The processor 301 is used to execute the application code stored in the memory 303 to implement the content shown in the foregoing method embodiments.

[0141] Figure 6 The cloud server 300 shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0142] The above are all preferred embodiments of this application, and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the structure, shape and principle of this application should be covered within the scope of protection of this application.

[0143] Additionally, it should be understood that relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. The terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

Claims

1. A visual image maintenance system for district heating balance status, characterized in that, Each user's heating pipe in each area is equipped with an on / off controller and a room temperature controller, which are electrically connected. Each area's building heating pipe inlet is also equipped with a heat meter and an electric regulating valve. Each area is also equipped with at least one data acquisition device, which includes a data collection module. The data collection module is electrically connected to the on / off controllers of multiple users and is used to collect user room temperature data. The data collection module is electrically connected to the heat meter and is used to collect building heat data; The system also includes a balance adjustment module, which is located in the data acquisition unit or built into the electric regulating valve. The balance adjustment module is electrically connected to the electric regulating valve and is used to send adjustment signals to the electric regulating valve to control the building's heating pipes. The system also includes a cloud server, and the data collector communicates with the cloud server. Alternatively, the data collector may further include a positioning module for acquiring location data of the data collector. The cloud server generates the adjustment signal based on the room temperature data, building heat data, communication quality data, or location data sent by the data acquisition device and sends it to the balance adjustment module. It also generates and displays a visualization image of the regional heating balance status.

2. A method for maintaining a visual image of the regional heating balance status, characterized in that, The method of the regional heating balance status visualization image maintenance system as described in claim 1, wherein the method is executed by the cloud server, includes: The data acquisition device acquires the heating status data after packaging and sending it. The heating status data includes the user's room temperature data, the building's heat data, communication quality data, or location data. If the heating status data includes communication quality data, then the location data of the data acquisition device is determined based on the communication quality data through triangulation. A visualization image of the regional heating balance is established. Based on the location data, the corresponding image point in the visualization image of the regional heating balance is determined. The display characteristics of the image point are determined based on the user room temperature data and the building heat data.

3. The method according to claim 2, characterized in that, The communication quality data includes location data of at least three cellular base stations, and also includes the communication signal strength and communication signal delay between the data collector and each cellular base station. The step of determining the location data of the data collector based on the communication quality data through triangulation includes: Based on the location data of the cellular base station, retrieve the operating frequency, transmit power and antenna gain of the cellular base station from the enterprise database; The sum of the transmit power and antenna gain of each cellular base station is calculated, and the sum is subtracted from the communication signal strength between the data collector and the corresponding cellular base station to calculate the path loss of the communication signal between the data collector and each cellular base station. Based on the path loss and the calculation formula for the distance between the data collector and the cellular base station, the first distance between the data collector and each of the cellular base stations is calculated; The second distance between the data collector and each cellular base station is calculated based on the product of the signal propagation speed and the communication signal delay between the data collector and each cellular base station. The first distance and the second distance between the data collector and each cellular base station are calculated according to a preset weight to obtain the corresponding distance; Based on the coordinate data of each cellular base station and the distance between the data collector and each cellular base station, the location data of the data collector is determined through triangulation.

4. The method according to claim 3, characterized in that, Before calculating the corresponding distance by applying a preset weight to the first distance and the second distance between the data collector and each cellular base station, the method further includes: Determine whether the difference between the first distance and the second distance is greater than a preset value; If so, then execute: Acquire multiple sets of new communication quality data and calculate multiple sets of new first distance and new second distance; The variance of the first distance is calculated based on multiple sets of the new first distances, and the variance of the second distance is calculated based on multiple sets of the new second distances; Calculate the ratio of the variance of the first distance to the variance of the second distance; Obtain the original preset weights corresponding to the first distance and the second distance; The corrected ratio is obtained by taking the reciprocal of the ratio. The updated preset weight is obtained by taking the original preset weight and the corrected ratio. The average of multiple sets of new first distances is taken as the updated first distance, and the average of multiple sets of new second distances is taken as the updated second distance.

5. The method according to claim 3, characterized in that, The step of determining the location data of the data collector through triangulation based on the coordinate data of each cellular base station and the distance between the data collector and each cellular base station includes: The data collector and any two adjacent cellular base stations are considered as a computing group; For each of the calculation groups, the base station distance d between the two cellular base stations is calculated based on the coordinate data of the two cellular base stations; Based on the distance between the base stations, and the distances a and b between the data collector and each of the cellular base stations, the cosine and sine values ​​of any included angle in the triangle formed between the data collector and two of the cellular base stations are calculated using the law of cosines: , ; Determine the direction angle α of the line connecting the two cellular base stations based on their coordinate data; Based on the coordinate data (x1, y1) of the cellular base station where any of the included angles are located, the orientation angle α, the sine value, and the cosine value, two candidate coordinate data in the calculation group are calculated: ； The overlapping candidate coordinate data in each of the calculation groups are determined as the location data of the data acquisition device.

6. The method according to claim 5, characterized in that, If no overlapping candidate coordinate data exists, the method further includes: Determine the fourth distance between each candidate coordinate data in each calculation group and each candidate coordinate data in other calculation groups; Delete the candidate coordinate data corresponding to the largest fourth distance in each calculation group; The center coordinates of the candidate coordinate data of each calculation group are used as the location data of the data acquisition device.

7. The method according to claim 2, characterized in that, The regional heating balance visualization image includes a regional map, and determining the corresponding image point in the regional heating balance visualization image based on the location data includes: Determine the corresponding location of the location data on the regional map; Determine whether an image point already exists at the corresponding location; If no image point exists at the corresponding location, then an image point is determined on the map of the area. If an image point already exists at the corresponding location, then it includes: If there are overlapping candidate coordinate data, the environmental information between the corresponding location and the cellular base station is determined on the area map. The environmental information includes building height and number of buildings. The environmental complexity is determined based on the building height and number of buildings. Based on the environmental complexity, the communication signal strength and communication signal delay between the data acquisition device and each cellular base station are adjusted, and the step of determining the location data of the data acquisition device through triangulation is repeated until an image point is determined on the regional map. If there are no overlapping candidate coordinate data, then the candidate locations corresponding to each candidate coordinate data are determined on the area map; Candidate regions are determined based on each of the candidate locations; In the candidate area, identify the building closest to the corresponding location, and on the area map, determine the image point at the location of the corresponding building.

8. The method according to claim 7, characterized in that, Before correcting the communication signal strength and communication signal delay between the data acquisition device and each cellular base station according to the environmental complexity, the process includes: Multiple environmental complexity ranges are defined based on environmental information; Acquire multiple sets of sample data, which include multiple sample environmental complexities located in each environmental complexity range, as well as the standard communication signal strength, actual communication signal strength, standard communication signal delay, and actual communication signal delay corresponding to each environmental complexity. Based on the actual communication signal strength, the environmental complexity, and the standard communication signal strength, a linear relationship model for communication signal strength is established. Based on the actual communication signal delay, the environmental complexity, and the standard communication signal delay, a linear relationship model for communication signal delay is established. Using the sample data, the parameters of the linear relationship model of the communication signal strength and the linear relationship model of the communication signal delay are fitted by regression analysis, so as to obtain the correct communication signal strength based on the environmental complexity and the communication signal strength, and / or, to obtain the correct communication signal delay based on the environmental complexity and the communication signal delay.

9. A visual image maintenance device for regional heating balance status, characterized in that, include: The data acquisition module is used to acquire the heating status data sent by the data collector after being packaged. The heating status data includes the user room temperature data, the building heat data, and the communication quality data. The location data calculation module is used to determine the location data of the data collector based on the communication quality data through triangulation. The regional heating balance status visualization image establishment module is used to establish a regional heating balance status visualization image, determine the corresponding image point in the regional heating balance status visualization image based on the location data, and determine the display characteristics of the image point based on the user room temperature data and the building heat data.

10. A cloud server, characterized in that, include: At least one processor; At least one memory; At least one computer program, wherein the at least one computer program is stored in the memory and configured to be executed by the at least one processor, the at least one computer program being configured to: perform the regional heating balance state visualization image maintenance method as claimed in any one of claims 2-8.

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