Species distribution data aggregation method, system and storage medium

Abstract

The present invention provides a method, system, and storage medium for aggregating species distribution data. The method comprises: obtaining raw species distribution data; determining the map grid scale for displaying species distribution, and obtaining raw species distribution data within each grid; and performing data enhancement processing on each grid, using the raw species distribution data of multiple other grids within a predetermined range around it as the central grid, thereby obtaining aggregated species distribution data for each grid. The species distribution data aggregation method provided by the present invention can enhance the display of species distribution data, thereby improving data display quality.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a method, system and storage medium for aggregating species distribution data. Background Art

[0002] The distribution of species around the world is uneven, and each location has different common species. A species database can be established to obtain a list of common species near a location based on geographic location, while also meeting users' personalized information needs. However, the data obtained from species observations is unevenly distributed, and the number of species within a certain range is generally small, resulting in poor display quality. Therefore, some data enhancement is required. Summary of the Invention

[0003] One of the purposes of the present disclosure is to provide a method for aggregating species distribution data, comprising the following steps:

[0004] Obtain raw species distribution data;

[0005] Determine the map grid scale for displaying species distribution and obtain the original species distribution data within each grid range;

[0006] For each grid, it is used as the central grid, and the original species distribution data of multiple other grids within the set range around it are used to perform data enhancement processing on the central grid, so as to obtain the aggregated results of species distribution data of each grid.

[0007] In some embodiments, the original species distribution data is obtained through a species distribution data source and species identification result information.

[0008] In some embodiments, the original species distribution data is obtained through species identification result information, including: obtaining wireless data or mobile data of the user and processing the data to obtain the original species distribution data.

[0009] In some embodiments, the method further includes: obtaining the original species distribution data within each grid range, processing the original species distribution data according to the species commonness to obtain processed species distribution data, and using the processed species distribution data for subsequent data enhancement processing.

[0010] In some embodiments, the method further includes: obtaining the altitude value of each grid, calculating the altitude value difference between each central grid and multiple other grids within a surrounding set range, and when the altitude value difference between any other grid and its central grid exceeds a set threshold, the original species distribution data of the other grid does not participate in the data enhancement processing of the central grid.

[0011] In some embodiments, the threshold value for the altitude difference is set and adjusted according to different regions.

[0012] In some embodiments, the map grid scale is adjusted according to different regions and / or different map grid scales are set for the same region.

[0013] In some embodiments, the data enhancement processing includes: obtaining the weight value of each other grid within a set range around the central grid according to the set attenuation coefficient, multiplying the original species distribution data of the other grids by the weight value and adding them to the data of the central grid, and finally obtaining the species distribution data aggregation result after data enhancement of the central grid.

[0014] In some embodiments, the attenuation coefficient is set and adjusted according to different regions.

[0015] In some embodiments, the method further includes: classifying the original species distribution data according to the time dimension, obtaining the original species distribution data at different times within each grid range, and performing data enhancement processing respectively according to the original species distribution data at different times.

[0016] According to another aspect of the present disclosure, a species distribution data aggregation system is proposed, including a processor and a memory, wherein the memory stores a program, and when the program is executed by the processor, the species distribution data aggregation method described above is implemented.

[0017] According to another aspect of the present disclosure, a storage medium is provided, on which a program is stored. When the program is executed, the species distribution data aggregation method described above is implemented.

[0018] Other features and advantages of the present disclosure will become more apparent from the following detailed description of exemplary embodiments of the present disclosure with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0020] The present disclosure can be more clearly understood from the following detailed description with reference to the accompanying drawings, in which:

[0021] Figure 1 FIG2 is a flow chart of a method for aggregating species distribution data according to an embodiment of the present invention.

[0022] Figure 2 FIG2 is a schematic diagram showing the weight values of each other grid within a set range around a central grid provided by an embodiment of the present invention.

[0023] Figure 3 FIG2 is a schematic diagram showing the weight values of each other grid within a set range around a central grid provided by another embodiment of the present invention.

[0024] Figure 4 Shown is a schematic diagram of the aggregation results of species distribution data in a certain area provided by one embodiment of the present invention.

[0025] Figure 5 FIG2 is a schematic diagram of the structure of a species distribution data aggregation system provided by an embodiment of the present invention.

[0026] Note that in the embodiments described below, the same reference numerals are sometimes used in common across different drawings to denote the same parts or parts having the same functions, and their repeated descriptions are omitted. In some cases, similar reference numerals and letters are used to denote similar items, so once an item is defined in one drawing, it need not be further discussed in subsequent drawings.

[0027] For ease of understanding, the positions, sizes, and ranges of various structures shown in the drawings and the like may not represent actual positions, sizes, and ranges, etc. Therefore, the present disclosure is not limited to the positions, sizes, and ranges disclosed in the drawings and the like. DETAILED DESCRIPTION

[0028] Various exemplary embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings. It should be noted that unless otherwise specifically stated, the relative arrangements of components and steps, numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present disclosure.

[0029] The following description of at least one exemplary embodiment is merely illustrative and is not intended to limit the present disclosure, its application, or use. In other words, the structures and methods herein are presented in an exemplary manner to illustrate various embodiments of the structures and methods of the present disclosure. However, those skilled in the art will appreciate that these are merely exemplary of the disclosure that may be implemented, and are not exhaustive. Furthermore, the drawings are not necessarily drawn to scale, and some features may be exaggerated to illustrate details of specific components.

[0030] Technologies, methods and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, such technologies, methods and equipment should be considered part of the authorization specification.

[0031] In all examples shown and discussed herein, any specific values should be interpreted as merely exemplary and not limiting. Therefore, other examples of the exemplary embodiments may have different values.

[0032] Figure 1FIG2 is a flow chart of a method for aggregating species distribution data according to an embodiment of the present invention. The method can be implemented in an application (app) installed on a smart terminal such as a mobile phone or tablet computer.

[0033] like Figure 1 As shown, the method includes:

[0034] Step S100: obtaining original species distribution data;

[0035] Step S200: determining the map grid scale for displaying species distribution, and obtaining original species distribution data within each grid range;

[0036] Step S300: For each grid, take it as the central grid, and perform data enhancement processing on the central grid using the original species distribution data of multiple other grids within a set range around it, so as to obtain the species distribution data aggregation result of each grid.

[0037] In some embodiments, the raw species distribution data is obtained from a species distribution data source and species identification information. The raw species distribution data can be obtained from a species distribution data source, for example, from various publicly available general species distribution databases (e.g., GBIF: Global Biodiversity Information Facility). Such data sources contain a large amount of field observation data and can serve as a source of data on species distribution in the wild.

[0038] In some embodiments, the raw species distribution data is obtained through species identification result information, including: obtaining the user's wireless data or mobile data and processing it to obtain the raw species distribution data. The data obtained by the user through species identification software can be fitted and processed according to the IP data of the user's wireless WIFI to obtain the location information of the species distribution, which serves as a reference for the distribution information of species data in specific areas such as residential areas or commercial industrial areas. At the same time, in areas without wireless signals such as WIFI, the approximate range of the species distribution area can be determined based on the user's mobile data (such as 3G, 4G or 5G), but this method cannot determine the precise location of the species distribution. The species identification software can identify the species of the plant based on the images taken by the user, and present the classification information and other relevant information of the species based on the identified species.

[0039] In some embodiments, the method further includes: obtaining the original species distribution data within each grid range, processing the original species distribution data according to the species commonness to obtain processed species distribution data, and using the processed species distribution data for subsequent data enhancement processing.

[0040] Species can be ranked by commonness, and this can be displayed as an extension of subsequent species information. Users can also choose to display only distribution data for species at or above a certain commonness level. Species commonness can be determined and displayed accordingly based on the species' location information and statistically analyzed across different regions and scales. Species commonness can be determined by first confirming that the species in the species list are listed in the national (state) or other regional species list. Rare species and protected species (e.g., IUCN species) are set to rare by default. Horticultural species must have their commonness confirmed based on data. Data from species distribution data sources (e.g., GBIF) is used as a reference for species commonness in the wild, and statistics are calculated based on a set regional scale (e.g., a 40x20km grid). User-identified species data, such as Wi-Fi or mobile data, can be used as a reference for ornamental plant commonness and can supplement GBIF data. Statistics can also be calculated based on a set regional scale (e.g., a 40x20km grid).

[0041] In some embodiments, the method further includes: obtaining the altitude value of each grid, calculating the altitude value difference between each central grid and multiple other grids within a surrounding set range, and when the altitude value difference between any other grid and its central grid exceeds a set threshold, the original species distribution data of the other grid does not participate in the data enhancement processing of the central grid.

[0042] Species observation data show that species distribution is uneven. If a species is common in a certain area, then it should also be distributed to a certain extent in the surrounding areas. At the same time, the difference in altitude also has a certain impact on the regional range of species distribution. At the same time, the number of species within a set grid scale (for example, 40*20km) is generally not large, and the display effect is poor. Therefore, the data needs to be enhanced to a certain extent.

[0043] Within an altitude difference of 1000 meters (although altitude effects can be disregarded), species distribution data observed within each grid cell should be diffused to the surrounding grid cells (i.e., the weight of species distribution data should be attenuated over the distance). For example, the weight of species distribution data can be gradually attenuated within 400 kilometers for the same latitude and within 100 kilometers for the same longitude. If the altitude difference exceeds 1000 meters, diffusion is not continued, because species distribution in different areas with large altitude differences will also vary significantly.

[0044] In some embodiments, the threshold value of the altitude difference is set and adjusted according to different regions, for example, different ranges such as 1500 meters and 2000 meters can be uniformly set to different values, or different values can be set according to the different species distribution in different regions.

[0045] In some embodiments, the map grid scale can be adjusted based on the region and / or different map grid scales can be set for the same region. For example, different scales can be set, such as 40 x 20 km or 50 x 50 km. Different regions can determine different display grid scales based on species distribution data, and users can also choose to display different grid scales. In other words, the default display can be a 50 km x 50 km grid, and the user can choose to reduce it to a 40 x 20 grid, or the default grid size can remain unchanged at 40 x 20 or 50 x 50.

[0046] Similarly, the distribution altitude and weight attenuation distance can be set according to the species distribution in different regions, because the degree of species distribution aggregation in different regions is different. For example, there are fewer species distributed in deserts, wastelands or Gobi, and more species distributed in warm tropical or tropical rainforest areas. Therefore, different data can be set for data enhancement processing to improve the data display effect. The influence of altitude and weight attenuation distance is also due to the same reason.

[0047] In some embodiments, the data enhancement processing includes: obtaining the weight value of each other grid within a set range around the central grid according to the set attenuation coefficient, multiplying the original species distribution data of the other grids by the weight value and adding them to the data of the central grid, and finally obtaining the species distribution data aggregation result after data enhancement of the central grid.

[0048] Furthermore, the attenuation coefficient is set and adjusted according to different regions. Figure 2 and Figure 3 , which shows a schematic diagram of the weight value of each other grid within a set range around a certain central grid provided by different embodiments. Figure 2 and Figure 3 As shown in the figure, the original weight value of the grid at the center point is set to 100 (that is, 100% of its data participates in the cumulative calculation), and the other surrounding grids are attenuated according to the weight values set in the figure (90 means 90% participates in the accumulation, 60 means 60% participates in the accumulation). Finally, the data of the grid in the center is accumulated by multiplying the data of each grid within the set range around it by the percentage of the weight value.

[0049] Figure 4The figure shows the results of the aggregation of species distribution data for a certain area provided by one embodiment of the present invention. When the species data of the 156.5*156km grid range is finally displayed, the data displayed in each grid are all species data that have undergone the above processing. It can display the overall species data and the data of each classified species and display them in order of quantity. After loading the map, the species distribution data is displayed on the map grid of the appropriate grid scale range according to the grid data, such as Figure 4 As shown, in response to the user's operation, for example, when the user moves or clicks on a certain grid, the species distribution data corresponding to the grid is displayed.

[0050] In some embodiments, the method further includes: classifying the original species distribution data according to the time dimension, obtaining the original species distribution data at different times within each grid range, and performing data enhancement processing respectively according to the original species distribution data at different times.

[0051] Species distribution data can be modeled as three-dimensional coordinates (wx hx t) by partitioning data by time, for example, by month. For example, m50-j12-w13-t4 represents aggregated data for April, the 12th month in longitude and the 13th month in latitude, using a 50km geographic scale. This key stores the relevant aggregated species distribution data. By adding a time dimension, you can define richer and more detailed displays of species distribution data, such as displaying species distribution data for a specific time period or for all time periods. Species location information is recorded based on the actual longitude and latitude distribution range or specific location information, allowing for data statistics at different grid scales and subsequent display based on needs.

[0052] In addition, species distribution coordinates can be used to calculate the species of cities, provinces or countries and aggregate the data for display.

[0053] Data aggregation of major cities:

[0054] Provide a list of cities

[0055] For each city, calculate the aggregate data

[0056] Get the coordinates of the city

[0057] Call the above species distribution data

[0058] Get species distribution data for the city

[0059] Data aggregation of major provinces:

[0060] Provide a list of provinces

[0061] Get a list of cities in a province and aggregate data for each city

[0062] Aggregate data for a city

[0063] Aggregation of data from major countries

[0064] Provide a list of countries

[0065] Get a list of provinces in a country and aggregate data for each province

[0066] Summarize the aggregated data of provinces

[0067] Basic urban data services

[0068] Get city / province / country based on latitude and longitude

[0069] Get a list of provinces in a country

[0070] Get a list of cities in a province

[0071] Get basic information about the city based on the city code

[0072] Obtaining user species information and updating the species distribution data aggregation database is done in the following ways:

[0073] 1. User authorized geographic location, or inferred the user's location latitude and longitude based on the user's IP information

[0074] 2. Calculate the longitude and latitude coding information of the user's location using the geohash algorithm based on the longitude and latitude, and match it to the geographic grid database

[0075] 3. Get the data of the grid

[0076] 4. Combine geographic grid data and user dynamic information (photographed plants, current time, etc.) to provide users with suggestions and information that fit the local environment

[0077] Geohash is a method of slicing the entire Earth's user grid, for example, into 40km*20km grid blocks. Any longitude and latitude coordinate can then be quickly located within these grid blocks. We can store local information within GeoHash grid blocks, including a list of common local plants based on a species distribution database and local weather information based on a basic meteorological and climate information database:

[0078] a. Daily minimum temperature, maximum temperature, average temperature

[0079] b. Monthly average temperature

[0080] c. Precipitation information, at least the monthly average precipitation

[0081] d. Humidity information, at least the monthly average humidity

[0082] e. Lighting information

[0083] f. Hardiness zone information

[0084] Other related information may include common species information, terrain information such as altitude, climate zone classification information, vegetation information, and so on. By associating and storing species distribution data from different species data sets, the data can be aggregated and displayed to facilitate different interactive displays and functional processing. For example, a list of common species near a location and climate and meteorological information about the species distribution area can be obtained based on the geographic location. This can also meet the user's personalized information needs, such as viewing classified aggregated information such as common poisonous plants near New York, common weeds near Los Angeles, and common fish species at various fishing spots.

[0085] Based on the same inventive concept, the present invention also provides a species distribution data aggregation system, comprising a processor and a memory, wherein the memory stores a program, and when the program is executed by the processor, the species distribution data aggregation method described above is implemented. Figure 5 , Figure 5 FIG. 1 is a schematic diagram of the structure of a species distribution data aggregation system provided by an embodiment of the present invention. Figure 5 As shown, the species distribution data aggregation system includes a processor 301 , a communication interface 302 , a memory 303 and a communication bus 304 .

[0086] The processor 301 , the communication interface 302 , and the memory 303 communicate with each other via the communication bus 304 .

[0087] The memory 303 is used to store computer programs.

[0088] The processor 301 is configured to execute the program stored in the memory 303 to implement the following steps:

[0089] Obtain raw species distribution data;

[0090] Determine the map grid scale for displaying species distribution and obtain the original species distribution data within each grid range;

[0091] For each grid, it is used as the central grid, and the original species distribution data of multiple other grids within the set range around it are used to perform data enhancement processing on the central grid, so as to obtain the aggregated results of species distribution data of each grid.

[0092] For the specific implementation of each step of this method and related explanations, please refer to the above Figure 1 The method implementation shown is not described in detail here.

[0093] In addition, other implementations of the species distribution data aggregation method implemented by the processor 301 executing the program stored in the memory 303 are the same as the implementations mentioned in the aforementioned method implementation section and will not be repeated here.

[0094] The communication bus 304 mentioned in the electronic device can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The communication bus 304 can be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, the figure shows only one thick line, but this does not mean that there is only one bus or only one type of bus.

[0095] The communication interface 302 is used for communication between the electronic device and other devices.

[0096] The processor 301 may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor. The processor 301 is the control center of the electronic device, connecting various parts of the entire electronic device using various interfaces and lines.

[0097] The memory 303 may be used to store the computer program. The processor 301 implements various functions of the electronic device by running or executing the computer program stored in the memory 303 and calling the data stored in the memory 303.

[0098] The memory 303 may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM).

[0099] According to another aspect of the present disclosure, the present invention further provides a storage medium having a program stored thereon, wherein the program, when executed, implements the following steps:

[0100] Obtain raw species distribution data;

[0101] Determine the map grid scale for displaying species distribution and obtain the original species distribution data within each grid range;

[0102] For each grid, it is used as the central grid, and the original species distribution data of multiple other grids within the set range around it are used to perform data enhancement processing on the central grid, so as to obtain the aggregated results of species distribution data of each grid.

[0103] The computer-readable storage medium of the embodiment of the present invention can adopt any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, a system, device or component of electricity, magnetism, light, electromagnetic, infrared or semiconductor, or any combination thereof. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer hard 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 thereof. In this article, a computer-readable storage medium can be any tangible medium containing or storing a program, which can be used by an instruction execution system, device or device or used in combination with it.

[0104] A computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0105] Computer program code for performing the operations of the present invention may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages such as Java, Smalltalk, C++, and conventional procedural programming languages such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0106] It should be noted that the devices and methods disclosed in the embodiments of this document may also be implemented in other ways. The device embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of the devices, methods, and computer program products according to the various embodiments of this document. In this regard, each box in the flowchart or block diagram may represent a module, program, or portion of code, wherein the module, program segment, or portion of code contains one or more executable instructions for implementing a specified logical function, and the module, program segment, or portion of code contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the boxes may also occur in an order different from that marked in the accompanying drawings. For example, two consecutive boxes may actually be executed substantially in parallel, or they may sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart, may be implemented by a dedicated hardware-based system for performing the specified function or action, or may be implemented by a combination of dedicated hardware and computer instructions.

[0107] In addition, the functional modules in the various embodiments of this document may be integrated together to form an independent part, or each module may exist independently, or two or more modules may be integrated to form an independent part.

[0108] The above description is only a description of the preferred embodiment of the present invention and does not limit the scope of the present invention. Any changes and modifications made by ordinary technicians in the field of the present invention based on the above disclosure are within the scope of protection of the claims.

Claims

1. A method for aggregating species distribution data, characterized in that: The following steps are involved: Obtain raw species distribution data; Determine the map grid scale for displaying species distribution and obtain the original species distribution data within each grid range; For each grid, take it as the central grid and perform data enhancement processing on the central grid using the original species distribution data of multiple other grids within the set range around it, so as to obtain the aggregated results of species distribution data of each grid; Among them, the data enhancement processing includes: obtaining the weight value of each other grid within the set range around the central grid according to the set attenuation coefficient, multiplying the original species distribution data of other grids by the weight value and adding them to the data of the central grid, and finally obtaining the species distribution data aggregation result after data enhancement of the central grid.

2. The species distribution data aggregation method according to claim 1, characterized in that: The original species distribution data is obtained through species distribution data source and species identification result information.

3. The method for aggregating species distribution data according to claim 2, characterized in that: The original species distribution data is obtained through species identification result information, including: obtaining wireless data or mobile data of the user and processing the data to obtain the original species distribution data.

4. The method for aggregating species distribution data according to claim 1, wherein: The method further includes: obtaining original species distribution data within each grid range, processing the original species distribution data according to species commonness to obtain processed species distribution data, and performing subsequent data enhancement processing using the processed species distribution data.

5. The method for aggregating species distribution data according to claim 1, wherein: The method also includes: obtaining the altitude value of each grid, calculating the altitude value difference between each central grid and multiple other grids within a set range around it, and when the altitude value difference between any other grid and its central grid exceeds a set threshold, the original species distribution data of the other grid does not participate in the data enhancement processing of the central grid.

6. The method for aggregating species distribution data according to claim 5, characterized in that: The threshold value for the altitude difference is set and adjusted according to different regions.

7. The method for aggregating species distribution data according to claim 1, wherein: The map grid scale is adjusted according to different regions and / or different map grid scales are set for the same region.

8. The method for aggregating species distribution data according to claim 1, wherein: The attenuation coefficient is set and adjusted according to different areas.

9. The method for aggregating species distribution data according to claim 1, wherein: The method also includes: classifying the original species distribution data according to the time dimension, obtaining the original species distribution data at different times within each grid range, and performing data enhancement processing respectively according to the original species distribution data at different times.

10. A species distribution data aggregation system, characterized in that: The method comprises a processor and a memory, wherein a program is stored in the memory, and when the program is executed by the processor, the species distribution data aggregation method according to any one of claims 1 to 9 is implemented.

11. A storage medium having a program stored thereon, characterized in that: When the program is executed, the species distribution data aggregation method according to any one of claims 1 to 9 is implemented.

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