Realization of building land map positioning system based on digital model

Abstract

The present invention relates to the technical field of building land map positioning, and in particular to a building land map positioning system based on a digital model, comprising: a data acquisition module, a data processing module, a data analysis module, a data storage module, and a central control management module; the system distinguishes between high-density power consumption areas and low-density power consumption areas through power consumption data, and then uses a drone to take on-site photos of the low-density power consumption areas, and at the same time compares them with planned regular building map data to determine whether they meet the requirements of building land or illegal construction, and then estimates the value of the building land through multi-faceted data, provides reference data for staff, improves the convenience of positioning and analyzing building land in urban areas, and improves the convenience of staff analyzing the building land by generating a three-dimensional digital model.

Description

Technical Field

[0001] The invention relates to the technical field of building land map positioning, and in particular to a building land map positioning system based on a digital model. Background Art

[0002] The mapping of construction land in urban areas is used to find construction land in urban areas, so as to facilitate the improvement or development of facilities in urban areas. Generally, images of urban areas are acquired by remote sensing or drones, and then three-dimensional digital modeling of the city is performed based on the acquired data, such as a method for generating a three-dimensional digital map of a city disclosed in the invention patent with publication number CN109035396A and a system and method for surveying and updating urban maps based on big data disclosed in the invention patent with publication number CN113505188A.

[0003] However, during use, it was found that it is difficult to obtain accurate data in the urban area through remote sensing, which affects the accuracy of three-dimensional digital modeling. It is difficult to obtain data for the entire urban area through drones, and the captured images are prone to contain parts that involve personal privacy and require post-processing. After the construction land is discovered, it is inconvenient to provide reference data for the staff, and the staff are required to analyze the value of the construction land, resulting in poor practicality. Therefore, there is an urgent need to implement a construction land map positioning system based on a digital model. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides a system for distinguishing high-density electricity consumption areas from low-density electricity consumption areas through electricity consumption data, and then using a drone to take on-site photos of the low-density electricity consumption areas, and at the same time comparing them with the planned regular building map data to determine whether they meet the requirements of building land or illegal buildings, and then estimating the value of the building land through various data to provide reference data for staff, thereby improving the convenience of locating and analyzing building land in urban areas, and generating a three-dimensional digital model to improve the convenience of staff in analyzing the building land, thereby realizing a building land map positioning system based on a digital model.

[0005] The digital model-based construction land map positioning system of the present invention comprises:

[0006] Data acquisition module: acquires urban electricity consumption data, images of low-density electricity consumption areas, relevant data of low-density electricity consumption areas on the network, and planned formal building map data;

[0007] Data processing module: adjusts the clarity of the image data of the low-density electricity consumption area, and retouches the part of the image suspected of personal privacy, and then transmits the retouched image to the data analysis module;

[0008] Data analysis module: Combined with the retouched image data, the relevant data of the low-density electricity consumption area on the network is cleaned, key information is extracted, and the value of the building land in the image is estimated to provide reference data for the staff, and a three-dimensional digital model is established to facilitate the staff to observe the area;

[0009] Data storage module: stores the acquired data, estimated results and staff operation data;

[0010] Central control management module: verify the identity information of the logged-in staff, record the staff's operation process, and assist the staff in centrally managing the data acquisition module, data processing module, data analysis module and data storage module;

[0011] Electricity consumption data is used to distinguish between high-density electricity consumption areas and low-density electricity consumption areas, and then drones are used to take on-site photos of low-density electricity consumption areas. At the same time, they are compared with the planned formal building map data to determine whether they meet the requirements of building land or illegal construction. The value of the building land is then estimated through various data to provide reference data for staff, thereby improving the convenience of locating and analyzing building land in urban areas. In addition, by generating a three-dimensional digital model, the convenience of staff in analyzing the building land is improved.

[0012] Preferably, the data acquisition module includes:

[0013] Relevant data acquisition unit: searching for relevant data of low-density electricity consumption areas on the Internet;

[0014] Electricity consumption data acquisition unit: acquires electricity consumption data in the urban area and distinguishes between high-density electricity consumption areas and low-density electricity consumption areas;

[0015] Image data acquisition unit: photographing the low-density electricity consumption area to acquire image data of the low-density electricity consumption area;

[0016] Map data acquisition unit: acquires the planned regular building map data and extracts the low-density electricity consumption area from the map.

[0017] Preferably, the image data acquisition unit:

[0018] Path planning unit: Before acquiring images, the flight route of the drone group is planned to ensure the integrity and efficiency of image acquisition in the area;

[0019] Drone swarm: Each drone is equipped with an obstacle avoidance unit, which enables each drone to avoid obstacles on the route during flight according to the planned route;

[0020] Shooting units: The number is the same as the number of drones in the drone swarm. Before the drone takes off, the map of the low-density electricity consumption area captured from the map is transmitted to the shooting unit. During the flight of the drone swarm, the area is photographed from high or low altitude to obtain image data of the area. The photographed image is compared with the map of the low-density electricity consumption area to determine whether there are any illegal buildings.

[0021] Preferably, the data processing module includes:

[0022] Image clarity adjustment unit: removes noise from the image, reduces random noise in the image, and then enhances the edges and details of the image through a sharpening algorithm. After that, the details and layering of the image are further enhanced by adjusting the local contrast of the image.

[0023] Image privacy harmony unit: mosaics the human images in the image according to the set threshold and marks the processing location;

[0024] Data transmission unit: transmits the processed image data to the data analysis module according to the TCP protocol.

[0025] Preferably, the data analysis module includes:

[0026] Data cleaning unit: cleans the relevant data of low-density electricity consumption areas found on the network and obtains key information from the relevant data;

[0027] Deep learning unit: Analyze and process the acquired images and key information, estimate the value of the construction land, and provide reference data for staff;

[0028] 3D modeling unit: Use CityEngine software to perform 3D digital modeling based on the acquired image data;

[0029] Database: Stores the training data of the deep learning unit.

[0030] Preferably, the deep learning processing unit adopts the PyTorch framework and convolutional neural network, and analyzes and processes the acquired images and key information through a pre-trained qualified model to estimate the value of the construction land.

[0031] Preferably, the data storage module includes:

[0032] Data classification unit: classifies the received data, and classifies the individual power consumption data, related data, image data and map data;

[0033] Data management unit: compress the classified data and process the data that exceeds the storage period;

[0034] Data storage unit: stores the compressed data and limits the storage period of the stored data.

[0035] Preferably, the data management unit includes:

[0036] Data compression unit: compress the classified data;

[0037] Data deletion unit: deletes data that exceeds the storage period;

[0038] Data recovery unit: recovers accidentally deleted data that has exceeded the recovery period.

[0039] Preferably, the central control management module includes:

[0040] Human-computer interaction unit: provides a platform for staff to control the system;

[0041] Login management unit: verify the identity information of the staff who log in to the system to ensure the security of the system;

[0042] Centralized control unit: assists staff to centrally manage the data acquisition module, data processing module, data analysis module and data storage module.

[0043] Preferably, the method for searching for building land in an urban area by implementing a building land map positioning system based on a digital model as described in claim 3 comprises the following steps:

[0044] S1. The data acquisition module is used to obtain the electricity consumption data of the urban area, and the high-density electricity consumption area and the low-density electricity consumption area are distinguished at the same time. Then, the image of the low-density electricity consumption area is obtained by shooting with a drone group, and the relevant data of the low-density electricity consumption area is searched on the Internet to obtain the formal building map data of the plan;

[0045] S2. The clarity of the image data of the low-density electricity consumption area obtained is adjusted by the data processing module, and the part of the image suspected of personal privacy is trimmed, and then the trimmed image is transmitted to the data analysis module;

[0046] S3. The data analysis module is combined with the trimmed image data to clean the relevant data of the low-density electricity consumption area on the network, extract the key information, and then estimate the value of the building land in the image to provide reference data for the staff, and establish a three-dimensional digital model to facilitate the staff to observe the area;

[0047] S4. The acquired data, estimated results and operation data of the staff are stored through the data storage module.

[0048] Compared with the prior art, the present invention has the following beneficial effects:

[0049] 1. Use electricity consumption data to distinguish high-density electricity consumption areas from low-density electricity consumption areas, then use drones to take on-site photos of low-density electricity consumption areas, and compare them with the planned formal building map data to determine whether they meet the requirements of building land or illegal construction, thereby improving the convenience of locating building land;

[0050] 2. Improve data processing efficiency by processing data separately;

[0051] 3. Through deep learning processing, the value of the construction land is estimated through multi-faceted data, providing reference data for staff, improving the convenience of locating and analyzing construction land in the urban area, and by generating a three-dimensional digital model, improving the convenience of staff in analyzing the construction land. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is a structural schematic diagram of a building land map positioning system implemented based on a digital model in the present invention;

[0053] Figure 2 It is a structural schematic diagram of the data acquisition module of the present invention;

[0054] Figure 3 It is a structural schematic diagram of an image data acquisition unit of the present invention;

[0055] Figure 4 It is a structural schematic diagram of a data processing module of the present invention;

[0056] Figure 5 It is a structural schematic diagram of the data analysis module of the present invention;

[0057] Figure 6 It is a structural schematic diagram of a data storage module of the present invention;

[0058] Figure 7 It is a structural schematic diagram of a data management unit of the present invention;

[0059] Figure 8 It is a structural diagram of the central control management module of the present invention. DETAILED DESCRIPTION

[0060] In order to facilitate understanding of the present invention, the present invention will be described more fully below with reference to the relevant drawings. The present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive.

[0061] Example

[0062] like Figures 1 to 8 As shown, a building land map positioning system is implemented based on a digital model, including:

[0063] Data acquisition module: acquires urban electricity consumption data, images of low-density electricity consumption areas, relevant data of low-density electricity consumption areas on the network, and planned formal building map data;

[0064] Data processing module: adjusts the clarity of the image data of the low-density electricity consumption area, and retouches the part of the image suspected of personal privacy, and then transmits the retouched image to the data analysis module;

[0065] Data analysis module: Combined with the retouched image data, the relevant data of the low-density electricity consumption area on the network is cleaned, key information is extracted, and the value of the building land in the image is estimated to provide reference data for the staff, and a three-dimensional digital model is established to facilitate the staff to observe the area;

[0066] Data storage module: stores the acquired data, estimated results and staff operation data;

[0067] Central control management module: verify the identity information of the logged-in staff, record the staff's operation process, and assist the staff in centrally managing the data acquisition module, data processing module, data analysis module and data storage module;

[0068] The data acquisition module comprises:

[0069] Relevant data acquisition unit: searching for relevant data of low-density electricity consumption areas on the Internet;

[0070] Electricity consumption data acquisition unit: acquires electricity consumption data in the urban area and distinguishes between high-density electricity consumption areas (such as residential areas, shopping malls and hospitals) and low-density electricity consumption areas (such as self-built houses, bungalows and wasteland);

[0071] Image data acquisition unit: photographing the low-density electricity consumption area to acquire image data of the low-density electricity consumption area;

[0072] Map data acquisition unit: acquires the planned regular building map data and extracts the low-density electricity consumption area from the map;

[0073] The image data acquisition unit:

[0074] Path planning unit: Before acquiring images, the flight route of the drone group is planned to ensure the integrity and efficiency of image acquisition in the area;

[0075] Drone swarm: Each drone is equipped with an obstacle avoidance unit, which enables each drone to avoid obstacles on the route during flight according to the planned route;

[0076] Photo shooting unit: The number is the same as the number of drones in the drone group. Before the drone takes off, the map of the low-density electricity consumption area intercepted from the map is transmitted to the photo shooting unit. During the flight of the drone group, the area is photographed from high or low altitude to obtain image data of the area. The photographed image is compared with the map of the low-density electricity consumption area to determine whether there is any illegal construction;

[0077] The data processing module comprises:

[0078] Image clarity adjustment unit: removes noise from the image, reduces random noise in the image, and then enhances the edges and details of the image through a sharpening algorithm. After that, the details and layering of the image are further enhanced by adjusting the local contrast of the image.

[0079] Image privacy harmony unit: mosaics the human images in the image according to the set threshold and marks the processing location;

[0080] Data transmission unit: transmits the processed image data to the data analysis module according to the TCP protocol;

[0081] The data analysis module includes:

[0082] Data cleaning unit: cleans the relevant data of low-density electricity consumption areas found on the Internet and obtains key information from the relevant data (such as the transaction status of the construction land in recent years, dispute news and relevant policies);

[0083] Deep learning unit: Analyze and process the acquired images and key information, estimate the value of the construction land, and provide reference data for staff;

[0084] 3D modeling unit: Use CityEngine software to perform 3D digital modeling based on the acquired image data;

[0085] Database: stores the training data of deep learning units;

[0086] The deep learning processing unit uses a PyTorch framework and a convolutional neural network, and analyzes and processes the acquired images and key information through a pre-trained qualified model to estimate the value of the building land;

[0087] The data storage module comprises:

[0088] Data classification unit: classifies the received data, and classifies the individual power consumption data, related data, image data and map data;

[0089] Data management unit: compress the classified data and process the data that exceeds the storage period;

[0090] Data storage unit: stores the compressed data and limits the storage period of the stored data;

[0091] The data management unit comprises:

[0092] Data compression unit: compress the classified data;

[0093] Data deletion unit: deletes data that exceeds the storage period;

[0094] Data recovery unit: recovers accidentally deleted data that exceeds the recovery period;

[0095] The central control management module includes:

[0096] Human-computer interaction unit: provides a platform for staff to control the system;

[0097] Login management unit: verify the identity information of the staff who log in to the system to ensure the security of the system;

[0098] Centralized control unit: assists staff to centrally manage data acquisition module, data processing module, data analysis module and data storage module;

[0099] The method for searching for building land in an urban area by implementing a building land map positioning system based on a digital model as described in claim 3 comprises the following steps:

[0100] S1. The data acquisition module is used to obtain the electricity consumption data of the urban area, and the high-density electricity consumption area and the low-density electricity consumption area are distinguished at the same time. Then, the image of the low-density electricity consumption area is obtained by shooting with a drone group, and the relevant data of the low-density electricity consumption area is searched on the Internet to obtain the formal building map data of the plan;

[0101] S2. The clarity of the image data of the low-density electricity consumption area obtained is adjusted by the data processing module, and the part of the image suspected of personal privacy is trimmed, and then the trimmed image is transmitted to the data analysis module;

[0102] S3. The data analysis module is combined with the trimmed image data to clean the relevant data of the low-density electricity consumption area on the network, extract the key information, and then estimate the value of the building land in the image to provide reference data for the staff, and establish a three-dimensional digital model to facilitate the staff to observe the area;

[0103] S4. The acquired data, estimated results and operation data of the staff are stored through the data storage module.

[0104] The deep learning unit can also use the real estate market analysis method, device and equipment based on deep transfer learning disclosed in the invention patent with announcement number CN111813893B in combination with an image processing model to perform estimation work.

[0105] The construction land map positioning system based on the digital model of the present invention has common mechanical installation, connection or setting methods, which can be implemented as long as they can achieve their beneficial effects. Technical personnel in the industry only need to install and operate it according to the accompanying instruction manual, without the need for technical personnel in this field to make creative labor.

[0106] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A building land map positioning system is implemented based on a digital model, characterized in that: include: Data acquisition module: acquires urban electricity consumption data, images of low-density electricity consumption areas, relevant data of low-density electricity consumption areas on the network, and planned formal building map data; Data processing module: adjusts the clarity of the image data of the low-density electricity consumption area, and retouches the part of the image suspected of personal privacy, and then transmits the retouched image to the data analysis module; Data analysis module: Combined with the retouched image data, the relevant data of the low-density electricity consumption area on the network is cleaned, key information is extracted, and the value of the building land in the image is estimated to provide reference data for the staff, and a three-dimensional digital model is established to facilitate the staff to observe the area; Data storage module: stores the acquired data, estimated results and staff operation data; Central control management module: verify the identity information of the logged-in staff, record the staff's operation process, and assist the staff in centrally managing the data acquisition module, data processing module, data analysis module and data storage module; The data acquisition module comprises: Relevant data acquisition unit: searching for relevant data of low-density electricity consumption areas on the Internet; Electricity consumption data acquisition unit: acquires electricity consumption data in the urban area and distinguishes between high-density electricity consumption areas and low-density electricity consumption areas; Image data acquisition unit: photographing the low-density electricity consumption area to acquire image data of the low-density electricity consumption area; Map data acquisition unit: acquires the planned regular building map data and extracts the low-density electricity consumption area from the map; The data processing module comprises: Image clarity adjustment unit: removes noise from the image, reduces random noise in the image, and then enhances the edges and details of the image through a sharpening algorithm. After that, the details and layering of the image are further enhanced by adjusting the local contrast of the image. Image privacy harmony unit: mosaics the human images in the image according to the set threshold and marks the processing location; Data transmission unit: transmits the processed image data to the data analysis module according to the TCP protocol.

2. The digital model-based construction site map positioning system according to claim 1, characterized in that: The image data acquisition unit: Path planning unit: Before acquiring images, the flight route of the drone group is planned to ensure the integrity and efficiency of image acquisition in the area; Drone swarm: Each drone is equipped with an obstacle avoidance unit, which enables each drone to avoid obstacles on the route during flight according to the planned route; Shooting units: The number is the same as the number of drones in the drone swarm. Before the drone takes off, the map of the low-density electricity consumption area captured from the map is transmitted to the shooting unit. During the flight of the drone swarm, the area is photographed from high or low altitude to obtain image data of the area. The photographed image is compared with the map of the low-density electricity consumption area to determine whether there are any illegal buildings.

3. The digital model-based construction site map positioning system according to claim 1, characterized in that: The data analysis module includes: Data cleaning unit: cleans the relevant data of low-density electricity consumption areas found on the network and obtains key information from the relevant data; Deep learning unit: Analyze and process the acquired images and key information, estimate the value of the construction land, and provide reference data for staff; 3D modeling unit: Use CityEngine software to perform 3D digital modeling based on the acquired image data; Database: Stores the training data of the deep learning unit.

4. The digital model-based construction site map positioning system according to claim 3, characterized in that: The deep learning processing unit adopts the PyTorch framework and convolutional neural network, and analyzes and processes the acquired images and key information through a pre-trained qualified model to estimate the value of the construction land.

5. The digital model-based construction site map positioning system according to claim 1, characterized in that: The data storage module comprises: Data classification unit: classifies the received data, and classifies the individual power consumption data, related data, image data and map data; Data management unit: compress the classified data and process the data that exceeds the storage period; Data storage unit: stores the compressed data and limits the storage period of the stored data.

6. The digital model-based construction site map positioning system according to claim 5, characterized in that: The data management unit comprises: Data compression unit: compress the classified data; Data deletion unit: deletes data that exceeds the storage period; Data recovery unit: recovers accidentally deleted data that has exceeded the recovery period.

7. The digital model-based construction site map positioning system according to claim 1, characterized in that: The central control management module includes: Human-computer interaction unit: provides a platform for staff to control the system; Login management unit: verify the identity information of the staff who log in to the system to ensure the security of the system; Centralized control unit: assists staff to centrally manage the data acquisition module, data processing module, data analysis module and data storage module.

8. The digital model-based construction site map positioning system according to claim 2, characterized in that: The method for finding building land in an urban area by implementing a building land map positioning system based on a digital model comprises the following steps: S1. The data acquisition module is used to obtain the electricity consumption data of the urban area, and the high-density electricity consumption area and the low-density electricity consumption area are distinguished at the same time. Then, the image of the low-density electricity consumption area is obtained by shooting with a drone group, and the relevant data of the low-density electricity consumption area is searched on the Internet to obtain the formal building map data of the plan; S2. The clarity of the image data of the low-density electricity consumption area obtained is adjusted by the data processing module, and the part of the image suspected of personal privacy is trimmed, and then the trimmed image is transmitted to the data analysis module; S3. The data analysis module is combined with the trimmed image data to clean the relevant data of the low-density electricity consumption area on the network, extract the key information, and then estimate the value of the building land in the image to provide reference data for the staff, and establish a three-dimensional digital model to facilitate the staff to observe the area; S4. The acquired data, estimated results and operation data of the staff are stored through the data storage module.

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