Data analysis method and device, computer equipment and computer program product

Abstract

The embodiment of the invention relates to a data analysis method and device, computer equipment and a computer program product. The method comprises the following steps: firstly, converting a target electronic fence into a hexagonal set comprising a plurality of hexagonal grids through a fitting algorithm according to latitude and longitude information of a boundary of the target electronic fence, and then, carrying out data query in a preset database according to indexes of the hexagonal grids, and finally, according to the multiple pieces of target business data, determining a business data analysis result of the target electronic fence. By adopting the method, when the target electronic fence is changed, the target electronic fence is converted into the hexagonal grid set again, and then the data is inquired, so that the business data analysis result associated with the changed target electronic fence can be obtained, and the obtained business data analysis result of the target electronic fence is more accurate.

Description

Technical Field

[0001] The embodiments of the present disclosure relate to the field of data processing technology, and in particular to a data analysis method, apparatus, computer equipment, and computer program product. Background Art

[0002] Geo-fencing is an application of LBS (Location-Based Services), which uses a set of longitude and latitude points to create a virtual geographic boundary. Analyzing the business data generated by devices within the geo-fence helps operators understand operational performance in different geographic areas and time periods, enabling them to make informed decisions.

[0003] Currently, business data is associated with electronic fence identification information. When the electronic fence changes, the business data analysis results of the electronic fence may be inaccurate. Summary of the Invention

[0004] The embodiments of the present disclosure provide a data analysis method, apparatus, computer device, and computer program product, which can be used to improve the accuracy of business data analysis results of electronic fences.

[0005] In a first aspect, an embodiment of the present disclosure provides a data analysis method, the method comprising:

[0006] According to the latitude and longitude information of the boundary of the target electronic fence, the target electronic fence is converted into a hexagonal set through a fitting algorithm; wherein the hexagonal set includes a plurality of hexagonal grids;

[0007] Performing data query in a preset database according to the index of each hexagonal grid to obtain target business data corresponding to each hexagonal grid;

[0008] The business data analysis result of the target electronic fence is determined based on the multiple target business data.

[0009] In one embodiment, the process of building the preset database includes:

[0010] Get the latitude and longitude information of multiple business data;

[0011] Determining the index of each business data at different index levels according to the latitude and longitude information of each business data; wherein the hexagonal grids at different index levels have different sizes;

[0012] The preset database is constructed according to each of the business data and the index of each of the business data at different index levels.

[0013] In one embodiment, converting the target electronic fence into a hexagonal set by a fitting algorithm based on the latitude and longitude information of the target electronic fence boundary includes:

[0014] Determining a target index level of the target electronic fence according to a preset expected number of grids;

[0015] The target electronic fence is converted into the hexagonal set through a fitting algorithm according to the target index level and the longitude and latitude information of the boundary of the target electronic fence.

[0016] In one embodiment, the method further comprises:

[0017] Obtaining the fitting accuracy and data query efficiency of the test electronic fence; wherein the fitting accuracy represents the overlap ratio between the test electronic fence space and the grid set space obtained by fitting the test electronic fence, and the data query efficiency represents the time length for obtaining the test electronic fence service data;

[0018] The expected number of grids is determined according to the fitting accuracy and the data query efficiency.

[0019] In one embodiment, the process of obtaining the fitting accuracy includes:

[0020] Acquire the space of the test electronic fence and the grid set space of the index level to be fitted;

[0021] The fitting accuracy is determined according to an overlap ratio between the space of the test electronic fence and the grid set space.

[0022] In one embodiment, the process of obtaining data query efficiency includes:

[0023] Performing a data query in a preset database for each grid in the grid set obtained by fitting the test electronic fence to obtain a query duration;

[0024] The data query efficiency is determined according to the query duration.

[0025] In one embodiment, determining the index level of the target electronic fence according to a preset expected number of grids includes:

[0026] Determining the area of ​​a single hexagonal grid according to the area of ​​the target electronic fence and the expected number of grids;

[0027] A preset level division table is queried according to the area of ​​the single hexagonal grid to determine the target index level.

[0028] In a second aspect, an embodiment of the present disclosure provides a data analysis device, the device comprising:

[0029] a conversion module, configured to convert the target electronic fence into a hexagonal set based on the latitude and longitude information of the target electronic fence boundary through a fitting algorithm; wherein the hexagonal set includes a plurality of hexagonal grids;

[0030] A query module, configured to perform data query in a preset database according to the index of each hexagonal grid, and obtain target business data corresponding to each hexagonal grid;

[0031] A determination module is used to determine the business data analysis result of the target electronic fence based on the multiple target business data.

[0032] In a third aspect, an embodiment of the present disclosure provides a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method described in the first aspect when executing the computer program.

[0033] In a fourth aspect, an embodiment of the present disclosure provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the method described in the first aspect is implemented.

[0034] In a fifth aspect, an embodiment of the present disclosure provides a computer program product, including a computer program, which implements the method described in the first aspect when executed by a processor.

[0035] The data analysis method, apparatus, computer device, and computer program product provided by the embodiments of the present disclosure first convert the target electronic fence into a hexagonal set comprising a plurality of hexagonal grids based on the latitude and longitude information of the boundary of the target electronic fence through a fitting algorithm. Then, data is queried in a preset database based on the index of each hexagonal grid, thereby obtaining the target business data corresponding to each hexagonal grid. Finally, the business data analysis results of the target electronic fence are determined based on the plurality of target business data. In this way, the target business data is corresponded to the hexagonal grids, and by converting the target electronic fence into a set of hexagonal grids, the business data analysis results of the target electronic fence are determined based on each hexagonal grid. When the target electronic fence changes, the target electronic fence is reconverted into a set of hexagonal grids and the data is then queried again to obtain the business data analysis results associated with the changed target electronic fence, thereby obtaining a more accurate business data analysis result of the target electronic fence. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 A diagram of an application environment of a data analysis method in one embodiment;

[0037] Figure 2 A system architecture diagram of a data analysis method in one embodiment;

[0038] Figure 3 1 is a flow chart of a data analysis method according to an embodiment;

[0039] Figure 4 A schematic diagram of electronic fence conversion in one embodiment;

[0040] Figure 5 A schematic flow chart of a preset database construction step in another embodiment;

[0041] Figure 6 is a flow chart of a data analysis method in another embodiment;

[0042] Figure 7 Schematic diagram of a flow chart of a target index level determination step in another embodiment;

[0043] Figure 8 A schematic flow chart of a step of determining the expected number of grids in another embodiment;

[0044] Figure 9 Schematic diagram of a flow chart of a fitting accuracy acquisition step in another embodiment;

[0045] Figure 10 A flowchart of a step for obtaining data query efficiency in another embodiment;

[0046] Figure 11 is a flow chart of a data analysis method in another embodiment;

[0047] Figure 12 is a flow chart of a data analysis method in another embodiment;

[0048] Figure 13 is a structural block diagram of a data analysis device in one embodiment;

[0049] Figure 14 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solutions and advantages of the embodiments of the present disclosure more clear, the embodiments of the present disclosure are further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the embodiments of the present disclosure and are not intended to limit the embodiments of the present disclosure.

[0051] First of all, before specifically introducing the technical solutions of the embodiments of the present disclosure, the technical background or technical evolution context on which the embodiments of the present disclosure are based is introduced. Generally, an electronic fence is an application of LBS, which uses a set of latitude and longitude points to enclose a virtual geographical boundary. By analyzing the business data generated by the devices within the electronic fence, it can help operators understand the operating conditions of different geographical areas and different time periods, so as to make further decisions based on the operating conditions. At present, business data is associated with electronic fence identification information. When the electronic fence changes, the business data analysis results of the electronic fence may be inaccurate.

[0052] The following describes the technical solutions involved in the embodiments of the present disclosure in conjunction with the scenarios to which the embodiments of the present disclosure are applied.

[0053] The data analysis method provided by the embodiment of the present disclosure can be applied to Figure 1 In the application environment shown, a computer device 102 communicates with a database server 104 via a network. The computer device 102 may be, but is not limited to, various personal computers, laptops, smartphones, tablet computers, and portable wearable devices. The database server 104 may be implemented as an independent server or a server cluster consisting of multiple servers.

[0054] The data analysis method provided by the embodiment of the present disclosure can be applied to Figure 2In the system architecture shown. The system architecture includes data source, authentication layer, capability layer, data cleaning / calculation, data management, real-time computing engine, log monitoring and data storage. Among them, the data source includes various systems that generate business data, such as order system, operation and maintenance system, asset system and users. The actual business data generated can be obtained through the data source. The authentication layer is used to perform security verification, including role authentication and regional authentication. Further operations can be performed after authentication. The capability layer is used to query business data in the electronic fence according to user needs, including real-time query of fence indicators, real-time query of fence data details, real-time query of custom fence indicators and real-time query of custom fence data details. Data cleaning / calculation is used to associate the acquired business data with different index levels, such as index levels 5 to 15. Data management is used to manage the acquired business data, including data source management and data indicator management. The real-time computing engine includes interface throttling, fitting cache, fitting calculation, and data routing. Interface throttling is used to limit query requests to prevent excessive system traffic and resulting overload. Fitting cache uses Local Cache and Redis to cache data during the fitting calculation process. Fitting calculation includes dynamic index selection and spatial index fitting. Data routing is used to parse and process acquired business data, including context initialization. Data cleaning / calculation, data management, and real-time computing processes are performed through the DataChain proxy to exchange data with the database, which can be a StarRocks database. Log monitoring is used to record logs generated during data analysis.

[0055] In one embodiment, Figure 3 As shown, a data analysis method is provided, which is applied to Figure 1 The computer device in the example is used to illustrate the process, including the following steps:

[0056] Step 301 : According to the latitude and longitude information of the boundary of the target electronic fence, the target electronic fence is converted into a hexagonal set through a fitting algorithm.

[0057] Wherein, the hexagonal set includes multiple hexagonal grids. The target electronic fence can be the geographical range where the user wants to view the business data, which is a virtual geographical boundary surrounded by the latitude and longitude information of the target electronic fence boundary. The H3 hexagonal geographic index is a geographic space index system using hexagonal grids. Each hexagonal grid uniquely corresponds to an H3 geographic index, and each H3 geographic index represents a fixed hexagonal geographic space. Therefore, the target electronic fence is converted into a hexagonal set through a fitting algorithm, and each hexagonal grid in the hexagonal set corresponds to an index. Optionally, for an irregular electronic fence such as Figure 4 As shown in the figure on the left, it can be converted into a set of multiple regular hexagonal grids as follows Figure 4 As shown in the figure on the right.

[0058] Step 302 : performing data query in a preset database according to the index of each hexagonal grid to obtain target business data corresponding to each hexagonal grid.

[0059] The preset database may be a database that stores the associations between business data and indexes at different index levels. Data queries are performed in the preset database based on the indexes of each hexagonal grid, and the target business data corresponding to each hexagonal grid can be obtained based on the associations. Optionally, the business data may include business data for multiple indicators, and the business data corresponding to the indicator to be queried by the user can be searched. The business data may also include business data for different time periods, and the business data for the corresponding time period can be selected as the target business data based on the user's query requirements.

[0060] Step 303: Determine the business data analysis result of the target electronic fence based on the multiple target business data.

[0061] According to the corresponding target business data queried from each hexagonal grid in the hexagonal set, the business data analysis results within the target electronic fence range are obtained through statistical processing.

[0062] In the above-mentioned data analysis method, first, based on the latitude and longitude information of the target electronic fence boundary, the target electronic fence is converted into a hexagonal set comprising multiple hexagonal grids through a fitting algorithm. Then, data is queried in a preset database based on the index of each hexagonal grid to obtain the target business data corresponding to each hexagonal grid. Finally, the business data analysis results of the target electronic fence are determined based on the multiple target business data. In this way, the target business data is associated with the hexagonal grids. By converting the target electronic fence into a set of hexagonal grids, the business data analysis results of the target electronic fence are determined based on each hexagonal grid. If the target electronic fence changes, the target electronic fence is reconverted into a set of hexagonal grids and the data is then queried again to obtain the business data analysis results associated with the changed target electronic fence, thereby obtaining more accurate business data analysis results for the target electronic fence.

[0063] In the embodiments of the present application, Figure 5 As shown in the figure, the construction process of the preset database includes:

[0064] Step 501: Obtain the latitude and longitude information of multiple business data.

[0065] The original business detail data of multiple business data and the latitude and longitude information of the original business detail data are obtained according to different data sources. The latitude and longitude information can represent the geographical location where the original business detail data is generated.

[0066] Step 502: Determine the index of each business data at different index levels according to the latitude and longitude information of each business data.

[0067] The hexagonal grids at different index levels have different sizes. Using the streaming computing framework, the latitude and longitude information of each business data item is converted to obtain the index corresponding to each business data item at different index levels. The H3 geographic index includes a total of 16 index levels. The hexagonal grids for indexes 0 to 4 have larger areas and are optional. Index levels 5-15 are generally considered.

[0068] Step 503 : constructing a preset database according to each business data and the index of each business data at different index levels.

[0069] The original detailed data of each business and the association relationship between the indexes of the original detailed data of each business at different index levels are stored in the database to obtain a preset database. Optionally, the preset database can be StarRocks, a large-scale parallel processing database that supports rapid analysis of massive data.

[0070] In the above embodiment, by constructing a preset database, the preset database stores various business data and indexes of the business data at different index levels, which can facilitate determining the business data corresponding to each hexagonal grid according to the index.

[0071] In one embodiment, Figure 6 As shown, the steps of converting the target geo-fence into a hexagonal set by a fitting algorithm based on the latitude and longitude information of the target geo-fence boundary include:

[0072] Step 601: Determine a target index level of a target geo-fence according to a preset expected number of grids.

[0073] The expected number of grids is the number of hexagonal grids in the hexagonal set obtained after fitting the target electronic fence. Optionally, the expected number of grids can be the number of grids with good fitting effect determined based on historical fitting of the target electronic fence. Figure 7 As shown, the target index level may be determined by:

[0074] Step 701: Determine the area of ​​a single hexagonal grid according to the area of ​​the target geo-fence and the expected number of grids.

[0075] According to the ratio of the actual area of ​​the target electronic fence to the expected number of grids, the area of ​​a single hexagonal grid after fitting can be calculated.

[0076] Step 702: query a preset level division table based on the area of ​​a single hexagonal grid to determine a target index level.

[0077] The preset hierarchical division table is shown in Table 1 below. H3 Resolution corresponds to different index levels, Average Hexagon Area is the area of ​​a single hexagonal grid, Average Hexagon Edge Length is the edge length of a single hexagonal grid, and Number of unique indexes is the number of unique indexes. Based on the calculated area of ​​a single hexagonal grid, query Table 1. When the area falls between two index levels, index downward. For example, if the area of ​​a single hexagonal grid falls between index levels 11 and 12, the target index level is determined to be 11. The target index level is also the index level used when fitting the target geo-fence.

[0078] Table 1 Preset level division table

[0079]

[0080] Step 602 : According to the target index level and the longitude and latitude information of the boundary of the target electronic fence, the target electronic fence is converted into a hexagonal set through a fitting algorithm.

[0081] After determining the target index level, the area of ​​the hexagonal grid corresponding to the target index level can be determined according to Table 1 above. The number of hexagons required for fitting is then determined based on the area of ​​the hexagonal grid and the actual area of ​​the target geo-fence. The fitting algorithm adjusts the hexagonal parameters to fit the target geo-fence according to the number of hexagons required, so that the geographic space of the fitted hexagonal set approaches the geographic space of the target geo-fence. Alternatively, you can import an algorithm package that supports H3, then call the fitting method, inputting the geo-fence boundary point set and the index level as parameters, to perform fitting.

[0082] In the above embodiment, the target electronic fence is converted into a hexagonal set, and then the business data is queried through the hexagonal grid in the hexagonal set. In this way, when the target electronic fence is changed, the new hexagonal set can be fitted to query the associated business data, so that the business data of the target electronic fence obtained is more accurate.

[0083] In the embodiment of the present application, in order to determine the expected number of grids, as shown in FIG. Figure 8 As shown, the method further includes:

[0084] Step 801: Obtain the fitting accuracy and data query efficiency of the test electronic fence.

[0085] Among them, the fitting accuracy represents the overlapping ratio of the test electronic fence space and the grid set space obtained by fitting the test electronic fence, and the data query efficiency represents the length of time it takes to obtain the test electronic fence business data. Generally, the fitting accuracy is inversely proportional to the data query efficiency. That is, the larger the hexagonal grid index area corresponding to the index level, the fewer hexagons are required to fit the electronic fence, the lower the fitting accuracy, and the faster the data query efficiency. The smaller the hexagonal grid index area corresponding to the index level, the more hexagons are required to fit the electronic fence, the higher the fitting accuracy, and the lower the data query efficiency. Optionally, the fitting accuracy and data query efficiency can be statistically determined through historical data.

[0086] Optionally, the fitting accuracy can also be determined statistically based on test data, and the test data acquisition process includes the following steps: Figure 9 Steps shown:

[0087] Step 901: Acquire the space of the test electronic fence and the grid set space of the index level to be fitted.

[0088] The grid set of the index level to be fitted is also the hexagonal set obtained by fitting the test electronic fence according to the index level to be fitted. The space of the test electronic fence can be determined according to the boundary longitude and latitude information of the test electronic fence, and the space of the grid set can be determined according to the grid set.

[0089] Step 902 : Determine the fitting accuracy based on the overlap ratio between the test geo-fence space and the grid set space.

[0090] The fitting accuracy can be calculated according to the following formula:

[0091] Dice = 2 * (A∩B) / (|A| + |B|)

[0092] Where A represents the test geo-fence space, B represents the fitted grid space, and Dice represents the overlap between the test geo-fence space and the fitted grid space, also known as the fitting accuracy. Calculations show that when index level ≥ 10, Dice is > 98%, when index level ≥ 11, Dice is > 99.8%, and when index level ≥ 14, Dice is 100%.

[0093] Optionally, data query efficiency acquisition process, such as Figure 10 Shown, including:

[0094] Step 1001 : performing a data query in a preset database for each grid in a grid set obtained by fitting the test electronic fence to obtain a query duration.

[0095] Based on the StarRocks database engine experiment, the data query time of the test electronic fence at different index levels is [40ms, 4300ms].

[0096] Step 1002: Determine the data query efficiency based on the query duration.

[0097] The data query efficiency can be determined based on the data query duration. The longer the query duration, the lower the data query efficiency, and the shorter the query duration, the higher the data query efficiency.

[0098] Step 802: Determine the expected number of grids based on the fitting accuracy and data query efficiency.

[0099] The desired number of grids represents the desired balance between fitting accuracy and data query efficiency. Optionally, you can set expectations for fitting accuracy and query efficiency, with the expected fitting accuracy range being [99.8%, 100%] and the expected query time being [40ms, 650ms]. Experiments with test geo-fences show that when the number of hexagonal grids obtained by fitting the test geo-fence is around 9000, the fitting accuracy and query time fall within the set expected fitting accuracy and query time. Therefore, when the expected number of fitted hexagons is factor = 9000, the expected fitting accuracy and query efficiency can be achieved. For example, if the area of ​​the fitted test geo-fence is s = 18.2 km², the calculated expected area of ​​a single index is s / factor = 0.00182 km². By consulting Table 1 above, indexes are retrieved downwards based on the area. Since 0.00182 km² falls between index levels 11 and 12, we use index level 11, resulting in an actual index area of ​​0.0021496 km². The estimated number of hexagons after fitting is 18.2 km² / 0.0021496 km², or 8466. Next, we fit the test geo-fence using index level 11. By inputting the geo-fence boundary point set and fitting the index level, we obtain a set of hexagons. We calculate the corresponding fitting accuracy and query efficiency, achieving a Dice of 99.86% and a query duration of 640 ms, both within the expected range. This confirms that the above fitting strategy achieves a balance between geo-fence fitting accuracy and data query efficiency.

[0100] In the above method, the expected number of grids is determined by testing the expected fitting accuracy and data query efficiency of the electronic fence, and then the index level can be determined by the expected number of grids, so that the fitting accuracy and query efficiency of the target electronic fence can be close to the expected values.

[0101] In the embodiments of the present application, Figure 11 The flowchart of the data analysis method provided in the embodiment of the present application includes:

[0102] Step 1101: Obtain the latitude and longitude information of multiple business data.

[0103] Step 1102 : Determine the index of each business data at different index levels according to the latitude and longitude information of each business data.

[0104] Step 1103 : constructing a preset database according to each business data and the index of each business data at different index levels.

[0105] Step 1104 : determining a target index level of the target geo-fence according to the preset expected number of grids.

[0106] Step 1105 : Convert the target geo-fence into a hexagonal set using a fitting algorithm according to the target index level and the latitude and longitude information of the target geo-fence boundary.

[0107] Step 1106 : perform data query in a preset database according to the index of each hexagonal grid to obtain target business data corresponding to each hexagonal grid.

[0108] Step 1107: Determine the business data analysis result of the target electronic fence based on the multiple target business data.

[0109] The following describes an embodiment of the present disclosure in conjunction with a specific electronic fence business data analysis scenario. Figure 12 As shown, the business data storage process includes the following steps:

[0110] (1) Monitor the business data of each data source business system and obtain the original detailed business data.

[0111] (2) The longitude and latitude information corresponding to the original business detail data is converted into indexes corresponding to different index levels through the real-time streaming computing framework, such as the index levels h5-h15 shown in the figure.

[0112] (3) The converted original business detail data and the indexes corresponding to different index levels are stored in the StarRocks database.

[0113] To query geo-fence business data, users can enter: <geo-fence latitude and longitude boundaries>, <geo-fence data metric to be calculated>, and <business data time period>. The geo-fence is fitted to a set of hexagons. The data source and corresponding business data are found based on the metric to be queried. The database then retrieves the business data associated with each hexagonal grid based on its index. The output is: <geo-fence data metric results for the input time period>, resulting in the geo-fence business data analysis results.

[0114] It should be understood that although Figure 3-12 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. In addition, Figure 3-12 At least part of the steps may include multiple steps or multiple stages. These steps or stages are not necessarily performed at the same time, but can be performed at different times. The order of execution of these steps or stages is not necessarily one by one, but can be performed in turn or alternately with other steps or at least part of the steps or stages in other steps.

[0115] In one embodiment, Figure 13 As shown, a data analysis device 1300 is provided, comprising: a conversion module 1301, a query module 1302 and a determination module 1303, wherein:

[0116] The conversion module 1301 is configured to convert the target electronic fence into a hexagonal set based on the latitude and longitude information of the target electronic fence boundary through a fitting algorithm; wherein the hexagonal set includes a plurality of hexagonal grids;

[0117] A query module 1302 is configured to perform a data query in a preset database according to the index of each hexagonal grid to obtain target business data corresponding to each hexagonal grid;

[0118] The determination module 1303 is configured to determine a business data analysis result of a target electronic fence based on a plurality of target business data.

[0119] In one embodiment, a construction module is further included, which is used to obtain the latitude and longitude information of multiple business data; determine the index of each business data at different index levels based on the latitude and longitude information of each business data; wherein the hexagonal grid sizes of different index levels are different; and construct a preset database based on each business data and the index of each business data at different index levels.

[0120] In one embodiment, the conversion module 1301 is specifically used to determine the target index level of the target electronic fence according to a preset expected number of grids; and convert the target electronic fence into a hexagonal set through a fitting algorithm based on the target index level and the longitude and latitude information of the target electronic fence boundary.

[0121] In one embodiment, an acquisition module is also included, which is used to obtain the fitting accuracy and data query efficiency of the test electronic fence; wherein the fitting accuracy represents the overlapping ratio of the test electronic fence space and the grid set space obtained by fitting the test electronic fence, and the data query efficiency represents the time length for obtaining the test electronic fence business data; based on the fitting accuracy and the data query efficiency, the expected number of grids is determined.

[0122] In one embodiment, the acquisition module is specifically configured to acquire the space of the test electronic fence and the grid set space of the index level to be fitted; and determine the fitting accuracy rate according to the overlap ratio between the space of the test electronic fence and the grid set space.

[0123] In one embodiment, the acquisition module is specifically configured to perform data query in a preset database based on each grid in the grid set obtained by fitting the test electronic fence to obtain query duration; and determine data query efficiency based on the query duration.

[0124] In one embodiment, the conversion module 1301 is specifically configured to determine the area of ​​a single hexagonal grid according to the area of ​​the target electronic fence and the expected number of grids; and to query a preset hierarchical division table according to the area of ​​the single hexagonal grid to determine the target index level.

[0125] For the specific definition of the data analysis device, please refer to the definition of the data analysis method above, which will not be repeated here. Each module in the above-mentioned data analysis device can be implemented in whole or in part by software, hardware, or a combination thereof. Each of the above-mentioned modules can be embedded in or independent of the processor in the electronic device in the form of hardware, or can be stored in the memory of the electronic device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0126] Figure 14 1 is a block diagram of a computer device 1300 according to an exemplary embodiment. For example, the computer device 1300 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, etc.

[0127] Reference Figure 14 The computer device 1300 may include one or more of the following components: a processing component 1302, a memory 1304, a power component 1306, a multimedia component 1308, an audio component 1310, an input / output (I / O) interface 1312, a sensor component 1314, and a communication component 1316. The memory stores computer programs or instructions that run on the processor.

[0128] Processing component 1302 generally controls the overall operation of computer device 1300, such as operations associated with display, phone calls, data communications, camera operation, and recording operations. Processing component 1302 may include one or more processors 1320 to execute instructions to perform all or part of the steps of the above-described methods. In addition, processing component 1302 may include one or more modules to facilitate interaction between processing component 1302 and other components. For example, processing component 1302 may include a multimedia module to facilitate interaction between multimedia component 1308 and processing component 1302.

[0129] The memory 1304 is configured to store various types of data to support the operations of the computer device 1300. Examples of such data include instructions for any application or method operating on the computer device 1300, contact data, phone book data, messages, pictures, videos, etc. The memory 1304 can be implemented by any type of volatile or non-volatile storage device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, or optical disk.

[0130] The power supply component 1306 provides power to the various components of the computer device 1300. The power supply component 1306 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the computer device 1300.

[0131] The multimedia component 1308 includes a touchscreen display that provides an output interface between the computer device 1300 and the user. In some embodiments, the touchscreen display may include a liquid crystal display (LCD) and a touch panel (TP). The touch panel includes one or more touch sensors to sense touches, slides, and gestures on the touch panel. The touch sensors can not only sense the boundaries of a touch or slide action, but also detect the duration and pressure associated with the touch or slide action. In some embodiments, the multimedia component 1308 includes a front-facing camera and / or a rear-facing camera. When the computer device 1300 is in an operating mode, such as a capture mode or a video mode, the front-facing camera and / or the rear-facing camera can receive external multimedia data. Each front-facing camera and the rear-facing camera can have a fixed optical lens system or have focal length and optical zoom capabilities.

[0132] The audio component 1310 is configured to output and / or input audio signals. For example, the audio component 1310 includes a microphone (MIC) configured to receive external audio signals when the computer device 1300 is in an operating mode, such as a call mode, a recording mode, or a speech recognition mode. The received audio signals may be further stored in the memory 1304 or transmitted via the communication component 1316. In some embodiments, the audio component 1310 also includes a speaker for outputting audio signals.

[0133] I / O interface 1312 provides an interface between processing component 1302 and peripheral interface modules, such as a keyboard, click wheel, buttons, etc. These buttons may include, but are not limited to, a home button, volume buttons, a start button, and a lock button.

[0134] Sensor assembly 1314 includes one or more sensors for providing various status assessments of computer device 1300. For example, sensor assembly 1314 can detect the open / closed state of computer device 1300, the relative positioning of components, such as the display and keypad of computer device 1300. Sensor assembly 1314 can also detect changes in the position of computer device 1300 or a component of computer device 1300, the presence or absence of user contact with computer device 1300, the orientation or acceleration / deceleration of computer device 1300, and changes in the temperature of computer device 1300. Sensor assembly 1314 can include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 1314 can also include an optical sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, sensor assembly 1314 can also include an accelerometer, a gyroscope, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0135] Communication component 1316 is configured to facilitate wired or wireless communication between computer device 1300 and other devices. Computer device 1300 can access a wireless network based on a communication standard, such as WiFi, 2G, or 3G, or a combination thereof. In one exemplary embodiment, communication component 1316 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, communication component 1316 also includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0136] In an exemplary embodiment, the computer device 1300 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above-described data analysis method.

[0137] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 1304 including instructions. The instructions can be executed by the processor 1320 of the electronic device 1300 to perform the above method. For example, the non-transitory computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0138] In an exemplary embodiment, a storage medium including instructions is also provided, such as memory 1422 including instructions. The instructions can be executed by a processor of server 1400 to perform the above method. The storage medium can be a non-transitory computer-readable storage medium, such as a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, optical data storage device, etc.

[0139] In an exemplary embodiment, a computer program product is also provided. When executed by a processor, the computer program can implement the above-described method. The computer program product includes one or more computer instructions. When these computer instructions are loaded and executed on a computer, they can implement part or all of the above-described method in whole or in part according to the processes or functions described in the embodiments of the present disclosure.

[0140] It should be noted that any solutions described in this specification and in the examples that involve the processing of personal information will be processed only with a legitimate basis (such as with the consent of the personal information subject or as necessary for the performance of a contract) and only within the prescribed or agreed scope. A user's refusal to process personal information other than that required for basic functions will not affect their use of these functions.

[0141] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the above-described method embodiments. Any reference to memory, storage, database, or other media used in the embodiments provided herein may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0142] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0143] The above-described embodiments merely represent several implementation methods of the embodiments of the present disclosure. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that a person of ordinary skill in the art can make several modifications and improvements without departing from the concept of the embodiments of the present disclosure, all of which fall within the scope of protection of the embodiments of the present disclosure. Therefore, the scope of protection of the patent for the embodiments of the present disclosure shall be based on the appended claims.

Claims

1. A data analysis method, characterized in that: The method comprises: According to the latitude and longitude information of the boundary of the target electronic fence, the target electronic fence is converted into a hexagonal set through a fitting algorithm; wherein the hexagonal set includes a plurality of hexagonal grids; Performing data query in a preset database according to the index of each hexagonal grid to obtain target business data corresponding to each hexagonal grid; The business data analysis result of the target electronic fence is determined based on the multiple target business data.

2. The method according to claim 1, characterized in that The process of building the preset database includes: Get the latitude and longitude information of multiple business data; Determining the index of each business data at different index levels according to the latitude and longitude information of each business data; wherein the hexagonal grids at different index levels have different sizes; The preset database is constructed according to each of the business data and the index of each of the business data at different index levels.

3. The method according to claim 1, characterized in that The step of converting the target electronic fence into a hexagonal set by a fitting algorithm based on the latitude and longitude information of the boundary of the target electronic fence includes: Determining a target index level of the target electronic fence according to a preset expected number of grids; The target electronic fence is converted into the hexagonal set through a fitting algorithm according to the target index level and the longitude and latitude information of the boundary of the target electronic fence.

4. The method according to claim 3, characterized in that The method further comprises: Obtaining the fitting accuracy and data query efficiency of the test electronic fence; wherein the fitting accuracy represents the overlap ratio between the test electronic fence space and the grid set space obtained by fitting the test electronic fence, and the data query efficiency represents the time length for obtaining the test electronic fence service data; The expected number of grids is determined according to the fitting accuracy and the data query efficiency.

5. The method according to claim 4, characterized in that The process of obtaining the fitting accuracy includes: Acquire the space of the test electronic fence and the grid set space of the index level to be fitted; The fitting accuracy is determined according to an overlap ratio between the space of the test electronic fence and the grid set space.

6. The method according to claim 4, characterized in that The process of obtaining the data query efficiency includes: Performing a data query in a preset database for each grid in the grid set obtained by fitting the test electronic fence to obtain a query duration; The data query efficiency is determined according to the query duration.

7. The method according to claim 3, characterized in that The step of determining the index level of the target electronic fence according to the preset expected number of grids includes: Determining the area of ​​a single hexagonal grid according to the area of ​​the target electronic fence and the expected number of grids; A preset level division table is queried according to the area of ​​the single hexagonal grid to determine the target index level.

8. A data analysis device, characterized in that: The device comprises: a conversion module, configured to convert the target electronic fence into a hexagonal set based on the latitude and longitude information of the target electronic fence boundary through a fitting algorithm; wherein the hexagonal set includes a plurality of hexagonal grids; A query module, configured to perform data query in a preset database according to the index of each hexagonal grid, and obtain target business data corresponding to each hexagonal grid; The determination module 1303 is configured to determine a business data analysis result of the target electronic fence based on the plurality of target business data.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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

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