City appearance environment monitoring method, device and equipment and storage medium

Abstract

The invention relates to a city appearance environment monitoring method, device and equipment and a storage medium, and the method comprises the following steps: carrying out the image collection of a target city through preset front-end equipment, and obtaining a target city image; performing city appearance environment monitoring on the target city image by using a preset AI monitoring algorithm to obtain a city appearance environment monitoring result; judging whether the city appearance environment monitoring result has city appearance problem environment characteristics or not, and if yes, positioning the city appearance environment problem characteristics to obtain positioning city appearance environment problem data; performing quantitative evaluation on the positioning city appearance environment problem data through an environment evaluation index system to obtain an evaluation result; formulating an environment management optimization scheme of the target city based on the evaluation result; the technical problems that due to the lack of effective data analysis means, the environmental problems can only be handled afterwards, and prevention and timely intervention are difficult to achieve are solved.

Description

Technical Field

[0001] The present invention relates to the field of image recognition technology, and in particular to a method, device, equipment and storage medium for monitoring urban environment. Background Art

[0002] With the continuous acceleration of urbanization, the quality of urban environment has become one of the important factors affecting the quality of life of residents. In the rapid expansion of cities, how to effectively manage and improve the urban environment has become an urgent problem to be solved. Traditional urban environmental monitoring methods often rely on manual inspections, which are not only inefficient but also difficult to achieve comprehensive coverage, especially in the face of increasingly complex environmental pollution, garbage dumping, illegal construction and other problems in cities. Traditional methods seem to be powerless. In addition, due to the lack of effective data analysis methods, environmental problems can often only be dealt with after the fact, and it is difficult to prevent and intervene in time.

[0003] In recent years, with the rapid development of artificial intelligence technology, especially the progress of computer vision and big data analysis technology, a new solution has been provided for urban environmental monitoring. By using front-end devices (such as cameras) installed throughout the city to collect real-time images and analyzing the collected data in combination with pre-set AI monitoring algorithms, various problems in the urban environment can be identified efficiently and accurately. This method can not only significantly improve monitoring efficiency, but also predict future environmental change trends by learning from historical data, thereby providing a scientific basis for decision makers. However, although AI technology has broad application prospects in environmental monitoring, it still faces many challenges in actual operation, such as algorithm accuracy, data security, and privacy protection.

[0004] In response to the above problems, this study proposed an AI-based urban environment monitoring method, which aims to achieve accurate identification and positioning of urban environmental problems by integrating advanced image processing technology and environmental evaluation index system. This method first obtains real-time image data of the target city through front-end equipment, and then uses AI algorithms to automatically detect abnormal conditions in the image, such as littering, illegal advertising, etc., and then accurately marks the location of these urban appearance problems. Finally, the identified problems are quantitatively evaluated according to the environmental evaluation standards, providing city managers with a set of scientific and reasonable environmental management optimization suggestions to help them plan and implement urban environmental improvement measures more effectively. This method not only improves the level of intelligence in urban environmental management, but also provides strong technical support for building a better and more harmonious urban living space. Summary of the invention

[0005] The main purpose of the present invention is to provide a method, device, equipment and storage medium for urban environment monitoring, which solves the technical problem that due to the lack of effective data analysis means, environmental problems can only be dealt with after the fact, and it is difficult to prevent and intervene in time.

[0006] To achieve the above object, the present invention provides a method for monitoring urban environment, comprising the following steps: The target city is imaged by collecting images through a preset front-end device to obtain an image of the target city; Using a preset AI monitoring algorithm to monitor the city appearance and environment of the target city image, and obtaining a city appearance and environment monitoring result; Determine whether the urban appearance and environment monitoring results have environmental features of urban appearance problems; if so, locate the environmental features of urban appearance problems to obtain located urban appearance and environment problem data; Conducting quantitative evaluation on the data of the city appearance and environmental problems through the environmental evaluation index system to obtain evaluation results; An environmental management optimization plan for the target city is formulated based on the evaluation results.

[0007] Furthermore, the preset front-end device is a camera installed on an unmanned aircraft, and the image acquisition of the target city by the preset front-end device to obtain the target city image includes: During the flight of the unmanned aircraft along the predetermined route, the target city is continuously imaged by a camera mounted on the unmanned aircraft to obtain an image of the target city; Performing geometric correction processing on the target city image to obtain a corrected target city image; Performing image stitching on the corrected target city image to obtain a panoramic target city image; The panoramic target city image is enhanced to obtain a target city image.

[0008] Furthermore, the use of a preset AI monitoring algorithm to monitor the target city image to obtain a city appearance and environment monitoring result includes: Performing regional segmentation on the target city image to obtain a target city regional image; wherein the target city regional image includes the outline of urban buildings, green areas, roads and public facilities; Performing multi-scale extraction on the urban environment in the target urban area image to obtain urban environment features; Performing time series modeling on the city environment characteristics to obtain a time series characteristic graph; By using a preset cross-attention mechanism, the time series feature graph is focused on the city appearance environment to obtain a focused city appearance environment graph; Performing pixel-level classification on the concerned urban environment map to obtain a pixel-level classification result; Based on the pixel-level classification result, the city environment is monitored to obtain a city environment monitoring result.

[0009] Furthermore, the positioning of the characteristics of the city appearance and environment problems to obtain the positioning city appearance and environment problem data includes: Initially positioning the features of the urban environment problem to obtain preliminary location information; wherein the preliminary location information is two-dimensional location information; Performing three-dimensional reconstruction on the preliminary position information of multiple perspectives through multi-perspective geometry technology to obtain three-dimensional position information; Obtaining geographic reference points of the target city from a database, and spatially mapping the three-dimensional location information based on the geographic reference points by adopting homologous point pair mapping technology to obtain geographic coordinate information; Dynamically update the geographic coordinate information through time-space fusion technology to obtain real-time location information; Using Internet of Things technology to verify the real-time location information to obtain verified location information; If the verified location information is consistent with the geographic coordinate information, the real-time location information is used as the positioning city appearance environment data; If the verified location information is inconsistent with the real-time location information, the urban environment feature is repositioned.

[0010] Furthermore, the environmental assessment index system is used to quantitatively assess the data of the located city appearance and environmental problems to obtain assessment results, including: Classifying the data of the located city appearance and environmental problems in the target city area to obtain environmental problem classification labels; wherein the environmental problem classification labels include green area problems, road problems, and advertising and signage problems; Calling the standardized index value of the environmental problem classification label in the database, and performing weight assignment on the environmental problem classification label to obtain the problem label weight value; Calculating a weighted index value of the target city area based on the standardized index value and the problem label weight value to obtain a weighted index value of the environmental problem; The target urban area is quantitatively evaluated based on the weighted index value of the environmental problem to obtain an evaluation result.

[0011] Furthermore, formulating an environmental management optimization plan for the target city based on the evaluation results includes: Based on the evaluation results, the main problems and key areas of the city appearance environment are identified to obtain a problem list; wherein the main problems in the problem list include the city appearance problem environment characteristics corresponding to the key areas; Performing spatial visualization on the problem list based on a preset geographic information system to obtain a problem distribution map; Perform trend analysis on the problem distribution graph by using a time series analysis method to obtain a change trend report; Conduct decision-making management and planning for the target city based on the change trend report to obtain a preliminary management plan; The preliminary management plan is reviewed and optimized to obtain an optimized plan for environmental management of the target city.

[0012] Furthermore, the problem list is spatially visualized based on the geographic information system to obtain a problem distribution map, including: Obtaining geographic coordinate information of environmental features of urban appearance problems corresponding to key areas in the problem list; Loading basic geographic data into a geographic information system to generate a basic map; wherein the basic map includes roads, buildings and green areas of the target city; Overlaying the geographic coordinate information onto the basic map to generate a preliminary problem distribution map; Using different icons to symbolize the preliminary problem distribution map to obtain a symbolized problem distribution map; Performing hot spot analysis on the symbolized problem distribution map to obtain a hot spot area distribution map; The hot spot area distribution map and the symbolized problem distribution map are combined to obtain a problem distribution map.

[0013] The present invention also provides a city environment monitoring device, comprising: The acquisition module is used to acquire images of the target city through a preset front-end device to obtain an image of the target city; An analysis module is used to use a preset AI monitoring algorithm to perform city appearance and environment monitoring on the target city image to obtain a city appearance and environment monitoring result; A positioning module is used to determine whether the urban appearance and environment monitoring results contain environmental features of urban appearance problems. If so, the urban appearance and environment problem features are located to obtain the located urban appearance and environment problem data; An evaluation module is used to quantitatively evaluate the located city appearance and environmental problem data through an environmental evaluation index system to obtain an evaluation result; A formulation module is used to formulate an environmental management optimization plan for the target city based on the evaluation results.

[0014] The present invention also provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any one of the above methods when executing the computer program.

[0015] The present invention also provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of any of the above-mentioned methods are implemented.

[0016] The city appearance and environment monitoring method provided by the present invention comprises the following steps: collecting images of a target city through a preset front-end device to obtain a target city image; using a preset AI monitoring algorithm to perform city appearance and environment monitoring on the target city image to obtain a city appearance and environment monitoring result; judging whether the city appearance and environment monitoring result has environmental characteristics of city appearance problems, and if so, locating the city appearance and environment problem characteristics to obtain located city appearance and environment problem data; quantitatively evaluating the located city appearance and environment problem data through an environmental evaluation index system to obtain an evaluation result; formulating an environmental management optimization plan for the target city based on the evaluation result; through the above-mentioned technical means, the technical problem that due to the lack of effective data analysis means, environmental problems can often only be dealt with after the fact, and it is difficult to prevent and intervene in time is solved, and the AI-based monitoring method can predict possible environmental problems in the future through historical data analysis, so that city managers can take preventive measures before problems occur, turn from passive response to active management, and effectively avoid or reduce the negative impact of environmental problems. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is a schematic diagram of the steps of a method for monitoring the city appearance and environment in one embodiment of the present invention; Figure 2 is a structural block diagram of a city environment monitoring device in one embodiment of the present invention; Figure 3 It is a schematic block diagram of the structure of a computer device according to an embodiment of the present invention.

[0018] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0020] like Figure 1 As shown, Figure 1 This is a schematic diagram of the steps of a method for monitoring urban environment in one embodiment of the present invention; In one embodiment of the present invention, a method for monitoring a city environment is provided, comprising the following steps: Step S1, collecting images of a target city through a preset front-end device to obtain an image of the target city.

[0021] Specifically, the target city is imaged through the preset front-end equipment. Obtaining the target city image means that a series of devices with image acquisition functions, such as high-definition cameras and drones, are pre-deployed in a specific urban area. These devices are started at predetermined time intervals or trigger conditions to capture images of the designated target city area in all directions and from multiple angles. For example, fixed high-definition cameras are installed in public areas such as the main streets, squares, and parks in the city, or drones are used to fly over these areas regularly to obtain high-definition images of the ground. These image data will then be transmitted to the back-end server as the basic data for subsequent urban appearance and environmental monitoring. In this way, it can be ensured that the collected image information is both comprehensive and timely, providing high-quality data support for the subsequent urban appearance and environmental monitoring using the preset AI monitoring algorithm. For example, in the core business district of a large city, by deploying multiple high-definition cameras and regularly arranging drone flight missions, the urban appearance of the area can be monitored in real time, and environmental problems such as littering and illegal advertising can be discovered and dealt with in a timely manner.

[0022] Step S2, using a preset AI monitoring algorithm to perform city appearance and environment monitoring on the target city image to obtain a city appearance and environment monitoring result.

[0023] Specifically, using a preset AI monitoring algorithm to monitor the target city image and obtain the city appearance and environment monitoring results means that after obtaining the target city image, the image data is analyzed and processed by a pre-trained artificial intelligence algorithm to identify the city appearance and environment problems that may exist in the image. Specifically, the AI ​​monitoring algorithm can automatically identify various abnormal situations in the image, such as littering, illegal advertising, illegal buildings, etc., and classify and mark these problems. For example, in the core business district of a large city, the deployed high-definition cameras and drones collect a large number of street view images. After these images are transmitted to the data center, the AI ​​monitoring algorithm will scan each image. Once littering or illegal advertising is found, the algorithm will automatically mark these locations and generate corresponding reports. In this way, city managers can take quick measures to solve the problem based on these monitoring results to ensure the cleanliness and beauty of the urban environment. Through this intelligent monitoring method, not only the efficiency and accuracy of discovering city appearance and environmental problems are improved, but also strong data support is provided for subsequent environmental management and optimization.

[0024] Step S3, determining whether the urban appearance and environment monitoring results contain environmental features of urban appearance problems; if so, locating the environmental features of urban appearance problems to obtain located urban appearance and environment problem data.

[0025] Specifically, it is determined whether there are environmental features of urban appearance problems in the urban appearance and environmental monitoring results. If there are, the urban appearance and environmental problem features are located to obtain the localized urban appearance and environmental problem data. This means that after the AI ​​monitoring algorithm has conducted a preliminary analysis of the target city image, the system will further check these analysis results to confirm whether there are indeed urban appearance and environmental problems. If the monitoring results show that there are environmental features of urban appearance problems, such as littering, illegal advertising, illegal construction, etc., the system will accurately locate the specific locations of these problems. For example, in the aforementioned application scenario of the core commercial district of a large city, the AI ​​algorithm has identified several cases of littering. At this time, the system will further analyze the specific locations of these phenomena, such as which corner of a street, which building's exterior wall, etc., and record the coordinate information of these locations to form localized urban appearance and environmental problem data. These data not only contain the type of problem, but also precise geographic location information, so that city managers can quickly and accurately find the location of the problem and dispatch personnel to clean up or rectify it in a timely manner. Through such a process, it can be ensured that urban appearance and environmental problems are handled in a timely and effective manner, further improving the level of urban management and the quality of life of residents.

[0026] Step S4, quantitatively evaluating the located city appearance environmental problem data through an environmental evaluation index system to obtain an evaluation result.

[0027] Specifically, the data of the located city appearance and environmental problems are quantitatively evaluated through the environmental evaluation index system to obtain the evaluation results, which means that after determining the specific location and type of the city appearance and environmental problems, a set of pre-designed environmental evaluation index systems are used to quantitatively analyze the impact of these problems to evaluate their specific impact on the urban environment. This set of index systems may include but is not limited to factors such as the severity of the problem, the scope of impact, and the impact on public safety and the quality of life of residents. For example, in the application scenario of the aforementioned core business district of a large city, the system has identified and located multiple littering phenomena through AI algorithms. Next, the environmental evaluation index system will quantitatively evaluate these littering phenomena. Assuming that the evaluation system includes multiple dimensions such as "the amount of garbage", "the impact on pedestrian traffic", and "the impact on surrounding commercial activities", each dimension has a specific scoring standard. The system will score each dimension according to the actual situation, and finally combine the scores of each dimension to obtain a total score. In this way, city managers can not only understand the location and type of specific problems, but also clearly know the severity of these problems and their impact on the urban environment, thereby providing a scientific basis for the subsequent formulation of targeted environmental management optimization plans. This quantitative assessment method can more effectively guide resource allocation, prioritize solving those issues that have a greater impact on the urban environment and residents' quality of life, and promote the overall improvement of the urban environment.

[0028] Step S5: formulating an environmental management optimization plan for the target city based on the evaluation results.

[0029] Specifically, formulating the environmental management optimization plan of the target city based on the evaluation results means formulating a set of targeted and feasible environmental management optimization plans based on the specific content of the evaluation results after the precise positioning and quantitative evaluation of the city appearance and environmental problems. This process needs to comprehensively consider the severity of the various problems reflected in the evaluation results, the scope of impact, and the impact on the quality of life of residents, so as to propose the most appropriate solutions. For example, in the application scenario of the core business district of the aforementioned large city, the severity of the littering phenomenon in many places and its specific impact on the surrounding environment have been clarified through the quantitative evaluation of the environmental evaluation index system. Based on these evaluation results, city managers can formulate specific optimization plans, such as adding trash cans in areas with serious problems and increasing the frequency of cleaning; at the same time, they can also strengthen the supervision of these areas by installing surveillance cameras and setting up warning signs to reduce the occurrence of littering. In addition, in order to fundamentally solve these problems, public publicity and education activities can also be carried out to improve citizens' environmental awareness and encourage everyone to jointly maintain a good urban environment. Through such a comprehensive optimization plan, we can not only effectively solve the existing urban environment problems, but also prevent the occurrence of similar problems in the future, thereby continuously improving the city's environmental quality and residents' satisfaction.

[0030] In a specific embodiment, the preset front-end device is a camera installed on an unmanned aircraft, and the image acquisition of the target city by the preset front-end device to obtain the target city image includes: During the flight of the unmanned aircraft along the predetermined route, the target city is continuously imaged by a camera mounted on the unmanned aircraft to obtain an image of the target city; Performing geometric correction processing on the target city image to obtain a corrected target city image; Performing image stitching on the corrected target city image to obtain a panoramic target city image; The panoramic target city image is enhanced to obtain a target city image.

[0031] Specifically, in the application scenario of the core business district of a large city, drones, as a flexible and efficient image acquisition tool, can fly along a predetermined route within a preset time to cover the entire monitoring area. The high-definition camera carried by the drone can continuously capture images of the ground during the flight to ensure that every corner can be captured. For example, the drone may fly along the path above important areas such as main streets, squares, parks, etc., and continuously capture high-definition images of the ground during the flight. These images not only include the overall view of the street, but also capture details such as sidewalks and green belts, providing a rich data source for subsequent analysis. However, since the drone will be affected by factors such as wind speed and altitude changes during flight, the captured images may be deformed or distorted to a certain extent. Therefore, it is particularly important to perform geometric correction processing on the target city image. The geometric correction processing mainly adjusts the image through a mathematical model to eliminate image distortion caused by shooting angles, lens distortion, etc., to ensure that each image can truly reflect the actual scene on the ground. The image after geometric correction processing is not only clearer and more natural visually, but more importantly, it ensures the relative position relationship between images, laying the foundation for subsequent image stitching. Next, the corrected target city image is stitched to obtain a panoramic target city image. Image stitching technology is the process of synthesizing multiple overlapping images into a panoramic image with a large field of view. At this stage, the system will use advanced image processing algorithms to automatically identify and match the overlapping areas between different images, and then seamlessly stitch these images together to form a complete panoramic image. This process not only requires the algorithm to have high robustness and accuracy, but also needs to consider the color consistency between images to ensure that the final synthesized panoramic image has no obvious stitching marks visually. In the aforementioned application scenario, through image stitching technology, multiple images taken by unmanned aircraft at different time periods and different locations can be synthesized into a panoramic image covering the entire core business district, providing data support from a macro perspective for subsequent urban environment monitoring. Finally, the panoramic target city image is enhanced to obtain a target city image. Image enhancement technology aims to improve the quality of the image to make it more suitable for human eye observation or machine analysis. Common image enhancement methods include contrast enhancement, brightness adjustment, noise removal, etc. In this case, image enhancement processing can make the details in the panoramic image more prominent and the colors more vivid, making it easier to identify problems in the city environment, such as littering and illegal advertisements. For example, after image enhancement processing, garbage piles or illegal advertisements that were not obvious in the original image become very obvious in the enhanced image, making it easier for subsequent AI monitoring algorithms to accurately identify and locate them.In summary, by continuously collecting images of the target city through the camera carried by the unmanned aircraft, and then through a series of processing steps such as geometric correction, image stitching and image enhancement, a high-quality image of the target city can be finally obtained. This image not only covers the panoramic view of the monitored area, but also retains rich detailed information, providing important data support for urban environment monitoring. Based on such image data, city managers can more intuitively understand the current status of the urban environment, discover and solve existing problems in a timely manner, thereby continuously improving the level of urban management and creating a better living environment.

[0032] In a specific embodiment, the use of a preset AI monitoring algorithm to perform city appearance and environment monitoring on the target city image to obtain a city appearance and environment monitoring result includes: Performing regional segmentation on the target city image to obtain a target city regional image; wherein the target city regional image includes the outline of urban buildings, green areas, roads and public facilities; Performing multi-scale extraction on the urban environment in the target urban area image to obtain urban environment features; Performing time series modeling on the city environment characteristics to obtain a time series characteristic graph; By using a preset cross-attention mechanism, the time series feature graph is focused on the city appearance environment to obtain a focused city appearance environment graph; Performing pixel-level classification on the concerned urban environment map to obtain a pixel-level classification result; Based on the pixel-level classification result, the city environment is monitored to obtain a city environment monitoring result.

[0033] Specifically, in the application scenario of the core business district of a large city, first, the preset AI monitoring algorithm is used to perform regional segmentation on the target city image collected by the drone. This process involves segmenting the entire image into multiple areas of specific significance, such as the outline of urban buildings, green areas, roads and public facilities. The purpose of regional segmentation is that by dividing the image into different functional areas, more attention can be paid to specific problems in each area, such as the distribution of garbage in green areas, damaged traffic signs on roads, etc. For example, the AI ​​algorithm can automatically identify which parts belong to buildings, which are green belts, and which are roads by learning a large amount of labeled training data, thereby decomposing the entire image into multiple logically clear sub-areas. Then, the urban environment in the target urban area image is multi-scale extracted to obtain the urban environment features. Multi-scale extraction is an important image processing technology that allows the algorithm to extract features at different scales of the same image, thereby capturing objects of different sizes and shapes. In urban environment monitoring, multi-scale extraction can help the algorithm more comprehensively identify problems in the environment, whether it is large areas of green belt garbage or small areas of illegal advertising. For example, the algorithm may identify a garbage accumulation area at a larger scale, while at a smaller scale it may be able to capture the specific location of individual illegal billboards. Subsequently, the city appearance and environmental characteristics are modeled in time series to obtain a time series feature map. This step is to introduce information in the time dimension, and by analyzing the city appearance and environmental characteristics at different time points, the changing trend of the environment over time can be captured. Time series modeling can predict future environmental conditions based on historical data and provide early warning information for city managers. For example, by analyzing the city appearance and environmental characteristics of the same location in the past few months, the algorithm can predict environmental problems that may occur in the area in a certain period of time in the future, such as an increase in the amount of garbage before and after holidays. Next, the city appearance and environmental attention of the time series feature map is performed through a preset cross-attention mechanism to obtain a focused city appearance and environmental map. The cross-attention mechanism is a technique used to improve model performance, which allows the model to pay more attention to other related input information when processing the current input. In city appearance and environmental monitoring, the cross-attention mechanism allows the model to pay more attention to areas that have often had problems in history when analyzing the environmental characteristics at a certain point in time. For example, if a park has accumulated garbage many times in the past, the model will automatically pay more attention to this area in the new image analysis, so as to find potential problems more quickly. Finally, the city environment map of concern is classified at the pixel level to obtain the pixel-level classification result. Pixel-level classification means that each pixel in the image is classified into a specific category, such as "garbage", "illegal advertising", "normal", etc. This process requires the use of deep learning models, such as convolutional neural networks (CNN), to analyze and classify images pixel by pixel.Through pixel-level classification, the specific location and scope of the problem can be determined very accurately, providing accurate guidance for subsequent processing. For example, after pixel-level classification, it can be clearly seen that there are several garbage accumulation points on a certain street, as well as their specific locations, which provides a direct basis for city managers to quickly dispatch cleaning staff. Based on the pixel-level classification results, the city appearance environment is monitored to obtain the city appearance environment monitoring results. This final step is to summarize all the previous processing results to form a detailed city appearance environment monitoring report. The report not only includes the specific location, type and severity of the problem, but also provides an analysis of historical change trends to help city managers fully understand the current city appearance and formulate scientific and reasonable management measures. For example, through this report, city managers can understand which areas have the most serious environmental problems and which measures have worked better in the past, so that they can be more targeted in future management and improve work efficiency. In summary, through this series of carefully designed processing steps, the city appearance environment can be comprehensively and meticulously monitored from multiple dimensions, which not only improves the accuracy and efficiency of problem discovery, but also provides strong technical support for the continuous improvement of the urban environment.

[0034] In a specific embodiment, the step of locating the features of the urban environment problem to obtain the located urban environment problem data includes: Initially positioning the features of the urban environment problem to obtain preliminary location information; wherein the preliminary location information is two-dimensional location information; Performing three-dimensional reconstruction on the preliminary position information of multiple perspectives through multi-perspective geometry technology to obtain three-dimensional position information; Obtaining geographic reference points of the target city from a database, and spatially mapping the three-dimensional location information based on the geographic reference points by adopting homologous point pair mapping technology to obtain geographic coordinate information; Dynamically update the geographic coordinate information through time-space fusion technology to obtain real-time location information; Using Internet of Things technology to verify the real-time location information to obtain verified location information; If the verified location information is consistent with the geographic coordinate information, the real-time location information is used as the positioning city appearance environment data; If the verified location information is inconsistent with the real-time location information, the urban environment feature is repositioned.

[0035] Specifically, in the application scenario of the core business district of a large city, first, the characteristics of the city appearance and environmental problems are initially located to obtain preliminary location information. The preliminary location information here is determined based on the coordinate system in the two-dimensional image. For example, when the AI ​​algorithm detects a phenomenon of littering in the image, it will record the two-dimensional coordinates of the image where the garbage is located, that is, the preliminary location information. Although this step can roughly determine the location of the problem, it cannot provide accurate spatial information due to the planar characteristics of the image. Next, the preliminary location information of multiple perspectives is three-dimensionally reconstructed through multi-perspective geometry technology to obtain three-dimensional location information. Multi-perspective geometry technology uses multiple images taken from different angles to reconstruct a three-dimensional model of an object by calculating the geometric relationship between these images. In this case, multiple images taken by unmanned aircraft at different heights and angles will be used to reconstruct the three-dimensional location of the city appearance and environmental problems. For example, through multi-perspective geometry technology, the garbage points in the preliminary location information can be converted from two-dimensional coordinates to three-dimensional coordinates to determine their exact location in space. Then, the geographic reference points of the target city are obtained from the database, and the three-dimensional location information is spatially mapped based on the geographic reference points by adopting the homologous point pair mapping technology to obtain geographic coordinate information. A geographic reference point refers to a landmark building or public facility in a city whose precise geographic coordinates are known. By aligning the three-dimensional location information with these geographic reference points, the three-dimensional coordinates can be converted into geographic coordinates. For example, if there is a precise latitude and longitude coordinate of a landmark building in the database, the location of the garbage point in the three-dimensional coordinates can be converted into longitude and latitude coordinates through homologous point pair mapping technology, thereby obtaining its position in the global coordinate system. Subsequently, the geographic coordinate information is dynamically updated through the time-space fusion technology to obtain real-time location information. The time-space fusion technology combines information in the two dimensions of time and space, and provides more accurate positioning results through comprehensive analysis of historical data and real-time data. In this case, the location changes of urban appearance and environmental problems can be tracked in real time through continuously updated image data. For example, if a garbage point is cleaned up within a period of time, the system will automatically update the status of the location to ensure the timeliness of the data. Next, the real-time location information is verified using the Internet of Things technology to obtain verified location information. The Internet of Things technology can collect environmental data in real time through sensors and devices deployed throughout the city. In this case, the accumulation of garbage cans can be monitored in real time through sensors installed near the garbage cans. If the sensor detects the existence of a garbage point, it can verify whether the positioning result of the AI ​​algorithm is accurate. For example, if the sensor detects that there is indeed garbage accumulation somewhere, and the AI ​​algorithm also locates the location, it means that the positioning result is correct. Finally, if the verified location information is consistent with the geographic coordinate information, the real-time location information is used as the positioning city appearance and environment data; if the verified location information is inconsistent with the real-time location information, the city appearance and environment features are relocated.This means that only when the results of IoT technology verification are consistent with the positioning results of the AI ​​algorithm, the location information will be used as the final data on urban appearance and environmental problems. If the verification results are inconsistent, it means that there may be false positives or false negatives, and image acquisition and analysis need to be performed again to ensure the accuracy of positioning. For example, if the sensor does not detect a garbage point, but the AI ​​algorithm locates the location, the system will automatically trigger the re-positioning process, and ensure the accurate positioning of the problem by collecting images and analyzing them again. In summary, through this series of fine positioning steps, the accurate positioning of urban appearance and environmental problems can be ensured, which not only improves the accuracy of problem discovery, but also provides real-time and reliable data support for urban managers. This multi-technology integration method not only improves the level of intelligence in urban appearance and environmental monitoring, but also provides a scientific basis for the continuous improvement of the urban environment.

[0036] In a specific embodiment, the quantitative evaluation of the located city appearance environmental problem data by the environmental evaluation index system to obtain the evaluation results includes: Classifying the data of the located city appearance and environmental problems in the target city area to obtain environmental problem classification labels; wherein the environmental problem classification labels include green area problems, road problems, and advertising and signage problems; Calling the standardized index value of the environmental problem classification label in the database, and performing weight assignment on the environmental problem classification label to obtain the problem label weight value; Calculating a weighted index value of the target city area based on the standardized index value and the problem label weight value to obtain a weighted index value of the environmental problem; The target urban area is quantitatively evaluated based on the weighted index value of the environmental problem to obtain an evaluation result.

[0037] Specifically, in the application scenario of the core business district of a large city, first, the data of the located city appearance and environmental problems in the target city area are classified to obtain the classification labels of the environmental problems. This means that through the previous image acquisition, AI monitoring and precise positioning, the specific location and type of all city appearance and environmental problems in the target city area have been obtained. Next, these problems need to be classified according to different categories. For example, green area problems may include garbage accumulation, vegetation damage, etc.; road problems may include road damage, missing traffic signs, etc.; advertising and signage problems may include illegal advertising posting, damaged signboards, etc. Through classification, the distribution of various problems can be more clearly understood, providing a basis for subsequent evaluation. Next, the standardized index value of the environmental problem classification label in the database is called, and the environmental problem classification label is weighted to obtain the problem label weight value. The standardized index value refers to a set of evaluation criteria set in advance for each type of environmental problem, which are usually formulated based on historical data and expert opinions. For example, for green area problems, there may be a range of index values, indicating different levels from mild to severe. Weight allocation is to assign different weight values ​​to various problems according to the severity of their impact on the urban environment. For example, road problems may have a greater impact on traffic safety, so the weight value is higher; while green area problems may have a greater impact on aesthetics, and the weight value is relatively low. In this way, it can be ensured that the evaluation results are more scientific and reasonable. Then, the weighted index value of the target urban area is calculated based on the standardized index value and the problem label weight value to obtain the weighted index value of the environmental problem. This step is to multiply the standardized index value of each environmental problem by its corresponding weight value, and then sum the weighted values ​​of all problems to obtain a comprehensive weighted index value. For example, suppose that 10 green area problems are found in a certain area, and the standardized index value of each problem is 3, and the weight value is 0.5; at the same time, 5 road problems are found, and the standardized index value of each problem is 4, and the weight value is 0.8. Then, the weighted index value of the green area problem is , the weighted index value of the road problem is . Finally, the weighted index value of the environmental problems in the area is (15 + 16 = 31). Finally, the target urban area is quantitatively evaluated based on the weighted index value of the environmental problems to obtain the evaluation results. The evaluation results not only reflect the overall severity of the urban appearance and environmental problems in the area, but can also be compared horizontally with other areas to help urban managers understand the environmental conditions in different areas. For example, if the weighted index value of environmental problems in a core business district is 31, while the weighted index value of another ordinary residential area is 20, it means that the environmental problems in the core business district are more serious and need to be treated first. In addition, the evaluation results can also be used to monitor the changing trend of environmental problems. For example, after the implementation of environmental remediation measures in a certain area, the weighted index value has dropped significantly, indicating that the treatment effect is significant. Through this series of quantitative evaluation steps, the urban appearance and environmental conditions of the target urban area can be comprehensively and objectively reflected, providing data support for urban managers to formulate scientific and reasonable environmental management optimization plans. This systematic and quantitative evaluation method not only improves the accuracy and scientificity of the evaluation, but also provides a strong technical guarantee for the continuous improvement of the urban environment. For example, in the aforementioned application scenario, through this evaluation method, city managers can clearly understand which areas have the most serious environmental problems and which types of environmental problems need to be addressed as a priority, so as to allocate resources more specifically and improve the efficiency and effectiveness of environmental management.

[0038] In a specific embodiment, formulating the environmental management optimization plan for the target city based on the evaluation results includes: Based on the evaluation results, the main problems and key areas of the city appearance environment are identified to obtain a problem list; wherein the main problems in the problem list include the city appearance problem environment characteristics corresponding to the key areas; Performing spatial visualization on the problem list based on a preset geographic information system to obtain a problem distribution map; Perform trend analysis on the problem distribution graph by using a time series analysis method to obtain a change trend report; Conduct decision-making management and planning for the target city based on the change trend report to obtain a preliminary management plan; The preliminary management plan is reviewed and optimized to obtain an optimized plan for environmental management of the target city.

[0039] Specifically, in the application scenario of the core business district of a large city, first, the main problems and key areas of the city appearance environment are identified based on the evaluation results to obtain a problem list. This means that through the previous quantitative evaluation, the weighted index values ​​of environmental problems in each area have been obtained, and these index values ​​reflect the severity of environmental problems in different areas. Next, it is necessary to identify the main city appearance and environmental problems and key areas based on these index values. For example, if the weighted index value of environmental problems in a core business district is the highest, it means that the environmental problems in this area are the most serious and need to be given priority attention. At the same time, the problem list can be further refined to list specific problems in the area, such as garbage accumulation in green areas, damaged traffic signs on roads, illegal posting of advertisements and signs, etc. Next, the problem list is spatially visualized based on the preset geographic information system to obtain a problem distribution map. Geographic Information System (GIS) is a powerful tool that can visualize various geographic data. Through GIS, each environmental problem in the problem list can be marked on the map to form an intuitive problem distribution map. For example, in a GIS system, different colors and icons can be used to represent different types of problems, such as green for greening area problems, red for road problems, and blue for advertising and signage problems. In this way, city managers can see at a glance which areas have the most concentrated problems and which types of environmental problems are the most common. Then, the problem distribution map is trend analyzed by a time series analysis method to obtain a change trend report. Time series analysis is a statistical method used to analyze the changing trend of data over time. By analyzing the problem distribution map at different time points, the changing pattern of environmental problems can be discovered. For example, the problem distribution of each month in the past year can be analyzed to see whether there are seasonal changes in environmental problems in certain areas, or whether certain types of environmental problems have a tendency to gradually worsen. These trend analysis results can provide important references for future environmental management. For example, if it is found that the greening area problem in a certain area is particularly serious in summer, a summer environmental maintenance plan can be made in advance. Next, decision management and planning are carried out on the target city based on the change trend report to obtain a preliminary management plan. This means that based on the results of the trend analysis, city managers can formulate a preliminary environmental management plan. The preliminary management plan should include specific measures and action plans, such as adding trash cans and cleaning frequency in areas with serious problems, repairing damaged traffic signs, and removing illegal advertisements. In addition, the plan should also clearly define the division of responsibilities and timetables to ensure that various measures can be effectively implemented. For example, a three-month environmental improvement plan can be formulated to focus on solving one type of problem each month and gradually improve the environmental conditions of the entire area. Finally, the preliminary management plan is reviewed and optimized to obtain the target city environmental management optimization plan. Review and optimization is an important link to ensure the scientificity and feasibility of the management plan.The preliminary management plan can be evaluated and improved through expert review, public opinion collection and other methods. For example, urban planning experts, environmental protection experts and others can be invited to review the plan and put forward improvement suggestions; at the same time, the opinions and suggestions of citizens can be solicited through questionnaires, public hearings and other forms. Through this process, the management plan can be continuously improved to make it more in line with actual needs and more operational. In the end, the target city environmental management optimization plan will be a comprehensive, scientific and feasible action plan, providing a strong guarantee for the continuous improvement of the urban environment. Through this series of steps, the systematic and scientific management of urban appearance and environmental problems can be ensured, which not only improves the efficiency of problem discovery and resolution, but also provides a solid foundation for the long-term improvement of the urban environment. For example, in the aforementioned application scenario, through this series of optimization solutions, urban managers can more accurately identify and solve environmental problems in the core business district, and improve the overall environmental quality of the city and the satisfaction of residents.

[0040] In a specific embodiment, the spatial visualization of the problem list based on the geographic information system to obtain a problem distribution map includes: Obtaining geographic coordinate information of environmental features of urban appearance problems corresponding to key areas in the problem list; Loading basic geographic data into a geographic information system to generate a basic map; wherein the basic map includes roads, buildings and green areas of the target city; Overlaying the geographic coordinate information onto the basic map to generate a preliminary problem distribution map; Using different icons to symbolize the preliminary problem distribution map to obtain a symbolized problem distribution map; Performing hot spot analysis on the symbolized problem distribution map to obtain a hot spot area distribution map; The hot spot area distribution map and the symbolized problem distribution map are combined to obtain a problem distribution map.

[0041] Specifically, in the application scenario of the core business district of a large city, first, the geographic coordinate information of the environmental characteristics of the city appearance problems corresponding to the key areas in the problem list is obtained. This means that through the previous image acquisition, AI monitoring and precise positioning, the specific location and type of all city appearance and environmental problems in the target city area have been obtained. For example, through positioning technology, the specific geographic coordinates of garbage accumulation points, illegal advertising posting points, etc. in a certain area have been determined. These geographic coordinate information is the basis for subsequent spatial visualization. Next, the basic geographic data is loaded in the geographic information system to generate a basic map. The basic geographic data includes information such as roads, buildings and green areas in the target city. These data are usually stored in the GIS system and can be called through the database. For example, the loaded map data can show the location of all main streets, building outlines and green belts in the core business district. The generated basic map provides background information for the subsequent problem distribution map, making the marking of problem points more intuitive. Then, the geographic coordinate information is superimposed on the basic map to generate a preliminary problem distribution map. This step is to mark the geographic coordinate information of the city appearance and environmental problems obtained in the early stage on the basic map in the form of points. For example, garbage accumulation points can be marked with red dots on the basic map, illegal advertisement posting points can be marked with blue dots, and traffic sign damaged points can be marked with yellow dots. In this way, the preliminary problem distribution map shows the specific locations of all problem points in the city. Next, the preliminary problem distribution map is symbolized using different icons to obtain a symbolized problem distribution map. Symbolization refers to using different icons and colors to represent different types of problems, making the problem distribution map clearer and easier to read. For example, a garbage bin icon can be used to represent a garbage accumulation point, a billboard icon can be used to represent an illegal advertisement posting point, and a traffic sign icon can be used to represent a traffic sign damaged point. Through symbolization, city managers can see at a glance the distribution of different types of environmental problems in the city. Then, a hot spot analysis is performed on the symbolized problem distribution map to obtain a hot spot area distribution map. Hot spot analysis is a statistical method used to identify areas where problems are concentrated. By analyzing the density of problem points in the problem distribution map, hot spot areas where problems are more concentrated can be found. For example, a hot spot analysis tool in GIS can be used to generate a hot spot area distribution map, in which dark colors are used to represent areas with higher density of problem points, and light colors are used to represent areas with lower density of problem points. The hotspot area distribution map can help city managers quickly identify areas that need to be focused on. Finally, the hotspot area distribution map and the symbolic problem distribution map are combined to obtain a problem distribution map. This step is to merge the hotspot area distribution map and the symbolic problem distribution map to generate a comprehensive problem distribution map. On this map, you can not only see different types of problem points and their specific locations, but also the density distribution of problem points. For example, you can highlight the hotspot area with a dark background and mark specific problem points with different icons.In this way, city managers can fully understand the distribution of urban environmental problems and provide a scientific basis for subsequent environmental management and optimization. Through this series of steps, the geographic information of urban environmental problems can be systematically and visually displayed, providing city managers with an intuitive and comprehensive distribution map of environmental problems. This visualization method not only improves the efficiency of problem discovery and resolution, but also provides strong technical support for the long-term improvement of the urban environment. For example, in the aforementioned application scenario, through this series of optimization steps, city managers can more accurately identify and solve environmental problems in the core business district, and improve the overall environmental quality of the city and the satisfaction of residents.

[0042] The above describes the city appearance and environment monitoring method in the embodiment of the present invention. The following describes the city appearance and environment monitoring system in the embodiment of the present invention. Figure 2 In one embodiment of the present invention, a city environment monitoring system includes: The acquisition module 21 is used to acquire images of the target city through a preset front-end device to obtain an image of the target city; The analysis module 22 is used to use a preset AI monitoring algorithm to perform city appearance and environment monitoring on the target city image to obtain a city appearance and environment monitoring result; The positioning module 23 is used to determine whether the urban environment monitoring result has an urban environment problem feature. If so, the urban environment problem feature is located to obtain the located urban environment problem data; An evaluation module 24 is used to quantitatively evaluate the located city appearance environmental problem data through an environmental evaluation index system to obtain an evaluation result; The formulation module 25 is used to formulate an environmental management optimization plan for the target city based on the evaluation results.

[0043] In this embodiment, for the specific implementation of each unit in the above system embodiment, please refer to the above method embodiment, which will not be repeated here.

[0044] Reference Figure 3 The present invention also provides a computer device in an embodiment, wherein the internal structure of the computer device can be as follows: Figure 3As shown. The computer device includes a processor, a memory, a display screen, an input device, a network interface and a database connected through a system bus. Among them, the processor designed by the computer is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the corresponding data in this embodiment. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the above method is implemented.

[0045] Those skilled in the art will understand that Figure 3 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied.

[0046] An embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the above method is implemented. It can be understood that the computer-readable storage medium in this embodiment can be a volatile readable storage medium or a non-volatile readable storage medium.

[0047] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media provided by the present invention and used in the embodiments may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double-speed data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM.

[0048] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, device, article or method including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, device, article or method. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the presence of other identical elements in the process, device, article or method including the element.

[0049] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for monitoring urban environment, characterized in that: The following steps are involved: The target city is imaged by collecting images through a preset front-end device to obtain an image of the target city; Using a preset AI monitoring algorithm to monitor the city appearance and environment of the target city image, and obtaining a city appearance and environment monitoring result; Determine whether the urban appearance and environment monitoring results have environmental features of urban appearance problems; if so, locate the environmental features of urban appearance problems to obtain located urban appearance and environment problem data; Conducting quantitative evaluation on the data of the city appearance and environmental problems through the environmental evaluation index system to obtain evaluation results; An environmental management optimization plan for the target city is formulated based on the evaluation results.

2. The method for monitoring the urban environment according to claim 1, characterized in that: The preset front-end device is a camera installed on an unmanned aircraft. The image acquisition of the target city by the preset front-end device to obtain the image of the target city includes: During the flight of the unmanned aircraft along the predetermined route, the target city is continuously imaged by a camera mounted on the unmanned aircraft to obtain an image of the target city; Performing geometric correction processing on the target city image to obtain a corrected target city image; Performing image stitching on the corrected target city image to obtain a panoramic target city image; The panoramic target city image is enhanced to obtain a target city image.

3. The method for monitoring the urban environment according to claim 1, characterized in that: The using of a preset AI monitoring algorithm to monitor the target city image for the city appearance and environment to obtain the city appearance and environment monitoring results includes: Performing regional segmentation on the target city image to obtain a target city regional image; wherein the target city regional image includes the outline of urban buildings, green areas, roads and public facilities; Performing multi-scale extraction on the urban environment in the target urban area image to obtain urban environment features; Performing time series modeling on the city environment characteristics to obtain a time series characteristic graph; By using a preset cross-attention mechanism, the time series feature graph is focused on the city appearance environment to obtain a focused city appearance environment graph; Performing pixel-level classification on the concerned urban environment map to obtain a pixel-level classification result; Utilizing a preset AI monitoring algorithm, based on the pixel-level classification results, the city appearance and environment are monitored to obtain a city appearance and environment monitoring result.

4. The method for monitoring the city environment according to claim 1, characterized in that: The step of locating the characteristics of the city appearance and environment problems to obtain the located city appearance and environment problem data includes: Initially positioning the features of the urban environment problem to obtain preliminary location information; wherein the preliminary location information is two-dimensional location information; Performing three-dimensional reconstruction on the preliminary position information of multiple perspectives through multi-perspective geometry technology to obtain three-dimensional position information; Obtaining geographic reference points of the target city from a database, and spatially mapping the three-dimensional location information based on the geographic reference points by adopting homologous point pair mapping technology to obtain geographic coordinate information; Dynamically update the geographic coordinate information through time-space fusion technology to obtain real-time location information; Using Internet of Things technology to verify the real-time location information to obtain verified location information; If the verified location information is consistent with the geographic coordinate information, the real-time location information is used as the positioning city appearance environment data; If the verified location information is inconsistent with the real-time location information, the urban environment feature is repositioned.

5. The method for monitoring the city environment according to claim 3, characterized in that: The quantitative evaluation of the located city appearance environmental problem data by the environmental evaluation index system to obtain the evaluation results includes: Classifying the data of the located city appearance and environmental problems in the target city area to obtain environmental problem classification labels; wherein the environmental problem classification labels include green area problems, road problems, and advertising and signage problems; Calling the standardized index value of the environmental problem classification label in the database, and performing weight assignment on the environmental problem classification label to obtain the problem label weight value; Calculating a weighted index value of the target city area based on the standardized index value and the problem label weight value to obtain a weighted index value of the environmental problem; The target urban area is quantitatively evaluated based on the weighted index value of the environmental problem to obtain an evaluation result.

6. The method for monitoring the city environment according to claim 4, characterized in that: The formulating of the environmental management optimization plan for the target city based on the evaluation results includes: Based on the evaluation results, the main problems and key areas of the city appearance environment are identified to obtain a problem list; wherein the main problems in the problem list include the city appearance problem environment characteristics corresponding to the key areas; Performing spatial visualization on the problem list based on a preset geographic information system to obtain a problem distribution map; Perform trend analysis on the problem distribution graph by using a time series analysis method to obtain a change trend report; Conduct decision-making management and planning for the target city based on the change trend report to obtain a preliminary management plan; The preliminary management plan is reviewed and optimized to obtain an optimized plan for environmental management of the target city.

7. The method for monitoring the city environment according to claim 6, characterized in that: The spatial visualization of the problem list based on the geographic information system to obtain a problem distribution map includes: Obtaining geographic coordinate information of environmental features of urban appearance problems corresponding to key areas in the problem list; Loading basic geographic data into a geographic information system to generate a basic map; wherein the basic map includes roads, buildings and green areas of the target city; Overlaying the geographic coordinate information onto the basic map to generate a preliminary problem distribution map; Using different icons to symbolize the preliminary problem distribution map to obtain a symbolized problem distribution map; Performing hot spot analysis on the symbolized problem distribution map to obtain a hot spot area distribution map; The hot spot area distribution map and the symbolized problem distribution map are combined to obtain a problem distribution map.

8. A city environment monitoring device, characterized in that: include: The acquisition module is used to acquire images of the target city through a preset front-end device to obtain an image of the target city; An analysis module is used to use a preset AI monitoring algorithm to perform city appearance and environment monitoring on the target city image to obtain a city appearance and environment monitoring result; A positioning module is used to determine whether the urban appearance and environment monitoring results contain environmental features of urban appearance problems. If so, the urban appearance and environment problem features are located to obtain the located urban appearance and environment problem data; An evaluation module is used to quantitatively evaluate the located city appearance and environmental problem data through an environmental evaluation index system to obtain an evaluation result; A formulation module is used to formulate an environmental management optimization plan for the target city based on the evaluation results.

9. A computer device comprising a memory and a processor, wherein a computer program is stored in the memory, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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