Data processing method and device, electronic equipment and storage medium

Abstract

The invention relates to the technical field of data processing, in particular to a data processing method and device, electronic equipment and a storage medium, and the method comprises the steps that a server obtains respective description data of each entity object in a target area range sent by client equipment, and an affiliation relationship between different types of entity objects, constructing each virtual entity corresponding to each entity object, and then obtaining position indication information of each interest point in the target area range; then, aiming at each target virtual entity which does not correspond to the interest point, calculating to obtain each corresponding analysis result; and according to the analysis result difference of the target virtual entities of the same type under the corresponding analysis indexes, obtaining an anomaly judgment result of each target virtual entity under the corresponding analysis indexes. Thus, efficient data collection can be achieved, complex analysis and evaluation can be achieved according to the obtained data, and the data processing efficiency and the processing effect are improved.

Description

Background Art

[0002] In the prior art, when processing data in scenarios such as urban planning, it is usually based on the data submitted offline and the manually constructed City Information Modeling (CIM) to perform manual analysis and processing to obtain corresponding data analysis results.

[0003] However, when processing data using the processing method in the prior art, it is necessary to invest too much human resources, which greatly reduces the data processing efficiency; moreover, the data content covered in the data submitted offline and the constructed CIM model has extremely poor timeliness, which increases the data processing difficulty, cannot achieve complex analysis and processing, is difficult to guarantee the data processing effect, and reduces the usability of the data analysis results; furthermore, in specific business scenarios, effective business processing cannot be carried out based on the data analysis results. Summary of the Invention

[0004] Embodiments of the present application provide a data processing method, apparatus, electronic device, and storage medium to improve data processing efficiency and ensure data processing effect.

[0005] In a first aspect, a data processing method is proposed, which is applied to a server and includes:

[0006] Obtain the description data of each entity object within the target area range sent by the client device, as well as the attribution relationships between different types of entity objects, and construct each virtual entity corresponding to each entity object, where the description data includes the location information of the entity object;

[0007] Obtain the location indication information of each point of interest within the target area range;

[0008] For each target virtual entity whose location information is not included in the location indication information of each point of interest, perform the following operations: determine each analysis index that matches the type of the target virtual entity, determine the attribution relationship between the target virtual entity and other virtual entities, and based on the location difference between the location information of the target virtual entity and the location indication information of each point of interest, combine the attribution relationship to obtain each analysis result corresponding to each analysis index;

[0009] Based on the difference in the analysis results of the target virtual entities of the same type under the corresponding analysis indexes, obtain the abnormal determination result of each target virtual entity under the corresponding analysis indexes.

[0010] In a second aspect, a data processing method is proposed, which is applied to a client device and includes:

[0011] In response to a data processing instruction triggered for a target area range, send the description data of each entity object within the target area range and the attribution relationships between different types of entity objects to the server, where the description data at least includes the location information of the entity object;

[0012] In response to a processing progress presentation instruction triggered for a target virtual entity on a content display page, display the processing progress of each analysis result of the target virtual entity obtained from the server in the content display area;

[0013] In response to an exception presentation instruction triggered for a target virtual entity on the content display page, display the exception determination results of each target virtual entity under the corresponding analysis metrics obtained from the server in the content display area.

[0014] In a third aspect, a data processing device is proposed, including:

[0015] A first acquisition unit, configured to acquire the description data of each entity object within the target area range sent by a client device and the attribution relationships between different types of entity objects, and construct each virtual entity corresponding to each entity object, where the description data includes the location information of the entity object;

[0016] A second acquisition unit, configured to acquire the location indication information of each point of interest within the target area range;

[0017] An execution unit, configured to perform the following operations for each target virtual entity whose location information is not included in the location indication information: determine the analysis metrics matching the type of the target virtual entity, determine the attribution relationship between the target virtual entity and other virtual entities, and obtain each analysis result corresponding to each analysis metric according to the location difference between the location information of the target virtual entity and the location indication information of each point of interest, in combination with the attribution relationship;

[0018] A determination unit, configured to obtain the exception determination results of each target virtual entity under the corresponding analysis metrics according to the analysis result differences of the target virtual entities of the same type under the corresponding analysis metrics.

[0019] Optionally, the description data further includes the status information and statistical information of the entity object. After obtaining the exception determination results of each target virtual entity under the corresponding analysis metrics, the device further includes a response unit, and the response unit is configured to:

[0020] In response to an interest point planning request triggered by a client device for a sub-area within the target area range, acquire the target interest point type and interest point evaluation metrics carried in the interest point planning request;

[0021] Based on the status information of each virtual entity in the sub-region, determine each candidate virtual entity that matches the point of interest planning request in the sub-region, and for each candidate virtual entity, according to the position information of the candidate virtual entity and each other virtual entity in the sub-region, combined with the statistical information and status information of each other virtual entity, determine the evaluation result corresponding to the point of interest evaluation index;

[0022] Based on each evaluation result, select the virtual entity to be planned that matches the point of interest planning request among the candidate virtual entities.

[0023] Optionally, the description data further includes the status information and statistical information of the entity object. After obtaining the position indication information of each point of interest within the target area, the execution unit of the device is further configured to:

[0024] For each point of interest, perform the following operations: determine the statistical indicators that match the type of the point of interest, and according to the position indication information of the point of interest and the position information of each virtual entity, combined with the status information and statistical information of each virtual entity, determine the statistical result corresponding to the statistical indicator;

[0025] Obtain the abnormal determination result of each point of interest under the corresponding statistical indicator according to the difference in the statistical results of the same type of points of interest under the corresponding statistical indicators.

[0026] Optionally, when obtaining the description data of each entity object within the target area sent by the client device, the execution unit is configured to:

[0027] Obtain the status information and position information sent by the client device for each entity object within the target area, and obtain the statistical information sent by other client devices for each entity object;

[0028] Determine the status information, position information, and statistical information of each entity object as the description data of each entity object.

[0029] Optionally, the device further includes a configuration unit, and the configuration unit is configured to:

[0030] Configure different initial entity presentation methods for different types of virtual entities;

[0031] For each type of target virtual entity, perform the following operations: determine the analysis indicators of each homogeneous virtual entity of one type, and for each analysis indicator, set the corresponding entity abnormal presentation method for each homogeneous virtual entity according to each value range of the analysis result under the analysis indicator.

[0032] Optionally, when obtaining the position indication information of each point of interest within the target area range, the second obtaining unit is configured to:

[0033] Obtain a preset area size, and divide the target area range according to the area size to obtain each sub-range area;

[0034] For each sub-range area, perform the following operations: determine the position range information corresponding to the sub-range area, and based on the position range information, call the map service interface multiple times until the total number of points of interest feedback by the map service interface once is lower than the set value, and obtain each point of interest obtained by each call to the map server interface and the position indication information of each point of interest.

[0035] Optionally, before obtaining the preset area size, the second obtaining unit is further configured to:

[0036] Start a point of interest obtaining thread, and after obtaining a distributed lock through the point of interest obtaining thread, execute the processing procedure;

[0037] After obtaining the position indication information of each point of interest within the target area range, before performing operations on each target virtual entity whose position information is not included in the position indication information, further include:

[0038] Start a result calculation thread, and obtain a distributed lock through the result calculation thread.

[0039] Optionally, when obtaining each analysis result corresponding to each analysis index according to the position difference between the target virtual entity and each point of interest and in combination with the attribution relationship, the execution unit is configured to:

[0040] When determining each basic virtual entity that belongs to the target virtual entity according to the attribution relationship, for each basic virtual entity, perform the following operations: determine each analysis index that matches the basic virtual entity, and according to the position indication information of each point of interest and the position information of the basic virtual entity, obtain each analysis result corresponding to each analysis index of the basic virtual entity;

[0041] Based on the analysis results corresponding to each basic virtual entity, obtain each analysis result determined for each analysis index corresponding to the target virtual entity.

[0042] Optionally, when determining that there are no basic virtual entities that belong to the target virtual entity according to the attribution relationship, the execution unit is further configured to:

[0043] Based on the location indication information of each of the points of interest and the location information of the target virtual entity, determine, among the points of interest, each target point of interest that satisfies a preset location difference condition with respect to the target virtual entity;

[0044] Based on the location differences between each of the target points of interest and the target virtual entity, obtain respective analysis results corresponding to the analysis indicators.

[0045] Fourthly, a data processing device is proposed, including:

[0046] A data sending unit, configured to, in response to a data processing instruction triggered for a target area range, send the description data of each entity object within the target area range and the attribution relationships between different types of entity objects to a server, where the description data at least includes the location information of the entity object;

[0047] A progress display unit, configured to, in response to a processing progress presentation instruction triggered for a target virtual entity on a content display page, display the processing progress of each analysis result of the target virtual entity obtained from the server in the content display area;

[0048] An exception display unit, configured to, in response to an exception presentation instruction triggered for a target virtual entity on the content display page, display the exception determination results of each target virtual entity under the corresponding analysis indicators obtained from the server in the content display area.

[0049] Optionally, the device further includes a content presentation unit, and the content presentation unit is configured to:

[0050] In response to a content presentation instruction triggered for the target area range, obtain the initial entity presentation modes configured for different types of virtual entities from the server, and present the virtual entities according to the obtained initial entity presentation modes;

[0051] In response to a trigger operation for a target analysis indicator, obtain the entity exception presentation modes of each same-type target virtual entity configured by the server for the target analysis indicator, and adjust the presentation modes of the same-type target virtual entities from the initial presentation modes to the corresponding entity exception presentation modes.

[0052] Optionally, after displaying the exception determination results of each target virtual entity under the corresponding analysis indicators obtained from the server in the content display area, the content presentation unit is further configured to:

[0053] In response to the point of interest planning instruction triggered for the sub-region range within the target region range, send a point of interest planning request carrying the target point of interest type and point of interest evaluation metrics to the server;

[0054] Receive the virtual entity to be planned fed back by the server, and identify the virtual entity to be planned among the presented virtual entities.

[0055] Optionally, after presenting the virtual entities, the content presentation unit is further configured to:

[0056] In response to the detail display instruction triggered for the target metric of a virtual entity, obtain the analysis result determined corresponding to the target metric from the server, and the description data of the virtual entity;

[0057] Present the processing result corresponding to the target metric and part of the description data on the detail display page.

[0058] Optionally, the data sending unit is further configured to:

[0059] In response to the data upload instruction triggered on the content display page, send the collected data to the server, so that the server determines the statistical information and status information of each entity object based on the obtained data.

[0060] In a fifth aspect, an electronic device is proposed, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor implements the above method when executing the computer program.

[0061] In a sixth aspect, a computer-readable storage medium is proposed, on which a computer program is stored, and the computer program implements the above method when executed by a processor.

[0062] In a seventh aspect, a computer program product is proposed, including a computer program, and the computer program implements the above method when executed by a processor.

[0063] The beneficial effects of this application are as follows:

[0064] The present application proposes a data processing method, apparatus, electronic device and storage medium. From the perspective of the server, by obtaining the description data of each entity object within the target area sent by the client device and the attribution relationship between different types of entity objects, efficient data collection can be achieved, the collection cost of the data to be processed can be reduced, and the timeliness and reliability of the data to be processed can be improved; afterwards, with the help of the position indication information of each point of interest obtained, the server can determine each point of interest included within the target area and the position of each point of interest within the target area, so that each point of interest can be effectively located within the target area;

[0065] Afterwards, each virtual entity is constructed for each of the entity objects, and each target virtual entity that does not correspond to a point of interest is determined according to the inclusion relationship between the position information of each virtual entity and the position indication information of each point of interest; for each target virtual entity, each analysis index matching the type of the target virtual entity is determined, and according to the position difference between the position information of the target virtual entity and the position indication information of each point of interest, in combination with the attribution relationship between the determined target virtual entity and other entity objects, the analysis results of the target virtual entity under each analysis index are respectively obtained; this enables consideration of the position relationship between the target virtual entity and each point of interest, as well as the attribution relationship between the target virtual entity and other virtual entities during the process of determining the analysis results corresponding to a target virtual entity, so that when faced with the calculation of complex analysis indexes, the relevant situation between each point of interest and the current target virtual entity, as well as the influence of other virtual entities on the current target virtual entity, can be effectively analyzed, thus enabling complex analysis processing, ensuring the data processing effect, and further improving the usability of each analysis result, so that in a specific business scenario, effective analysis processing can be carried out based on each analysis result, thereby enabling efficient data processing while reducing the data processing cost and data processing time;

[0066] Furthermore, according to the differences in the analysis results of the target virtual entities of the same type under the corresponding analysis indexes, the abnormal determination results of each target virtual entity under the corresponding analysis indexes can be obtained, which enables the effective determination of the abnormal situation of the target virtual entity based on the analysis results obtained from objective information processing. That is, the calculated analysis results provide a determination basis for evaluating the abnormal situation of the target virtual entity.

[0067] From the perspective of the client device, in response to the data processing instruction triggered for the target area range, the description data of each entity object within the target area range is directly uploaded to the server, enabling the collation and upload of data; subsequently, in response to the processing progress presentation instruction triggered for a target virtual entity, the processing progress of each analysis result corresponding to a target entity can be displayed within the content display area, and moreover, in response to the exception presentation instruction triggered for the target virtual entity in the content display page, the exception determination results of each target virtual entity under the corresponding analysis metrics obtained from the server can be displayed; based on this, the client device can display the analysis progress and determination results obtained from the server in response to different presentation instructions. On the one hand, this can facilitate the acquisition of statistical results related to the target area range, and on the other hand, it can intuitively present the processing progress and abnormal conditions of the analysis results. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 FIG. is a schematic diagram of a possible application scenario in an embodiment of the present application;

[0069] Figure 2A FIG. is a schematic diagram of the data processing process in an embodiment of the present application;

[0070] Figure 2B FIG. is a schematic diagram of the process of expanding and determining the target area range in an embodiment of the present application;

[0071] Figure 2C FIG. is a schematic diagram of a process for obtaining description data in an embodiment of the present application;

[0072] Figure 2D FIG. is a schematic diagram of another process for obtaining description data in an embodiment of the present application;

[0073] Figure 3 FIG. is a schematic diagram of the process for obtaining each point of interest in an embodiment of the present application;

[0074] Figure 4 FIG. is a schematic diagram of the process for obtaining each analysis result in an embodiment of the present application;

[0075] Figure 5A FIG. is a schematic diagram of the data processing process of the client device in an embodiment of the present application;

[0076] Figure 5B FIG. is a schematic diagram of the page for triggering the data processing instruction for the target area range in an embodiment of the present application;

[0077] Figure 5C FIG. is a schematic diagram of the overview content presented in an embodiment of the present application;

[0078] Figure 5DSchematic diagram of the processing progress of each analysis result presented in the embodiments of this application;

[0079] Figure 5E Schematic diagram of the presentation of the abnormal determination result in the embodiments of this application;

[0080] Figure 5F Schematic diagram of the process of uploading custom data in the embodiments of this application;

[0081] Figure 5G Schematic diagram of the content presentation effect in the embodiments of this application;

[0082] Figure 5H Schematic diagram of the result presentation of the virtual entity under the target index in the embodiments of this application;

[0083] Figure 6 Schematic diagram of the information type in the data table in the embodiments of this application;

[0084] Figure 7 Schematic diagram of the logical structure of a data processing device in the embodiments of this application;

[0085] Figure 8 Schematic diagram of the logical structure of another data processing device in the embodiments of this application;

[0086] Figure 9 Schematic diagram of the hardware composition structure of an electronic device applying the embodiments of this application;

[0087] Figure 10 Schematic diagram of the hardware composition structure of another electronic device applying the embodiments of this application. Detailed implementation manners

[0088] To make the objectives, technical solutions and advantages of the embodiments of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are part of rather than all of the embodiments of the technical solutions of this application. Based on the embodiments recorded in this application document, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the technical solutions of this application.

[0089] Terms such as "first" and "second" in the specification, claims and above-mentioned drawings of this application are used to distinguish similar objects and do not necessarily need to be used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances so that the embodiments of the present invention described here can be implemented in an order other than those illustrated or described here.

[0090] In the embodiments of the present application, the term "module" or "unit" refers to a computer program with a predetermined function or a part of a computer program, which works together with other related parts to achieve a predetermined goal, and can be fully or partially implemented by using software, hardware (such as a processing circuit or a memory), or a combination thereof. Similarly, one processor (or multiple processors or memories) can be used to implement one or more modules or units. In addition, each module or unit can be a part of an overall module or unit that includes the functions of the module or unit.

[0091] The following explains some terms in the embodiments of the present application to facilitate the understanding of those skilled in the art.

[0092] Location information: Specifically, it can refer to spatial location information. Among them, the spatial location information can be described by using points, lines, and planes, and specifically by means of coordinates such as points, multipolygons, and polygons. When storing spatial location information, it is stored in the database using the geometry type; in the Java Topology Suite (JTS) class library, it is also stored in the form of a geometry object, which encapsulates many functions, such as functions for covering, distance, area, and finding the coordinates of the center point.

[0093] Point of interest: In a feasible implementation, a point of interest, also known as a place of interest, is used to indicate a location where a specified function is implemented.

[0094] The following briefly introduces the design concept of the embodiments of the present application:

[0095] Currently, in the urban planning scenario, the following situation exists: In each district of first-tier cities, urban survey and planning design institutes or similar functional departments will, every year, combine the data reported by government departments and produce an analysis report - "Urban Operation Report" manually, such as by using urban CMI. It generally starts in October every year and ends in February or March of the following year.

[0096] Moreover, in the existing data processing methods, due to the fact that data is submitted offline, the technology is backward and the efficiency is low. A large number of personnel are invested, and the effect is not ideal, resulting in a long data processing time. In addition, the data submitted offline has extremely poor timeliness, which increases the difficulty of data processing and cannot achieve complex analysis and processing.

[0097] In view of this, the present application provides a data processing method, apparatus, electronic device, and storage medium. From the perspective of the server, by obtaining the description data of each entity object within the target area sent by the client device, as well as the attribution relationships between different types of entity objects, efficient data collection can be achieved, the collection cost of the data to be processed can be reduced, and the timeliness and reliability of the data to be processed can be improved; subsequently, with the help of the obtained position indication information of each point of interest, the server can determine each point of interest included within the target area and the position of each point of interest within the target area, enabling effective positioning of each point of interest within the target area;

[0098] Subsequently, virtual entities are constructed for each entity object, and each target virtual entity that does not correspond to a point of interest is determined according to the inclusion relationship between the position information of each virtual entity and the position indication information of each point of interest; then, for each target virtual entity, analysis indicators that match the type of the target virtual entity are determined, and based on the position difference between the position information of the target virtual entity and the position indication information of each point of interest, combined with the attribution relationship between the determined target virtual entity and other entity objects, the analysis results of the target virtual entity under each analysis indicator are obtained respectively; this enables consideration of the position relationship between the target virtual entity and each point of interest, as well as the attribution relationship between the target virtual entity and other virtual entities during the process of determining the analysis results corresponding to a target virtual entity, enabling effective analysis of the relevant situation between each point of interest and the current target virtual entity, as well as the influence of other virtual entities on the current target virtual entity when faced with the calculation of complex analysis indicators, so complex analysis processing can be achieved, the data processing effect can be guaranteed, and further the usability of each analysis result can be improved, enabling effective analysis and processing based on each analysis result in a specific business scenario, thereby achieving efficient data processing while reducing the data processing cost and data processing time;

[0099] Furthermore, according to the differences in the analysis results of target virtual entities of the same type under the corresponding analysis indicators, the abnormal determination results of each target virtual entity under the corresponding analysis indicators can be obtained, which enables effective determination of the abnormal situation of the target virtual entity based on the analysis results obtained from objective information processing. That is, the calculated analysis results provide a determination basis for evaluating the abnormal situation of the target virtual entity.

[0100] From the perspective of the client device, in response to the data processing instruction triggered for the target area range, the description data of each entity object within the target area range is directly uploaded to the server, enabling the collation and upload of data. Subsequently, in response to the processing progress presentation instruction triggered for a target virtual entity, the processing progress of each analysis result corresponding to a target entity can be displayed within the content display area. Moreover, in response to the abnormal presentation instruction triggered for the target virtual entity on the content display page, the abnormal determination results of each target virtual entity under the corresponding analysis metrics obtained from the server can be displayed. Based on this, the client device can display the analysis progress and determination results obtained from the server in response to different presentation instructions. On the one hand, this can facilitate the acquisition of statistical results related to the target area range, and on the other hand, it can intuitively present the processing progress and abnormal conditions of the analysis results.

[0101] The preferred embodiments of the present application will be described below in conjunction with the accompanying drawings of the specification. It should be understood that the preferred embodiments described herein are only for the purpose of illustrating and explaining the present application and are not used to limit the present application. Moreover, without conflict, the embodiments of the present application and the features in the embodiments can be combined with each other.

[0102] Refer to Figure 1 As shown, it is a schematic diagram of a possible application scenario in an embodiment of the present application. In this application scenario schematic diagram, it includes a client device 110 and a server 120.

[0103] In a feasible embodiment of the present application, the server 120 can obtain the description data of each entity object within the target area range sent by the client device 110, as well as the attribution relationships between different types of entity objects, and construct each virtual entity corresponding to each entity object. It can also obtain the position indication information of each point of interest within the target area range. Additionally, the server 120 can perform data processing on the target area range based on the obtained data to obtain the statistical results of the points of interest and the statistical results of the virtual entities, and respectively process each analysis result for each target virtual entity that does not correspond to a point of interest. The server 120 can also obtain the abnormal determination results of each target virtual entity under the corresponding analysis metrics based on the differences in various analysis results between the same type of target virtual entities. Furthermore, for each point of interest, the corresponding statistical results can be determined.

[0104] Correspondingly, the client device 110 can, in response to a data processing instruction triggered by a user, send the description data of each entity object within the target area to the server 120, and display the analysis results obtained by the server processing, display the processing progress of the server for the analysis results, and display various abnormal determination results determined by the server for each target virtual entity; in addition, it can display the layout of each virtual entity as a whole, the positions of each point of interest in each virtual entity, and display each analysis index that can be triggered to further present details.

[0105] The client device 110 includes, but is not limited to, mobile phones, tablet computers, laptops, e-book readers, intelligent voice interaction devices, smart home appliances, vehicle terminals, aircraft, etc.

[0106] The server 120 can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms.

[0107] In the embodiments of the present application, the client device 110 and the server 120 can communicate through a wired network or a wireless network.

[0108] The following is a schematic description of the data processing process in combination with possible application scenarios:

[0109] Application scenario 1: In the urban planning scenario, process the data involved in the urban planning process.

[0110] In the processing process corresponding to Application scenario 1, after the client device triggers a data processing instruction based on a certain area (assumed to be Area 1) in the urban planning, the description data of each building, each street, and each area in Area 1, as well as the attribution relationships between each street, each area, and each building, are sent to the server.

[0111] The server constructs corresponding virtual entities for each building, each street, and each area, and obtains the location indication information of each point of interest (such as bus stops, parks, schools, supermarkets, etc.) in Area 1. After that, the statistical results of each point of interest are obtained, and for each virtual entity, the statistical results of the virtual entity are obtained; at the same time, for each target virtual entity that is not a corresponding point of interest, based on the location difference from each point of interest, combined with the attribution relationship with other virtual entities, for each analysis index matching the type of the target virtual entity, the analysis results are obtained. In addition, based on the analysis results, the abnormal conditions of each target virtual entity under the corresponding analysis indexes can be determined.

[0112] Application Scenario 2: In the scenario where different business processing nodes are implemented in different links of business processing, the deployment of each business processing node is analyzed and processed.

[0113] In the processing process corresponding to Application Scenario 2, the client sends the description data of each business processing node in a region and the attribution relationship between different types of business processing nodes to the server. The server uses the storage nodes for data storage as points of interest and obtains the location description information of each point of interest, where different types of storage nodes are used to store different types of data.

[0114] After that, for the business processing nodes that do not implement the data storage function, the server obtains the analysis results of the business processing nodes under each analysis index according to the location difference between the business processing nodes and the data storage nodes, combined with the attribution relationship between the business processing nodes and other processing nodes. The attribution relationship between the business processing nodes and other processing nodes is used to represent the dependency relationship between different business contents. For example, if the business processing of business processing node A depends on the processing result of business processing node B, then business processing node A belongs to business processing node B.

[0115] It should be noted that in Application Scenario 2, the analysis results determined from the perspective of business processing nodes may include the distance matching degree between business processing nodes and different types of storage nodes; the statistical results determined from the perspective of storage nodes may include the coverage degree of different types of business processing nodes for different business links.

[0116] Furthermore, based on the processing results, the deployment locations of business processing nodes and storage nodes can be determined, and whether the deployment of different types of business processing nodes and different types of storage nodes is reasonable can be determined.

[0117] In addition, it should be understood that in the specific embodiments of the present application, data processing is involved. When the embodiments described in the present application are applied to specific products or technologies, the collection, use, and processing of relevant data need to comply with the relevant laws, regulations, and standards of relevant countries and regions.

[0118] The following will, with reference to the accompanying drawings, first explain the data processing process from the perspective of the server:

[0119] Refer to Figure 2A As shown, it is a schematic diagram of the data processing process in the embodiments of the present application. The following will, with reference to the accompanying Figure 2A drawings, explain the data processing process:

[0120] Step 201: The server obtains the description data of each entity object within the target area range sent by the client device, as well as the attribution relationships between different types of entity objects, and constructs virtual entities corresponding to each entity object.

[0121] In the embodiments of the present application, when the server determines the target area range, in some feasible implementation manners, it can directly obtain the target area range indicated by the client device; in other feasible implementation manners, the server can expand a certain range on the basis of the entity object range indicated by the client device to obtain the target area range.

[0122] For example, refer to Figure 2B As shown, it is a schematic diagram of the process of expanding to determine the target area range in the embodiments of the present application. According to the content shown in the accompanying Figure 2B drawings, it can be known that assuming that the target area range is expanded on the basis of the entity object range indicated by the client device, then, according to the three manners shown in the accompanying Figure 2B drawings, in the first feasible manner, the quadrilateral contour of the entity object range can be determined on the basis of the entity object range, where there are a finite number of tangent points between the sides of the quadrilateral contour and the entity object range; then, expand 1000 m in the four directions of the quadrilateral to obtain the target area range. In the second feasible manner, the contour of the entity object range can be determined on the basis of the entity object range, and then, the radius of the circular contour can be increased by 1000 m to obtain the target area range, where the circular contour contains the entity object range and there are a finite number of tangent points between the circular contour and the entity object range. In the third feasible implementation manner, the target area range can be obtained by expanding 1000 m in each direction without changing the shape of the entity object range.

[0123] For the description data of each entity object obtained, in some feasible implementation manners, among the description data of each entity object obtained by the server, location information of the entity object is included; in some other feasible implementation manners, among the description data of each entity object obtained by the server, location information, status information, and statistical information of the entity object are included, where the location information is used to describe the location of the entity object; the status information corresponds to different contents for different entity object types; the statistical information refers to the data obtained by counting the entity object, for example, it can be the total number of people accommodated, or the total load number.

[0124] In the case where status information and statistical information are included in the description data obtained by the server, when the server obtains the description data of each entity object within the target area range sent by the client device, in some feasible implementation manners, the server can obtain the status information and location information sent by the client device for each entity object within the target area range, and obtain the statistical information sent by other client devices for each entity object; then determine the status information, location information, and statistical information of each entity object as the description data of each entity object.

[0125] Specifically, the description data obtained by the server may be sent by one client, or can be sent by multiple clients, and this application does not make specific restrictions on this. Taking obtaining status information and location information from the client device and obtaining statistical information from other client devices as an example, the server can receive the information uploaded by the client device in the form of a table, etc., where the uploaded table can specifically be an excel table, and the statistical information may include the calculation results of each corresponding defined index obtained through external calculation; the target area range is used to define the spatial range for the processing.

[0126] It should be understood that in different application scenarios, the description data may further include some basic information describing the entity object from other dimensions, such as startup time, shape information, etc. This application only takes the description data including statistical information, status information, and location information as an example for illustrative description. Moreover, in the case where the uploading times of the statistical information, status information, and location information are different, when the server establishes the association relationship between the statistical information, status information, and location information of the same entity object, it can integrate the statistical information, status information, and location information of the same entity object according to the identification information of the entity object; or, it can determine the entity objects with close locations as the same entity object according to the location information of the entity object, and integrate the statistical information, status information, and location information of the same entity object.

[0127] In addition, with regard to the form of existence of the described data, the server can store the attribution relationships between various entity objects and the respective description data of each entity object in a table. In some feasible implementation manners, the description data of each entity object can be integrated in one table; in other feasible implementation manners, the description data of the same type of entity objects can be integrated in one table, and the superior entity object to which the same type of entity objects belong is identified. In a feasible implementation manner, the relationships between various virtual entities (or the relationships between entity objects) are all described by geometry in the data structure.

[0128] For example, taking the target area range as X Street, B District, A City (abbreviated as X Street) as an example, the buildings, sub-streets, communities, plots, areas, and points of interest covered by X Street are all data within the scope of X Street. Among them, buildings, sub-streets, communities, plots, and areas are different types of entity objects.

[0129] Another example, in combination with the attached Figure 2C As shown, it is a schematic diagram of a process for obtaining description data in an embodiment of the present application. According to Figure 2C the content shown, the data sent by the client device received by the server is in the form shown in Table 1. Table 1 indicates the types of entity objects, that is, areas, plots, streets, communities, and buildings; moreover, it indicates the attribution relationships between different types of entity objects. Among them, an area is a piece of area defined by the user; a plot can be understood as a piece of area planned by the housing construction; a street contains communities, and a community contains buildings. There is an inclusion relationship between standard administrative regions, but areas and plots are not standard administrative regions. After the server obtains partial description data of each entity object sent by the client device, corresponding identification (ID) information is configured for each entity object; furthermore, when other devices upload statistical information based on custom metrics, the ID of the entity object can be carried in the uploaded data, so that the server can integrate the content shown in Table 4 based on the ID. Among them, the crossed-out part is the part without content.

[0130] Another example, referring to Figure 2D As shown, it is another schematic diagram of a process for obtaining description data in an embodiment of the present application. According to Figure 2D the content shown, among the statistical information uploaded by other clients obtained, the location information of the entity object may be carried. Based on this, the server can integrate the statistical information in Table 3 into Table 2 according to the matching situation of the location information to obtain the form shown in Table 4.

[0131] In this way, various data information sent by different client devices for each entity object can be integrated to obtain the description data of each entity object, so that based on the obtained description data, each entity object can be described from different information dimensions.

[0132] Furthermore, the server can construct virtual entities for each entity object, where the existence form of the virtual entity is determined according to the preset data presentation effect, and specifically can be a two-dimensional virtual entity or a three-dimensional virtual entity.

[0133] For example, assume that the preset data presentation effect is to present each entity object within the target area in the form of a three-dimensional model. In the finally constructed three-dimensional model, one entity object corresponds to one three-dimensional virtual entity.

[0134] For another example, assume that the preset data presentation data is to present each entity object within the target area in the form of a two-dimensional model. Then in the finally constructed two-dimensional model, one entity object corresponds to one two-dimensional virtual entity.

[0135] Step 202: The server obtains the position indication information of each point of interest within the target area.

[0136] When executing step 202, the server first obtains the preset area size, divides the target area according to the area size to obtain each sub-area; then for each sub-area, the following operations are performed: determining the position range information corresponding to the sub-area, and based on the position range information, calling the map service interface multiple times until the total number of points of interest feedback by the map service interface each time is lower than the set value, and obtaining each point of interest and the position indication information of each point of interest obtained by each call to the map server interface.

[0137] It should be noted that in the embodiments of the present application, considering that when starting multiple threads simultaneously to obtain points of interest, it is necessary to call the map service interface in parallel for data acquisition, which increases the call cost of the map service interface and is prone to duplicate data acquisition; based on this, optionally, the server can use the point of interest acquisition thread to execute the process of obtaining the position indication information of each point of interest, and after obtaining the distributed lock through the point of interest acquisition thread, execute the specific process of obtaining points of interest, where the present application can adopt the acquisition logic of the distributed lock in related technologies to obtain the distributed lock through the point of interest acquisition thread, and the present application does not make specific limitations on this.

[0138] For example, the server can be set to start the point of interest acquisition thread at a specified time, such as 2 am, and after obtaining the distributed lock through the point of interest acquisition thread, start to execute the process of obtaining the preset area size and dividing the target area according to the area size.

[0139] In this way, by obtaining a distributed lock, when obtaining each point of interest within the target area range and the position indication information of each point of interest, only one point of interest acquisition thread is used for information acquisition, which helps to reduce the number of interface calls and save processing resources.

[0140] In the embodiments of the present application, considering that when obtaining points of interest based on a request for a relatively large range, there may be omissions in the acquisition of points of interest. Therefore, the server can first divide the target area according to a preset area size to obtain each sub-range area obtained by the division. The specific size of the area size is set according to actual processing needs, and the present application does not make specific limitations on this; then, for each sub-range area, the map service interface is called multiple times based on the position range information of the sub-range area, and the points of interest within the sub-range area and the position indication information of the points of interest feedback by the map service interface multiple times are obtained, and finally all the points of interest within the sub-range area and the position indication information of the points of interest are obtained.

[0141] It should be noted that in the embodiments of the present application, when requesting to obtain each point of interest maintained by the map service with the help of the map service interface, in some feasible implementation manners, different types of points of interest can be obtained in sequence by means of the type information of the points of interest; in some other feasible implementation manners, the map service interface can be requested to feedback various types of points of interest without distinguishing the types of points of interest; among them, the type information of the points of interest in the map service is consistent with the type information of the points of interest used by the server.

[0142] For example, refer to Figure 3 As shown, it is a schematic diagram of the process of obtaining each point of interest in the embodiments of the present application. The following will describe the process of the server obtaining each point of interest in conjunction with the drawings:

[0143] Step 301: The server starts a point of interest acquisition thread and obtains a distributed lock through this point of interest acquisition thread.

[0144] Step 302: The server determines the target area range and cuts the target area range with a preset area size to obtain each sub-range area.

[0145] Step 303: The server reads a sub-range area.

[0146] Step 304: The server obtains a type of point of interest.

[0147] Step 305: The server calls the map service interface once based on the interface call keyword, based on the type information of the point of interest and the position range information of the sub-range area.

[0148] Among them, the interface call keyword refers to the keyword (key) used when calling the map service interface. In the embodiments of the present application, multiple interface call keywords can be registered in advance using multiple accounts for use when calling the map service interface.

[0149] Step 306: The server obtains each point of interest and the position indication information of each point of interest fed back by the map service interface, and stores each point of interest and the corresponding position indication information in the database.

[0150] Step 307: The server determines whether the total number of each point of interest obtained is lower than the set value. If so, it executes Step 308; otherwise, it executes Step 305.

[0151] Specifically, the server determines whether the total number of each point of interest obtained in a single time is lower than the set value. When it is determined to be lower than the set value, it can be determined that no new point of interest information will be obtained. Among them, the value of the set value can be 1.

[0152] Step 308: The server determines whether all types of points of interest have been obtained. If so, it executes Step 309; otherwise, it executes Step 304.

[0153] Step 309: The server determines whether all sub-range areas have been read. If so, it executes Step 310; otherwise, it executes Step 303.

[0154] Step 310: The server obtains each point of interest within each sub-range area, and the position indication information of each point of interest.

[0155] Specifically, for each sub-range area, the server can respectively obtain the name information of each point of interest inside and the position indication information of each point of interest; based on this, for the target area range, the position indication information of all the points of interest covered can be obtained.

[0156] In this way, by dividing the target area range into small-range areas and obtaining points of interest for the split sub-range areas, a comprehensive acquisition of each point of interest within the target area range can be achieved.

[0157] In a feasible implementation manner, the server can perform statistical processing on each point of interest to obtain a statistical result, and perform statistical processing on each virtual entity to obtain statistical results of different types of virtual entities.

[0158] Specifically, the server can count the total number of points of interest included within the target area range, and for different types of virtual entities, count the total number of corresponding types of virtual entities within the target area range. Among them, since the virtual entity is constructed corresponding to the entity object, the type of the entity object is the type of the corresponding virtual entity; the attribution relationship between entity objects is also the attribution relationship between the corresponding virtual entities.

[0159] In this way, the server can count different types of virtual entities that need attention within the target area, as well as count each point of interest involved within the target area, so as to organize the data as a whole.

[0160] Step 203: The server performs the following operations on each target virtual entity whose location information is not included in each location indication information: determine each analysis index matching the type of the target virtual entity, determine the attribution relationship between the target virtual entity and other virtual entities, and based on the location difference between the location information of the target virtual entity and the location indication information of each point of interest, combined with the attribution relationship, obtain each analysis result corresponding to each analysis index.

[0161] When performing Step 203, the server calculates the analysis results for each target virtual entity that does not correspond to a point of interest. Among them, the server determines a virtual entity whose location information is not included in the location indication information of each point of interest as a target virtual entity that does not correspond to a point of interest. Moreover, the server can specifically start a result calculation thread, obtain a distributed lock through the result calculation thread, and then subsequently calculate each analysis result corresponding to each target virtual entity with the help of the result calculation thread.

[0162] The following takes the calculation of each analysis result corresponding to a single target virtual entity as an example to illustrate the relevant processing process:

[0163] The server needs to first determine each analysis index that matches according to the type of the target virtual entity. Among them, different types of target virtual entities may correspond to different analysis indexes.

[0164] For example, for a target virtual entity of the "building" type, the corresponding analysis indexes may be: the reachable time of each point of interest, the convenience index, etc.; for a target virtual entity of the "street, plot, community, and area" type, the corresponding analysis indexes may be: the average reachable time of each building covered for each point of interest, the average convenience index, etc.

[0165] After that, the server determines the attribution relationship between the target virtual entity and other virtual entities. Among them, this attribution relationship is used to limit the coverage relationship between virtual entities on the one hand, and on the other hand, to assist in determining the virtual entities that can affect the calculation of each analysis index of the target virtual entity.

[0166] Furthermore, when the server determines that there are basic virtual entities belonging to the target virtual entity based on the attribution relationship, for each basic virtual entity, the following operations are performed: determine the analysis indicators matching the basic virtual entity, and based on the location indication information of each point of interest and the location information of the basic virtual entity, obtain each analysis result corresponding to each analysis indicator of the basic virtual entity; then, based on each analysis result corresponding to each basic virtual entity, obtain each analysis result determined by the analysis indicators corresponding to the target virtual entity.

[0167] Specifically, considering that the analysis indicators corresponding to a large range of virtual entities are usually determined based on the analysis indicators of the most basic virtual entities, when the server determines that the target virtual entity covers a basic virtual entity, for each basic virtual entity covered by the target virtual entity and not covering a basic virtual entity, determine the analysis indicators corresponding to the basic virtual entity, and obtain each analysis result corresponding to each analysis indicator; then, based on each analysis result corresponding to each basic virtual entity, obtain each analysis result determined by the analysis indicators corresponding to the target virtual entity.

[0168] It should be noted that when calculating the analysis result corresponding to the analysis indicator, for the analysis indicator representing the reachable time of the point of interest, in some feasible implementation manners, based on the location information of the point of interest and the virtual entity, the map service interface can be called to directly obtain the distance information and time information obtained through route planning; in some other feasible implementation manners, based on the location information provided by the geometry class method, the org.locationtech.jts.geom.Geometry.distance function can be used, and the following formula can be adopted: from.distance(to)*2*3.1415926*6371008.7714 / 360 to calculate the spherical distance between two locations, where from.distance(to) refers to the distance between two geometric shapes. In the case of calculating the spherical distance between the target virtual entity and the point of interest, the two geometric shapes refer to the shapes of the target virtual entity and the point of interest; 3.1415926 is the value of π, and 6371008.7714 is the radius of the earth; then, according to the speed in different scenarios, the reachable time is calculated based on the spherical distance.

[0169] In addition, when calculating the analysis result corresponding to the analysis indicator of the target virtual entity, the consideration of the distance relationship with multiple points of interest may be involved. At this time, the corresponding data of the points of interest can be obtained and processed according to the preset calculation method of the analysis indicator result.

[0170] Refer to Figure 4 shown, which is a schematic diagram of the process of obtaining each analysis result in the embodiment of the present application. The following combines the attached Figure 4, taking the case where each basic virtual entity is covered in the target virtual entity as an example, the process involved in calculating each analysis result of the target virtual entity is described as follows:

[0171] Step 401: The server starts a result calculation thread and obtains a distributed lock through this result calculation thread.

[0172] Specifically, before calculating each analysis metric, obtaining a distributed lock through the started result calculation thread is to avoid multiple service instances initiating calculations simultaneously and wasting resources. The metric calculation only needs to be performed once by a certain instance.

[0173] Step 402: The server obtains a target virtual entity that is not the corresponding point of interest and determines each analysis metric corresponding to the target virtual entity.

[0174] Specifically, after the server determines each virtual entity according to the target area range, it determines the virtual entities that are not the corresponding points of interest within the target area range as the target virtual entities.

[0175] Step 403: The server obtains a basic virtual entity covered by the target virtual entity.

[0176] For example, metrics such as plots and streets are calculated by aggregating buildings. When determining the basic virtual entity, the MBRCover function under the database postgis specification can be used to determine the building as the basic virtual entity from the data table constructed based on the description data of each virtual entity.

[0177] Step 404: The server obtains an analysis metric of a basic virtual entity.

[0178] Specifically, the server needs to traverse and obtain each analysis metric. Moreover, the analysis metrics that need to be calculated can be combined into an array because the calculation processes of each analysis metric are relatively similar, and the calculation process of the analysis metric can be reused.

[0179] Step 405: The server obtains an analysis result corresponding to the current analysis metric based on the obtained point-of-interest data corresponding to the analysis metric and the description data of the basic virtual entity, and records the metric calculation status.

[0180] It should be noted that recording the metric calculation status is to skip certain metric calculations or recalculate when the calculation process of the analysis metric is interrupted or stopped and recalculated next time.

[0181] Step 406: The server stores the analysis result corresponding to the current analysis metric into the database.

[0182] Step 407: The server determines whether all the analysis metrics of the basic virtual entity have been obtained. If so, it proceeds to Step 408; otherwise, it proceeds to Step 404.

[0183] Step 408: The server determines whether all the basic virtual entities covered by the target virtual entity have been read. If so, it proceeds to Step 409; otherwise, it proceeds to Step 403.

[0184] Step 409: The server determines the analysis results of the target virtual entity under each analysis metric based on the analysis results of each basic virtual entity.

[0185] Step 410: The server stores the analysis results of the target virtual entity under each analysis metric in the database and records the metric calculation status of the target virtual entity under each analysis metric.

[0186] It should be noted that recording the metric calculation status is for the purpose of being able to skip certain metric calculations or recalculate when the calculation process of the analysis metric is interrupted or stopped, and then recalculating next time.

[0187] In this way, when the target virtual entity covers each basic virtual entity, by integrating the analysis results of each basic virtual entity, the analysis results of the target virtual entity under each analysis metric can be obtained, enabling the effective calculation of the analysis results of the target virtual entity.

[0188] Specifically, when the server determines that there are no basic virtual entities belonging to the target virtual entity based on the attribution relationship between the target virtual entity and other virtual entities, the server can, according to the location indication information of each point of interest and the location information of the target virtual entity, determine, among each point of interest, the target points of interest that satisfy the preset location difference condition with the target virtual entity; and then, based on the location difference between each target point of interest and the target virtual entity, obtain each analysis result corresponding to each analysis metric.

[0189] Specifically, when the target virtual entity does not cover the basic virtual entity, that is, when the target virtual entity itself is the basic virtual entity, the server can screen the points of interest for the target virtual entity according to the distance difference between each point of interest and the target virtual entity, so that the points of interest within a certain distance range from the target virtual entity can participate in the calculation of the analysis metric; then, according to the preset result calculation method of the analysis metric, based on the location difference between the screened points of interest and the target virtual entity, obtain each analysis result corresponding to each analysis metric, where the analysis metric is related to the arrival time or distance between the point of interest and the target virtual entity.

[0190] It should be noted that since there may be multiple interest points of the same type among the interest points, assuming the analysis index is: the reachable time between the target virtual entity and an interest point of a certain type. Then, the determined shortest reachable time can be determined as the reachable time between the target virtual entity and this type of interest point; or, the average value of the determined reachable times can be determined as the reachable time between the target virtual entity and this type of interest point.

[0191] In this way, according to whether the target virtual entity covers the basic virtual entity, two feasible calculation methods for the results of the analysis index can be obtained, enabling effective calculation of various analysis indexes related to the distance based on the distance between the target virtual entity and each interest point when the target virtual entity is the basic virtual entity; moreover, with the help of various analysis indexes, an effective analysis of the relationship between the target virtual entity and the interest point can be achieved.

[0192] In addition, in the embodiments of the present application, when the described data further includes the status information and statistical information of the entity object, after the server obtains the location indication information of each interest point within the target area range, the following operations can also be performed for each interest point: determine the statistical index matching the type of the interest point, and according to the location indication information of the interest point and the location information of each virtual entity, combined with the status information and statistical information of each virtual entity, determine the statistical result corresponding to the statistical index. Then, according to the difference in the statistical results of the same type of interest points under the corresponding statistical index, obtain the abnormal determination result of each interest point under the corresponding statistical index.

[0193] Specifically, the server can determine the statistical indexes matching each type of interest point from the perspective of each interest point, and obtain the statistical results corresponding to each interest point or each type of interest point for the corresponding statistical index, where the number of statistical indexes is at least one. After calculating the statistical results for each interest point, the abnormal determination result of each interest point under the corresponding statistical index can be obtained according to the difference in the statistical results of the same type of interest points under the corresponding statistical index.

[0194] For example, assuming that for an interest point - Kindergarten A, the determined corresponding statistical index is: population coverage rate; then, a population evaluation range can be determined with the center point position of Kindergarten A as the center and a specified length as the radius, and based on the population statistical information associated with each virtual entity within this population evaluation range, determine the total population within this population evaluation range, and use the determined total population as the coverage rate, where the specified length can specifically be the walking length of M minutes, or directly measured by N meters, and the values of M and N are set according to actual processing needs.

[0195] For another example, assume that for a type of point of interest - kindergarten, the corresponding statistical indicator is determined to be population coverage rate. Then, the population coverage rate corresponding to each kindergarten within the target area can be calculated, and the average value of the population coverage rates can be used as the population coverage rate of the kindergarten.

[0196] For example, assume that there are 5 kindergartens within the target area, and the population coverage rates are 2500, 2300, 3200, 500, and 2700 respectively. Then, it is obvious that the population coverage rate of the fourth kindergarten is much lower than the average population coverage rate. At this time, it can be determined that the population coverage rate of the fourth kindergarten is abnormal, and the location planning of the fourth kindergarten is unreasonable.

[0197] In this way, from the perspective of points of interest, the corresponding statistical indicators for each point of interest can be determined, enabling the radiation situation of each point of interest for various consideration indicators to be determined within the target area. The points of interest can be evaluated and judged based on the statistical results of the points of interest, and the rationality of the deployment of the points of interest can be effectively evaluated and judged.

[0198] Step 204: The server obtains the abnormal determination result of each target virtual entity under the corresponding analysis indicators according to the difference in the analysis results of the target virtual entities of the same type under the corresponding analysis indicators.

[0199] In the embodiment of the present application, after obtaining the respective analysis results corresponding to each target virtual entity, the abnormal determination of each target virtual entity can be performed based on the respective analysis results corresponding to each target virtual entity.

[0200] Specifically, considering that the target virtual entities of the same type correspond to the same type of analysis indicators, when the server determines the abnormal situation of the target virtual entity, it can combine the result difference between the target virtual entity and other target virtual entities of the same type under the same analysis indicator to determine the abnormal determination result of the target virtual entity under this analysis indicator.

[0201] It should be noted that when determining that a target virtual entity is abnormal under an analysis indicator, the following determination methods can be included but are not limited to: the analysis result of the target virtual entity under an analysis indicator is lower than the average value of the analysis results of the target virtual entities of the same type under this analysis indicator; or, the analysis result of the target virtual entity under an analysis indicator is higher than (or lower than) the preset result threshold for this analysis indicator.

[0202] In addition, in the embodiments of the present application, in order to ensure the rendering effect of the processed data on the client device subsequently, the server may configure different initial entity rendering methods for different types of virtual entities; moreover, for each type of target virtual entity, the following operations are performed: determining the corresponding analysis metrics for each virtual entity of the same type, and for each analysis metric, setting the corresponding entity abnormal rendering method for each virtual entity according to the value ranges of the analysis results under the corresponding analysis metric.

[0203] Specifically, according to actual processing requirements, the server may set the rendering methods of each virtual entity when initially rendering the virtual entities. Among them, the initially set rendering methods may constrain the rendering position, rendering color, rendering shape, etc.; in a feasible implementation manner, when a virtual entity is rendered, it may be rendered by constructing a model corresponding to an entity object in the real world with the help of digital twin technology. At this time, the virtual entity is also called a twin; in addition, in order to reflect the differences in the analysis results among the same type of target virtual entities under the same analysis metric, for each type of target virtual entity, for each corresponding analysis metric, according to the value range of the analysis metric, the entity abnormal rendering method of the target virtual entity under this analysis metric is configured. Among them, the content constrained by the entity abnormal rendering method includes the rendering color, rendering position, and rendering shape, etc.

[0204] In this way, by setting the content display method, it is possible to personalize the rendering effects of each virtual entity within the target area according to actual processing requirements, and with the help of the differentially set entity abnormal rendering methods, it is possible to intuitively display the differences in the analysis results corresponding to the same type of target virtual entities under the same analysis metric on the client device.

[0205] In addition, in the embodiments of the present application, in the case where the described data includes the position information, status information, and statistical information of the entity object, after the server obtains the abnormal determination results of each target virtual entity under the corresponding analysis metrics, it may respond to the interest point planning request triggered by the client device for the sub-area within the target area, and obtain the target interest point type and interest point evaluation metric carried in the interest point planning request; then, based on the status information of each virtual entity in the sub-area, determine each candidate virtual entity that matches the interest point planning request in the sub-area, and for each candidate virtual entity, according to the position information of the candidate virtual entity and each other virtual entity in the sub-area, combined with the statistical information and status information of each other virtual entity, determine the evaluation result corresponding to the interest point evaluation metric; after that, based on each evaluation result, select the virtual entity to be planned that matches the interest point planning request among each candidate virtual entity.

[0206] Specifically, in a feasible implementation, the server can, in response to a point-of-interest planning request triggered by a client device for a sub-region within a target region range, determine a location within the sub-region where a point of interest can be deployed. When specifically implementing, candidate locations can be first selected, that is, each candidate virtual entity is determined, and then the candidate virtual entity with the best value of the corresponding point-of-interest evaluation metric is determined as the virtual entity to be planned. Based on this, the location of the virtual entity to be planned can be determined as the preferred location for deploying a point of interest of the target point-of-interest type.

[0207] It should be noted that the point-of-interest planning request obtained by the server carries the target point-of-interest type and the point-of-interest evaluation metric selected for the target point-of-interest type; when the server processes the point-of-interest planning request, it calculates candidate virtual entities that can be planned and deployed with points of interest based on the status information of the virtual entities in the sub-region.

[0208] For example, assume that the target point-of-interest type carried in the point-of-interest planning request is a kindergarten, the corresponding point-of-interest evaluation metric is population coverage rate, and the types of virtual entities include buildings, communities, streets, plots, and areas; then, among the virtual entities of the plot type, plots with a status information of idle status are selected as each candidate virtual entity. After that, the population coverage rate is calculated for each selected candidate virtual entity, and then the candidate virtual entity corresponding to the maximum population coverage rate is used as the selected virtual entity to be planned.

[0209] In this way, the server can, in response to the point-of-interest planning request, by means of the status information of each virtual entity maintained, and when meeting the evaluation requirements of the point-of-interest evaluation metric, through traversing the target virtual entities whose status information meets the requirements, efficiently calculate the deployment location of the point of interest, obtain a planning result that meets the planning requirements, and improve the data processing effect.

[0210] Next, in combination with the accompanying drawings, the data processing process will be described from the perspective of the client device:

[0211] Refer to Figure 5A shown, which is a schematic diagram of the data processing process of the client device in an embodiment of the present application. Next, in combination with the attached Figure 5A , the relevant data processing process will be described:

[0212] Step 501: In response to a data processing instruction triggered for a target region range, the client device sends the description data of each entity object within the target region range and the attribution relationship between different types of entity objects to the server, where the description data at least includes the location information of the entity object.

[0213] In the embodiments of the present application, the client device may, in response to a data processing instruction triggered by the user on an operable page for a target area range, send the description data of each entity object within the target area range uploaded by the user, as well as the attribution relationships between different types of entity objects, to the server. Specifically, the operable page may belong to any one of a client application, a web application, or a mini-program application. Based on this, the client device allows the user to trigger processing, upload data, and view data on the client application, web application, or mini-program application.

[0214] For example, refer to Figure 5B As shown, it is a schematic diagram of a page for triggering a data processing instruction for a target area range in the embodiments of the present application. After the client device inputs or selects the target area range, according to the user's upload instruction, it obtains partial description data of each entity object that needs to be concerned within the target area range sorted out by the user; then, in response to the user's Figure 5B "New Project" operation (i.e., the data processing instruction triggered for the target area range) triggered within the operable page shown, it sends the target area range indicated by the user, as well as the description data of each entity object within the target area range and the attribution relationships between each entity object, to the server.

[0215] In a feasible implementation manner, the client device may, in response to a content overview instruction triggered for a target area range in the content display page, display the statistical results of each point of interest within the target area range and the statistical results of different types of virtual entities obtained from the server in the content display area of the content display page.

[0216] For example, refer to Figure 5C As shown, it is a schematic diagram of the overview content presented in the embodiments of the present application. According to Figure 5C the content shown, the client device, in response to the trigger instruction for the "Spatial Twin Information Library", presents the area indicated by the target area range in the content display area, and presents the statistical results of various points of interest and the statistical results of various virtual entities, where the types of virtual entities are the same as the types of the corresponding entity objects.

[0217] Step 502: The client device, in response to a processing progress presentation instruction triggered for a target virtual entity in the content display page, displays the processing progress of each analysis result of a target virtual entity obtained from the server in the content display area.

[0218] Specifically, the client device, in response to a processing progress presentation instruction triggered for a target virtual entity in the content display page, presents the processing progress of each analysis result of the target virtual entity in the content display area of the content display page.

[0219] For example, referring to Figure 5D as shown, which is a schematic diagram of the processing progress of each analysis result presented in the embodiment of the present application. According to the Figure 5D content shown, in response to the user's trigger instruction for "reachable full workflow", the client device presents the processing progress of each analysis result related to a target virtual entity within the content display area on the content display page.

[0220] Step 503: In response to the abnormal presentation instruction triggered on the content display page, the client device displays the abnormal determination results of each target virtual entity under the corresponding analysis metrics obtained from the server in the content display area.

[0221] Specifically, in response to the abnormal presentation instruction triggered on the content display page, the client device displays the abnormal determination results of each target virtual entity determined by the server under the respective analysis metrics in the content display area.

[0222] For example, referring to Figure 5E as shown, which is a schematic diagram of the presentation of the abnormal determination results in the embodiment of the present application. When the user triggers the abnormal result display instruction, the abnormal determination results of each virtual entity under the corresponding analysis metrics as shown in Figure 5E can be presented. Among them, the types of virtual entities include many, such as building, land, community, subdistrict, zone.

[0223] In addition, the client can, in response to the data upload instruction triggered on the content display page, send the collected data to the server so that the server determines the statistical information and status information of each entity object based on the obtained data.

[0224] For example, referring to Figure 5F as shown, which is a schematic diagram of the process of uploading custom data in the embodiment of the present application. According to the Figure 5F content shown, in response to the user's custom metric configuration instruction on the page associated with "custom upload sub-table data", the data content selected by the user for upload is uploaded to the server, where the uploaded content can be the statistical information and status information of each entity object. In addition, in response to the user's preview original table instruction on the associated page, the custom metrics related to the twin (or virtual entity) can be presented. Among them, according to the Figure 5F content shown, Figure 5FThe ID therein can specifically be the ID of an entity object, or, it can be the ID of a virtual entity constructed for the entity object; the ID of the entity object and the ID of the corresponding virtual entity can be kept consistent; in a feasible implementation manner, the custom metrics may include analysis metrics related to points of interest, and moreover, in the case where the custom metrics uploaded by the client include the target analysis metrics for server calculation, it can be indicated that the server does not perform the result calculation for the target analysis metrics.

[0225] In this way, it is possible to upload the result data of the custom metrics to the server according to the custom needs, thus being able to provide more data for the server to calculate based on.

[0226] In addition, in a feasible implementation manner, the client can, in response to a content presentation instruction triggered for a target area range, obtain from the server the initial entity presentation modes configured for different types of virtual entities, and present each virtual entity according to the obtained initial entity presentation modes; furthermore, in response to a trigger operation for a target analysis metric, it can obtain the entity abnormal presentation modes of each same-kind target virtual entity configured by the server for the target analysis metric, and adjust the presentation modes of each same-kind target virtual entity from the initial presentation modes to the corresponding entity abnormal presentation modes.

[0227] For example, referring to Figure 5G shown, it is a schematic diagram of the content presentation effect in an embodiment of the present application. According to the content Figure 5G indicated in the schematic diagram, the client device, in response to the effect presentation instruction triggered by the user for the target area range, displays Figure 5G each virtual entity shown in the schematic diagram, and displays various types of calculated metrics.

[0228] In this way, in response to the content presentation instruction triggered by the user, it is possible to obtain the content to be presented and the presentation mode of the content from the server, so that it is possible to present each virtual entity in the target area range as a whole, and present the metrics used to trigger the viewing of detailed content.

[0229] Furthermore, in the presented content, the client device, in response to a detailed display instruction triggered for a target metric of a virtual entity, obtains from the server the analysis result determined corresponding to the target metric, and the description data of a virtual entity; then, on the detailed display page, it presents the processing result corresponding to the target metric and partial description data, where the detailed display page can be a window page on top of the presented content.

[0230] Specifically, based on the user's trigger display for the presented virtual entity and target metrics, the client device can present the processing results of the virtual entity under the target metrics and some description data related to the virtual entity on the details display page, where the details display page can be a window page on the content presentation page.

[0231] For example, refer to Figure 5H As shown, it is a schematic diagram of the result presentation of the virtual entity under the target metrics in the embodiment of the present application. According to Figure 5H the content shown, assuming that for the twin with twin ID 19608 and the resident population quantity, a details display instruction is triggered, then the type, name, and resident population quantity of the twin are displayed. Among them, when the virtual entity is in a three-dimensional environment, the virtual entity can also be called a twin, which can be specifically understood as a twin entity in the three-dimensional space corresponding to the entity object in the real world.

[0232] In this way, it is possible to specifically display the details content of the specified virtual entity under the target metrics, enabling the user to better view various processing results within the target area range on the client device.

[0233] In a feasible implementation manner of the present application, the client device provides a function for triggering interest point planning for the user. When the user determines a sub-region where it is necessary to deploy interest points within the target area range by viewing the anomaly discrimination result, the client device responds to the interest point planning instruction triggered for the sub-region range within the target area range, and sends an interest point planning request carrying the target interest point type and interest point evaluation metrics to the server; then, receives the virtual entity to be planned feedback by the server and identifies the virtual entity to be planned among the presented virtual entities.

[0234] Specifically, when the user triggers an interest point planning instruction based on the selected sub-region on the specified page, the client device triggers an interest point planning request to the server and can obtain the virtual entity to be planned feedback by the server. Furthermore, it can Figure 5G highlight the virtual entity to be planned in the content presentation result shown, where the highlighting methods include but are not limited to flashing marks, color deepening, etc.

[0235] In this way, the client device can respond to the user's planning requirements, efficiently obtain the planning result from the server, and can intuitively present the planning result.

[0236] The following will take the processing of data involved in the urban planning process as an example in combination with specific application scenarios to illustrate the relevant data processing process. In this scenario, one project corresponds to one target area range, and the target area range can specifically be Area X of City A; each point of interest is a supermarket, express delivery, barbershop, pharmacy, repair shop, restaurant, laundromat, park, bus stop, etc.; each type of entity object includes: buildings, communities, streets, plots, and areas; for the basic virtual entity - building, the matching analysis indicators include: medical rescue reach time, park and green space reach time, cultural facility reach time, fire rescue reach time, primary school reach time, kindergarten reach time, subway station reach time, bus stop reach time, elderly service reach time, and convenient commercial service index, etc.

[0237] The description data of each entity object in Area X of City A obtained by the server from the client device, also known as twin data, is saved in the form of a data table.

[0238] For example, refer to Figure 6 As shown, it is a schematic diagram of the information type in the data table in the embodiment of the present application. According to the appendix Figure 6 It can be known that the server and the client device have agreed on the data types included in the data table, and for each defined data type, the content form specifically shown in the Figure 6 table can be obtained.

[0239] In addition, the server can obtain custom metrics calculated externally for each entity object. Among them, each custom metric is reported to the server in the form of a table; the content format in the table refers to: building / plot / street / area id or spatial location information, custom metric 1, custom metric 2, etc.

[0240] After that, the server can use the calculation results under the custom data reported by external devices (such as client devices) as statistical information in the description data to achieve the fusion of the description data.

[0241] Specifically, if there is a twin ID in the data table uploaded by the external device, it can be directly associated; if there is no twin ID, the matching situation between the location information in the data table uploaded by the external device and the location information in the maintenance data table is required, and the results calculated externally for the custom metrics are associated with the twin.

[0242] For example, if the location information of twin A covers or is adjacent to a target spatial location information recorded in the externally uploaded data table, then twin A can be associated with the index calculation results corresponding to the target spatial location information.

[0243] After that, the server can pull each point of interest in Area X of City A to obtain the name and location indication information of each point of interest; furthermore, for each twin of the non-corresponding point of interest, the analysis results under various analysis metrics can be calculated.

[0244] Taking the analysis metric of the medical rescue or fire rescue reachable time as an example, when calculating this analysis metric, the shortest time for a medical facility and a fire facility POI to drive to a building or plot can be calculated.

[0245] For example, based on the central position of the twin, the central position of the hospital, and the specified driving mode, the map service interface can be called. The relevant call form is like "https: / / apis.map.qq.com / ws / direction / v1 / driving / ?from=39.915285,116.403857&to=39.915285,116.803857&output=json&callback=cb&key=" to obtain the driving time obtained according to the driving route planning algorithm provided by the map service.

[0246] Refer to Table 1 below, which is a schematic table of the information fed back after calling the map service interface in the embodiments of the present application. According to the content shown in Table 1, when calling the map service interface to obtain the feedback result in the driving situation based on the location information of the twin and the location information of the point of interest, the total distance and the estimated time in the driving situation can be obtained; furthermore, based on the obtained total distance and estimated time, the reachable time of medical rescue or fire rescue can be determined.

[0247] Table 1

[0248]

[0249] Similarly, when considering the reachable time in the walking situation, the map service interface can be called in the same way to obtain the estimated time of the solution in the walking situation, and the obtained estimated time of the solution can be determined as the reachable time.

[0250] Optionally, in the case where only a rough calculation of the reachable time is required, after calculating the spherical distance between two location points, the reachable time can be calculated based on the determined spherical distance and the speeds in different scenarios.

[0251] Specifically, when calculating the processing result of the analysis index - the convenient commercial service index of a basic twin (i.e., a building), the area within 1000 meters from the basic twin or the area that can be reached within a 15-minute walk can be determined as the consideration range of convenient points of interest. Among them, the spherical distance between the basic twin and the point of interest can be determined with the help of the st_distance result of the postgis function, and the points of interest with a spherical distance greater than 1000m are directly discarded.

[0252] After that, a decay function is used to calculate the utility y of each point-of-interest facility (y = 1 - x / 1000, 0 ≤ x ≤ 1000m), where y is the utility of the point-of-interest facility and x is the distance. The utilities of the point-of-interest facilities are weighted according to the quantity of each type of facility; the convenient commercial service index is used to measure the convenience degree of a building to reach different types of point-of-interest facilities.

[0253] For example, the weight distribution is 5 for housekeeping, 5 for express delivery, 3 for laundry, 10 for haircuts, 5 for repairs, 10 for pharmacies, 5 for supermarkets, and 20 for catering. Then, the utilities of various types of point-of-interest facilities are weighted (including eight types of facilities: supermarkets, express delivery facilities, barbershops, laundries, pharmacies, repair points, housekeeping stores, and restaurants, and the weights of each type of facility can be adjusted), and the convenient commercial service index of each building is calculated. Finally, the index is sum(sum(y)*weight). The larger the value, the better the representation of convenience.

[0254] Another example, assume that within a range of 1000m from Building 1, there are three types of points of interest, namely, a supermarket, an express delivery, and a barbershop; assume that Building 1 is 800 meters away from the supermarket, 600 meters away from a certain express delivery, and 400 meters away from a certain barbershop. Then, its convenient commercial service facility index is (1 - 800 / 1000)*5 + (1 - 600 / 1000)*5 + (1 - 400 / 1000)*10.

[0255] The analysis results corresponding to plots, streets, areas, etc. are usually calculated from the average value accumulated from the analysis results of the buildings included in the spatial access.

[0256] At the same time, the server can calculate coverage rate indicators related to people for the points of interest, such as bus stop coverage rate, elderly service coverage rate, kindergarten coverage rate, etc. When the client device imports building and people-related data and binds them to each twin, the coverage rate of relevant indicators can be calculated. Taking the kindergarten coverage rate as an example, with the central position of the kindergarten as the center point, assuming that the target range is calculated as within a 15-minute walk or 1000 meters, then the number of people on each building within this target range is the coverage rate.

[0257] After that, the server can analyze the differences in the analysis results of each basic twin (or basic virtual entity) in different types of analysis metrics, and obtain the anomaly determination results of the type twin under the corresponding analysis metrics. Based on this, when presenting the data content, the server can set the value ranges of different analysis results and identify them with different colors, so as to visually present the anomalies of the twin under different analysis metrics. In addition, it is possible to analyze the result distribution of any one analysis metric for buildings, plots, streets or areas, and determine the adjustment direction of urban planning according to the result distribution.

[0258] For example, according to the user's needs, if it is determined that the accessible time to the park green space in a certain area is relatively long, it means that there is a lack of park green space construction. Then, location selection recommendations can be planned to reduce the accessible time to the park green space.

[0259] For another example, after the user selects an area to be improved (usually a street or a larger administrative region), selects to build one or more schools, and then selects whether to prioritize accessible time or the number of covered people, that is, selects the point of interest evaluation metric. Then, the server can traverse these plots according to the idle plots and land use properties within the selected area, and then calculate the accessible time or the number of covered people from all buildings to this school when the school is placed in this plot. Combining with the recommended algorithm selected by the user, it decides where the school is most suitable to be built and marks the location with a prominent color.

[0260] For another example, various indices can be compared with other cities or streets, such as the convenience service business index, to evaluate which urban area or street layout is more reasonable; in addition, through interface display and analysis, the areas and metrics that need to be improved can be marked.

[0261] In this way, this application can utilize Internet data, combined with map service interfaces, geographic location information technology, AI and other technologies, to automatically analyze the industrial layout and convenience service index of the city; moreover, it can intelligently recommend the deployment locations of points of interest, which not only helps to improve the rationality of the urban industrial layout, but also helps to improve the convenience service index, etc., providing reference data processing results for urban planning.

[0262] Based on the same inventive concept, refer to Figure 7 As shown, it is a schematic logical structure diagram of a data processing device in an embodiment of this application. The data processing device 700 includes a first acquisition unit 701, a second acquisition unit 702, an execution unit 703, and a determination unit 704, where,

[0263] The first acquisition unit 701 is configured to acquire the description data of each entity object within the target area sent by the client device, as well as the attribution relationships between different types of entity objects, and construct each virtual entity corresponding to each entity object, where the description data includes the location information of the entity object;

[0264] The second acquisition unit 702 is configured to acquire the location indication information of each point of interest within the target area;

[0265] The execution unit 703 is configured to perform the following operations for each target virtual entity whose location information is not included in the location indication information of each point of interest: determine each analysis index matching the type of the target virtual entity, determine the attribution relationship between the target virtual entity and other virtual entities, and based on the location difference between the location information of the target virtual entity and the location indication information of each point of interest, combined with the attribution relationship, obtain each analysis result corresponding to each analysis index;

[0266] The determination unit 704 is configured to obtain the anomaly determination result of each target virtual entity under the corresponding analysis indexes according to the difference in the analysis results of the target virtual entities of the same type under the corresponding analysis indexes.

[0267] Optionally, the description data further includes the status information and statistical information of the entity object. After obtaining the anomaly determination result of each target virtual entity under the corresponding analysis indexes, the apparatus further includes a response unit 705, and the response unit 705 is configured to:

[0268] In response to an interest point planning request triggered by the client device for a sub-region within the target area, obtain the target interest point type and interest point evaluation index carried in the interest point planning request;

[0269] Based on the status information of each virtual entity in the sub-region, determine each candidate virtual entity matching the interest point planning request in the sub-region, and for each candidate virtual entity, according to the location information of the candidate virtual entity and each other virtual entity in the sub-region, combined with the statistical information and status information of each other virtual entity, determine the evaluation result corresponding to the interest point evaluation index;

[0270] Based on each evaluation result, select the virtual entity to be planned matching the interest point planning request among each candidate virtual entity.

[0271] Optionally, the description data further includes the status information and statistical information of the entity object. After obtaining the location indication information of each point of interest within the target area, the execution unit 703 of the apparatus is further configured to:

[0272] For each point of interest, perform the following operations: determine a statistical metric that matches the type of the point of interest, and based on the location indication information of the point of interest and the location information of each virtual entity, combine the status information and statistical information of each virtual entity, and determine the statistical result corresponding to the statistical metric;

[0273] Based on the difference in the statistical results of points of interest of the same type under the corresponding statistical metrics, obtain the anomaly determination result of each point of interest under the corresponding statistical metric.

[0274] Optionally, when obtaining the description data of each entity object within the target area sent by the client device, the execution unit 703 is used for:

[0275] Obtain the status information and location information sent by the client device for each entity object within the target area, and obtain the statistical information sent by other client devices for each entity object;

[0276] Determine the status information, location information, and statistical information of each entity object as the description data of each entity object.

[0277] Optionally, the apparatus further includes a configuration unit 706, and the configuration unit 706 is used for:

[0278] Configure different initial entity presentation methods for different types of virtual entities;

[0279] For each type of target virtual entity, perform the following operations: determine the corresponding analysis metrics for each virtual entity of the same type, and for each analysis metric, set the corresponding entity anomaly presentation method for each virtual entity of the same type according to the value ranges of the analysis results under the corresponding analysis metric.

[0280] Optionally, when obtaining the location indication information of each point of interest within the target area, the second obtaining unit 702 is used for:

[0281] Obtain the preset area size, and divide the target area range according to the area size to obtain each sub-range area;

[0282] For each sub-range area, perform the following operations: determine the corresponding location range information of the sub-range area, and based on the location range information, call the map service interface multiple times until the total number of points of interest feedback by the map service interface once is lower than the set value, and obtain each point of interest and the location indication information of each point of interest obtained by each call to the map server interface.

[0283] Optionally, before obtaining the preset area size, the second obtaining unit 702 is further used for:

[0284] Start a point of interest acquisition thread, and after obtaining a distributed lock through the point of interest acquisition thread, perform the processing procedure;

[0285] After obtaining the position indication information of each point of interest within the target area, before performing an operation on each target virtual entity whose position information is not included in the position indication information, it further includes:

[0286] Start a result calculation thread and obtain a distributed lock through the result calculation thread.

[0287] Optionally, when obtaining each analysis result corresponding to each analysis index according to the position difference between the target virtual entity and each point of interest and in combination with the attribution relationship, the execution unit 703 is configured to:

[0288] When determining that there are basic virtual entities belonging to the target virtual entity according to the attribution relationship, for each basic virtual entity, perform the following operations: determine each analysis index matching the basic virtual entity, and according to the position indication information of each point of interest and the position information of the basic virtual entity, obtain each analysis result corresponding to each analysis index of the basic virtual entity;

[0289] Based on the analysis results corresponding to each basic virtual entity, obtain each analysis result determined by each analysis index corresponding to the target virtual entity.

[0290] Optionally, when determining that there are no basic virtual entities belonging to the target virtual entity according to the attribution relationship, the execution unit 703 is further configured to:

[0291] According to the position indication information of each point of interest and the position information of the target virtual entity, determine each target point of interest that satisfies the preset position difference condition with the target virtual entity among each point of interest;

[0292] Based on the position difference between each target point of interest and the target virtual entity, obtain each analysis result corresponding to each analysis index.

[0293] Based on the same inventive concept, refer to Figure 8 as shown Figure 8 which is a schematic logical structure diagram of another data processing device in the embodiment of the present application. The data processing device 800 includes a data sending unit 801, a progress display unit 802, and an exception display unit 803, where

[0294] The data sending unit 801 is configured to, in response to a data processing instruction triggered for the target area range, send the description data of each entity object within the target area range and the attribution relationship between different types of entity objects to the server, where the description data at least includes the position information of the entity object;

[0295] A progress display unit 802, configured to display, in a content display area, the processing progress of analysis results of a target virtual entity obtained from a server in response to a processing progress presentation indication triggered for the target virtual entity in a content display page;

[0296] An exception display unit 803, configured to display, in the content display area, the exception determination results of each target virtual entity under corresponding analysis metrics obtained from the server in response to an exception presentation indication triggered for the target virtual entity in the content display page.

[0297] Optionally, the apparatus further includes a content presentation unit 804, and the content presentation unit 804 is configured to:

[0298] In response to a content presentation indication triggered for a target area range, obtain entity initial presentation manners configured for different types of virtual entities from the server, and present each virtual entity according to the obtained entity initial presentation manners;

[0299] In response to a trigger operation for a target analysis metric, obtain entity exception presentation manners of each same-type target virtual entity configured by the server for the target analysis metric, and adjust the presentation manners of each same-type target virtual entity from the initial presentation manners to the corresponding entity exception presentation manners.

[0300] Optionally, after displaying, in the content display area, the exception determination results of each target virtual entity under corresponding analysis metrics obtained from the server, the content presentation unit 804 is further configured to:

[0301] In response to an interest point planning indication triggered for a sub-area range in the target area range, send an interest point planning request carrying a target interest point type and an interest point evaluation metric to the server;

[0302] Receive a virtual entity to be planned fed back by the server, and identify the virtual entity to be planned among the presented virtual entities.

[0303] Optionally, after presenting each virtual entity, the content presentation unit 804 is further configured to:

[0304] In response to a detail display indication triggered for a target metric of a virtual entity, obtain an analysis result determined according to the target metric and description data of a virtual entity from the server;

[0305] Present the processing result corresponding to the target metric and partial description data on a detail display page.

[0306] Optionally, the data sending unit 801 is further configured to:

[0307] In response to a data upload instruction triggered on the content display page, the collected data is sent to the server so that the server determines the statistical information and status information of each entity object based on the obtained data.

[0308] For the convenience of description, the above parts are divided into respective modules (or units) according to functions and described separately. Of course, when implementing the present application, the functions of the respective modules (or units) can be implemented in the same or multiple software or hardware.

[0309] After introducing the data processing method and apparatus of the exemplary embodiments of the present application, next, an electronic device according to another exemplary embodiment of the present application is introduced.

[0310] Those skilled in the art can understand that various aspects of the present application can be implemented as a system, a method, or a program product. Therefore, various aspects of the present application can be specifically implemented in the following forms, namely: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or an implementation combining hardware and software aspects, which can be collectively referred to as "circuitry", "module", or "system" here.

[0311] Based on the same inventive concept as the above method embodiment, an electronic device is further provided in an embodiment of the present application. Refer to Figure 9 As shown, it is a schematic diagram of the hardware composition structure of an electronic device applying the embodiment of the present application. In one embodiment, the electronic device can be Figure 1 the server 120 shown. In this embodiment, the structure of the electronic device can be as Figure 9 shown, including a memory 901, a communication module 903, and one or more processors 902.

[0312] The memory 901 is used to store the computer program executed by the processor 902. The memory 901 may mainly include a program storage area and a data storage area. Among them, the program storage area may store an operating system and programs required to run the instant messaging function, etc.; the data storage area may store various instant messaging information and operation instruction sets, etc.

[0313] The memory 901 can be a volatile memory, such as a random-access memory (RAM); the memory 901 can also be a non-volatile memory, such as a read-only memory, a flash memory, a hard disk drive (HDD), or a solid-state drive (SSD); or the memory 901 is any other medium that can be used to carry or store a desired computer program in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory 901 can be a combination of the above memories.

[0314] The processor 902 can include one or more central processing units (CPUs) or be a digital processing unit, etc. The processor 902 is used to implement the above data processing method when calling the computer program stored in the memory 901.

[0315] The communication module 903 is used to communicate with the client device and the server.

[0316] In the embodiments of the present application, the specific connection medium between the above memory 901, communication module 903, and processor 902 is not limited. In the embodiments of the present application Figure 9 it is connected between the memory 901 and the processor 902 through a bus 904, and the bus 904 is described in thick lines in Figure 9 The connection manners between other components are only for illustrative purposes and are not to be taken as limiting. The bus 904 can be divided into an address bus, a data bus, a control bus, etc. For ease of description, Figure 9 only a thick line is used to describe it in

[0317] The computer storage medium is stored in the memory 901, and computer-executable instructions are stored in the computer storage medium. The computer-executable instructions are used to implement the data processing method of the embodiments of the present application. The processor 902 is used to execute the above data processing method, as Figure 2A shown.

[0318] In another embodiment, the electronic device can also be other electronic devices. Refer to Figure 10 shown, which is a schematic diagram of the hardware composition structure of another electronic device applying the embodiments of the present application. The electronic device can specifically be Figure 1 the client device 110 shown in Figure 10As shown, it includes components such as communication component 1010, memory 1020, display unit 1030, camera 1040, sensor 1050, audio circuit 1060, Bluetooth module 1070, processor 1080, etc.

[0319] The communication component 1010 is used to communicate with the server. In some embodiments, it may include a Wireless Fidelity (WiFi) module. The WiFi module belongs to short - range wireless transmission technology, and through the WiFi module, the electronic device can help users send and receive information.

[0320] The memory 1020 can be used to store software programs and data. The processor 1080 executes various functions and data processing of the client device 110 by running the software programs or data stored in the memory 1020. In this application, the memory 1020 can store the operating system and various application programs, and can also store the computer programs for executing the data - processing method of the embodiments of this application.

[0321] The display unit 1030 can also be used to display information input by the user or information provided to the user, as well as the graphical user interface (GUI) of various menus of the client device 110. Specifically, the display unit 1030 may include a display screen 1032 disposed on the front of the client device 110. The display unit 1030 can be used to display the operable pages and display pages in the embodiments of this application, etc.

[0322] The display unit 1030 can also be used to receive input digital or character information, generating signal inputs related to the user settings and function controls of the client device 110. Specifically, the display unit 1030 may include a touch screen 1031 disposed on the front of the client device 110, which can collect touch operations of the user on or near it.

[0323] Among them, the touch screen 1031 can cover the display screen 1032, or the touch screen 1031 and the display screen 1032 can be integrated to implement the input and output functions of the client device 110. After integration, it can be simply called a touch display screen. In this application, the display unit 1030 can display application programs and corresponding operation steps.

[0324] The camera 1040 can be used to capture static images, and the user can post comments on the images captured by the camera 1040 through an application. An object generates an optical image through a lens and projects it onto a photosensitive element. The photosensitive element can be a charge coupled device (CCD) or a complementary metal-oxide-semiconductor (CMOS) phototransistor. The photosensitive element converts the optical signal into an electrical signal, and then transmits the electrical signal to the processor 1080 to be converted into a digital image signal.

[0325] The client device may further include at least one sensor 1050, such as an acceleration sensor 1051, a distance sensor 1052, a fingerprint sensor 1053, and a temperature sensor 1054. The client device may also be configured with other sensors such as a gyroscope, a barometer, a hygrometer, a thermometer, an infrared sensor, a light sensor, and a motion sensor.

[0326] The audio circuit 1060, the speaker 1061, and the microphone 1062 can provide an audio interface between the user and the client device 110. The audio circuit 1060 can transmit the electrical signal converted from the received audio data to the speaker 1061, and the speaker 1061 converts it into a sound signal for output. On the other hand, the microphone 1062 converts the collected sound signal into an electrical signal, which is received by the audio circuit 1060, converted into audio data, and then the audio data is output to the communication component 1010 to be sent to, for example, another client device 110, or the audio data is output to the memory 1020 for further processing.

[0327] The Bluetooth module 1070 is used to interact with other Bluetooth devices having Bluetooth modules through the Bluetooth protocol.

[0328] The processor 1080 is the control center of the client device, connecting various parts of the entire terminal using various interfaces and lines. By running or executing software programs stored in the memory 1020 and calling data stored in the memory 1020, it performs various functions of the client device and processes data. In some embodiments, the processor 1080 may include at least one processing unit; the processor 1080 may also integrate an application processor and a baseband processor. In this application, the processor 1080 can run an operating system, application programs, user interface display and touch response, as well as the data processing method of the embodiments of this application. Additionally, the processor 1080 is coupled to the display unit 1030.

[0329] In some possible embodiments, various aspects of the data processing method provided in this application can also be implemented in the form of a program product, which includes a computer program. When the program product runs on an electronic device, the computer program is used to cause the electronic device to execute the steps in the data processing method according to various exemplary embodiments of this application described above in this specification. For example, the electronic device can execute steps such as Figure 5A shown in

[0330] The program product can adopt any combination of one or more readable media. The readable media can be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (non-exhaustive list) of the readable storage medium include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0331] The program product of the embodiments of this application can adopt a portable compact disk read-only memory (CD-ROM) and include a computer program, and can run on an electronic device. However, the program product of this application is not limited to this. In this document, the readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with a command execution system, apparatus, or device.

[0332] The readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, which carries a readable computer program. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The readable signal medium can also be any readable medium other than the readable storage medium, which can send, propagate, or transmit a program for use by or in conjunction with a command execution system, apparatus, or device.

[0333] The computer program contained on the readable medium can be transmitted by any suitable medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the above.

[0334] The computer program for performing the operations of this application can be written in any combination of one or more programming languages, including object-oriented programming languages such as Java, C++, etc., and also including conventional procedural programming languages such as the "C" language or similar programming languages. The computer program can be executed entirely on the user's electronic device, partially on the user's electronic device, executed as an independent software package, partially on the user's electronic device and partially on a remote electronic device, or entirely on a remote electronic device or server. In the case of a remote electronic device, the remote electronic device can be connected to the user's electronic device through any type of network including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external electronic device (for example, by connecting through the Internet using an Internet service provider).

[0335] It should be noted that although several units or subunits of the device are mentioned in the above detailed description, this division is merely exemplary and not mandatory. In fact, according to the embodiments of this application, the features and functions of the two or more units described above can be embodied in one unit. Conversely, the features and functions of one unit described above can be further divided and embodied by multiple units.

[0336] In addition, although the operations of the method of this application are described in a specific order in the drawings, this does not require or imply that these operations must be performed in that specific order, or that all the operations shown must be performed to achieve the desired result. Additionally or alternatively, some steps can be omitted, multiple steps can be combined into one step for execution, and / or one step can be decomposed into multiple steps for execution.

[0337] Those skilled in the art should understand that the embodiments of this application can be provided as a method, a system, or a computer program product. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable computer programs.

[0338] This application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processors of general-purpose computers, special-purpose computers, embedded processors, or other programmable data servers to generate a machine, such that the instructions executed by the processors of the computer or other programmable data servers generate means for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or means for implementing the functions specified in multiple blocks.

[0339] Although the preferred embodiments of the present application have been described, those skilled in the art can make additional changes and modifications once they learn the basic creative concepts. Therefore, the appended claims are intended to be construed to include the preferred embodiments as well as all changes and modifications falling within the scope of the present application.

[0340] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalent technologies, the present application is also intended to include these changes and modifications.

Claims

1. A data processing method, characterized in that, Applied to a server, including: Obtain the respective description data of each entity object within the target area range sent by the client device, as well as the attribution relationships between different types of entity objects, and construct respective virtual entities corresponding to the respective entity objects, where the description data includes the location information of the entity object; Obtain the location indication information of each point of interest within the target area range; For each target virtual entity whose location information is not included in the respective location indication information, perform the following operations: determine the respective analysis metrics that match the type of the target virtual entity, determine the attribution relationships between the target virtual entity and other virtual entities, and based on the location differences between the location information of the target virtual entity and the location indication information of the respective points of interest, combined with the attribution relationships, obtain respective analysis results corresponding to the respective analysis metrics; Based on the differences in the analysis results of the target virtual entities of the same type under the respective corresponding analysis metrics, obtain the anomaly determination results of each target virtual entity under the respective corresponding analysis metrics.

2. The method according to claim 1, characterized in that, The description data further includes the status information and statistical information of the entity object. After obtaining the anomaly determination results of each target virtual entity under the respective corresponding analysis metrics, it further includes: In response to an interest point planning request triggered by the client device for a sub-region within the target area range, obtain the target interest point type and interest point evaluation metrics carried in the interest point planning request; Based on the status information of the respective virtual entities in the sub-region, determine the respective candidate virtual entities that match the interest point planning request in the sub-region, and for each candidate virtual entity, based on the location information of the candidate virtual entity and the respective other virtual entities in the sub-region, combined with the statistical information and status information of the respective other virtual entities, determine the evaluation results corresponding to the interest point evaluation metrics; Based on the respective evaluation results, select the virtual entity to be planned that matches the interest point planning request among the respective candidate virtual entities.

3. The method according to claim 1, characterized in that The description data further includes the status information and statistical information of the entity object. After obtaining the location indication information of each point of interest within the target area range, the method further includes: For each point of interest, perform the following operations: determine the statistical metrics that match the type of the point of interest, and based on the location indication information of the point of interest and the location information of the respective virtual entities, combined with the status information and statistical information of the respective virtual entities, determine the statistical results corresponding to the statistical metrics; Based on the differences in the statistical results of the points of interest of the same type under the respective corresponding statistical metrics, obtain the anomaly determination results of each point of interest under the respective corresponding statistical metrics.

4. The method according to claim 3, wherein The obtaining the respective description data of each entity object within the target area range sent by the client device includes: Obtain the status information and location information sent by the client device for each entity object within the target area range, and obtain the statistical information sent by other client devices for the respective entity objects; Determine the status information, location information, and statistical information of the respective entity objects as the respective description data of the respective entity objects.

5. The method according to claim 1, wherein Also includes: Configure different initial entity presentation methods for different types of virtual entities; For each type of target virtual entity, perform the following operations: Determine the corresponding analysis metrics for each virtual entity of the same type, and for each analysis metric, set the corresponding entity abnormal presentation method for each virtual entity of the same type according to each value range of the analysis results under the analysis metric.

6. The method according to claim 1, wherein The obtaining of the position indication information of each point of interest within the target area range includes: Obtain a preset area size, and divide the target area range according to the area size to obtain each sub-range area; For each sub-range area, perform the following operations: Determine the corresponding position range information of the sub-range area, and based on the position range information, call the map service interface multiple times until the total number of points of interest feedback by the map service interface each time is lower than the set value, and obtain each point of interest obtained by each call to the map server interface and the position indication information of each point of interest.

7. The method according to claim 6, wherein Before obtaining the preset area size, it further includes: Start a point of interest acquisition thread, and after obtaining a distributed lock through the point of interest acquisition thread, execute the processing process; After obtaining the position indication information of each point of interest within the target area range, before performing operations on each target virtual entity whose position information is not included in the position indication information, it further includes: Start a result calculation thread and obtain a distributed lock through the result calculation thread.

8. The method according to any one of claims 1-7, characterized in that, The obtaining of each analysis result corresponding to each analysis metric according to the position difference between the target virtual entity and each point of interest, in combination with the attribution relationship, includes: According to the attribution relationship, when it is determined that there are basic virtual entities belonging to the target virtual entity, for each basic virtual entity, perform the following operations: Determine the corresponding analysis metrics for the basic virtual entity, and according to the position indication information of each point of interest and the position information of the basic virtual entity, obtain each analysis result corresponding to the analysis metrics of the basic virtual entity; Based on the analysis results corresponding to each basic virtual entity, obtain each analysis result determined by the analysis metrics corresponding to the target virtual entity.

9. The method according to claim 8, wherein According to the attribution relationship, when it is determined that there are no basic virtual entities belonging to the target virtual entity, the method further includes: According to the position indication information of each point of interest and the position information of the target virtual entity, determine each target point of interest that satisfies the preset position difference condition with the target virtual entity among each point of interest; Based on the position difference between each target point of interest and the target virtual entity, obtain each analysis result corresponding to each analysis metric.

10. A data processing method, characterized in that, Applied to a client device, it includes: In response to a data processing instruction triggered for a target area range, send the description data of each entity object within the target area range and the attribution relationship between different types of entity objects to the server, where the description data at least includes the position information of the entity object; In response to a processing progress presentation indication triggered for a target virtual entity on a content display page, display the processing progress of each analysis result of the target virtual entity obtained from the server in the content display area; In response to an exception presentation indication triggered for a target virtual entity on the content display page, display the exception determination results of each target virtual entity under corresponding analysis metrics obtained from the server in the content display area.

11. The method according to claim 10, wherein The method further includes: In response to a content presentation indication triggered for the target area range, obtain from the server the initial entity presentation methods configured for different types of virtual entities, and present the virtual entities according to the obtained initial entity presentation methods; In response to a trigger operation for a target analysis metric, obtain the entity exception presentation methods of each same-type target virtual entity configured by the server for the target analysis metric, and adjust the presentation methods of the same-type target virtual entities from the initial presentation methods to the corresponding entity exception presentation methods.

12. The method according to claim 11, characterized in that, After displaying the exception determination results of each target virtual entity under corresponding analysis metrics obtained from the server in the content display area, the method further includes: In response to a point of interest planning indication triggered for a sub-area range within the target area range, send a point of interest planning request carrying the target point of interest type and point of interest evaluation metrics to the server; Receive the virtual entities to be planned feedback by the server, and identify the virtual entities to be planned among the presented virtual entities.

13. The method according to claim 11, wherein After presenting the virtual entities, the method further includes: In response to a detail display indication triggered for a target metric of a virtual entity, obtain from the server the analysis results determined corresponding to the target metric and the description data of the virtual entity; Present the processing results corresponding to the target metric and part of the description data on a detail display page.

14. The method according to any one of claims 10-13, characterized in that, The method further includes: In response to a data upload indication triggered on the content display page, send the collected data to the server, so that the server determines the statistical information and status information of each entity object based on the obtained data.

15. A data processing device, characterized in that, Including: A first acquisition unit, configured to acquire the description data of each entity object within the target area range sent by the client device, and the attribution relationship between different types of entity objects, and construct each virtual entity corresponding to the entity objects, where the description data includes the location information of the entity object; A second acquisition unit, configured to acquire the location indication information of each point of interest within the target area range; An execution unit, configured to perform the following operations for each target virtual entity whose location information is not included in the location indication information: determine the analysis metrics matching the type of the target virtual entity, determine the attribution relationship between the target virtual entity and other virtual entities, and obtain each analysis result corresponding to the analysis metrics according to the location difference between the location information of the target virtual entity and the location indication information of each point of interest, in combination with the attribution relationship; A determination unit, configured to obtain an abnormality determination result of each target virtual entity under corresponding analysis metrics according to the differences in the analysis results of target virtual entities of the same type under corresponding analysis metrics.

16. The device according to claim 15, characterized in that, The description data further includes the status information and statistical information of the entity objects. After obtaining the abnormality determination result of each target virtual entity under corresponding analysis metrics, the apparatus further includes a response unit, and the response unit is configured to: In response to a point of interest planning request triggered by a client device for a sub-region within the target region range, obtain the target point of interest type and point of interest evaluation metrics carried in the point of interest planning request; Based on the status information of each virtual entity in the sub-region, determine each candidate virtual entity that matches the point of interest planning request in the sub-region, and for each candidate virtual entity, according to the position information of the candidate virtual entity and each other virtual entity in the sub-region, and in combination with the statistical information and status information of each other virtual entity, determine an evaluation result corresponding to the point of interest evaluation metric; Based on each evaluation result, select a virtual entity to be planned that matches the point of interest planning request from among the candidate virtual entities.

17. A data processing device, characterized in that, including: A data sending unit, configured to, in response to a data processing instruction triggered for a target region range, send the description data of each entity object within the target region range and the attribution relationships between entity objects of different types to a server, where the description data at least includes the position information of the entity objects; A progress display unit, configured to, in response to a processing progress presentation instruction triggered for a target virtual entity on a content display page, display the processing progress of each analysis result of the target virtual entity obtained from the server in the content display area; An abnormality display unit, configured to, in response to an abnormality presentation instruction triggered for a target virtual entity on the content display page, display the abnormality determination result of each target virtual entity under corresponding analysis metrics obtained from the server in the content display area.

18. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the computer program, the method described in any one of claims 1-14 is implemented.

19. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method described in any one of claims 1-14 is implemented.

20. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, the method described in any one of claims 1-14 is implemented.

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