Mobile terminal data acquisition and uploading system for urban and rural planning research

Abstract

The invention relates to the technical field of data processing, in particular to a mobile terminal data collecting and uploading system for urban and rural planning research. The system comprises a memory and a processor, and the processor executes a computer program stored in the memory so as to realize the following steps: acquiring a survey boundary of a survey area; obtaining an abnormal evaluation value according to the shape similarity between the survey boundary in each segment and the original planning path boundary, and determining an error path segment and a normal segment; according to an error path segment, investigating an abnormal evaluation value of the boundary to obtain a boundary abnormal evaluation discrete index, and determining a preliminary influence segment and a non-influence segment; dividing the preliminary influence segment into a suspected invasion segment and a non-invasion segment according to a boundary anomaly evaluation discrete index and an anomaly evaluation value; and fitting the survey boundaries of each type of segments to obtain an optimal boundary and uploading the optimal boundary. According to the method, the accuracy of determining the boundary of the investigation area and the credibility and accuracy of investigation data are improved.

Description

Technical Field

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

[0002] Traditional urban and rural planning surveys often rely on paper records and manual measurements, which are inefficient, delayed, and inaccurate. The transmission and aggregation of data is cumbersome, making it difficult to respond quickly to changing needs. The development of mobile data collection technology, especially the popularity of portable devices such as smartphones and tablets, has enabled urban and rural planning surveys to collect data in real time and accurately. Through the application of technologies such as GPS and geographic information systems, surveyors can directly collect various types of data such as geographic location, buildings, land use, and transportation facilities in the region through mobile devices on site, and upload them to the cloud or central database in real time. This real-time uploaded data can provide planners with accurate and up-to-date information, which helps to better analyze, evaluate, and formulate planning plans.

[0003] In the process of mobile data collection for urban and rural planning surveys, multiple investigators are required to collect regional boundary data in the same area. Since the original planned boundary may change due to the construction of buildings in the later stage, the boundary of the area cannot be determined. The existing method uses the template matching method to determine the regional boundary, which is easy to cause low accuracy of the regional boundary, affecting the subsequent urban and rural planning decision-making and implementation, and reducing the credibility and accuracy of the survey process. Summary of the invention

[0004] In order to solve the problem of low survey accuracy and credibility in the existing methods when investigating the boundaries of urban and rural planning areas, the purpose of the present invention is to provide a mobile terminal data collection and uploading system for urban and rural planning surveys. The technical scheme adopted is as follows: The present invention provides a mobile terminal data collection and uploading system for urban and rural planning research, comprising a memory and a processor, wherein the processor executes a 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; The survey boundary and encroachment sub-path of each type of segment are fitted 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.

[0005] Preferably, the step of obtaining the abnormality evaluation value of each investigation boundary in each segment during each investigation according to the shape similarity between the investigation boundary in each segment and the boundary of the original planned path includes: 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.

[0006] Preferably, the use of abnormal evaluation values ​​to screen abnormal boundary areas 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 abnormal area.

[0007] Preferably, the step of combining the number of times of determining the abnormal boundary area in each segment in all 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.

[0008] Preferably, the abnormal evaluation values ​​of all survey boundaries of all surveys of the error path segmentation are used to obtain the discrete index of boundary abnormality evaluation of the 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 of the variance to the mean is taken as the coefficient of variation; and the product of the coefficient of variation and the range is determined as a discrete index for evaluating boundary anomaly of any error path segment.

[0009] Preferably, determining preliminary impact segments and non-impact segments includes: 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.

[0010] Preferably, the encroachment contribution value of each preliminary impact segment is obtained according to the boundary anomaly evaluation discrete index and the anomaly evaluation value of each preliminary impact segment, and the suspected encroachment segment and the non-encroachment segment are determined by using the encroachment contribution value, including: For any preliminary impact segment: count the percentage of abnormal evaluation values ​​of the survey boundary of any preliminary impact segment that are greater than the preset second abnormal threshold value during all surveys; 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; the preset second abnormal threshold value is greater than the preset first abnormal threshold value; 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.

[0011] Preferably, the step of determining the encroachment subpath by combining the abnormal evaluation value of the investigated boundary of the suspected encroachment segment, the encroachment contribution value and the position distribution at each investigation comprises: 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.

[0012] Preferably, 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.

[0013] Preferably, fitting the survey boundary and the occupied 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; calculating the average length of all the investigation boundaries in any non-encroaching segment; determining the investigation boundaries in any non-encroaching segment whose length is greater than or equal to the average length as reference boundaries; fitting all the reference boundaries in any non-encroaching segment to a line, and recording 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.

[0014] The present invention has at least the following beneficial effects: The present invention first obtains the survey boundary of the survey area during multiple surveys, and preliminarily evaluates the abnormality of the 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, obtains the abnormal evaluation value, and preliminarily screens the segment using the abnormal evaluation value to obtain the abnormal boundary area; the segment of the abnormal boundary area is divided into error path segmentation and normal segmentation in combination with the number of determinations of the abnormal boundary area in each segment in all surveys, considering that the error path segmentation may be due to the differences in the surveyor's own collection angle or standing position selection when performing the data collection task, resulting in the deviation between the collected survey boundary and the original planned boundary path, or the construction of buildings in the later stage of the urban and rural planning process has caused the original planned path boundary to change, therefore, according to the abnormal evaluation values ​​of all survey boundaries of all surveys of the error path segmentation, all error path segments are further divided into two categories, namely, preliminary impact segmentation and non-impact segmentation; in the survey process There may be temporary accumulation of debris on the road, which requires the change of the survey path, resulting in a certain deviation from the original planned path, and it is mistakenly believed that there is an occupied area in this part. It is necessary to further analyze the preliminary occupied segment to determine the occupied segment. The present invention divides the preliminary impact segment into suspected occupied segments and non-occupied segments according to the discrete index and abnormal evaluation value of the boundary abnormality evaluation of the preliminary impact segment. For the suspected occupied segment, since it has been surveyed many times, there are multiple survey paths. In the process of surveying it, not every time there is debris accumulation, which requires the change of the survey path. The abnormal evaluation value of the survey boundary of the suspected occupied segment, the occupation contribution value and the position distribution during each survey are comprehensively considered, and the occupied sub-paths are screened from all the survey boundaries of the suspected occupied segment. Further, the optimal boundary of each type of segment is determined, and then the optimal path of the survey area is obtained, which reduces the impact of mobile terminal data collection in urban and rural planning surveys, improves the accuracy of determining the boundary of the survey area and the credibility and accuracy of the survey data. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] 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.

[0016] Figure 1 The present invention provides a flowchart of the steps performed by a mobile terminal data collection and uploading system for urban and rural planning research. DETAILED DESCRIPTION

[0017] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined purpose of the invention, a mobile terminal data collection and uploading system for urban and rural planning research proposed in accordance with the present invention is described in detail below in combination with the accompanying drawings and preferred embodiments.

[0018] 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.

[0019] The following is a detailed description of a specific scheme of a mobile terminal data collection and uploading system for urban and rural planning research provided by the present invention in conjunction with the accompanying drawings.

[0020] Example of a mobile terminal data collection and upload system for urban and rural planning research: The specific scenario targeted by this embodiment is: in the process of collecting data on the boundaries of the urban and rural planning survey area, multiple investigators collect boundary data of the survey area, that is, the boundary data of the survey area is collected multiple times. Since the surveyors may need to change the survey route due to temporary accumulation of debris on the road during the collection process, there will be certain differences between the survey route and the original planned route. This embodiment corrects the collected boundary data based on the results of multiple surveys to obtain the accurate boundary of the survey area, that is, the optimal boundary.

[0021] This embodiment proposes a mobile terminal data collection and uploading system for urban and rural planning research, the system includes a memory and a processor, the processor executes a computer program stored in the memory to implement the following Figure 1 The specific steps are as follows: Step S1, obtaining the original planned path boundary of the survey area of ​​urban and rural planning and the survey boundary of each survey.

[0022] During the urban and rural planning survey, for the survey area, select a mobile data collection application that supports GPS positioning and path recording, train the surveyors participating in the survey, ensure that each surveyor's mobile device has a good GPS function, and has installed the selected application. After the surveyor arrives at the survey area, open the mobile application, and the surveyors start to conduct path surveys in the survey area respectively. All surveyors upload the collected survey boundary data, that is, they complete multiple surveys of the survey area boundaries and obtain the survey boundaries of each survey. In this embodiment, the number of surveyors is 10. In specific applications, the implementer can set it according to the specific situation. At the same time, the original planned path boundary of the survey area is obtained.

[0023] So far, the original planned path boundaries of the survey area and the survey boundaries of each survey have been collected.

[0024] Step S2, according to the shape similarity between the survey boundary in each segment and the boundary of the original planned path, obtain the abnormal evaluation value of each survey boundary in each segment during each survey, and use the abnormal evaluation value to screen the abnormal boundary area of ​​each survey; combine the number of determinations of the abnormal boundary area in each segment in all surveys to determine the error path segment and the normal segment.

[0025] Since there may be buildings under construction on the original planned path boundary of the survey area, which may cause changes in the original planned path boundary, the survey path areas with errors are first screened based on the length difference between the survey boundary collected in each survey and the original planned boundary and the degree of matching between their boundary shapes.

[0026] First, the original planned path boundary of the survey area is divided to obtain multiple segments of equal length. 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. In each survey process, 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.

[0027] Next, this embodiment is described by taking any segment on the boundary of the survey area as an example, and other segments can be processed by the method provided in this embodiment.

[0028] Specifically, any segment on the boundary of the survey area is recorded as a segment to be analyzed. For any survey: the dynamic time warping (DTW) distance between the position coordinates of the original planned path boundary of the segment to be analyzed and the survey boundary, and the length difference between the original planned path boundary of the segment to be analyzed and the survey boundary are calculated; the abnormal evaluation value of the segment to be analyzed is obtained 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. 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 actual application. In this embodiment, the normalized result of the product between the DTW distance and the length difference is used as the abnormal 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 and minimum normalization method is used to normalize the product between the DTW distance and the length difference. By adopting the above method, the abnormal evaluation value of the segment to be analyzed can be obtained in each survey. It should be noted that: each survey segment to be analyzed has a corresponding abnormal evaluation value. If the abnormal evaluation value is greater than or equal to the preset first abnormal threshold, the corresponding segment is determined as a boundary abnormal area. In this embodiment, the preset first abnormal threshold is 0.5. In specific applications, the implementer can set it according to specific circumstances.

[0029] If the number of times the segment to be analyzed is determined to be an abnormal boundary area is greater than or equal to the preset number threshold during all surveys, the segment to be analyzed is determined to be an error path segment; if the number of times the segment to be analyzed is determined to be an abnormal boundary area is less than the preset number threshold during all surveys, the segment to be analyzed is determined to be a normal segment. In this embodiment, the preset number threshold is ,in, Indicates the number of surveys, Indicates the rounding up symbol. In specific applications, implementers can also set a preset quantity threshold according to specific circumstances.

[0030] At this point, using the above method, all segments on the boundary of the original planned path in the survey area can be divided into two categories, namely normal segments and error path segments.

[0031] Step S3, 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 influencing segmentation and non-influencing segmentation are determined.

[0032] In the above steps, multiple error path segments were screened out. Error path segments may be caused by differences in the survey angles or standing positions of the surveyors when performing data collection tasks, resulting in deviations between the survey boundary data collected by different surveyors when measuring the area boundary and the original planned boundary path. In addition, in the process of urban and rural planning, with the passage of time, especially with the construction of later buildings, the original planned boundaries may change. New buildings may be built within the original planned boundaries or in the surrounding areas, thereby encroaching or changing the original boundary range, which may also cause the survey boundary data collected by the surveyors to deviate from the original planned path. Therefore, it is necessary to further distinguish the specific causes of the error path segments determined above.

[0033] When the investigators investigate the boundary of the investigation area, due to their own factors, the error path segmentation is generated. In this segment, the difference in the path length of each investigator is relatively small, and the shape of the investigation boundary is similar to the original planned boundary path. Therefore, the boundary anomaly evaluation of the investigation boundary collected by each investigator in each error path segment is relatively concentrated; for the error path segmentation caused by the occupied area, the occupied area occupies a certain area, and the investigators may pass through different locations in the occupied area, and there may be detours or shortcuts, resulting in inconsistent path lengths for each investigator when passing through the occupied area, and the boundary shape is quite different. Therefore, in each error path segment, the boundary anomaly evaluation of the investigation boundary collected by each investigation is relatively discrete.

[0034] Based on the above characteristics, the discreteness of the boundary anomaly evaluation of the error path segment is evaluated next, and then the error path segment is divided into preliminary impact segment and non-impact segment.

[0035] Next, this embodiment is described by taking one error path segment as an example, and other error path segments can be processed by the method provided in this embodiment.

[0036] For any error path segment: The range of the abnormal evaluation value of the survey boundary of the error path segmentation in all survey processes, the variance of the abnormal evaluation value of the survey boundary of the error path segmentation and the mean of the abnormal evaluation value of the survey boundary of the error path segmentation are calculated respectively; the ratio between the variance and the mean is taken as the coefficient of variation; the product of the coefficient of variation and the range is determined as the discrete index of the boundary abnormal evaluation of any error path segment.

[0037] In this embodiment, a specific calculation formula for the discrete index of boundary anomaly evaluation is given, and the discrete index of boundary anomaly evaluation of the b-th error path segment can be expressed as:

[0038] in, represents the discrete index of boundary anomaly evaluation of the b-th error path segment, Indicates the number of surveys, represents the abnormal evaluation value of the survey boundary of the bth error path segment in the mth survey process, It represents the maximum value of the abnormal evaluation value of the survey boundary of the b-th error path segment in all survey processes, It represents the minimum value of the abnormal evaluation value of the survey boundary of the b-th error path segment in all survey processes, represents the variance of the abnormal evaluation value of the survey boundary of the b-th error path segment in all survey processes, Represents the average value of the abnormal evaluation value of the survey boundary of the b-th error path segment in all survey processes.

[0039] Characterizes the extreme value of the abnormal evaluation of the survey boundary of the bth error path segment in all survey processes. The larger the value, the greater the difference in the survey boundaries collected in different surveys, and the more likely it is that there is an encroachment area. Characterizes the coefficient of variation. The larger its value, the higher the degree of data dispersion, and the more likely the investigators are to pass through the occupied area. When the range of the abnormal evaluation value of the survey boundary of the bth error path segment in all survey processes is larger and the coefficient of variation is larger, it means that the boundary abnormal evaluation of the bth error path segment is more discrete, that is, the discrete index of the boundary abnormal evaluation of the bth error path segment is larger.

[0040] By adopting 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 it is that there is an occupied area; the smaller the boundary anomaly evaluation discrete index, the more likely the anomaly of the corresponding segment is caused by the investigator himself. Therefore, the error path segment whose boundary anomaly evaluation discrete index is greater than or equal to the preset discrete threshold is determined as the preliminary impact segment; the error path segment whose boundary anomaly evaluation discrete index is less than the preset discrete threshold is determined as the non-impact segment. In this embodiment, the preset discrete threshold is 0.4. In specific applications, the implementer can set it according to the specific situation.

[0041] So far, the error path segments are divided into two categories by using the above method provided by this embodiment, namely, preliminary impact segments and non-impact segments.

[0042] Step S4, according to the discrete index and abnormal evaluation value of the boundary abnormality evaluation 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 the non-encroachment segment; combine the abnormal evaluation value of the survey boundary of the suspected encroachment segment, the encroachment contribution value and the position distribution at each survey to determine the encroachment sub-path.

[0043] In the process of investigating the boundary data, there may be temporary accumulation of debris on the road, which requires changing the survey route. The replaced survey route has a certain deviation from the boundary of the original planned route, and is mistakenly believed to be an occupied area. Therefore, it is necessary to further analyze the preliminary impact segments determined above.

[0044] During a survey, the change of survey route due to temporary debris accumulation on the road will cause the boundary anomaly evaluation of the collected route to increase. However, temporary debris accumulation will be removed, and the route will not be changed every time the survey is conducted. Therefore, when the boundary anomaly evaluation of the survey boundary in a certain segment is larger and the number is smaller, it means that the surveyor has taken the wrong path in this segment, otherwise there is an occupied area. When determining whether the initial impact segment is an error caused by the occupied area, the discrete index of the boundary anomaly evaluation of the segment is used as the weight. The larger the value, the greater the possibility that the initial impact segment is occupied.

[0045] Next, this embodiment is described by taking any preliminary impact segment as an example, and other preliminary impact segments can be processed by the method provided in this embodiment.

[0046] Specifically, for any preliminary impact segment: count the percentage of abnormal evaluation values ​​of the survey boundary of the preliminary impact segment that are greater than the preset second abnormal threshold value during all surveys; use the discrete index of the abnormal evaluation of the boundary of the preliminary impact segment as a weight to correct the percentage of the number, and obtain the encroachment contribution value of the preliminary impact segment; the preset second abnormal threshold value is greater than the preset first abnormal threshold value. The preset second abnormal threshold value in this embodiment is 0.6, and in specific applications, the implementer can set it according to specific circumstances.

[0047] In this embodiment, a specific calculation formula for the encroachment contribution value is given. The encroachment contribution value of a preliminary impact segment can be expressed as:

[0048] in, Indicates The encroachment contribution value of the initial impact segment, Indicates A discrete index for evaluating boundary anomalies that initially affect segmentation, Indicates that all the research The number of survey boundaries whose abnormal evaluation values ​​of the survey boundaries of the preliminary impact segment are greater than the preset second abnormal threshold, Indicates that all the research The number of investigation boundaries for each preliminary impact segment.

[0049] Characterization The percentage of abnormal evaluation values ​​of the survey boundary of the preliminary impact segment that are greater than the preset second abnormal threshold value; the discrete index of boundary abnormal evaluation is used as the weight to correct the percentage of the number, and the corrected result is recorded as the encroachment contribution value.

[0050] By adopting the above method, the encroachment contribution value of each preliminary impact segment can be obtained.

[0051] The initial impact segment whose encroachment contribution value is greater than or equal to the preset encroachment threshold is determined as a suspected encroachment segment; the initial impact segment whose encroachment contribution value is less than the preset encroachment threshold is determined as a non-encroachment segment. In this embodiment, the preset encroachment threshold is 0.5. In specific applications, the implementer can set it according to specific circumstances.

[0052] Using the above method, several suspected encroachment segments were screened out.

[0053] When a new building appears in a suspected encroachment segment and is constructed within the boundary of the original planned path, the building has multiple boundaries. Investigators may conduct investigations based on different boundaries and need to select the boundary with the greatest contribution as the actual encroachment boundary of the investigation area in the suspected encroachment segment.

[0054] All the continuous suspected encroachment segments are connected to form a number of suspected encroachment continuous segments. It should be noted that: some suspected encroachment segments may not have a continuous suspected encroachment segment, in which case each suspected encroachment segment is still recorded as a suspected encroachment continuous segment.

[0055] Next, a suspected occupation continuous segment is taken as an example for description. The method provided in this embodiment can be used to process other suspected occupation continuous segments.

[0056] For any suspected encroachment on a continuous segment: The length of the investigation boundary of the suspected continuous segment of encroachment in all investigation processes is obtained, and based on the length, the investigation boundary of the suspected continuous segment of encroachment in all investigation processes is clustered using a hierarchical clustering algorithm to obtain multiple cluster clusters. The hierarchical clustering algorithm is a prior art and will not be described in detail here. According to the distribution discreteness of the abnormal evaluation value of each investigation boundary in each cluster cluster and the range of the encroachment contribution value, the effective contribution value of the investigation boundary of each cluster cluster of the suspected continuous segment of encroachment is obtained, and the distribution discreteness and the range of the encroachment contribution value are both negatively correlated with the effective contribution value.

[0057] Among them, the negative correlation relationship 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 practical applications.

[0058] In this embodiment, a specific calculation formula for the effective contribution value is given. Suspected occupation of consecutive sections The effective contribution value of the survey boundary within a cluster can be expressed as:

[0059] in, Indicates Suspected occupation of consecutive sections The effective contribution value of the survey boundary within each cluster; Indicates Continuous occupation segment The variance of the abnormal evaluation value of each survey boundary within the cluster; Indicates Suspected occupation of consecutive sections The encroachment contribution value of the suspected encroachment segment in each cluster; Indicates Suspected occupation of consecutive sections The maximum value of the encroachment contribution value of all suspected encroachment segments in the cluster, Indicates Suspected occupation of consecutive sections The minimum value of the encroachment contribution value of all the encroachment segments in the cluster; Indicates The number of clusters suspected of encroaching on the continuous segment, The value of Any integer in ; To preset the first adjustment parameter, It is the preset second adjustment parameter.

[0060] In this embodiment, the preset first adjustment parameter and the preset second adjustment parameter are introduced into the specific calculation formula of the effective contribution value in order to prevent the denominator from being 0. 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 the specific situation.

[0061] No. Suspected occupation of consecutive sections The variance of the boundary anomaly evaluation of each survey boundary within a cluster is used to characterize the distribution dispersion of the boundary anomaly evaluation. The smaller the value of Suspected occupation of consecutive sections The more discrete the distribution of the abnormal evaluation value of each survey boundary within a cluster, the more invalid the survey boundary is. Characterization Suspected occupation of consecutive sections The range of the encroachment contribution values ​​of all suspected encroachment segments in the cluster is smaller, indicating that the Suspected occupation of consecutive sections The greater the encroachment contribution evaluation of the suspected encroachment segment within a cluster, the more effective the survey boundary is.

[0062] Using the above method, we can obtain The effective contribution value of the investigated boundary in all clusters of the suspected encroachment continuous segment is calculated. The larger the effective contribution value, the more likely the investigated boundary in the corresponding cluster is to be a real encroachment boundary. Therefore, in this embodiment, the investigated boundary in the cluster whose effective contribution value is 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.

[0063] By adopting the above method, multiple encroachment sub-paths corresponding to each suspected encroachment continuous segment can be obtained.

[0064] Step S5, respectively fitting the survey boundary and the encroachment sub-path of each type of segment to determine the optimal boundary of the survey area and upload its information, wherein the types of segments include normal segments, non-impact segments and non-encroachment segments.

[0065] Through the above steps provided in this embodiment, the boundaries of the survey area are divided into multiple categories, namely normal segments, non-impact segments, non-occupied segments and suspected occupied continuous segments, and the occupied sub-paths are screened out from all survey boundaries of the suspected occupied continuous segments. Next, each type of segment will be fitted separately to determine the optimal boundary of the survey area.

[0066] For normal segments and non-influence segments, these two types of segments are processed in the same way. Specifically, the least squares method is used to fit all the survey boundaries in the normal segment and the non-influence segment to a line, and the fitted lines are recorded as the optimal boundaries of the normal segment and the non-influence segment, respectively.

[0067] For any non-occupied segment; calculate the mean length of all survey boundaries in the non-occupied segment; determine the survey boundary in the non-occupied segment whose length is greater than or equal to the mean length as the reference boundary; use the least squares method to fit all reference boundaries in the non-occupied segment to a line, and record the fitted line as the optimal boundary of the non-occupied segment.

[0068] For any suspected continuous segment of encroachment: all encroachment sub-paths in the suspected continuous segment of encroachment are fitted to a line using the least squares method, and the fitted line is recorded as the optimal boundary of the suspected continuous segment of encroachment.

[0069] The above method can be used to obtain the optimal boundary of each segment in each type of segment. The optimal boundaries of all normal segments, all non-affected segments, all non-invading segments and all continuously invading segments together constitute the optimal boundary of the survey area.

[0070] After obtaining the optimal boundaries of the survey area, the location information of these optimal boundaries is transmitted to the base station via 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 system. The base station transmits the data to the remote server via wired transmission to ensure the stability and security of the data. The server receives the data from the base station, and the server will clean and organize the data to ensure the accuracy of its format and content. Finally, the data will be uploaded to the database system, which contains the coordinates of the optimal boundaries of the survey area and data attributes. The database will be backed up to prevent data loss and provide reliable storage support for subsequent queries and analysis.

[0071] After completing the data upload, you can use professional data analysis tools, such as Excel, to perform statistical analysis on the data and generate detailed urban and rural planning analysis reports based on the survey data to support decision makers in making scientific plans for regional development, land use, infrastructure construction and other aspects.

[0072] This embodiment first obtains the survey boundary of the survey area during multiple surveys, and preliminarily evaluates the abnormality of the 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, obtains the abnormal evaluation value, and preliminarily screens the segment using the abnormal evaluation value to obtain the abnormal boundary area; the abnormal boundary area is segmented into error path segmentation and normal segmentation based on the number of determinations of the abnormal boundary area in each segment in all surveys, considering that the error path segmentation may be due to the differences in the surveyor's own collection angle or standing position selection when performing the data collection task, resulting in the deviation between the collected survey boundary and the original planned boundary path, or the construction of buildings in the later stage of the urban and rural planning process has caused the original planned path boundary to change, so all error path segments are further divided into two categories according to the abnormal evaluation values ​​of all survey boundaries of all surveys of the error path segmentation, namely, preliminary impact segmentation and non-impact segmentation; in the survey process There may be temporary accumulation of debris on the road, which requires the change of the survey path, resulting in a certain deviation from the original planned path, and it is mistakenly believed that there is an occupied area in this part. It is necessary to further analyze the preliminary occupied segment to determine the occupied segment. In this embodiment, the preliminary impact segment is divided into suspected occupied segments and non-occupied segments according to the discrete index and abnormal evaluation value of the boundary abnormality evaluation of the preliminary impact segment. For the suspected occupied segment, since it has been surveyed many times, there are multiple survey paths. In the process of surveying it, not every time there is debris accumulation, which requires the change of the survey path. The abnormal evaluation value of the survey boundary of the suspected occupied segment, the occupation contribution value and the position distribution during each survey are combined. The occupied sub-paths are screened from all the survey boundaries of the suspected occupied segment. Further, the optimal boundary of each type of segment is determined, and then the optimal path of the survey area is obtained, which reduces the impact of mobile terminal data collection in urban and rural planning surveys, improves the accuracy of determining the boundary of the survey area, and improves the credibility and accuracy of the survey data.

[0073] In other embodiments, a mobile terminal 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 executed by the mobile terminal data collection and upload system for urban and rural planning surveys. The device can be specifically a chip, a component or a module, and the chip may include a connected processor and a memory; wherein the memory is used to store instructions, and when the processor calls and executes the instructions, the chip can execute the method executed by the mobile terminal data collection and upload system for urban and rural planning surveys provided in the above embodiment.

[0074] In other embodiments, a computer program product is also provided. When the computer program product is run on a computer, the computer is enabled to execute the above-mentioned related steps to implement a mandibular angle osteotomy range auxiliary marking method provided in the above-mentioned embodiment.

[0075] In other embodiments, a computer-readable storage medium is also provided, in which a computer program code is stored. When the computer program code runs on a computer, the computer executes the above-mentioned related method steps to implement a method executed by a mobile data collection and upload system for urban and rural planning surveys provided in the above embodiment.

[0076] Among them, the provided devices, computer program products, and computer-readable storage media 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 repeated here.

[0077] It should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should 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; The survey boundary and encroachment sub-path of each type of segment are fitted 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.

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 investigation boundary in each segment during each investigation is obtained according to the shape similarity between the investigation 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 abnormal area.

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: The abnormal evaluation values ​​of all survey boundaries of all surveys of the error path segmentation are used to obtain the discrete indicators of boundary abnormality evaluation of the 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 of the variance to the mean is taken as the coefficient of variation; and the product of the coefficient of variation and the range is determined as a discrete index for evaluating boundary anomaly of any error path segment.

6. 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.

7. The mobile terminal data collection and uploading system for urban and rural planning research according to claim 3 is characterized in that: According to the discrete index and abnormal evaluation value of the boundary abnormality of each preliminary impact segment, the encroachment contribution value of each preliminary impact segment is obtained, and the suspected encroachment segment and non-encroachment segment are determined by using the encroachment contribution value, including: For any preliminary impact segment: count the percentage of abnormal evaluation values ​​of the survey boundary of any preliminary impact segment that are greater than the preset second abnormal threshold value during all surveys; 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; the preset second abnormal threshold value is greater than the preset first abnormal threshold value; 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.

8. 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.

9. The mobile terminal data collection and uploading system for urban and rural planning research according to claim 8 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.

10. The mobile terminal data collection and uploading system for urban and rural planning research according to claim 9 is characterized in that: The step of fitting the survey boundary and the occupied 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; calculating the average length of all the investigation boundaries in any non-encroaching segment; determining the investigation boundaries in any non-encroaching segment whose length is greater than or equal to the average length as reference boundaries; fitting all the reference boundaries in any non-encroaching segment to a line, and recording 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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