Mobile Data Collection and Upload System for Urban and Rural Planning Research

Abstract

The present invention relates to the technical field of data processing, and particularly to a mobile data collection and upload system for urban and rural planning research. The system includes a memory and a processor, and the processor executes the computer program stored in the memory to implement the following steps: obtaining the research boundary of the research area; obtaining an abnormal evaluation value according to the shape similarity between the research boundary in each segment and the original planned path boundary, and determining the error path segment and the normal segment; obtaining a boundary abnormal evaluation discrete index according to the abnormal evaluation value of the research boundary of the error path segment, and determining the preliminary influence segment and the non-influence segment; dividing the preliminary influence segment into a suspected encroachment segment and a non-encroachment segment according to the boundary abnormal evaluation discrete index and the abnormal evaluation value; respectively fitting the research boundaries of each type of segment to obtain the optimal boundary and uploading it. The present invention improves the accuracy of determining the research area boundary and the credibility and accuracy of the research data.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and particularly to a mobile data collection and upload system for urban and rural planning research. Background Art

[0002] Traditional urban and rural planning research methods often rely on paper records and manual measurements, suffering from problems such as low efficiency, information lag, inaccurate data, etc. Moreover, the transmission and summary of data are rather cumbersome, making it difficult to quickly respond to changing needs. The development of mobile data collection technology, especially the popularization of portable devices such as smart phones and tablets, enables real-time and accurate data collection in urban and rural planning research. Through the application of technologies such as GPS and geographic information systems, researchers can directly collect various types of data such as geographical locations, buildings, land use, and transportation facilities within the area on-site through mobile devices and immediately upload them to the cloud or central database. This real-time uploaded data can provide planners with accurate and up-to-date information, helping to better analyze, evaluate, and formulate planning schemes.

[0003] During the mobile data collection process for urban and rural planning research, multiple researchers are required to collect the boundary data of the area in the same region. Since the original planned boundary may change due to subsequent building construction, the boundary of this area cannot be determined. When the existing method uses the template matching method to determine the area boundary, it is easy to cause a low accuracy of the area boundary, affecting subsequent urban and rural planning decisions and implementations, and reducing the credibility and accuracy of the research process. Summary of the Invention

[0004] In order to solve the problem of low research accuracy and credibility existing in the existing method when researching the boundary of urban and rural planning areas, the purpose of the present invention is to provide a mobile data collection and upload system for urban and rural planning research. The specific technical solution adopted is as follows:

[0005] The present invention provides a mobile data collection and upload system for urban and rural planning research, including a memory and a processor. The processor executes the computer program stored in the memory to implement the following steps:

[0006] Obtain the original planned path boundary of the research area for urban and rural planning and the research boundary during each research;

[0007] Obtain the abnormal evaluation value of each research boundary in each segment during each research according to the shape similarity between the research boundary and the original planned path boundary in each segment, and use the abnormal evaluation value to screen the boundary abnormal areas during each research; Combine the determination times of the boundary abnormal areas in each segment during all researches to determine the error path segments and normal segments;

[0008] Segment the abnormal evaluation values of all survey boundaries of all sub-surveys according to the error path to obtain the discrete index of boundary abnormal evaluation for error path segmentation, and determine the preliminary affected segments and non-affected segments;

[0009] According to the discrete index of boundary abnormal evaluation and the abnormal evaluation value of each preliminary affected segment, obtain the encroachment contribution value of each preliminary affected segment, and use the encroachment contribution value to determine the suspected encroachment segments and non-encroachment segments; Combine the abnormal evaluation value, encroachment contribution value of the survey boundary of the suspected encroachment segment and the position distribution at each survey to determine the encroachment sub-path;

[0010] Fit the survey boundaries of each type of segment and the encroachment sub-path respectively to determine the optimal boundary of the survey area and upload its information, where the types of segments include normal segments, non-affected segments and non-encroachment segments.

[0011] Preferably, the obtaining of the abnormal evaluation value of each survey boundary in each segment according to the shape similarity between the survey boundary in each segment and the boundary of the original planned path includes:

[0012] For any survey:

[0013] Calculate the DTW distance between the position coordinates of the boundary of the original planned path and the survey boundary of the segment to be analyzed, and the length difference between the boundary of the original planned path and the survey boundary of the segment to be analyzed; Obtain the abnormal evaluation value of the segment to be analyzed according to the DTW distance and the length difference; Both the DTW distance and the length difference are positively correlated with the abnormal evaluation value;

[0014] The segment to be analyzed is any segment on the boundary of the survey area.

[0015] Preferably, the screening of the boundary abnormal area of each survey by using the abnormal evaluation value includes:

[0016] Determine the segments with abnormal evaluation values greater than or equal to the preset first abnormal threshold as the boundary abnormal areas.

[0017] Preferably, the determination of the error path segments and normal segments by combining the determination times of the boundary abnormal areas in each segment of all surveys includes:

[0018] If the number of times the segment to be analyzed is determined as the boundary abnormal area during all surveys is greater than or equal to the preset quantity threshold, then determine the segment to be analyzed as the error path segment;

[0019] If the number of times the segment to be analyzed is determined as the boundary abnormal area during all surveys is less than the preset quantity threshold, then determine the segment to be analyzed as the normal segment.

[0020] Preferably, all the abnormal evaluation values of all the research boundaries segmented according to the error path are obtained to get the boundary abnormal evaluation discrete index of the error path segmentation, including:

[0021] For any error path segmentation:

[0022] Calculate the range, variance and mean of the abnormal evaluation values of the research boundaries of any error path segmentation in all the research processes;

[0023] Take the ratio between the variance and the mean as the coefficient of variation; determine the product of the coefficient of variation and the range as the boundary abnormal evaluation discrete index of any error path segmentation.

[0024] Preferably, determining the preliminary influence segmentation and non-influence segmentation includes:

[0025] Determine the error path segmentations with boundary abnormal evaluation discrete indexes greater than or equal to the preset discrete threshold as the preliminary influence segmentations;

[0026] Determine the error path segmentations with boundary abnormal evaluation discrete indexes less than the preset discrete threshold as the non-influence segmentations.

[0027] Preferably, according to the boundary abnormal evaluation discrete index and abnormal evaluation value of each preliminary influence segmentation, obtain the encroachment contribution value of each preliminary influence segmentation, and use the encroachment contribution value to determine the suspected encroachment segmentations and non-encroachment segmentations, including:

[0028] For any preliminary influence segmentation: count the proportion of the number of abnormal evaluation values of the research boundaries of any preliminary influence segmentation greater than the preset second abnormal threshold in all the research processes; correct the proportion with the boundary abnormal evaluation discrete index of any preliminary influence segmentation as the weight to obtain the encroachment contribution value of any preliminary influence segmentation; the preset second abnormal threshold is greater than the preset first abnormal threshold;

[0029] Determine the preliminary influence segmentations with encroachment contribution values greater than or equal to the preset encroachment threshold as the suspected encroachment segmentations; determine the preliminary influence segmentations with encroachment contribution values less than the preset encroachment threshold as the non-encroachment segmentations.

[0030] Preferably, combining the abnormal evaluation value, encroachment contribution value and position distribution at each research of the research boundary of the suspected encroachment segmentation to determine the encroachment sub-path, including:

[0031] Connect all the continuous suspected encroachment segmentations to form several suspected encroachment continuous segments;

[0032] For any suspected encroachment continuous segment: Cluster the research boundaries of any suspected encroachment continuous segment described during all research processes. According to the distribution dispersion degree of the abnormal evaluation values of each research boundary within each clustering cluster and the range of the encroachment contribution values, obtain the effective contribution value of the research boundary of each clustering cluster of the any suspected encroachment continuous segment. Both the distribution dispersion degree and the range of the encroachment contribution values are negatively correlated with the effective contribution value;

[0033] Use the effective contribution value to screen the encroachment sub-paths.

[0034] Preferably, the using the effective contribution value to screen the encroachment sub-paths includes:

[0035] Determine the research boundaries within the clustering cluster with the effective contribution value greater than or equal to the preset contribution threshold as the encroachment sub-paths.

[0036] Preferably, the respectively fitting the research boundaries of each type of segmented section and the encroachment sub-paths to determine the optimal boundary of the research area includes:

[0037] For the normal segmented section and the non-influencing segmented section: Fit all the research boundaries within the normal segmented section and the non-influencing segmented section to a line respectively, and record the fitted lines as the optimal boundary of the normal segmented section and the optimal boundary of the non-influencing segmented section respectively;

[0038] For any non-encroachment segmented section; Calculate the mean value of the lengths of all the research boundaries within the any non-encroachment segmented section; Determine the research boundaries within the any non-encroachment segmented section with the length greater than or equal to the mean value of the lengths as the reference boundaries; Fit all the reference boundaries within the any non-encroachment segmented section to a line, and record the fitted line as the optimal boundary of the any non-encroachment segmented section;

[0039] For any suspected encroachment continuous segment: Fit all the encroachment sub-paths within the any suspected encroachment continuous segment to a line, and record the fitted line as the optimal boundary of the any suspected encroachment continuous segment;

[0040] The optimal boundaries of all the normal segmented sections, the non-influencing segmented sections, the non-encroachment segmented sections and the suspected encroachment continuous segments together constitute the optimal boundary of the research area.

[0041] The present invention has at least the following beneficial effects:

[0042] The present invention first obtains the survey boundaries of the survey area during multiple surveys. According to the shape similarity between the survey boundaries in each segment and the original planned path boundaries, the abnormality of the survey boundaries in each segment is preliminarily evaluated respectively, obtaining abnormality evaluation values, and using the abnormality evaluation values to preliminarily screen the segments to obtain boundary abnormal areas. Combining the determination times of the boundary abnormal areas in each segment of all surveys, the segments of the boundary abnormal areas are divided into error path segments and normal segments. Considering that the error path segments may be due to differences in the collector's own collection angle or standing position selection when performing data collection tasks, resulting in a deviation between the collected survey boundary and the original planned boundary path, or may be due to the construction of buildings in the later stage of the urban and rural planning process, causing changes in the original planned path boundary. Therefore, according to the abnormality evaluation values of all survey boundaries of all error path segments in all surveys, all error path segments are further divided into two categories, namely preliminary influence segments and non-influence segments. During the survey process, there may be temporary debris accumulation on the road, resulting in the need to change the survey path, causing a certain deviation from the original planned path, and being misidentified as an encroached area in this part. It is necessary to further analyze the preliminary encroached segments to determine the encroached segments. The present invention divides the preliminary influence segments into suspected encroached segments and non-encroached segments according to the boundary abnormality evaluation discrete index and abnormality evaluation value of the preliminary influence segments. For the suspected encroached segments, since multiple surveys are conducted on them, there are multiple survey paths. During the survey process, it is not the case that there is debris accumulation every time, resulting in the need to change the survey path. By synthesizing the abnormality evaluation values, encroachment contribution values, and position distributions during each survey of the survey boundaries of the suspected encroached segments, encroachment sub-paths are screened from all survey boundaries of the suspected encroached segments. Further, the optimal boundary of each type of segment is determined, and then the optimal path of the survey area is obtained, reducing the impact caused by mobile data collection in urban and rural planning surveys, and improving the accuracy of determining the survey area boundary and the credibility and accuracy of survey data. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0044] Figure 1 It is a flowchart of the steps executed by a mobile data collection and upload system for urban and rural planning surveys provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0045] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the following provides a detailed description of a mobile data collection and upload system for urban and rural planning research proposed according to the present invention in combination with the accompanying drawings and preferred embodiments as follows.

[0046] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs.

[0047] The following specifically describes the specific solution of a mobile data collection and upload system for urban and rural planning research provided by the present invention in combination with the accompanying drawings.

[0048] Embodiment of a mobile data collection and upload system for urban and rural planning research:

[0049] The specific scenario targeted by this embodiment is as follows: During the process of collecting boundary data of the urban and rural planning research area, multiple researchers collect the boundary data of the research area, that is, the boundary data of the research area is collected multiple times. Since there may be temporary debris accumulation on the road during the collection process by the researchers, resulting in the need to change the research path, there are certain differences between the research path and the original planned path. This embodiment combines the results of multiple researches to correct the collected boundary data and obtain the accurate boundary of the research area, that is, the optimal boundary.

[0050] This embodiment proposes a mobile data collection and upload system for urban and rural planning research. The system includes a memory and a processor. The processor executes the computer program stored in the memory to implement the steps as Figure 1 shown below. The specific steps are as follows:

[0051] Step S1, obtain the original planned path boundary of the research area for urban and rural planning and the research boundary during each research.

[0052] During the urban and rural planning research process, for the research area, select a mobile data collection application that supports GPS positioning and path recording, and train the researchers participating in the research to ensure that each researcher's mobile device has good GPS function and the selected application program has been installed. After the researchers arrive at the research area, open the mobile application, and the researchers start to conduct path research on the research area respectively. All researchers upload the collected research boundary data, that is, the boundary of the research area has been researched multiple times, and the research boundary of each research has been obtained. In this embodiment, the number of researchers is 10. In specific applications, the implementer can set it according to the specific situation. At the same time, obtain the original planned path boundary of the research area.

[0053] So far, the original planned path boundary of the research area and the research boundary during each research have been collected.

[0054] In step S2, according to the shape similarity between the survey boundary and the original planned path boundary within each segment, the abnormal evaluation value of each survey boundary within each segment during each survey is obtained, and the boundary abnormal area of each survey is screened using the abnormal evaluation value; combining the determination times of the boundary abnormal areas within each segment for all surveys, the error path segments and normal segments are determined.

[0055] Since there may be building construction on the original planned path boundary of the survey area, which causes changes to the original planned path boundary, therefore, first, according to the length difference between the survey boundary collected during each survey and the original planned boundary and their boundary shape matching degree, the survey path areas with errors are screened.

[0056] First, the original planned path boundary of the survey area is divided. After division, multiple segments with equal lengths are obtained. In this embodiment, the number of segments is set to 20. In specific applications, the implementer can set the number of segments according to the length of the original planned path boundary of the survey area. During each survey, each segment has its corresponding survey boundary segment. It should be noted that: the survey boundaries of the segments mentioned later are all their corresponding survey boundary segments.

[0057] Next, this embodiment takes any segment on the boundary of the survey area as an example for illustration. For other segments, the method provided in this embodiment can be used for processing.

[0058] Specifically, any segment on the boundary of the research area is denoted as the segment to be analyzed. For any research: calculate the Dynamic Time Warping (DTW) distance between the original planned path boundary and the research boundary of the segment to be analyzed, as well as the length difference between the original planned path boundary and the research boundary of the segment to be analyzed; obtain the anomaly evaluation value of the segment to be analyzed based on the DTW distance and the length difference; both the DTW distance and the length difference are positively correlated with the anomaly evaluation value. Among them, the positive correlation means that the dependent variable will increase as the independent variable increases, and the dependent variable will decrease as the independent variable decreases. It can be an additive relationship, a multiplicative relationship, etc., which is determined by the actual application. In this embodiment, the normalized result of the product between the DTW distance and the length difference is used as the anomaly evaluation value of the segment to be analyzed. Among them, there are many data normalization methods, and the implementer can select according to the specific situation. In this embodiment, the maximum-minimum normalization method is used to normalize the product between the DTW distance and the length difference. By using the above method, the anomaly evaluation value of the segment to be analyzed can be obtained for each research. It should be noted that: there is a corresponding anomaly evaluation value for the segment to be analyzed in each research. If the anomaly evaluation value is greater than or equal to the preset first anomaly threshold, the corresponding segment is determined as the boundary anomaly area. In this embodiment, the preset first anomaly threshold is 0.5. In specific applications, the implementer can set it according to the specific situation.

[0059] If the number of times the segment to be analyzed is determined as the boundary anomaly area in all research processes is greater than or equal to the preset quantity threshold, then it is determined that the segment to be analyzed is an error path segment; if the number of times the segment to be analyzed is determined as the boundary anomaly area in all research processes is less than the preset quantity threshold, then it is determined that the segment to be analyzed is a normal segment. In this embodiment, the preset quantity threshold is , where represents the number of research times, represents the ceiling symbol. In specific applications, the implementer can also set the preset quantity threshold according to the specific situation.

[0060] So far, by using the above method, all segments on the original planned path boundary of the research area can be divided into two categories, namely normal segments and error path segments.

[0061] Step S3, according to the anomaly evaluation values of all research boundaries of all research times of the error path segments, obtain the boundary anomaly evaluation discrete index of the error path segments, and determine the preliminary influence segments and non-influence segments.

[0062] In the above steps, multiple error path segments are screened out. The error path segments may deviate from the original planned boundary path in the survey boundary data collected by different surveyors due to differences in the surveyors' own collection angles or standing position selections when performing data collection tasks. In addition, during the urban and rural planning process, over time, especially during the construction of buildings in the later stage, the original planned boundary may change. New buildings may be constructed within or around the original planned boundary, thus encroaching on or changing the original boundary range, which will also cause deviations between the survey boundary data collected by the surveyors and the original planned path. Therefore, it is necessary to further distinguish the specific causes of the determined error path segments above.

[0063] When the surveyor conducts a survey on the boundary of the survey area, for the error path segments generated due to their own factors, on this segment, the difference in the path lengths surveyed by each surveyor is relatively small, and the shape of the surveyed boundary is relatively similar to the original planned boundary path. Therefore, the boundary anomaly evaluations of the surveyed boundaries collected by each surveyor within each error path segment are relatively concentrated; for the error path segments caused by the encroached area, the encroached area occupies a certain area, and the surveyors may pass through different positions of the encroached area, and there may be detours or shortcuts, resulting in inconsistent path lengths for each surveyor when passing through the encroached area and large differences in the shapes of the boundaries. Therefore, the boundary anomaly evaluations of the surveyed boundaries collected by each survey during each error path segment are relatively discrete.

[0064] Based on the above characteristics, next, evaluate the discreteness of the boundary anomaly evaluations of the error path segments, and then divide the error path segments into preliminary influence segments and non-influence segments.

[0065] Next, this embodiment takes an error path segment as an example for illustration, and the method provided in this embodiment can be used to process other error path segments.

[0066] For any error path segment:

[0067] Calculate the range of the anomaly evaluation values of the surveyed boundaries of this error path segment, the variance of the anomaly evaluation values of the surveyed boundaries of this error path segment, and the mean value of the anomaly evaluation values of the surveyed boundaries of this error path segment during all surveys respectively; take the ratio between the variance and the mean value as the coefficient of variation; determine the product of the coefficient of variation and the range as the boundary anomaly evaluation discreteness index of any error path segment.

[0068] In this embodiment, a specific calculation formula for the boundary anomaly evaluation discreteness index is given. The boundary anomaly evaluation discreteness index of the b-th error path segment can be expressed as:

[0069]

[0070] Among them, represents the boundary anomaly evaluation discrete index of the b-th error path segment, represents the number of investigations, represents the anomaly evaluation value of the investigation boundary of the b-th error path segment during the m-th investigation, represents the maximum value of the anomaly evaluation values of the investigation boundaries of the b-th error path segment during all investigations, represents the minimum value of the anomaly evaluation values of the investigation boundaries of the b-th error path segment during all investigations, represents the variance of the anomaly evaluation values of the investigation boundaries of the b-th error path segment during all investigations, represents the average value of the anomaly evaluation values of the investigation boundaries of the b-th error path segment during all investigations.

[0071] Characterizes the range of the anomaly evaluation values of the investigation boundaries of the b-th error path segment during all investigations. The larger this value, the greater the difference in the investigation boundaries collected in different investigations, and the more likely there is an encroached area. Characterizes the coefficient of variation. The larger its value, the relatively higher the degree of data dispersion, and the more likely the investigator has passed through the encroached area. When the range of the anomaly evaluation values of the investigation boundaries of the b-th error path segment during all investigations is larger and the coefficient of variation is also larger, it indicates that the boundary anomaly evaluation of the b-th error path segment is more discrete, that is, the boundary anomaly evaluation discrete index of the b-th error path segment is larger.

[0072] By using the above method, the boundary anomaly evaluation discrete index of each error path segment can be obtained. The larger the boundary anomaly evaluation discrete index, the more likely there is an encroached area; the smaller the boundary anomaly evaluation discrete index, the more likely the anomaly of the corresponding segment is caused by the investigator's own reasons. Therefore, the error path segments with the boundary anomaly evaluation discrete index greater than or equal to the preset discrete threshold are determined as the preliminary influence segments; the error path segments with the boundary anomaly evaluation discrete index less than the preset discrete threshold are determined as the non-influence segments. In this embodiment, the preset discrete threshold is 0.4. In specific applications, the implementer can set it according to the specific situation.

[0073] So far, by using the above method provided in this embodiment, the error path segments are divided into two categories, namely the preliminary influence segments and the non-influence segments.

[0074] Step S4: Based on the boundary anomaly evaluation discrete index and anomaly evaluation value of each preliminary impact segment, obtain the encroachment contribution value of each preliminary impact segment, and use the encroachment contribution value to determine the suspected encroachment segment and non-encroachment segment; combine the anomaly evaluation value, encroachment contribution value of the research boundary of the suspected encroachment segment, and the position distribution during each research to determine the encroachment sub-path.

[0075] During the process of researching boundary data, there may be a situation where there are temporary sundries piled up on the road, resulting in the need to change the research path. There is a certain deviation between the boundary of the changed research path and the original planned path boundary, which is misjudged as an encroachment area in this part. Therefore, it is necessary to further analyze the above-determined preliminary impact segments.

[0076] When conducting a certain research, due to the accumulation of temporary sundries on the road, changing the research path will cause the boundary anomaly evaluation of this section of the path collected to increase. However, the temporary sundries will be removed, and it is not the case that the research path needs to be changed every time. Therefore, when the boundary anomaly evaluation of the research boundary within a certain segment is larger and the quantity is smaller, it indicates that the researcher has taken the wrong path in this segment, otherwise there is an encroachment area. When determining whether the error of the preliminary impact segment is caused by an encroachment area, use the boundary anomaly evaluation discrete index of this segment as the weight. The larger this value is, the stronger the possibility that the preliminary impact segment is encroached.

[0077] Next, this embodiment takes any preliminary impact segment as an example for illustration, and the methods provided in this embodiment can be used to process other preliminary impact segments.

[0078] Specifically, for any preliminary impact segment: count the proportion of the number of anomaly evaluation values of the research boundary of this preliminary impact segment during all researches that are greater than the preset second anomaly threshold; use the boundary anomaly evaluation discrete index of this preliminary impact segment as the weight to correct the proportion to obtain the encroachment contribution value of this preliminary impact segment; the preset second anomaly threshold is greater than the preset first anomaly threshold. The preset second anomaly threshold in this embodiment is 0.6, and in specific applications, the implementer can set it according to specific situations.

[0079] In this embodiment, the specific calculation formula of the encroachment contribution value is given. The encroachment contribution value of the th preliminary impact segment can be expressed as:

[0080]

[0081] Among them, represents the encroachment contribution value of the th preliminary impact segment, represents the boundary anomaly evaluation discrete index of the th preliminary impact segment, Indicates the number of research boundaries where the abnormal evaluation value of the research boundary of the th preliminary impact segment during all research processes is greater than the preset second abnormal threshold. Indicates the number of research boundaries of the th preliminary impact segment during all research processes.

[0082] Characterizes the proportion of the number of research boundaries where the abnormal evaluation value of the th preliminary impact segment is greater than the preset second abnormal threshold; using the boundary abnormal evaluation discrete index as the weight, correct this proportion, and record the corrected result as the encroachment contribution value.

[0083] By using the above method, the encroachment contribution value of each preliminary impact segment can be obtained.

[0084] Determine the preliminary impact segments with encroachment contribution value greater than or equal to the preset encroachment threshold as suspected encroachment segments; determine the preliminary impact segments with encroachment contribution value less than the preset encroachment threshold as non-encroachment segments. In this embodiment, the preset encroachment threshold is 0.5. In specific applications, the implementer can set it according to specific circumstances.

[0085] By using the above method, multiple suspected encroachment segments are screened out.

[0086] When new buildings are constructed within the original planned path boundary in a suspected encroachment segment, since there are multiple boundaries for the buildings, the researchers may conduct research based on different boundaries, and it is necessary to select the boundary with the highest contribution degree as the actual encroachment boundary of the research area in this suspected encroachment segment.

[0087] Connect all consecutive suspected encroachment segments to form several suspected encroachment continuous segments. It should be noted that: some suspected encroachment segments may not have consecutive suspected encroachment segments, in this case, each suspected encroachment segment is still recorded as a suspected encroachment continuous segment.

[0088] Next, take a suspected encroachment continuous segment as an example for illustration, and the methods provided in this embodiment can be used to process other suspected encroachment continuous segments.

[0089] For any suspected encroachment continuous segment:

[0090] Obtain the length of the research boundary of the suspected encroachment continuous segment during all research processes. Based on this length, use the hierarchical clustering algorithm to cluster the research boundaries of the suspected encroachment continuous segment during all research processes to obtain multiple clustering clusters. The hierarchical clustering algorithm is a prior art and will not be elaborated here. According to the distribution dispersion degree of the abnormal evaluation values of each research boundary within each clustering cluster and the range of the encroachment contribution values, obtain the effective contribution value of the research boundary of each clustering cluster of the suspected encroachment continuous segment. Both the distribution dispersion degree and the range of the encroachment contribution values are negatively correlated with the effective contribution value.

[0091] Among them, the negative correlation means that the dependent variable will decrease as the independent variable increases, and the dependent variable will increase as the independent variable decreases. It can be a subtraction relationship, a division relationship, etc., which is determined by the actual application.

[0092] In this embodiment, a specific calculation formula for the effective contribution value is given. The th suspected encroachment continuous segment, the effective contribution value of the research boundary within the th clustering cluster can be expressed as:

[0093]

[0094] Among them, represents the effective contribution value of the research boundary within the th suspected encroachment continuous segment and the th clustering cluster; represents the variance of the abnormal evaluation values of each research boundary within the th continuous encroachment segment and the th clustering cluster; represents the encroachment contribution value of the suspected encroachment segment within the th suspected encroachment continuous segment and the th clustering cluster; represents the maximum value of the encroachment contribution values of all suspected encroachment segments within the th suspected encroachment continuous segment and the th clustering cluster, represents the minimum value of the encroachment contribution values of all encroachment segments within the th suspected encroachment continuous segment and the th clustering cluster; represents the number of clustering clusters within the th suspected encroachment continuous segment, The value of is any integer in is a preset first adjustment parameter, is a preset second adjustment parameter.

[0095] In this embodiment, a preset first adjustment parameter and a preset second adjustment parameter are introduced into the specific calculation formula of the effective contribution value to prevent the denominator from being zero. In this embodiment, the values of the preset first adjustment parameter and the preset second adjustment parameter are both 0.01. In specific applications, the implementer can set them according to specific circumstances.

[0096] The variance of the boundary anomaly evaluation of each survey boundary within the th cluster of the th suspected encroachment continuous segment is used to characterize the distribution dispersion degree of the boundary anomaly evaluation. The smaller the value is, the more discrete the distribution of the anomaly evaluation values of each survey boundary within the th cluster of the th suspected encroachment continuous segment is, and the more ineffective the survey boundary is. Characterize the th suspected encroachment continuous segment The range of the encroachment contribution values of all suspected encroachment segments within the th cluster. The smaller the range is, the greater the encroachment contribution evaluation of the suspected encroachment segments within the th cluster of the

[0097] th suspected encroachment continuous segment is, and the more effective the survey boundary is.

[0097] By using the above method, the effective contribution values of the survey boundaries within all clusters of the th suspected encroachment continuous segment can be obtained. The greater the effective contribution value is, the more likely the survey boundary within the corresponding cluster is the true encroachment boundary. Therefore, in this embodiment, the survey boundary within the cluster with an effective contribution value greater than or equal to the preset contribution threshold is determined as the encroachment sub-path. In this embodiment, the preset contribution threshold is 2. In specific applications, the implementer can set it according to specific circumstances.

[0098] By using the above method, multiple encroachment sub-paths corresponding to each suspected encroachment continuous segment can be obtained.

[0099] Step S5: Fit the survey boundaries and encroachment sub-paths of each type of segment respectively to determine the optimal boundary of the survey area and upload its information, where the types of segments include normal segments, non-influential segments, and non-encroachment segments.

[0100] Through the above steps provided in this embodiment, the boundaries of the survey area are divided into multiple categories, namely normal segments, non-influential segments, non-encroachment segments, and suspected encroachment continuous segments. The encroachment sub-paths are screened out from all the survey boundaries of the suspected encroachment continuous segments. Next, each type of segment will be fitted separately to determine the optimal boundary of the survey area.

[0101] For normal segments and non - influencing segments, the same method is used for processing. Specifically, the least - squares method is used to fit all the survey boundaries within the normal segment and the non - influencing segment to a line respectively. The lines obtained by fitting are denoted as the optimal boundary of the normal segment and the optimal boundary of the non - influencing segment respectively.

[0102] For any non - encroaching segment; calculate the mean value of the lengths of all the survey boundaries within this non - encroaching segment; determine the survey boundaries within this non - encroaching segment whose lengths are greater than or equal to the mean value as reference boundaries; use the least - squares method to fit all the reference boundaries within this non - encroaching segment to a line, and denote the line obtained by fitting as the optimal boundary of this non - encroaching segment.

[0103] For any suspected encroachment continuous segment: use the least - squares method to fit all the encroachment sub - paths within this suspected encroachment continuous segment to a line, and denote the line obtained by fitting as the optimal boundary of this suspected encroachment continuous segment.

[0104] By using the above - mentioned method, the optimal boundary of each segment in each type of segment can be obtained. The optimal boundaries of all normal segments, the optimal boundaries of all non - influencing segments, the optimal boundaries of all non - encroaching segments, and the optimal boundaries of all continuous encroachment segments together constitute the optimal boundary of the survey area.

[0105] After obtaining the optimal boundary of the survey area, the position information of these optimal boundaries is transmitted to the base station through wireless signals. The base station, as a bridge between the wireless network and the wired network, is responsible for receiving data from mobile devices and forwarding it to the relevant wired network systems. The base station transmits the data to the remote server through wired transmission to ensure the stability and security of the data. The server receives the data from the base station and will clean and organize the data to ensure the accuracy of its format and content. Finally, these data will be uploaded to the database system. The database contains the coordinates and data attributes of the optimal boundary of the survey area. The database will perform backup operations to prevent data loss and provide reliable storage support for subsequent queries and analyses.

[0106] After completing the data upload, professional data analysis tools such as Excel can be used to perform statistical analysis on the data. According to the survey data, a detailed urban - rural planning analysis report can be generated to support decision - makers in making scientific plans for aspects such as regional development, land use, and infrastructure construction.

[0107] In this embodiment, the survey boundaries of the survey area during multiple surveys are first obtained. According to the shape similarity between the survey boundaries in each segment and the original planned path boundary, the abnormality of the survey boundaries in each segment is preliminarily evaluated respectively, and the abnormal evaluation values are obtained. Then, the segments are preliminarily screened using the abnormal evaluation values to obtain the boundary abnormal areas. Combining the determination times of the boundary abnormal areas in each segment of all surveys, the segments of the boundary abnormal areas are divided into error path segments and normal segments. Considering that the error path segments may be due to differences in the collector's own collection angle or standing position selection during the data collection task, resulting in a deviation between the collected survey boundary and the original planned boundary path, or it may be due to the construction of buildings in the later stage of the urban and rural planning process, which causes changes in the original planned path boundary. Therefore, all error path segments are further divided into two categories according to the abnormal evaluation values of all survey boundaries of all surveys in the error path segments, namely preliminary influence segments and non-influence segments. During the survey process, there may be temporary debris accumulation on the road, resulting in the need to change the survey path, which causes a certain deviation from the original planned path and is misjudged as an encroached area in this part. It is necessary to further analyze the preliminary encroached segments to determine the encroached segments. In this embodiment, according to the boundary abnormal evaluation discrete index and abnormal evaluation value of the preliminary influence segments, the preliminary influence segments are divided into suspected encroached segments and non-encroached segments. For the suspected encroached segments, since multiple surveys are conducted on them, there are multiple survey paths. During the survey process, it is not the case that debris accumulation causes the need to change the survey path every time. By comprehensively considering the abnormal evaluation values, encroachment contribution values, and position distributions during each survey of the survey boundaries of the suspected encroached segments, encroachment sub-paths are screened from all survey boundaries of the suspected encroached segments. Further, the optimal boundary of each type of segment is determined, and then the optimal path of the survey area is obtained, reducing the impact caused by mobile data collection in urban and rural planning surveys, and improving the accuracy of determining the survey area boundary, as well as the credibility and accuracy of survey data.

[0108] In other embodiments, a mobile data collection and upload device for urban and rural planning surveys is also provided, including a memory and a processor. The memory is used to store executable program code, and the processor is used to call and run the executable program code from the memory, so that the device executes the method performed by the above-mentioned mobile data collection and upload system for urban and rural planning surveys. The device may specifically be a chip, a component, or a module. The chip may include a processor and a memory connected thereto; wherein, the memory is used to store instructions, and when the processor calls and executes the instructions, the chip can execute the method performed by the mobile data collection and upload system for urban and rural planning surveys provided in the above embodiment.

[0109] In other embodiments, a computer program product is further provided. When the computer program product runs on a computer, the computer is caused to execute the above-related steps to implement a method for assisting in marking the osteotomy range of the mandibular angle provided in the above embodiments.

[0110] In other embodiments, a computer-readable storage medium is further provided. Computer program code is stored in the computer-readable storage medium. When the computer program code runs on a computer, the computer is caused to execute the above-related method steps to implement a method executed by a mobile data collection and upload system for urban and rural planning research provided in the above embodiments.

[0111] Among them, the provided device, computer program product, and computer-readable storage medium are all used to execute the corresponding methods provided above. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding methods provided above, and will not be elaborated here.

[0112] It should be noted that the above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A mobile terminal data collection and upload system for urban and rural planning research, comprising a memory and a processor, characterized in that: The processor executes the computer program stored in the memory to implement the following steps: Obtain the original planning path boundary of the survey area of ​​urban and rural planning and the survey boundary of each survey; According to the shape similarity between the survey boundary in each segment and the boundary of the original planned path, the abnormal evaluation value of each survey boundary in each segment is obtained at each survey, and the abnormal boundary area of ​​each survey is screened using the abnormal evaluation value; the error path segment and the normal segment are determined by combining the number of determinations of the abnormal boundary area in each segment in all surveys; According to the abnormal evaluation values ​​of all survey boundaries of all surveys of the error path segmentation, the discrete index of the boundary abnormal evaluation of the error path segmentation is obtained, and the preliminary impact segmentation and non-impact segmentation are determined; According to the discrete index and abnormal evaluation value of the boundary abnormality evaluation of each preliminary impact segment, the encroachment contribution value of each preliminary impact segment is obtained, and the suspected encroachment segment and the non-encroachment segment are determined by using the encroachment contribution value; the encroachment sub-path is determined by combining the abnormal evaluation value of the survey boundary of the suspected encroachment segment, the encroachment contribution value and the location distribution at each survey; Fit the survey boundary and encroachment sub-path of each type of segment to determine the optimal boundary of the survey area and upload its information. The segment categories include normal segment, non-impact segment and non-encroachment segment; Obtaining discrete indicators for boundary anomaly evaluation of error path segmentation, including: For any error path segment: Calculate the range, variance and mean of the abnormal evaluation value of the survey boundary of any error path segment in all survey processes; The ratio between the variance and the mean is used as the coefficient of variation; the product between the coefficient of variation and the range is determined as a discrete index for evaluating the boundary anomaly of any error path segment; Obtaining the encroachment contribution value of each preliminary impact segment includes: For any preliminary impact segment: count the percentage of abnormal evaluation values ​​of the survey boundary of any preliminary impact segment during all surveys that are greater than the preset second abnormal threshold; use the discrete index of the abnormal evaluation of the boundary of any preliminary impact segment as a weight to correct the percentage of the number, and obtain the encroachment contribution value of any preliminary impact segment.

2. The mobile terminal data collection and uploading system for urban and rural planning research according to claim 1 is characterized in that: The abnormal evaluation value of each survey boundary in each segment during each survey is obtained according to the shape similarity between the survey boundary in each segment and the boundary of the original planned path, including: For any survey: Calculate the DTW distance between the position coordinates of the original planned path boundary and the investigation boundary of the segment to be analyzed, and the length difference between the original planned path boundary and the investigation boundary of the segment to be analyzed; obtain the abnormal evaluation value of the segment to be analyzed according to the DTW distance and the length difference; the DTW distance and the length difference are both positively correlated with the abnormal evaluation value; The segment to be analyzed is any segment on the boundary of the survey area.

3. The mobile terminal data collection and uploading system for urban and rural planning research according to claim 1 is characterized in that: The method of using the abnormal evaluation value to screen the abnormal boundary area of ​​each survey includes: The segment whose abnormality evaluation value is greater than or equal to the preset first abnormality threshold is determined as a boundary abnormality area; the preset second abnormality threshold is greater than the preset first abnormality threshold.

4. The mobile terminal data collection and uploading system for urban and rural planning research according to claim 2 is characterized in that: The determination of the number of determinations of the abnormal boundary area in each segment in combination with all the surveys to determine the error path segment and the normal segment includes: If the number of times that the segment to be analyzed is determined to be a boundary abnormal area during all surveys is greater than or equal to a preset number threshold, the segment to be analyzed is determined to be an error path segment; If the number of times that the segment to be analyzed is determined to be a boundary abnormal area during all surveys is less than a preset number threshold, the segment to be analyzed is determined to be a normal segment.

5. The mobile terminal data collection and uploading system for urban and rural planning research according to claim 1 is characterized in that: Determine the initial impact and non-impact segments, including: The error path segments whose discrete index of boundary anomaly evaluation is greater than or equal to a preset discrete threshold are determined as preliminary impact segments; The error path segments whose discrete indicators of boundary anomaly evaluation are less than the preset discrete threshold are determined as non-influencing segments.

6. The mobile terminal data collection and uploading system for urban and rural planning research according to claim 3 is characterized in that: The encroachment contribution value is used to determine the suspected encroachment segment and the non-encroachment segment, including: The preliminary impact segment whose encroachment contribution value is greater than or equal to the preset encroachment threshold is determined as the suspected encroachment segment; the preliminary impact segment whose encroachment contribution value is less than the preset encroachment threshold is determined as the non-encroachment segment.

7. The mobile terminal data collection and uploading system for urban and rural planning research according to claim 1 is characterized in that: The step of combining the abnormal evaluation value of the investigated boundary of the suspected encroachment segment, the encroachment contribution value and the location distribution at each investigation to determine the encroachment subpath includes: Connect all the continuous suspected encroachment segments to form several suspected encroachment continuous segments; For any suspected encroachment continuous segment: cluster the survey boundaries of any suspected encroachment continuous segment in all survey processes, and obtain the effective contribution value of each cluster survey boundary of any suspected encroachment continuous segment according to the distribution dispersion degree of the abnormal evaluation value of each survey boundary in each cluster and the range of the encroachment contribution value. The distribution dispersion degree and the range of the encroachment contribution value are both negatively correlated with the effective contribution value. The effective contribution value is used to screen the occupied sub-path.

8. The mobile terminal data collection and uploading system for urban and rural planning research according to claim 7 is characterized in that: The using the effective contribution value to screen the occupied sub-paths includes: The survey boundary within the cluster whose effective contribution value is greater than or equal to the preset contribution threshold is determined as an occupied sub-path.

9. The mobile terminal data collection and uploading system for urban and rural planning research according to claim 8 is characterized in that: The step of fitting the survey boundary and the encroachment sub-path of each type of segment to determine the optimal boundary of the survey area includes: For normal segmentation and non-influence segmentation: fit all the investigated boundaries in normal segmentation and non-influence segmentation to a line respectively, and record the fitted lines as the optimal boundary of normal segmentation and the optimal boundary of non-influence segmentation respectively; For any non-encroaching segment: calculate the average length of all investigation boundaries in any non-encroaching segment; determine the investigation boundary in any non-encroaching segment whose length is greater than or equal to the average length as the reference boundary; fit all reference boundaries in any non-encroaching segment to a line, and record the fitted line as the optimal boundary of any non-encroaching segment; For any suspected encroachment continuous segment: all encroachment sub-paths in the suspected encroachment continuous segment are fitted to a line, and the fitted line is recorded as the optimal boundary of the suspected encroachment continuous segment; The optimal boundaries of all normal segments, the optimal boundaries of non-affected segments, the optimal boundaries of non-occupied segments and the optimal boundaries of suspected occupied continuous segments together constitute the optimal boundaries of the survey area.

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