Geographic data analysis method and apparatus, computer device, and storage medium

Abstract

The application relates to a geographic data analysis method and device, computer equipment and a storage medium. The method comprises the following steps: obtaining target geographic position information of a target site, determining geographic landmark information of geographic landmark points in a preset range of the target site according to the target geographic position information, dividing each geographic landmark point set according to the sub-function attribute category of each geographic landmark point, determining the weight corresponding to each category landmark point set according to the distance between each geographic landmark point and the target site, determining a reference category landmark point set from each category landmark point set based on the weight corresponding to each category landmark point set, determining a target category landmark point set based on the relationship between the weight corresponding to the reference category landmark point set and a threshold value, and determining the regional function attribute category corresponding to the region where the target site is located based on the target function attribute category corresponding to the target category landmark point set. The method can improve the accuracy of geographic data analysis.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular to a geographic data analysis method, apparatus, computer equipment, and storage medium. Background Technology

[0002] With the development of computer technology, the amount of data in the field of information technology has grown on a large scale, and research on data mining and analysis in various industries has become increasingly widespread. Among them, the analysis of geographic data is an important application area of ​​data analysis.

[0003] In traditional technologies, web crawling is used to obtain the target's geographical location and directly acquire the corresponding geographic attribute information, but the accuracy of this technical solution is low. Summary of the Invention

[0004] Therefore, it is necessary to provide a geographic data analysis method, apparatus, computer equipment, and readable storage medium to address the aforementioned technical problems. This can effectively improve the accuracy of geographic data analysis.

[0005] A geographic data analysis method, comprising:

[0006] Obtain the target geographic location information;

[0007] Based on the target's geographic location information, determine the geographic landmark information of each geographic landmark within a preset range of the target location. The geographic landmark information is used to characterize the sub-functional attribute category and geographic location information of the corresponding geographic landmark.

[0008] Based on the sub-functional attribute categories of each geographic landmark, geographic landmarks belonging to the same target functional attribute category are divided into the same category landmark set, resulting in each category landmark set. Each category landmark set corresponds to the target functional attribute category, and the granularity of the sub-functional attribute category is less than or equal to the granularity of the target functional attribute category.

[0009] The weights of each category of landmark set are determined based on the distance between each geographic landmark and the target location;

[0010] Based on the weights corresponding to each category of marker point set, a reference category marker point set is determined from each category of marker point set;

[0011] The target category marker set is determined based on the relationship between the weights and thresholds corresponding to the reference category marker set.

[0012] The regional functional attribute category corresponding to the area where the target location is located is determined based on the target functional attribute category corresponding to the target category marker set.

[0013] In one embodiment, based on the sub-functional attribute categories of each geographic landmark, geographic landmarks belonging to the same target functional attribute category are grouped into the same category of landmark sets, resulting in various category of landmark sets, including:

[0014] Based on the sub-functional attribute categories of each geographic landmark, geographic landmarks belonging to the same target functional attribute category are divided into the same category landmark set to obtain the full category landmark set;

[0015] Based on the target functional attribute categories corresponding to the full set of category markers, a correlation analysis is performed to determine the set of each relevant category marker.

[0016] Each category of marker point set is obtained by combining the sets of marker points of each relevant category.

[0017] In one embodiment, the weights corresponding to each category of marker set are determined based on the distances of each geographic marker to the target location, including:

[0018] Obtain the spatial distance between the target location and each geographic landmark, as well as the preset distance constants;

[0019] The spatial distances of each geographic landmark are fused with preset distance constants to obtain various fusion terms;

[0020] The weights corresponding to the set of markers for each category are obtained by taking the reciprocal of each fusion term.

[0021] In one embodiment, the weights corresponding to each category of marker set are determined based on the distances of each geographic marker to the target location, including:

[0022] Obtain the spatial distances between the target location and each geographic landmark;

[0023] The spatial distances of each geographic landmark are input into a Gaussian function to obtain the function values.

[0024] The function values ​​of each geographic landmark in each category landmark set are weighted and fused to obtain the corresponding weight, and then the weight corresponding to each category landmark set is determined.

[0025] In one embodiment, before determining the reference category marker set from each category marker set based on the weights corresponding to each category marker set, the method further includes:

[0026] Get the number of sets of marker points for each category;

[0027] When the number of sets is less than or equal to a preset threshold, the regional functional attribute category corresponding to the area where the target location is located is determined to be an unknown attribute category.

[0028] In one embodiment, determining a reference category marker set from each category marker set based on the weights corresponding to each category marker set includes:

[0029] Based on the weights corresponding to each category of marker point set, determine the first category marker point set and the second category marker point set from each category marker point set. The first category marker point set is the category marker point set with the largest weight among all category marker point sets, and the second category marker point set is the category marker point set with the second largest weight among all category marker point sets.

[0030] In one embodiment, determining the target category marker set based on the relationship between the weights and thresholds corresponding to the reference category marker set includes:

[0031] When the first weight is greater than or equal to the first threshold, the first category marker set is determined as the target category marker set, and the first weight is the weight corresponding to the first target category marker set.

[0032] When the first weight is greater than or equal to the second threshold and less than the first threshold, and the second weight is greater than or equal to the second threshold, the first category marker set and the second category marker set are determined as the target category marker set, and the second weight is the weight corresponding to the second target category marker set.

[0033] A geographic data analysis device, comprising:

[0034] The acquisition module is used to acquire the target geographic location information of the target location;

[0035] The first determining module is used to determine the geographic landmark information of each geographic landmark within a preset range of the target location based on the target geographic location information. The geographic landmark information is used to characterize the sub-functional attribute category and geographic location information of the corresponding geographic landmark. Based on the sub-functional attribute category of each geographic landmark, geographic landmarks belonging to the same target functional attribute category are divided into the same category of landmarks to obtain each category of landmarks. Each category of landmarks corresponds to the target functional attribute category, and the granularity of the sub-functional attribute category is less than or equal to the granularity of the target functional attribute category.

[0036] The second determination module is used to determine the weights corresponding to each category of marker set based on the distance between each geographic marker set and the target location; determine a reference category marker set from each category marker set based on the weights corresponding to each category marker set; and determine the target category marker set based on the relationship between the weights corresponding to the reference category marker set and a threshold.

[0037] The judgment module is used to determine the regional functional attribute category of the area where the target location is located based on the target functional attribute category corresponding to the target category marker point set.

[0038] A computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to perform the following steps:

[0039] Obtain the target geographic location information;

[0040] Based on the target's geographic location information, determine the geographic landmark information of each geographic landmark within a preset range of the target location. The geographic landmark information is used to characterize the sub-functional attribute category and geographic location information of the corresponding geographic landmark.

[0041] Based on the sub-functional attribute categories of each geographic landmark, geographic landmarks belonging to the same target functional attribute category are divided into the same category landmark set, resulting in each category landmark set. Each category landmark set corresponds to the target functional attribute category, and the granularity of the sub-functional attribute category is less than or equal to the granularity of the target functional attribute category.

[0042] The weights of each category of landmark set are determined based on the distance between each geographic landmark and the target location;

[0043] Based on the weights corresponding to each category of marker point set, a reference category marker point set is determined from each category of marker point set;

[0044] The target category marker set is determined based on the relationship between the weights and thresholds corresponding to the reference category marker set.

[0045] The regional functional attribute category corresponding to the area where the target location is located is determined based on the target functional attribute category corresponding to the target category marker set.

[0046] A computer-readable storage medium storing a computer program, which, when executed by a processor, performs the following steps:

[0047] Obtain the target geographic location information;

[0048] Based on the target's geographic location information, determine the geographic landmark information of each geographic landmark within a preset range of the target location. The geographic landmark information is used to characterize the sub-functional attribute category and geographic location information of the corresponding geographic landmark.

[0049] Based on the sub-functional attribute categories of each geographic landmark, geographic landmarks belonging to the same target functional attribute category are divided into the same category landmark set, resulting in each category landmark set. Each category landmark set corresponds to the target functional attribute category, and the granularity of the sub-functional attribute category is less than or equal to the granularity of the target functional attribute category.

[0050] The weights of each category of landmark set are determined based on the distance between each geographic landmark and the target location;

[0051] Based on the weights corresponding to each category of marker point set, a reference category marker point set is determined from each category of marker point set;

[0052] The target category marker set is determined based on the relationship between the weights and thresholds corresponding to the reference category marker set.

[0053] The regional functional attribute category corresponding to the area where the target location is located is determined based on the target functional attribute category corresponding to the target category marker set.

[0054] The aforementioned geographic data analysis method, apparatus, computer equipment, and storage medium acquire the target geographic location information of a target location, determine the geographic landmark information of each geographic landmark within a preset range of the target location based on the target geographic location information, classify geographic landmarks belonging to the same target functional attribute category into the same category of landmarks based on the sub-functional attribute category of each geographic landmark, obtain each category of landmarks set, determine the weight corresponding to each category of landmarks set based on the distance of each geographic landmark to the target location, determine the reference category of landmarks set from each category of landmarks set based on the weights corresponding to each category of landmarks set, determine the target category of landmarks set based on the relationship between the weights corresponding to the reference category of landmarks set and a threshold, and determine the regional functional attribute category corresponding to the target location area based on the target functional attribute category corresponding to the target category of landmarks set. In this way, by using other geographic landmarks within the preset range of the target location, and then performing cluster analysis on the geographic landmark information of other geographic landmarks to remove redundant data, a concise and highly relevant reference category of landmarks set is obtained. Finally, based on the weight characteristics of the reference category of landmarks set, the regional functional attribute category corresponding to the target location area is determined, effectively improving the accuracy of geographic data analysis. Attached Figure Description

[0055] Figure 1 This is a diagram illustrating the application environment of a geographic data analysis method in one embodiment.

[0056] Figure 2 This is a flowchart illustrating a geographic data analysis method in one embodiment;

[0057] Figure 3This is a flowchart illustrating the process of determining a set of category markers in one embodiment;

[0058] Figure 4 This is a flowchart illustrating the process of determining the weights of a set of category markers in one embodiment.

[0059] Figure 5 This is a flowchart illustrating the process of determining the weights of a set of category markers in one embodiment.

[0060] Figure 6 This is a flowchart illustrating the process of determining the functional attribute category of a region based on the number of category markers in one embodiment.

[0061] Figure 7 This is a flowchart illustrating the process of determining the first category of marker set and the second category of marker set in one embodiment;

[0062] Figure 8 This is a flowchart illustrating the process of determining the set of target category markers in one embodiment;

[0063] Figure 9 This is a structural block diagram of a geographic data analysis device in one embodiment;

[0064] Figure 10 This is an internal structural diagram of a computer device in one embodiment;

[0065] Figure 11 This is a flowchart illustrating the process of determining the set of target category markers in one embodiment;

[0066] Figure 12 This is a graph of the Gaussian function in one embodiment. Detailed Implementation

[0067] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0068] This application provides a method for geographic data analysis, which can be applied to, for example... Figure 1In the application environment shown, computer device 102 acquires the target geographic location information of the target location, determines the geographic landmark information of each geographic landmark within a preset range of the target location based on the target geographic location information, classifies geographic landmarks belonging to the same target functional attribute category into the same category of landmark set based on the sub-functional attribute category of each geographic landmark, obtains each category of landmark set, determines the weight corresponding to each category of landmark set based on the distance between each geographic landmark and the target location, determines the reference category of landmark set from each category of landmark set based on the weight corresponding to each category of landmark set, determines the target category of landmark set based on the relationship between the weight corresponding to the reference category of landmark set and the threshold, and determines the regional functional attribute category corresponding to the area where the target location is located based on the target functional attribute category corresponding to the target category of landmark set. Specifically, computer device 102 may include, but is not limited to, various personal computers, laptops, servers, smartphones, tablets, smart cameras, and portable wearable devices.

[0069] In one embodiment, such as Figure 2 As shown, a geographic data analysis method is provided, which can be applied to... Figure 1 Taking computer device 102 as an example, the following steps are included:

[0070] Step S202: Obtain the target geographical location information of the target location.

[0071] The target location's geographic location information includes its latitude and longitude.

[0072] Step S204: Determine the geographic landmark information of each geographic landmark within the preset range of the target location based on the target geographic location information. The geographic landmark information is used to characterize the sub-functional attribute category and geographic location information of the corresponding geographic landmark.

[0073] Specifically, the computer equipment determines each geographic landmark within a preset range around the target location based on the target location's geographic location information, and obtains the geographic landmark information of each geographic landmark, including the functional attribute information, latitude and longitude information, and spatial distance information from the target location of each geographic landmark.

[0074] Step S206: Based on the sub-functional attribute categories of each geographic landmark, geographic landmarks belonging to the same target functional attribute category are divided into the same category landmark set to obtain each category landmark set. Each category landmark set corresponds to the target functional attribute category, and the granularity of the sub-functional attribute category is less than or equal to the granularity of the target functional attribute category.

[0075] Specifically, the computer equipment divides the sub-functional attribute categories of each geographic landmark determined in the aforementioned steps into different sets according to the functional attribute categories. That is, geographic landmarks belonging to the same target functional attribute category are divided into the same category landmark set, thereby obtaining various category landmark sets. The sub-functional attribute categories of geographic landmarks in each category landmark set belong to the same target functional attribute category. For example, the target functional attribute categories corresponding to geographic landmarks with the sub-functional attribute category of library and geographic landmarks with the sub-functional attribute category of bookstore are both functional places that provide reading and books. Therefore, libraries and bookstores can be classified into the same category landmark set.

[0076] Step S208: Determine the weights of each category of marker set based on the distance between each geographic marker and the target location.

[0077] Specifically, the computer equipment calculates the spatial distance between the target location and each geographic landmark. Then, it merges the distances between the geographic landmarks belonging to the same category of landmarks determined in the previous steps and the target location to obtain the weight of the corresponding category of landmark set. The fusion method can be summation, weighted summation, multiplication, etc.

[0078] Step S210: Determine the reference category marker set from each category marker set based on the weights corresponding to each category marker set.

[0079] Among them, the reference category marker set is the two category marker sets with the largest weights among all category marker sets.

[0080] Specifically, the computer device determines the reference category marker set based on the weights corresponding to the category marker sets determined in the aforementioned steps. That is, it determines the category marker set corresponding to the larger weight among the category marker sets. Specifically, it compares the weights of each category marker set with a preset threshold, thereby determining the reference category marker set based on the weights of the category marker sets.

[0081] Step S212: Determine the target category marker set based on the relationship between the weights and thresholds corresponding to the reference category marker set.

[0082] Among them, the reference category marker set consists of the two category marker sets with the largest weights among all category marker sets. The reference category marker set with the larger weight is the first reference category marker set, and the reference category marker set with the smaller weight is the second reference category marker set.

[0083] Specifically, the computer device compares the weights corresponding to the first reference category marker set with the first threshold. When the weights are greater than or equal to the first threshold, the corresponding first reference category marker set is determined as the target category marker set. When the weights are greater than or equal to the second threshold and less than the first threshold, and the weights of the second reference category marker set are greater than or equal to the second threshold, both the first reference category marker set and the second reference category marker set are determined as the target category marker set.

[0084] Step S214: Determine the regional functional attribute category corresponding to the area where the target location is located based on the target functional attribute category corresponding to the target category marker set.

[0085] In this embodiment, by acquiring the target geographic location information of the target location, the geographic landmark information of each geographic landmark within a preset range of the target location is determined based on the target geographic location information. Based on the sub-functional attribute category of each geographic landmark, geographic landmarks belonging to the same target functional attribute category are grouped into the same category of landmark set, resulting in various category landmark sets. The weights corresponding to each category landmark set are determined based on the distance between each geographic landmark and the target location. Based on the weights corresponding to each category landmark set, a reference category landmark set is determined from these categories. Based on the relationship between the weights of the reference category landmark sets and a threshold, a target category landmark set is determined. Finally, based on the target functional attribute category corresponding to the target category landmark set, the regional functional attribute category corresponding to the area where the target location is located is determined. In this way, by using other geographic landmarks within the preset range of the target location, and then performing cluster analysis on the geographic landmark information of these other geographic landmarks to remove redundant data, a concise and highly relevant reference category landmark set is obtained. Finally, based on the weight characteristics of the reference category landmark set, the regional functional attribute category corresponding to the area where the target location is located is determined, effectively improving the accuracy of geographic data analysis.

[0086] In one embodiment, such as Figure 3 As shown, based on the sub-functional attribute categories of each geographic landmark, geographic landmarks belonging to the same target functional attribute category are grouped into the same category of landmark sets, resulting in the following category of landmark sets:

[0087] Step S302: Based on the sub-functional attribute categories of each geographic landmark, geographic landmarks belonging to the same target functional attribute category are divided into the same category landmark set to obtain the full category landmark set.

[0088] The full set of category markers is generated by clustering all geographic markers to form the category marker set of all geographic markers.

[0089] Specifically, the computer equipment classifies geographical landmarks belonging to the same target functional attribute category into the same category of landmarks based on the sub-functional attribute categories of each geographical landmark. For example, the target functional attribute categories corresponding to geographical landmarks with the sub-functional attribute category of library and geographical landmarks with the sub-functional attribute category of bookstore are both places that provide reading and books. Therefore, the library and the bookstore can be classified into the same category of landmarks, thereby obtaining the full set of category landmarks corresponding to all geographical landmarks.

[0090] Step S304: Perform correlation analysis based on the target functional attribute categories corresponding to the full set of category markers to determine the sets of relevant category markers.

[0091] Specifically, the computer device obtains the full set of category markers according to the aforementioned steps, then performs correlation analysis on each pair of category marker sets in the full set of category markers to obtain the correlation degree corresponding to each set, compares the correlation degree with a preset threshold, determines the category marker set corresponding to the correlation degree greater than or equal to the preset threshold, and determines these category marker sets with correlation degrees greater than the preset threshold as related category marker sets, thereby determining each related category marker set.

[0092] Step S306: Combine the sets of marker points for each relevant category to obtain the set of marker points for each category.

[0093] In this embodiment, firstly, based on the sub-functional attribute categories of each geographic landmark, geographic landmarks belonging to the same target functional attribute category are divided into the same category landmark set to obtain the full category landmark set. Then, based on the target functional attribute categories corresponding to the full category landmark set, correlation analysis is performed to determine each related category landmark set. Finally, the related category landmark sets are combined to obtain each category landmark set, effectively eliminating redundant data in the full category landmark set and improving the correlation of the related category landmark sets.

[0094] In one embodiment, such as Figure 4 As shown, the weights of each category of marker set are determined based on the distance between each geographic marker and the target location, including:

[0095] Step S402: Obtain the spatial distance between the target location and each geographic landmark, as well as the preset distance constant.

[0096] Specifically, the computer equipment calculates the spatial distance between the target location and each geographical landmark. The spatial distance can be the straight-line distance or the transportation distance between the target location and each geographical landmark. The transportation distance can be the length of the road between the target location and the geographical landmark.

[0097] Step S404: The spatial distance of each geographic landmark is fused with the preset distance constant to obtain each fusion item.

[0098] Specifically, the computer equipment performs weighted fusion of the spatial distance of each geographic landmark with a preset distance constant to obtain the fusion item corresponding to each geographic landmark. The fusion method can be addition, subtraction, multiplication, division, etc. between two numbers.

[0099] Step S406: Obtain the weights corresponding to each category marker set based on the reciprocals of each fusion term.

[0100] Specifically, the computer device can obtain the weights corresponding to each category of marker set in the manner shown in Formula 1 below:

[0101] weight=1 / (distance+const), formula 1

[0102] Where weight is the weight corresponding to the set of category markers, distance is the spatial distance, and const is a preset distance constant.

[0103] In this embodiment, the spatial distance between the target location and each geographic landmark and a preset distance constant are obtained respectively. The spatial distance of each geographic landmark and the preset distance constant are fused to obtain each fusion term. The weight corresponding to each category of landmark set is obtained according to the reciprocal of each fusion term. When the distance is larger, the weight is smaller, and when the distance is smaller, the weight is larger. In this way, the weight of the corresponding geographic landmark is reasonably configured according to the distance between each geographic landmark and the target location, which effectively improves the reliability of the weight.

[0104] In one embodiment, such as Figure 5 As shown, the weights of each category of marker set are determined based on the distance between each geographic marker and the target location, including:

[0105] Step S502: Obtain the spatial distance between the target location and each geographic landmark.

[0106] Specifically, the computer equipment calculates the spatial distance between the target location and each geographical landmark. The spatial distance can be the straight-line distance or the transportation distance between the target location and each geographical landmark. The transportation distance can be the length of the road between the target location and the geographical landmark.

[0107] Step S504: Input the spatial distance of each geographic landmark into the Gaussian function to obtain the function values.

[0108] Specifically, the computer equipment calculates the spatial distance between the target location and each geographic landmark according to the aforementioned steps, normalizes each geographic landmark, and then uses the normalized spatial distance of each geographic landmark as the independent variable of the function, inputting it into the Gaussian function to obtain the function values.

[0109] Step S506: The function values ​​of each geographic landmark in each category landmark set are weighted and fused to obtain the corresponding weight, thereby determining the weight corresponding to each category landmark set.

[0110] Specifically, the computer equipment sums up the weights of all geographic landmarks in each category of landmark set to obtain the weight of the corresponding category of landmark set, and then determines the weight corresponding to each category of landmark set.

[0111] In this embodiment, the computer device acquires the spatial distance between the target location and each geographic landmark, inputs the spatial distance of each geographic landmark into a Gaussian function to obtain each function value, and weights and fuses the function values ​​of each geographic landmark in each category of landmark set to obtain the corresponding weight. This determines the weight corresponding to each category of landmark set, thereby ensuring that the weight does not decay too quickly as the distance between the geographic landmark and the target location increases, and that the weight does not increase too quickly as the distance between the geographic landmark and the target location decreases, thus improving the reliability of the weight corresponding to each geographic landmark.

[0112] In one embodiment, such as Figure 6 As shown, before determining the reference category marker set from each category marker set based on the weights corresponding to each category marker set, the process also includes:

[0113] Step S602: Obtain the set number of each category of marker point set.

[0114] Step S604: When the number of sets is less than or equal to a preset threshold, determine that the regional functional attribute category corresponding to the area where the target location is located is an unknown attribute category.

[0115] In this embodiment, the computer device determines the regional functional attribute category corresponding to the area where the target location is located based on the number of category marker sets. When the number of each category marker set is too small, that is, less than a preset threshold, it means that it is insufficient to determine the regional functional attribute category of the area where the target location is located based on the current number of category marker sets, and the regional functional attribute category corresponding to the area where the target location is located is determined to be an unknown attribute category.

[0116] In one embodiment, such as Figure 7As shown, a reference category marker set is determined from each category marker set based on the weights corresponding to those sets, including:

[0117] Step S702: Determine the first category marker set and the second category marker set from each category marker set according to the weights corresponding to each category marker set. The first category marker set is the category marker set with the largest weight among all category marker sets, and the second category marker set is the category marker set with the second largest weight among all category marker sets.

[0118] In this embodiment, after the computer device determines the weights corresponding to each category of marker set according to the aforementioned steps, it then determines the reference category marker set corresponding to the two largest weights in each category of marker set, thereby improving the reliability of geographic data analysis.

[0119] In one embodiment, such as Figure 8 As shown, based on the relationship between the weights and thresholds corresponding to the reference category marker set, the target category marker set is determined, including:

[0120] Step S802: When the first weight is greater than or equal to the first threshold, the first category marker set is determined as the target category marker set, and the first weight is the weight corresponding to the first target category marker set.

[0121] Step S804: When the first weight is greater than or equal to the second threshold and less than the first threshold and the second weight is greater than or equal to the second threshold, the first category marker set and the second category marker set are determined as the target category marker set, and the second weight is the weight corresponding to the second target category marker set.

[0122] In this embodiment, the computer device compares the weights corresponding to the reference category marker set with the first threshold and the second threshold, and then determines the target category marker set in the reference category marker set based on the size relationship. This allows the target category marker set to be determined based on the weights of the reference category marker set, and the target category marker set is then used as the basis for judging the regional functional attribute category of the area where the target location is located, effectively improving the accuracy of judging the regional functional attribute category of the area where the target location is located.

[0123] This application also provides an application scenario in which the above-mentioned geographic data analysis method is applied to the scenario of determining regional business attributes. Specifically, the geographic data analysis method is applied in this application scenario as follows:

[0124] The latitude and longitude coordinates of the target location are determined. The LBS latitude and longitude data of the business registration address are used to retrieve the external address service interface. The fields returned by the interface are parsed to obtain information such as POI (Point of Interest) name, address, category, and distance between the POI and the business address. The POIs are classified into categories. Based on the POI category and distance, the weighted POI labels of the business registration address are calculated, thereby obtaining the business location labels of the business address.

[0125] (1) Analysis Sample Description

[0126] The analysis sample consisted of 1.66 million business address data entries obtained from the external service interface "Baihang+". The data was then analyzed by retrieving the LBS latitude and longitude data of the business registration addresses from the external address service interface.

[0127] (2) Data processing

[0128] Adjusting the address radius: The interface returns points of interest within 1000m of the registered business address. Considering that 1000m is a wide range, the address radius is reduced to improve clustering accuracy. Statistical analysis of the address radius distribution shows that 74.7% of the points of interest are within 300m, and 90.9% are within 500m. Points of interest within a radius of 300m are selected as samples.

[0129] (3) Integration of POI category labels

[0130] The POI category labels returned by the interface were divided into 10 custom category labels as shown in Table 1 below. POI data that are not relevant to the identification of the business location in the sample (including place name and address information, access facilities, indoor facilities, events and activities, etc., which have no classification significance) were removed.

[0131] Table 1

[0132]

[0133]

[0134] (4) Feature extraction

[0135] Considering that the analysis sample consists of Points of Interest (POIs) within 300m of the business registration address, the parameters are selected as: mean = 0, standard deviation = 200. A Gaussian function is constructed, and the distance between each POI and the target location is used as an independent variable input into the Gaussian function to obtain the corresponding function values. These function values ​​are then used as the weights for the corresponding POIs, thus enabling different weights to be assigned to the POI labels based on their distance. The proportion of 10 custom POI labels for each business registration address is statistically analyzed to obtain a feature wide table. The curve corresponding to the Gaussian function is shown in the figure below. Figure 12 As shown.

[0136] (5) Define clustering labeling rules

[0137] Based on the feature wide table generated in step (4), clustering rules are defined, and the specific logic is as follows: Figure 11 As shown, labels were then configured for all samples, resulting in 55 groups. Groups with a sample percentage greater than or equal to 1% were selected to obtain the grouping results shown in Table 2 below. The two groups with the highest weights among the 55 groups (public facilities land & industrial land) were then used to represent the regional operating attributes of the target location.

[0138] Table 2

[0139]

[0140]

[0141] The aforementioned geographic data analysis methods, devices, computer equipment, and storage media, through other POI points within a preset range of the target location, perform cluster analysis on the geographic indication information of the other POI points, eliminate redundant data, and thus obtain a concise and highly relevant group of reference POI points. Then, based on the weight characteristics of the reference POI point groupings, the regional functional attribute category corresponding to the area where the target location is located is determined, effectively improving the accuracy of geographic data analysis.

[0142] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0143] In one embodiment, such as Figure 9 As shown, a geographic data analysis device is provided. This device can employ software modules, hardware modules, or a combination of both as part of a computer device. Specifically, the device includes: an acquisition module 902, a first determination module 904, a second determination module 906, and a judgment module 908, wherein:

[0144] Module 902 is used to acquire the target geographic location information of the target location;

[0145] The first determining module 904 is used to determine the geographic landmark information of each geographic landmark within a preset range of the target location based on the target geographic location information. The geographic landmark information is used to characterize the sub-functional attribute category and geographic location information of the corresponding geographic landmark. Based on the sub-functional attribute category of each geographic landmark, geographic landmarks belonging to the same target functional attribute category are divided into the same category of landmarks to obtain each category of landmarks. Each category of landmarks corresponds to the target functional attribute category, and the granularity of the sub-functional attribute category is less than or equal to the granularity of the target functional attribute category.

[0146] The second determining module 906 is used to determine the weights corresponding to each category of marker set based on the distance between each geographic marker set and the target location; determine a reference category marker set from each category marker set based on the weights corresponding to each category marker set; and determine the target category marker set based on the relationship between the weights corresponding to the reference category marker set and a threshold.

[0147] The judgment module 908 is used to determine the regional functional attribute category of the area where the target location is located based on the target functional attribute category corresponding to the target category marker point set.

[0148] The aforementioned geographic data analysis device acquires the target geographic location information of a target location, determines the geographic landmark information of each geographic landmark within a preset range of the target location based on the target geographic location information, and groups geographic landmarks belonging to the same target functional attribute category into the same category of landmarks based on the sub-functional attribute category of each geographic landmark. It then determines the weight corresponding to each category of landmarks based on the distance between each geographic landmark and the target location. Based on the weights of each category of landmarks, it determines a reference category of landmarks from each category of landmarks. Based on the relationship between the weights of the reference category of landmarks and a threshold, it determines the target category of landmarks. Finally, it determines the regional functional attribute category corresponding to the area where the target location is located based on the target functional attribute category corresponding to the target category of landmarks. In this way, by using other geographic landmarks within the preset range of the target location and performing cluster analysis on the geographic landmark information of these other geographic landmarks to remove redundant data, a concise and highly relevant reference category of landmarks is obtained. Finally, based on the weight characteristics of the reference category of landmarks, the regional functional attribute category corresponding to the area where the target location is located is determined, effectively improving the accuracy of geographic data analysis.

[0149] In one embodiment, the first determining module 904 is further configured to classify geographic landmarks belonging to the same target functional attribute category into the same category landmark set based on the sub-functional attribute category of each geographic landmark, thereby obtaining a full category landmark set; perform correlation analysis based on the target functional attribute category corresponding to the full category landmark set to determine each related category landmark set; and combine each related category landmark set to obtain each category landmark set.

[0150] In one embodiment, the second determining module 906 is further configured to obtain the spatial distance between the target location and each geographic landmark and a preset distance constant; fuse the spatial distance between each geographic landmark and the preset distance constant to obtain each fusion term; and obtain the weight corresponding to each category of landmark set based on the reciprocal of each fusion term.

[0151] In one embodiment, the second determining module 906 is further configured to obtain the spatial distance between the target location and each geographic landmark; input the spatial distance of each geographic landmark into a Gaussian function to obtain each function value; and weight and fuse the function values ​​of each geographic landmark in each category of landmark set to obtain the corresponding weight, thereby determining the weight corresponding to each category of landmark set.

[0152] In one embodiment, the second determining module 906 is further configured to obtain the set number of each category of marker point set; when the set number is less than or equal to a preset threshold, the regional functional attribute category corresponding to the area where the target location is located is determined to be an unknown attribute category.

[0153] In one embodiment, the second determining module 906 is further configured to determine a first category marker set and a second category marker set from each category marker set according to the weights corresponding to each category marker set. The first category marker set is the category marker set with the largest weight among all category marker sets, and the second category marker set is the category marker set with the second largest weight among all category marker sets.

[0154] In one embodiment, the second determining module 906 is further configured to determine the first category marker set as the target category marker set when the first weight is greater than or equal to the first threshold, and the first weight is the weight corresponding to the first target category marker set; and to determine the first category marker set and the second category marker set as the target category marker set when the first weight is greater than or equal to the second threshold and less than the first threshold and the second weight is greater than or equal to the second threshold.

[0155] Specific limitations regarding the geographic data analysis device can be found in the limitations of the geographic data analysis method described above, and will not be repeated here. Each module in the aforementioned geographic data analysis device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0156] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 10 As shown, the computer device includes a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements a geographic data analysis method. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0157] Those skilled in the art will understand that Figure 10 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0158] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0159] In one embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0160] In one embodiment, a computer program product or computer program is provided, the computer program product or computer program including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and executes the computer instructions, causing the computer device to perform the steps in the above method embodiments.

[0161] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0162] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0163] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A geographic data analysis method, characterized in that, The method includes: Obtain the target geographic location information; Based on the target geographic location information, determine the geographic landmark information of each geographic landmark point within a preset range of the target location. The geographic landmark information is used to characterize the sub-functional attribute category and geographic location information of the corresponding geographic landmark point. Based on the sub-functional attribute categories of each geographic landmark, geographic landmarks belonging to the same target functional attribute category are divided into the same category landmark set to obtain each category landmark set. Each category landmark set corresponds to the target functional attribute category, and the granularity of the sub-functional attribute category is less than or equal to the granularity of the target functional attribute category. The weights corresponding to each category of marker set are determined based on the distance between each geographic marker and the target location; Based on the weights corresponding to each category of marker point set, a reference category marker point set is determined from each category of marker point set; Based on the relationship between the weights and thresholds corresponding to the reference category marker set, the target category marker set is determined; Based on the target functional attribute category corresponding to the set of target category markers, the regional functional attribute category corresponding to the area where the target location is located is determined.

2. The method according to claim 1, characterized in that, The sub-functional attribute categories of each geographic landmark are used to classify geographic landmarks belonging to the same target functional attribute category into the same category of landmark sets, resulting in various category of landmark sets, including: Based on the sub-functional attribute categories of each geographic landmark, geographic landmarks belonging to the same target functional attribute category are divided into the same category landmark set to obtain the full category landmark set; Based on the target functional attribute categories corresponding to the full set of category markers, a correlation analysis is performed to determine the set of each relevant category marker. Each category of marker set is obtained by combining the sets of markers of each relevant category.

3. The method according to claim 1, characterized in that, The step of determining the weights corresponding to each category of marker set based on the distance between each geographic marker and the target location includes: The spatial distances between the target location and each geographic landmark, as well as preset distance constants, are obtained respectively. The spatial distances of each geographic landmark are respectively fused with the preset distance constant to obtain each fusion item; The weights corresponding to each category of marker set are obtained by taking the reciprocal of each fusion term.

4. The method according to claim 1, characterized in that, The step of determining the weights corresponding to each category of marker set based on the distance between each geographic marker and the target location includes: The spatial distances between the target location and each geographic landmark are obtained respectively; The spatial distances of each geographic landmark are input into a Gaussian function to obtain the function values. The function values ​​of each geographic landmark in each category landmark set are weighted and fused to obtain the corresponding weight, and then the weight corresponding to each category landmark set is determined.

5. The method according to claim 1, characterized in that, Before determining the reference category marker set from the category marker set based on the weights corresponding to each category marker set, the method further includes: Obtain the set size of each category of marker point set; When the number of sets is less than or equal to a preset threshold, the regional functional attribute category corresponding to the area where the target location is located is determined to be an unknown attribute category.

6. The method according to claim 1, characterized in that, The step of determining a reference category marker set from the category marker set based on the weights corresponding to each category marker set includes: Based on the weights corresponding to each set of category markers, a first category marker set and a second category marker set are determined from each set of category markers. The first category marker set is the set of category markers with the largest weight among all the set of category markers, and the second category marker set is the set of category markers with the second largest weight among all the set of category markers.

7. The method according to claim 6, characterized in that, The step of determining the target category marker set based on the relationship between the weights and thresholds corresponding to the reference category marker set includes: When the first weight is greater than or equal to the first threshold, the first category marker set is determined to be the target category marker set, and the first weight is the weight corresponding to the first category marker set. When the first weight is greater than or equal to the second threshold and less than the first threshold, and the second weight is greater than or equal to the second threshold, the first category marker set and the second category marker set are determined to be the target category marker set, and the second weight is the weight corresponding to the second category marker set.

8. A geographic data analysis device, characterized in that, The device includes: The acquisition module is used to acquire the target geographic location information of the target location; The first determining module is used to determine the geographic landmark information of each geographic landmark point within a preset range of the target location based on the target geographic location information. The geographic landmark information is used to characterize the sub-functional attribute category and geographic location information of the corresponding geographic landmark point. Based on the sub-functional attribute category of each geographic landmark point, geographic landmark points belonging to the same target functional attribute category are divided into the same category of landmark points to obtain each category of landmark points. Each category of landmark points corresponds to the target functional attribute category, and the granularity of the sub-functional attribute category is less than or equal to the granularity of the target functional attribute category. The second determining module is used to determine the weights corresponding to each category of marker set based on the distance between each geographic marker set and the target location; determine a reference category marker set from each category marker set based on the weights corresponding to ...; and determine a target category marker set based on the relationship between the weights corresponding to the weights corresponding to the weights corresponding to the weights corresponding to the weights corresponding to the weights corresponding to the weights corresponding to the weights corresponding to the weights corresponding to the weights; The judgment module is used to determine the regional functional attribute category corresponding to the area where the target location is located based on the target functional attribute category corresponding to the target category marker point set.

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

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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