Environmental data collection and updating spatial analysis methods for the renewal of old residential areas

Abstract

The present invention relates to the field of image processing technology, and in particular to an environmental data collection and update space analysis method for updating old residential communities. The update space analysis method comprises: obtaining a remote sensing image of a target old residential community, the number of mobile personnel, and continuous frame images, dividing the remote sensing image into regions, and obtaining each inefficiently utilized region; based on each inefficiently utilized region, a preset time constant, the number of mobile personnel, and continuous frame images of the target old residential community, obtaining a user demand factor for each inefficiently utilized region; based on the inefficient utilization factor of each region and the user demand factor of each inefficiently utilized region, obtaining a renewal and transformation degree factor for each region; based on the renewal and transformation degree factor of each region, analyzing the renewal space of the old residential community to obtain an analysis result. The purpose of the present invention is to solve the problem that the accuracy of the actual utilization rate of the update scheme of the prior art is low, resulting in low reliability in updating and transforming old residential communities.

Description

Technical Field

[0001] The present invention relates to the field of image processing technology, and in particular to an environmental data collection and updating space analysis method for updating old residential areas. Background Art

[0002] Against the backdrop of rapid urbanization and urban aging, a large number of old residential areas have been formed. The buildings, facilities and community services in these areas often lag behind the needs of urban development, directly affecting the living comfort and health of residents. Therefore, it is necessary to update and renovate old residential areas. Environmental data collection is an important prerequisite and basis for updating old residential areas. Further analysis of the spatial structure of old residential areas is conducted to provide data support for residential area planning, ensuring that the update plan can meet the actual needs and expectations of residents, thereby improving the overall living environment of the city and the quality of life of residents.

[0003] The existing environmental data collection and updating spatial analysis logic for the renewal of old communities usually rely on physical indicators such as regional surface space utilization. In actual operation, due to the different adaptability and demand differences of residents in different residential areas for different living environments, the renewal schemes obtained by traditional analysis systems have certain limitations, resulting in a lack of accuracy in the actual utilization rate, which reduces the reliability of the final renewal and transformation results of old communities. Therefore, a more efficient and reliable environmental data collection and updating spatial analysis method for the renewal of old communities is urgently needed to improve the actual utilization rate of old communities and the quality of life of residents. Summary of the invention

[0004] In order to solve the technical problem that the actual utilization rate of the updating scheme in the prior art is low in accuracy, resulting in low reliability in updating and renovating old communities, the purpose of the present invention is to provide an environmental data collection and updating space analysis method for updating old communities. The technical scheme adopted is as follows:

[0005] A renewal space analysis method for renewal of old residential areas, the method comprising:

[0006] Obtain remote sensing images of the target old residential area, as well as the number of mobile personnel and continuous frame images in the target old residential area within a certain period of time;

[0007] Based on the remote sensing image, obtaining each inefficiently utilized area;

[0008] Based on each of the inefficiently utilized areas, a preset time constant, the number of mobile personnel in the target old residential area, and continuous frame images, a user demand factor for each inefficiently utilized area is obtained;

[0009] Based on the inefficient utilization factor of each area and the user demand factor of each inefficiently utilized area, the renewal and transformation degree factor of each area is obtained;

[0010] Based on the renewal and reconstruction degree factor of each area, the renewal space of old communities is analyzed and the analysis results are obtained.

[0011] Preferably, based on the remote sensing image, the specific steps of obtaining each inefficiently utilized area include:

[0012] Dividing the remote sensing image into regions to obtain each divided region in the remote sensing image;

[0013] Based on each divided area in the remote sensing image, each inefficiently utilized area is obtained.

[0014] Preferably, based on each divided area in the remote sensing image, the specific steps of obtaining each inefficiently utilized area include:

[0015] Based on each divided area in the remote sensing image, obtaining the mean value and information entropy of the pixel values ​​of all divided areas adjacent to each divided area and each divided area;

[0016] Based on each divided area in the remote sensing image and the average of the pixel values ​​of each divided area, obtaining a pixel protrusion coefficient of each divided area;

[0017] Based on the information entropy of the pixel value of each divided area, the pixel prominence coefficient of each local small area, all divided areas adjacent to each divided area and the factor threshold, each inefficiently utilized area is obtained.

[0018] Preferably, based on the information entropy of the pixel value of each divided area, the pixel prominence coefficient of each local small area, all divided areas adjacent to each divided area and the factor threshold, the specific steps of obtaining each inefficiently utilized area include:

[0019] Based on the information entropy of the pixel values ​​of each divided area, the pixel prominence coefficient of each local small area and all the divided areas adjacent to each divided area, determining the inefficient utilization factor of each divided area;

[0020] The inefficient utilization factor of each divided area is compared with the factor threshold to obtain each inefficient utilization area.

[0021] Preferably, based on each inefficiently utilized area, a preset time constant, the number of mobile personnel in the target old residential area, and continuous frame images, the specific steps of obtaining the user demand factor of each inefficiently utilized area include:

[0022] Based on each of the inefficiently utilized areas, a preset time constant and the number of mobile personnel in the target old residential area, a resident mobility coefficient of each inefficiently utilized area is obtained;

[0023] Based on the number of mobile personnel and continuous frame images in each inefficiently utilized area and the target old residential area, the interaction coefficient between different residents in each inefficiently utilized area is obtained;

[0024] Based on the resident mobility coefficient of each inefficiently utilized area and the interaction coefficient between different residents in each inefficiently utilized area, the user demand factor of each inefficiently utilized area is obtained.

[0025] Preferably, based on each inefficiently utilized area, a preset time constant and the number of mobile personnel in the target old residential area, the specific steps of obtaining the resident mobility coefficient of each inefficiently utilized area include:

[0026] Based on the number of mobile personnel in each of the inefficiently utilized areas and the target old residential quarters, confirm the periodicity of the personnel flow curve of each inefficiently utilized area;

[0027] According to the preset time constant, the number of mobile personnel in the target old residential area and the periodicity of the personnel flow curve of each inefficiently utilized area, the resident mobility coefficient of each inefficiently utilized area is obtained.

[0028] Preferably, based on the number of floating personnel in each inefficiently utilized area and the target old residential area and the continuous frame images, the specific steps of obtaining the interaction coefficient between different residents in each inefficiently utilized area include:

[0029] Based on the number of mobile personnel in each inefficiently utilized area and the target old residential area and the continuous frame images, obtain each angle between the movement direction vector of the personnel in each inefficiently utilized area and the centroid line between two different personnel in each inefficiently utilized area;

[0030] Based on each inefficiently utilized area, the number of mobile personnel in the target old residential area and each angle, the interaction coefficient between different residents in each inefficiently utilized area is obtained.

[0031] Preferably, the specific steps of obtaining the user demand factor of each inefficiently utilized area based on the resident mobility coefficient of each inefficiently utilized area and the interaction coefficient between different residents in each inefficiently utilized area include:

[0032] The resident mobility coefficient of each inefficiently utilized area is multiplied by the interaction coefficient between different residents in each inefficiently utilized area to obtain the user demand factor of each inefficiently utilized area.

[0033] The present invention also provides an environmental data collection method for updating old residential communities, which is used to collect remote sensing images of the target old residential communities in any of the above items, and the method comprises:

[0034] Obtain remote sensing data of the target old residential area over a period of time;

[0035] Convert and process remote sensing data information to obtain remote sensing images with spatial resolution;

[0036] Analyzing and preprocessing the remote sensing image to obtain a preprocessed remote sensing image;

[0037] The pre-processed remote sensing image is integrated with image contours to obtain a remote sensing image of the target old residential area.

[0038] Preferably, the specific steps of analyzing and preprocessing the remote sensing image to obtain the preprocessed remote sensing image include:

[0039] The remote sensing image is sequentially subjected to grayscale image conversion and smoothing filter denoising processing to obtain a preprocessed remote sensing image.

[0040] The present invention has the following beneficial effects:

[0041] The present invention obtains each inefficiently utilized area based on the remote sensing image; obtains the user demand factor of each inefficiently utilized area based on each inefficiently utilized area, a preset time constant, the number of mobile personnel in the target old community and continuous frame images; obtains the renewal and transformation degree factor of each area based on the inefficient utilization factor of each area and the user demand factor of each inefficiently utilized area; analyzes the renewal space of the old community based on the renewal and transformation degree factor of each area to obtain the analysis result. The inefficient utilization factor of each area is corrected by the user demand factor, and the renewal and transformation degree factor of each area is finally obtained to achieve a more accurate analysis result, thereby ensuring the reliability of the renewal and transformation results of the old community. The target old community can be better optimized through the analysis result, thereby improving the actual utilization rate of the old community and the quality of life of the residents. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0043] Figure 1 A flow chart of an environmental data collection method for updating old residential areas provided by an embodiment of the present invention;

[0044] Figure 2 A flowchart of an update space analysis method for updating old communities provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0045] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined purpose of the invention, the following is a detailed description of the specific implementation method, structure, characteristics and effects of an environmental data collection and update space analysis method for the renewal of old communities proposed by the present invention in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0046] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0047] The existing renewal space analysis method for the renewal of old residential areas makes a preliminary regional division based on the acquired local area land type and functional characteristics, and conducts regional space utilization analysis.

[0048] The following is a detailed description of a specific scheme of an environmental data collection and updating spatial analysis method for updating old residential areas provided by the present invention in conjunction with the accompanying drawings.

[0049] See also Figure 1 , which shows a flow chart of an environmental data collection method for updating old communities provided by an embodiment of the present invention. In this embodiment, the present invention provides an environmental data collection method for updating old communities, which is used to collect remote sensing images of target old communities, and includes the following steps:

[0050] S110, obtaining remote sensing data of the target old residential area within a period of time, wherein the period of time is one year.

[0051] S120: Convert and process the remote sensing data information to obtain a remote sensing image with spatial resolution, thereby realizing the generation of a remote sensing image with housing, environment and transportation facilities from the remote sensing data.

[0052] S130, performing analysis and preprocessing on the remote sensing image to obtain a preprocessed remote sensing image; this step specifically comprises: performing grayscale image conversion and smoothing filter denoising processing on the remote sensing image in sequence to obtain a preprocessed remote sensing image.

[0053] S140, integrating the image contours of the pre-processed remote sensing image to obtain the remote sensing image of the target old residential area. In this step, the image contours in the pre-processed remote sensing image are integrated using the closing operation in mathematical morphology, so that the remote sensing image of the target old residential area is clearer and more complete.

[0054] See also Figure 2 , which shows a flow chart of an update space analysis method for updating old communities provided by an embodiment of the present invention. Based on the above environmental data collection method for updating old communities, the present invention also provides an update space analysis method for updating old communities, the method comprising the following steps:

[0055] S100, obtaining a remote sensing image of a target old residential area and the number of mobile personnel and continuous frame images of the target old residential area within a certain period of time. In this embodiment, the certain period of time is one week.

[0056] S200: Acquire each inefficiently utilized area based on the remote sensing image.

[0057] S300. Based on each of the inefficiently utilized areas, a preset time constant, the number of mobile personnel in the target old residential area, and continuous frame images, a user demand factor for each inefficiently utilized area is obtained.

[0058] S400: Obtaining a renovation degree factor for each area based on the inefficient utilization factor of each area and the user demand factor of each inefficiently utilized area.

[0059] S500. Based on the renewal and reconstruction degree factor of each area, the renewal space of the old residential area is analyzed to obtain the analysis result.

[0060] The present invention obtains each inefficiently utilized area based on the remote sensing image; obtains the user demand factor of each inefficiently utilized area based on each inefficiently utilized area, a preset time constant, the number of mobile personnel in the target old community and continuous frame images; obtains the renewal and transformation degree factor of each area based on the inefficient utilization factor of each area and the user demand factor of each inefficiently utilized area; analyzes the renewal space of the old community based on the renewal and transformation degree factor of each area to obtain the analysis result. The inefficient utilization factor of each area is corrected by the user demand factor, and the renewal and transformation degree factor of each area is finally obtained to achieve a more accurate analysis result, thereby ensuring the reliability of the renewal and transformation results of the old community. The target old community can be better optimized through the analysis result, thereby improving the actual utilization rate of the old community and the quality of life of the residents.

[0061] The specific steps of S200 include:

[0062] The remote sensing image is divided into regions to obtain each divided region in the remote sensing image. In this embodiment, for the remote sensing image of the target old residential area obtained, a sliding window is constructed starting from the upper left corner of the remote sensing image. The area of ​​the sliding window can be 25 square centimeters to represent the actual area of ​​25 square meters; the area within the sliding window is regarded as a divided region. The sliding window is iterated from left to right and from top to bottom in the remote sensing image to traverse the entire remote sensing image and obtain all divided regions.

[0063] All the partitioned areas in the old residential areas have various functional types; some of them may be abandoned and inefficiently used due to long-term lack of management or planning, for example, the unmanaged partitioned areas become garbage dumps, and the abandoned bare land partitioned areas form bare roads due to the passage of a large number of residents. Therefore, the abandoned partitioned areas usually appear in more messy colors in remote sensing images, such as gray and irregular color distribution, which is quite different from the colors of the surrounding normal partitioned areas.

[0064] Based on each divided area in the remote sensing image, each inefficiently utilized area is obtained. This step specifically includes:

[0065] Based on each divided area in the remote sensing image, the mean value and information entropy of the pixel values ​​of all divided areas adjacent to each divided area and each divided area are obtained.

[0066] Based on each divided area in the remote sensing image and the average of the pixel values ​​of each divided area, the pixel protrusion coefficient of each divided area is obtained. Specifically, the pixel protrusion coefficient of each local small area is calculated by the pixel protrusion coefficient formula, and the pixel protrusion coefficient formula is as follows:

[0067] ;

[0068] In the formula, represents the pixel prominence coefficient of the i-th partition area, Represents the i-th partition area in the remote sensing image of the target old community, represents the mean value of the pixel values ​​of the ith partition area, The mean of the pixel means of all regions except the i-th partitioned region in the remote sensing image representing the target old community. The pixel values ​​of normal partitioned regions in remote sensing images have certain similarity and stability. At the same time, the number of normal regions is much larger than the number of inefficiently utilized partitioned regions. Therefore, the pixel prominence coefficient of each partitioned region is used as the weight of the inefficient utilization factor of each partitioned region to optimize the inefficient utilization factor of each partitioned region.

[0069] Based on the information entropy of the pixel value of each divided area, the pixel prominence coefficient of each local small area, all the divided areas adjacent to each divided area and the factor threshold, each inefficiently utilized area is obtained. This step specifically includes:

[0070] Based on the information entropy of the pixel value of each divided area, the pixel prominence coefficient of each local small area and all the divided areas adjacent to each divided area, the inefficient utilization factor of each divided area is determined. The calculation formula of the inefficient utilization factor of each divided area is as follows:

[0071]

[0072] In the formula, represents the inefficient utilization factor of the i-th partition area, represents the information entropy of the pixel value of the i-th partition area, represents the number of all partitioned areas adjacent to the i-th partitioned area, represent The nth partition area in represents the pixel prominence coefficient of the nth partition area, Represents the average of the pixel values ​​of the nth partition area among all the partition areas adjacent to the ith partition area. The larger the value, the greater the possibility that the area is an inefficiently utilized area.

[0073] The inefficient utilization factor of each divided area is compared with the factor threshold to obtain each inefficient utilization area. In this embodiment, the factor threshold is set to 0.4. When the inefficient utilization factor of each divided area is greater than the factor threshold, the divided area corresponding to the inefficient utilization factor of each divided area is used as the inefficient utilization initial area, and the adjacent inefficient utilization initial areas are merged to finally obtain each inefficient utilization area in the target old community.

[0074] The inefficiently used areas in the target old communities may be caused by the residents' autonomous needs or environmental abandonment factors; for example: designated garbage dumping sites in the target old communities and abandoned weed-covered areas or simple garbage dumping areas due to poor management. Therefore, the activity of residents in the inefficiently used areas in the target communities is analyzed to optimize the spatial analysis logic of the community renewal and ensure that the spatial layout and functional design after the renovation can better serve the needs and habits of residents.

[0075] Specifically, the specific steps of S300 include:

[0076] Based on each inefficiently utilized area, the preset time constant and the number of mobile personnel in the target old residential area, the resident mobility coefficient of each inefficiently utilized area is obtained. This step specifically includes:

[0077] Based on the number of mobile personnel in each inefficiently utilized area and the target old residential area, the periodicity of the personnel flow curve of each inefficiently utilized area is confirmed. Among them, surveillance cameras are installed in the inefficiently utilized areas of the target old residential area (to ensure that all inefficiently utilized areas are covered), and the number of people entering and leaving the inefficiently utilized areas is obtained in real time using the YOLO target detection algorithm. The personnel flow curve of the inefficiently utilized area is constructed based on the number of mobile personnel in the inefficiently utilized area within a week, and then the correlation between the personnel flow curve and its own lag curve is calculated using the autocorrelation function to obtain the periodicity of the personnel flow curve of each inefficiently utilized area.

[0078] According to the preset time constant, the number of mobile personnel in the target old residential area, and the periodicity of the personnel flow curve of each inefficient utilization area, the resident mobility coefficient of each inefficient utilization area is obtained. Specifically, the preset time constant is one day, that is, 24 hours. In this embodiment, the calculation formula of the resident mobility coefficient of each inefficient utilization area is as follows:

[0079] ;

[0080] In the formula, Representative The mobility coefficient of residents in the inefficiently utilized areas, Representative The periodicity of the personnel flow curve in the inefficiently utilized area, Representatives enter the Number of people in inefficiently utilized areas, Representatives leave every day Number of people in inefficiently utilized areas, Represents a preset time constant. The larger the The flow of people in the underutilized area is very frequent and active throughout the day. The larger the There are relatively regular and frequent personnel activities in the inefficiently utilized areas, that is, the resident mobility coefficient in this area is relatively large, which indirectly reflects that the residents have a high demand for this area.

[0081] Among them, for inefficiently utilized areas with less personnel mobility, there may be strong interactive behaviors between different people in the divided area, resulting in strong autonomous demand for specific groups of people in the inefficiently utilized area. For example, some inefficiently utilized areas become gathering places for specific interest groups (playing basketball, badminton and other sports). Therefore, when analyzing the demand of residents in the target old community for inefficiently utilized areas, the interaction coefficient between different people in the local area is analyzed, and then the resident mobility coefficient is corrected and adjusted.

[0082] Based on the number of mobile personnel in each inefficiently utilized area and the target old residential area and the continuous frame images, the interaction coefficient between different residents in each inefficiently utilized area is obtained. This step specifically includes:

[0083] Based on the number of mobile personnel in each inefficiently utilized area and the target old community and continuous frame images, each angle between the movement direction vector of the personnel in each inefficiently utilized area and the center of mass connection line between two different personnel in each inefficiently utilized area is obtained.

[0084] Based on the number of mobile personnel in each inefficiently utilized area and the target old residential area and each angle, the interaction coefficient between different residents in each inefficiently utilized area is obtained. In this embodiment, the optical flow method is used to calculate the optical flow vectors of all personnel in the area in the continuous frame images of each inefficiently utilized area, and the mean vector of the optical flow vector is used as the movement direction vector of each person.

[0085] For any two people in each inefficiently utilized area, connect their centroids to obtain the centroid line, and obtain the angle between the movement direction vector of each person and the centroid line. The smaller the value, the more similar the movement directions of the two people are, indicating that they are doing similar activities or sports, such as playing basketball or badminton, moving in the same direction or taking similar actions. And based on the number of mobile people in the target old community, obtain the number of people in each inefficiently utilized area. The calculation formula for the interaction coefficient between different residents in each inefficiently utilized area is as follows:

[0086] ;

[0087] In the formula, Representative The interaction coefficient between different residents in the inefficiently utilized area, Representative The number of all mobile personnel in the inefficiently utilized area, Represents The Inefficient use of regional personnel, represent Except for The number of mobile personnel in all inefficiently utilized areas except for the inefficiently utilized areas, represent Middle Inefficient use of regional personnel, Representative The movement direction vector of personnel in the inefficiently utilized area is The angle between the centroids of people in the inefficiently utilized areas, Representative The movement direction vector of personnel in the inefficiently utilized area is The angle between the centroids of people in the inefficiently utilized areas. Indicates Inefficiently utilize all possible regional personnel pairs in the area to traverse and calculate each pair of personnel ( and ). Then, all the interaction coefficients are averaged to obtain the interaction coefficient of the entire region, The closer the value of is to 1, the stronger the interaction between different people in the inefficient utilization area is, and they are more likely to be engaged in similar activities or sports; The closer the value is to 0, the weaker the interaction between different people in the inefficiently utilized area. People in the inefficiently utilized area may just pass by the area, which reflects that the residents of the target old community have little demand for the inefficiently utilized area.

[0088] Based on the resident mobility coefficient of each inefficiently utilized area and the interaction coefficient between different residents in each inefficiently utilized area, the user demand factor of each inefficiently utilized area is obtained. This step specifically includes:

[0089] The resident mobility coefficient of each inefficient utilization area is multiplied by the interaction coefficient between different residents in each inefficient utilization area to obtain the user demand factor of each inefficient utilization area. The resident mobility coefficient is corrected and adjusted by the interaction coefficient between different residents in each inefficient utilization area to obtain the user demand factor of each inefficient utilization area. The calculation formula of the user demand factor of each inefficient utilization area is as follows:

[0090] ;

[0091] In the formula, Representative User demand factors for inefficiently utilized areas, Representative The interaction coefficient between different residents in the inefficiently utilized area, Representative The mobility coefficient of residents in the inefficiently utilized areas. When the mobility coefficient of residents in each inefficiently utilized area is small, but the interaction coefficient between different residents is large, it means that there may be specific groups of people with interest in the area, so The larger the value, the The user demand factor of the inefficiently utilized area is high, and in the subsequent renewal space transformation, extra attention needs to be paid to the target inefficiently utilized area.

[0092] The calculation formula of the renovation degree factor of each area in the specific steps of S400 is as follows:

[0093] ;

[0094] In the formula, Representative The renewal degree factor of a region, Representative Inefficient utilization factor of the region, Representative User demand factors in a region.

[0095] Finally, by obtaining the renewal degree factor of each area in the target old community, the renewal degree of the target old community is evaluated, and a targeted renewal optimization strategy is formulated based on the results of the degree evaluation. For example, for areas with a high renewal degree factor (i.e. abandoned areas), the renewal optimization strategy is to build facilities such as garbage disposal stations to improve the spatial efficiency utilization rate of the area. This ensures the reliability of the renewal and transformation results of the target old community and improves the actual utilization rate of the old community and the quality of life of residents.

[0096] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0097] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A spatial analysis method for updating old residential areas, characterized by: The method comprises: Obtain remote sensing images of the target old residential area, as well as the number of mobile personnel and continuous frame images in the target old residential area within a certain period of time; Based on the remote sensing image, obtaining each inefficiently utilized area; Based on each of the inefficiently utilized areas, a preset time constant, the number of mobile personnel in the target old residential area, and continuous frame images, a user demand factor for each inefficiently utilized area is obtained; Based on the inefficient utilization factor of each area and the user demand factor of each inefficiently utilized area, the renewal and transformation degree factor of each area is obtained; Based on the renewal and reconstruction degree factor of each area, the renewal space of old communities is analyzed and the analysis results are obtained; Based on the remote sensing image, the specific steps of obtaining each inefficiently utilized area include: Dividing the remote sensing image into regions to obtain each divided region in the remote sensing image; Based on each divided area in the remote sensing image, obtaining each inefficiently utilized area; Based on each divided area in the remote sensing image, the specific steps of obtaining each inefficiently utilized area include: Based on each divided area in the remote sensing image, obtaining the mean value and information entropy of the pixel values ​​of all divided areas adjacent to each divided area and each divided area; Based on each divided area in the remote sensing image and the average of the pixel values ​​of each divided area, obtaining a pixel protrusion coefficient of each divided area; Based on the information entropy of the pixel value of each divided area, the pixel prominence coefficient of each local small area, all divided areas adjacent to each divided area and the factor threshold, each inefficiently utilized area is obtained; Based on the information entropy of the pixel value of each divided area, the pixel prominence coefficient of each local small area, all divided areas adjacent to each divided area and the factor threshold, the specific steps of obtaining each inefficiently utilized area include: Based on the information entropy of the pixel values ​​of each divided area, the pixel prominence coefficient of each local small area and all the divided areas adjacent to each divided area, determining the inefficient utilization factor of each divided area; Compare the inefficient utilization factor of each divided area with the factor threshold to obtain each inefficient utilization area; Based on each of the inefficiently utilized areas, the preset time constant, the number of mobile personnel in the target old residential area, and the continuous frame images, the specific steps of obtaining the user demand factor of each inefficiently utilized area include: Based on each of the inefficiently utilized areas, a preset time constant and the number of mobile personnel in the target old residential area, a resident mobility coefficient of each inefficiently utilized area is obtained; Based on the number of mobile personnel and continuous frame images in each inefficiently utilized area and the target old residential area, the interaction coefficient between different residents in each inefficiently utilized area is obtained; Based on the resident mobility coefficient of each inefficiently utilized area and the interaction coefficient between different residents in each inefficiently utilized area, the user demand factor of each inefficiently utilized area is obtained; Based on each of the inefficiently utilized areas, the preset time constant and the number of mobile personnel in the target old residential quarter, the specific steps of obtaining the resident mobility coefficient of each inefficiently utilized area include: Based on the number of mobile personnel in each of the inefficiently utilized areas and the target old residential quarters, confirm the periodicity of the personnel flow curve of each inefficiently utilized area; According to the preset time constant, the number of mobile personnel in the target old residential area and the periodicity of the personnel flow curve of each inefficiently utilized area, the resident mobility coefficient of each inefficiently utilized area is obtained; Based on each inefficiently utilized area, the number of mobile personnel in the target old community and the continuous frame images, the specific steps of obtaining the interaction coefficient between different residents in each inefficiently utilized area include: Based on the number of mobile personnel in each inefficiently utilized area and the target old residential area and the continuous frame images, obtain each angle between the movement direction vector of the personnel in each inefficiently utilized area and the centroid line between two different personnel in each inefficiently utilized area; Based on each inefficiently utilized area, the number of mobile personnel in the target old residential area, and each angle, the interaction coefficient between different residents in each inefficiently utilized area is obtained; The specific steps of obtaining the user demand factor of each inefficiently utilized area based on the resident mobility coefficient of each inefficiently utilized area and the interaction coefficient between different residents in each inefficiently utilized area include: The resident mobility coefficient of each inefficiently utilized area is multiplied by the interaction coefficient between different residents in each inefficiently utilized area to obtain the user demand factor of each inefficiently utilized area.

2. A method for collecting environmental data for updating old residential areas, characterized in that: Used to collect remote sensing images of target old communities in the update space analysis method of claim 1, the method comprising: Obtain remote sensing data of the target old residential area over a period of time; Convert and process remote sensing data information to obtain remote sensing images with spatial resolution; Analyzing and preprocessing the remote sensing image to obtain a preprocessed remote sensing image; The pre-processed remote sensing image is integrated with image contours to obtain a remote sensing image of the target old residential area.

3. The environmental data collection method for updating old residential areas according to claim 2 is characterized by: The specific steps of analyzing and preprocessing the remote sensing image to obtain the preprocessed remote sensing image include: The remote sensing image is sequentially subjected to grayscale image conversion and smoothing filter denoising processing to obtain a preprocessed remote sensing image.

Images