WO2024041275A1 - 物种分布数据聚合方法、系统及存储介质 - Google Patents

物种分布数据聚合方法、系统及存储介质 Download PDF

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Publication number
WO2024041275A1
WO2024041275A1 PCT/CN2023/108385 CN2023108385W WO2024041275A1 WO 2024041275 A1 WO2024041275 A1 WO 2024041275A1 CN 2023108385 W CN2023108385 W CN 2023108385W WO 2024041275 A1 WO2024041275 A1 WO 2024041275A1
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distribution data
species distribution
grid
data
species
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PCT/CN2023/108385
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English (en)
French (fr)
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徐青松
李青
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杭州睿胜软件有限公司
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Publication of WO2024041275A1 publication Critical patent/WO2024041275A1/zh

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/29—Geographical information databases
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/906—Clustering; Classification
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
    • G06T17/05—Geographic models

Definitions

  • the invention relates to the field of computer technology, and in particular to a species distribution data aggregation method, system and storage medium.
  • each species in various parts of the world is uneven, and each place has different common species.
  • a species database can be established to obtain a list of common species near the place based on geographical location, and it can also meet the user's personalization Message requirement.
  • the distribution of data obtained by species observation is uneven.
  • the number of species within a certain range is generally not very large, and the display effect is poor. Therefore, the data needs to be enhanced to a certain extent.
  • One of the purposes of this disclosure is to provide a species distribution data aggregation method, which includes the following steps:
  • each grid use it as the central grid, and use the original species distribution data of multiple other grids within a set range around it to perform data enhancement processing on the central grid to obtain the species distribution of each grid. Data aggregation results.
  • the original species distribution data is obtained from species distribution data sources and species identification result information.
  • obtaining the original species distribution data through species identification result information includes: obtaining the user's wireless data or mobile data and processing the data to obtain the original species distribution data.
  • the method further includes: obtaining original species distribution data within each grid range and then processing the original species distribution data according to species commonness to obtain processed species distribution data, and using the processed species distribution data. Species distribution data are subjected to subsequent data enhancement processing.
  • the method further includes: obtaining the altitude value of each grid, calculating each The altitude value difference between the central grid and multiple other grids within the surrounding set range. When the altitude value difference between any other grid and its central grid exceeds the set threshold, the original species of the other grid Distributed data does not participate in the data enhancement processing of the central grid.
  • the setting threshold of the altitude value difference is set and adjusted according to different regions.
  • the map grid scale is adjusted separately according to different areas and/or different map grid scales are set for the same area.
  • the data enhancement process includes: obtaining the weight value of each other grid within a set range around the central grid according to the set attenuation coefficient, and multiplying the original species distribution data of other grids by The weight values are then accumulated into the data of the central grid, and finally the species distribution data aggregation result after the data of the central grid is enhanced is obtained.
  • the attenuation coefficient is adjusted according to different settings in different regions.
  • 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 classifying the original species distribution data according to the original species distribution data at different times. Perform data enhancement processing.
  • a species distribution data aggregation system including a processor and a memory.
  • a program is stored on the memory.
  • the program is executed by the processor, the species distribution as described above is achieved.
  • Data aggregation methods including a processor and a memory.
  • a storage medium is proposed on which a program is stored, which when executed implements the species distribution data aggregation method as described above.
  • Figure 1 shows a schematic flow chart of a species distribution data aggregation method provided by an embodiment of the present invention.
  • Figure 2 shows a schematic diagram of the weight values of each other grid within a set range around a certain central grid provided by an embodiment of the present invention.
  • Figure 3 shows a schematic diagram of the weight values of each other grid within a set range around a certain central grid provided by yet another embodiment of the present invention.
  • Figure 4 shows a schematic diagram of the aggregation results of species distribution data in a certain area provided by an embodiment of the present invention.
  • Figure 5 shows a schematic structural diagram of a species distribution data aggregation system provided by an embodiment of the present invention.
  • any specific values are to be construed as illustrative only , rather than as a limitation. Accordingly, other examples of the exemplary embodiments may have different values.
  • Figure 1 shows a schematic flow chart of a species distribution data aggregation method provided by an embodiment of the present invention. This method can be implemented in an application (app) installed on a smart terminal such as a mobile phone or tablet computer. As shown in Figure 1, the method includes:
  • Step S100 Obtain original species distribution data
  • Step S200 Determine the map grid scale for species distribution display, and obtain original species distribution data within each grid range;
  • Step S300 For each grid, use it as the central grid, use the original species distribution data of multiple other grids within the set range around it to perform data enhancement processing on the central grid, thereby obtaining each grid The aggregation results of species distribution data.
  • the original species distribution data is obtained from species distribution data sources and species identification result information.
  • Original species distribution data can be obtained through species distribution data sources, such as through various public general species distribution databases (such as GBIF: Global Biodiversity Information Network), which contain a large number of field observations. The data can be used as a source of data on species distribution in the wild.
  • species distribution data sources such as through various public general species distribution databases (such as GBIF: Global Biodiversity Information Network), which contain a large number of field observations.
  • GBIF Global Biodiversity Information Network
  • obtaining the original species distribution data through species identification result information includes: obtaining the user's wireless data or mobile data and processing the data to obtain the original species distribution data.
  • the data that users use to identify species through species identification software can be processed based on the IP data of the user's wireless WIFI to obtain the location information of species distribution, which can be used as a reference for the distribution information of species data in specific areas such as residential areas or commercial industrial areas.
  • the user's mobile data (such as 3G, 4G or 5G) can also be processed to determine the approximate species distribution area, but this method cannot determine the precise location of species distribution.
  • Species identification software can identify plant species based on images taken by users, and present classification information and other related information of the species based on the identified species.
  • the method further includes: obtaining original species distribution data within each grid range and then processing the original species distribution data according to species commonness to obtain processed species distribution data, and using the processed species distribution data. Species distribution data are subjected to subsequent data enhancement processing.
  • Different species can be ranked according to their commonness, and the species commonness can be used as a subsequent display Expanded information of species information is displayed.
  • users can also choose to display only species distribution data at a specific level of commonness or above a certain level.
  • the species commonness can be statistically confirmed and displayed accordingly based on the location information of the species in different regions and scales.
  • the species commonness can be carried out in the following ways: First, confirm the species in the species list in this country (state) or other regional species lists, rare substances and protected species (such as IUCN species) are set to rare by default, horticultural species need to confirm their commonness based on data, and data from species distribution data sources (such as GBIF data) are used as the commonness of wild species.
  • statistics are carried out according to the set regional scale (such as 40*20km grid).
  • User-identified species data such as WIFI wireless data or mobile data serve as a reference for the commonness of ornamental plants and can be used as a supplement to the GBIF data. It can also be based on Statistics are performed within the set regional scale range (for example, 40*20km grid).
  • 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 the surrounding set range.
  • the numerical difference in altitude between a grid and its central grid exceeds a set threshold, the original species distribution data of the other grids will not participate in the data enhancement processing of the central grid.
  • Species observation data show that the distribution of species is uneven. If a species is a common species in a certain area, then there should also be a certain distribution of that species in its surrounding areas. At the same time, the difference between different altitudes has an important impact on the species distribution. Regional scope also has a certain impact. At the same time, the number of species within a set-scale grid range (for example, 40*20km) is generally not very large, and the display effect is poor, so the data needs to be enhanced to a certain extent.
  • the species distribution data observed within each grid range need to be diffused to the surrounding grids (that is, the distance at which the weight of the species distribution data attenuates), for example It is set that the same dimension gradually attenuates within 400 kilometers, and the same longitude gradually attenuates within 100 kilometers. If the altitude difference exceeds 1000 meters, the spread will not continue, because the species distribution in different areas with too large altitude differences will also be very different.
  • the setting threshold of the altitude value difference is set and adjusted according to different regions, such as 1500 meters, 2000 meters and other different ranges. It can be uniformly set to different values, or it can also be set according to the species distribution in different regions. to set different values respectively.
  • the map grid scale is adjusted and/or adjusted according to different regions.
  • Set different map grid scales for the same area can be set to different scale ranges such as 40*20km or 50*50km.
  • Different areas can determine different display grid scales based on species distribution data.
  • Users can also choose to display different grid scales. That is to say, the data of the 50km*50km grid can be displayed by default, and the user can choose to reduce it to a 40*20 grid, or the default is the 40*20 grid or the 50*50 grid remains unchanged.
  • the value of the distribution altitude and the distance of weight attenuation can also be set according to the distribution of species in different regions, because the degree of aggregation of species distribution in different regions is different. For example, areas such as deserts, wastelands or Gobi have less species distribution and are warmer. There are many species distributed in belts or tropical rainforest areas, so different data can be set for data enhancement processing to improve the data display effect. The same reason applies to the impact of altitude and weight attenuation distance.
  • the data enhancement process includes: obtaining the weight value of each other grid within a set range around the central grid according to the set attenuation coefficient, and multiplying the original species distribution data of other grids by The weight values are then accumulated into the data of the central grid, and finally the species distribution data aggregation result after the data of the central grid is enhanced is obtained.
  • the attenuation coefficient is set and adjusted according to different regions.
  • Figures 2 and 3 show schematic diagrams of the weight values of each other grid within a set range around a certain central grid provided by different embodiments.
  • the original weight value of the grid at the center point is set to 100 (that is, 100% of its data participates in the accumulation calculation), and other surrounding grids perform attenuation calculations according to the weight values set in the figure (90 is 90 % participates in the accumulation, 60 means 60% participates in the accumulation), the data of the final grid in the center is accumulated according to the data of each grid within the surrounding set range multiplied by the percentage of the weight value.
  • Figure 4 shows a schematic diagram of the aggregation results of species distribution data in a certain area provided by an embodiment of the present invention.
  • the data displayed in each grid are the species data processed above. It can display the overall species data, as well as the data of each classified species and sort them by quantity.
  • the species distribution data is displayed on the map grid in the appropriate grid scale range, as shown in Figure 4, corresponding to the user's operation, such as when the user moves or clicks on a certain grid, Displays the species distribution data corresponding to this grid.
  • 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 classifying the original species distribution data according to the original species distribution data at different times. Perform data enhancement processing.
  • the data of different months can be divided according to time, for example, by month, so that the species distribution data can be modeled as three-dimensional coordinates of w*h*t, for example: m50-j12-w13-t4: means that 50km is used as the geographical scale, and the longitude direction is the 12th , the 13th in latitude direction, divided in time by month, aggregated data in April.
  • the relevant species distribution aggregate data is saved under this key.
  • the location information of species is based on the actual longitude and latitude distribution range or Specific location information is recorded so that data statistics can be carried out at different grid scales and subsequently displayed according to needs.
  • species distribution coordinates can also be used to calculate and display the aggregated data of species in a city, province or country.
  • Obtaining user species information and updating the species distribution data aggregation database are carried out in the following ways:
  • the user authorizes the geographical location, or infers the longitude and latitude of the user's location based on the user's IP information.
  • the geographical location algorithm Geohash is a way to cut the entire earth user grid, such as cutting it into 40km*20km grid blocks, and then any longitude and latitude coordinates can quickly locate these grids.
  • Others can also include common species information, terrain information such as altitude, climate zone classification information, vegetation information and other related information.
  • Different species data sets and species distribution data are stored in association, so that after the species distribution data are aggregated and displayed, different interactive displays and functional processing can be facilitated. For example, a list of common species near the place and the climate of the species distribution area can be obtained based on the geographical location.
  • Meteorological information can also meet users' personalized information needs, such as viewing common poisonous plants near New York, common weeds near Los Angeles, common fish at various fishing spots, and other classified and aggregated information.
  • the present invention also provides a species distribution data aggregation system, including a processor and a memory.
  • a program is stored on the memory.
  • the species distribution data as described above is realized. Aggregation method.
  • Figure 5 is a schematic structural diagram of a species distribution data aggregation system provided by an embodiment of the present invention.
  • the species distribution data aggregation system includes a processor 301, a communication interface 302, and a memory 303 and communication bus 304.
  • the processor 301, the communication interface 302, and the memory 303 complete communication with each other through the communication bus 304.
  • the memory 303 is used to store computer programs.
  • processor 301 When the processor 301 is used to execute the program stored in the memory 303, it implements the following steps:
  • each grid use it as the central grid, and use the original species distribution data of multiple other grids within a set range around it to perform data enhancement processing on the central grid to obtain the species distribution of each grid. Data aggregation results.
  • the communication bus 304 mentioned in the above-mentioned electronic equipment may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc.
  • PCI Peripheral Component Interconnect
  • EISA Extended Industry Standard Architecture
  • the communication bus 304 can be divided into an address bus, a data bus, a control bus, etc. For ease of presentation, only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus.
  • the communication interface 302 is used for communication between the above-mentioned electronic device and other devices.
  • the processor 301 may be a central processing unit (CPU), or other general-purpose processor, digital signal processor (Digital Signal Processor, DSP), or specialized processor. Use integrated circuits (Application Specific Integrated Circuit, ASIC), field-programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.
  • the general processor may be a microprocessor or the processor may be any conventional processor, etc.
  • the processor 301 is the control center of the electronic device and uses various interfaces and lines to connect various parts of the entire electronic device.
  • the memory 303 can 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. Function.
  • 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.
  • RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous chain Synchlink DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.
  • the present invention also proposes a storage medium on which a program is stored.
  • the program When the program is executed, the following steps are implemented:
  • each grid use it as the central grid, and use the original species distribution data of multiple other grids within a set range around it to perform data enhancement processing on the central grid to obtain the species distribution of each grid. Data aggregation results.
  • the computer-readable storage medium in the embodiment of the present invention may be any combination of one or more computer-readable media.
  • the computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium.
  • the computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared or semiconductor system, device or device, or any combination thereof.
  • the computer can More specific examples (non-exhaustive list) of reading storage media include: electrical connections with one or more wires, portable computer hard drives, hard drives, random access memory (RAM), read only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination of the above.
  • a computer-readable storage medium may be any tangible medium that contains or stores a program for use by or in combination with an instruction execution system, apparatus, or device.
  • a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave carrying computer-readable program code therein. Such propagated data signals may take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above.
  • a computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device .
  • 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 language - such as "C" or similar programming language.
  • the program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server.
  • the remote computer can be connected to the user's computer through any kind of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (such as an Internet service provider) through the Internet. ).
  • LAN local area network
  • WAN wide area network
  • Internet service provider such as an Internet service provider
  • each block in the flowchart or block diagrams may represent a module, program, or portion of code that contains one or more operable functions for implementing the specified logical functions.
  • Execution instructions, the module, program segment or part of the code contains one or more executable functions for realizing the specified logical function. line instructions.
  • each block in the block diagram and/or flowchart illustration, and combinations of blocks in the block diagram and/or flowchart illustration can be designed into specialized hardware-based systems that perform the specified functions or acts. Implemented, or may be implemented using a combination of dedicated hardware and computer instructions.
  • each functional module in each embodiment of this article can be integrated together to form an independent part, each module can exist alone, or two or more modules can be integrated to form an independent part.

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Abstract

本发明提供一种物种分布数据聚合方法、系统及存储介质，所述方法包括：获取原始物种分布数据；确定物种分布显示的地图网格尺度，并获取各个网格范围内的原始物种分布数据；针对每个网格，以其作为中心网格，利用其周围设定范围内的多个其他网格的原始物种分布数据对该中心网格进行数据增强处理，从而获取每个网格的物种分布数据聚合结果。本发明提供的物种分布数据聚合方法能够对物种分布数据进行增强显示，以便提升数据显示效果。

Description

物种分布数据聚合方法、系统及存储介质 技术领域
本发明涉及计算机技术领域，特别涉及一种物种分布数据聚合方法、系统及存储介质。
背景技术
每个物种在全球各个地方的分布是不均匀的，每个地方都有不同的常见物种，可以建立一个物种数据库，根据地理位置获得该地附近常见物种的列表，同时还可以满足用户的个性化信息需求。然而物种观测获取的数据分布是不均匀的，同时一定范围内的物种数量一般不会很多，显示效果较差，因此需要对数据进行一定程度的增强。
发明内容
本公开的目的之一是提供一种物种分布数据聚合方法，包括下列步骤：
获取原始物种分布数据；
确定物种分布显示的地图网格尺度，并获取各个网格范围内的原始物种分布数据；
针对每个网格，以其作为中心网格，利用其周围设定范围内的多个其他网格的原始物种分布数据对该中心网格进行数据增强处理，从而获取每个网格的物种分布数据聚合结果。
在一些实施例中，所述原始物种分布数据通过物种分布数据源以及物种识别结果信息获取。
在一些实施例中，所述原始物种分布数据通过物种识别结果信息获取包括：获取用户的无线数据或移动数据并进行处理后得到原始物种分布数据。
在一些实施例中，该方法还包括：获取各个网格范围内的原始物种分布数据后根据物种常见度对所述原始物种分布数据进行处理以得到处理后的物种分布数据，并利用处理后的物种分布数据进行后续数据增强处理。
在一些实施例中，该方法还包括：获取每个网格的海拔数值，计算每个 中心网格和周围设定范围内的多个其他网格之间的海拔数值差距，当任一其他网格和其中心网格的海拔数值差距超过设定阈值时，该其他网格的原始物种分布数据不参与所述中心网格的数据增强处理。
在一些实施例中，所述海拔数值差距的设定阈值根据不同区域分别设定调整。
在一些实施例中，所述地图网格尺度根据不同区域分别设定调整和/或对同一区域设定不同的地图网格尺度。
在一些实施例中，所述数据增强处理包括：根据设置的衰减系数得到所述中心网格周围设定范围内的每个其他网格的权重值，将其他网格的原始物种分布数据乘以权重值后累加到中心网格的数据中，最终得到所述中心网格的数据增强后的物种分布数据聚合结果。
在一些实施例中，所述衰减系数根据不同区域分别设定调整。
在一些实施例中，该方法还包括：根据时间维度对所述原始物种分布数据进行分类处理，获取各个网格范围内处于不同时间的原始物种分布数据，并根据不同时间的原始物种分布数据分别进行数据增强处理。
根据本公开的另一方面，提出了一种物种分布数据聚合系统，包括处理器和存储器，所述存储器上存储有程序，所述程序被所述处理器执行时，实现如上所述的物种分布数据聚合方法。
根据本公开的另一方面，提出了一种存储介质，其上存储有程序，所述程序被执行时实现如上所述的物种分布数据聚合方法。
通过以下参照附图对本公开的示例性实施例的详细描述，本公开的其它特征及其优点将会变得更为清楚。
附图说明
构成说明书的一部分的附图描述了本公开的实施例，并且连同说明书一起用于解释本公开的原理。
参照附图，根据下面的详细描述，可以更加清楚地理解本公开，其中：
图1所示为本发明一实施例提供的物种分布数据聚合方法的流程示意图。
图2所示为本发明一实施例提供的某一中心网格周围设定范围内的每个其他网格的权重值示意图。
图3所示为本发明又一实施例提供的某一中心网格周围设定范围内的每个其他网格的权重值示意图。
图4所示为本发明一实施例提供的某一区域的物种分布数据聚合结果示意图。
图5所示为本发明一实施例提供的物种分布数据聚合系统的结构示意图。
注意，在以下说明的实施方式中，有时在不同的附图之间共同使用同一附图标记来表示相同部分或具有相同功能的部分，而省略其重复说明。在一些情况中，使用相似的标号和字母表示类似项，因此，一旦某一项在一个附图中被定义，则在随后的附图中不需要对其进行进一步讨论。
为了便于理解，在附图等中所示的各结构的位置、尺寸及范围等有时不表示实际的位置、尺寸及范围等。因此，本公开并不限于附图等所公开的位置、尺寸及范围等。
具体实施方式
下面将参照附图来详细描述本公开的各种示例性实施例。应注意到：除非另外具体说明，否则在这些实施例中阐述的部件和步骤的相对布置、数字表达式和数值不限制本公开的范围。
以下对至少一个示例性实施例的描述实际上仅仅是说明性的，决不作为对本公开及其应用或使用的任何限制。也就是说，本文中的结构及方法是以示例性的方式示出，来说明本公开中的结构和方法的不同实施例。然而，本领域技术人员将会理解，它们仅仅说明可以用来实施的本公开的示例性方式，而不是穷尽的方式。此外，附图不必按比例绘制，一些特征可能被放大以示出具体组件的细节。
对于相关领域普通技术人员已知的技术、方法和设备可能不作详细讨论，但在适当情况下，所述技术、方法和设备应当被视为授权说明书的一部分。
在这里示出和讨论的所有示例中，任何具体值应被解释为仅仅是示例性 的，而不是作为限制。因此，示例性实施例的其它示例可以具有不同的值。
图1所示为本发明一实施例提供的物种分布数据聚合方法的流程示意图，该方法可以在例如手机、平板电脑等智能终端上安装的应用程序(app)中实现。如图1所示，该方法包括：
步骤S100：获取原始物种分布数据；
步骤S200：确定物种分布显示的地图网格尺度，并获取各个网格范围内的原始物种分布数据；
步骤S300：针对每个网格，以其作为中心网格，利用其周围设定范围内的多个其他网格的原始物种分布数据对该中心网格进行数据增强处理，从而获取每个网格的物种分布数据聚合结果。
在一些实施例中，所述原始物种分布数据通过物种分布数据源以及物种识别结果信息获取。原始物种分布数据可以通过物种分布数据源来进行获取，例如通过各类公开的通用物种分布数据库(如GBIF：全球生物多样性信息网络)来获取原始物种分布数据，这类数据源包含大量野外观测数据,可以作为野外物种分布的数据来源。
在一些实施例中，所述原始物种分布数据通过物种识别结果信息获取包括：获取用户的无线数据或移动数据并进行处理后得到原始物种分布数据。用户通过物种识别软件进行物种识别的数据,可以根据用户无线WIFI的IP数据拟合处理，获取物种分布的位置信息，作为居民区或商业工业区等特定区域的物种数据的分布信息参考。同时在没有WIFI等无线信号的区域还可以根据用户的移动数据(例如3G、4G或5G)进行处理确定大致的物种分布区域范围，不过这种方式无法确定物种分布的精确位置。物种识别软件可以基于用户拍摄的影像识别植物的物种，并基于识别出的物种呈现该物种的分类信息和其他相关信息。
在一些实施例中，该方法还包括：获取各个网格范围内的原始物种分布数据后根据物种常见度对所述原始物种分布数据进行处理以得到处理后的物种分布数据，并利用处理后的物种分布数据进行后续数据增强处理。
不同物种可以按照常见程度进行分级，物种常见度可以作为后续显示物 种信息的扩展信息进行显示，此外用户还可以选择只显示特定常见程度等级或某等级以上的物种分布数据。物种常见度可以根据物种的位置信息在不同区域和尺度范围内的数据进行统计确认常见度和相应展示，物种常见度可以根据以下方式进行：首先在确认物种列表里面的物种在这个国家(州)或其他区域范围的物种清单里面，稀有物质以及保护物种(例如IUCN物种)默认设为少见，园艺物种需要根据数据确认其常见度，物种分布数据源的数据(例如GBIF数据)作为野外物种常见程度参考,按照设定的区域尺度范围(例如40*20km网格)进行统计，用户识别的物种数据例如WIFI无线数据或移动数据作为观赏植物常见程度参考,可以作为GBIF数据的补充，其也可以按照设定的区域尺度范围(例如40*20km网格)进行统计。
在一些实施例中，该方法还包括：获取每个网格的海拔数值，计算每个中心网格和周围设定范围内的多个其他网格之间的海拔数值差距，当任一其他网格和其中心网格的海拔数值差距超过设定阈值时，该其他网格的原始物种分布数据不参与所述中心网格的数据增强处理。
物种观测数据体现出物种的分布是不均匀的，如果某个物种在某个地区是常见物种,那么在它的周围地区应该也有该物种的一定分布，同时不同海拔高度的差值对于物种分布的区域范围也有一定影响，同时设定尺度的网格范围(例如40*20km)内的物种数量一般不会很多，显示效果较差，因此需要对数据进行一定程度的增强。
在海拔高度相差1000米以内(当然也可以不考虑海拔影响),各个网格范围内观测的物种分布数据需要向其周围的网格进行扩散(即物种分布数据的权重衰减的距离)，例如可以设定同维度在400公里内逐步衰减,同经度在100公里内逐步衰减。如果海拔高度相差超过1000米,则不继续扩散，因为海拔高度相差过大的不同区域物种分布也会有很大不同。
在一些实施例中，所述海拔数值差距的设定阈值根据不同区域分别设定调整，例如1500米，2000米等不同范围，可以统一设定为不同数值，也可以根据不同区域的物种分布情况的不同来分别设定不同的数值。
在一些实施例中，所述地图网格尺度根据不同区域分别设定调整和/或对 同一区域设定不同的地图网格尺度。例如可以设置为40*20km或50*50km等不同的尺度范围，不同区域可以根据物种分布数据的情况确定不同的显示网格尺度，用户也可以选择显示不同的网格尺度。也就是说可以默认显示50km*50km的网格的数据，用户可以选择缩小到40*20网格，或者是默认就是40*20网格或者50*50网格不变。
同样的，也可以按照不同区域物种分布的情况来设定分布海拔的数值以及权重衰减的距离，因为不同区域的物种分布聚合程度不同，例如沙漠、荒原或戈壁等地区物种分布较少，温热带或热带雨林区域的物种分布较多，因此可以设置不同的数据来进行数据增强处理，以便提升数据显示效果，对于海拔以及权重衰减的距离的影响也是相同的原因。
在一些实施例中，所述数据增强处理包括：根据设置的衰减系数得到所述中心网格周围设定范围内的每个其他网格的权重值，将其他网格的原始物种分布数据乘以权重值后累加到中心网格的数据中，最终得到所述中心网格的数据增强后的物种分布数据聚合结果。
进一步的，所述衰减系数根据不同区域分别设定调整。请参考图2和图3，其显示不同的实施例提供的某一中心网格周围设定范围内的每个其他网格的权重值示意图。如图2和图3所示，中心点的网格原始权重值设置为100(即其数据100％参与累加计算)，周围的其他网格按照图中设置的权重值进行衰减计算(90即90％参与累加，60即60％参与累加)，最终中心的网格的数据按照其周围设定范围内的各个网格的数据乘以权重值的百分比后进行累加获取。
图4所示为本发明一实施例提供的某一区域的物种分布数据聚合结果示意图。在最终显示156.5*156km网格范围的物种数据时，各个网格显示的数据都是经过以上处理的物种数据，其可以显示整体物种数据，以及各个分类物种的数据并按照数量进行排序显示。加载地图后按照网格数据，在合适的网格尺度范围的地图网格上显示物种分布数据，如图4所示，相应于用户的操作，例如当用户移动或点击到某一网格时，显示该网格所对应的物种分布数据。
在一些实施例中，该方法还包括：根据时间维度对所述原始物种分布数据进行分类处理，获取各个网格范围内处于不同时间的原始物种分布数据，并根据不同时间的原始物种分布数据分别进行数据增强处理。
可以根据时间例如按月划分不同月份的数据，从而将物种分布数据建模为w*h*t的三维坐标，例如：m50-j12-w13-t4:表示以50km作为地理刻度，经度方向第12个，纬度方向第13个，时间上按月划分，4月份的聚合数据。这个key下保存相关的物种分布聚合数据。加入时间维度后，可以定义更加丰富和细节的物种分布数据显示方式，例如某一时间段的物种分布数据或者全部时间段的物种分布数据来进行显示，物种的位置信息根据实际的经纬度分布范围或具体位置信息来进行记录，以便可以在不同的网格尺度下进行数据统计和后续根据需求区分显示。
此外，还可以通过物种分布坐标计算城市、省份或者国家的物种分别聚合数据进行显示。
主要城市数据聚合：
·提供城市列表
·对于每一个城市，计算聚合数据
·获取城市的坐标
·调用上面的物种分布数据
·获取该城市的物种分布数据
主要省份数据聚合：
·提供省份列表
·获取省份的城市列表，及每个城市的聚合数据
·汇总城市的聚合数据
主要国家数据聚合
·提供国家列表
·获取国家的省份列表，及每个省份的聚合数据
·汇总省份的聚合数据
基础城市数据服务
·根据经纬度，获取城市/省份/国家
·获取国家的省份列表
·获取省份的城市列表
·根据城市code，获取城市的基础信息
获取用户物种信息更新物种分布数据聚合数据库采取以下方式进行：
1.用户授权地理位置,或者根据用户IP信息推理出用户的所在地经纬度
2.根据经纬度算出用户所在地的地理位置算法(Geohash)的经纬度编码信息，并匹配到地理网格化数据库
3.获取该网格的数据
4.结合地理网格数据和用户动态信息(拍到的植物,当前的时间等)给出用户契合当地环境的建议和信息
地理位置算法Geohash是一种把整个地球用户网格切割的方式,比如切割成40km*20km的网格块,然后任何经纬度坐标都可以快速定位到这些网格。我们可以在Geohash网格块里面存储当地相关的信息，包含根据物种分布数据库,存储当地常见的植物列表，根据气象气候信息基础数据库,存储当地气象信息：
a.日最低温,最高温,平均温度
b.月平均气温
c.降水信息,至少有月平均降水量
d.湿度信息,至少有月平均湿度
e.光照信息
f.耐寒区信息Hardiness zone
其他还可以包括常见物种信息、海拔等地形信息、气候带分类信息、植被信息等等相关信息。将不同的物种数据集合物种分布数据关联存储，以便在物种分布数据聚合显示后，方便进行不同的交互显示和功能处理，例如可以根据地理位置获得该地附近常见物种的列表，物种分布地的气候气象信息，同时还可以满足用户的个性化信息需求，例如查看纽约附近常见的有毒植物，洛杉矶附近常见的杂草，各个钓点的常见鱼类等归类聚合信息。
基于同一发明构思，本发明还提供一种物种分布数据聚合系统，包括处理器和存储器，所述存储器上存储有程序，所述程序被所述处理器执行时，实现如上所述的物种分布数据聚合方法。请参考图5，图5所示为本发明一实施例提供的物种分布数据聚合系统的结构示意图，如图5所示，所述物种分布数据聚合系统包括处理器301、通信接口302、存储器303和通信总线304。
其中，所述处理器301、所述通信接口302、所述存储器303通过所述通信总线304完成相互间的通信。
所述存储器303，用于存放计算机程序。
所述处理器301，用于执行存储器303上所存放的程序时，实现如下步骤：
获取原始物种分布数据；
确定物种分布显示的地图网格尺度，并获取各个网格范围内的原始物种分布数据；
针对每个网格，以其作为中心网格，利用其周围设定范围内的多个其他网格的原始物种分布数据对该中心网格进行数据增强处理，从而获取每个网格的物种分布数据聚合结果。
关于该方法各个步骤的具体实现以及相关解释内容可以参见上述图1所示的方法实施方式，在此不做赘述。
另外，处理器301执行存储器303上所存放的程序而实现的物种分布数据聚合方法的其他实现方式，与前述方法实施方式部分所提及的实现方式相同，这里也不再赘述。
上述电子设备提到的通信总线304可以是外设部件互连标准(Peripheral Component Interconnect，PCI)总线或扩展工业标准结构(Extended Industry Standard Architecture，EISA)总线等。该通信总线304可以分为地址总线、数据总线、控制总线等。为便于表示，图中仅用一条粗线表示，但并不表示仅有一根总线或一种类型的总线。
通信接口302用于上述电子设备与其他设备之间的通信。
所述处理器301可以是中央处理单元(Central Processing Unit，CPU)，还可以是其他通用处理器、数字信号处理器(Digital Signal Processor，DSP)、专 用集成电路(Application Specific Integrated Circuit，ASIC)、现场可编程门阵列(Field-Programmable Gate Array，FPGA)或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件等。通用处理器可以是微处理器或者该处理器也可以是任何常规的处理器等，所述处理器301是所述电子设备的控制中心，利用各种接口和线路连接整个电子设备的各个部分。
所述存储器303可用于存储所述计算机程序，所述处理器301通过运行或执行存储在所述存储器303内的计算机程序，以及调用存储在存储器303内的数据，实现所述电子设备的各种功能。
所述存储器303可以包括非易失性和/或易失性存储器。非易失性存储器可包括只读存储器(ROM)、可编程ROM(PROM)、电可编程ROM(EPROM)、电可擦除可编程ROM(EEPROM)或闪存。易失性存储器可包括随机存取存储器(RAM)或者外部高速缓冲存储器。作为说明而非局限，RAM以多种形式可得，诸如静态RAM(SRAM)、动态RAM(DRAM)、同步DRAM(SDRAM)、双数据率SDRAM(DDRSDRAM)、增强型SDRAM(ESDRAM)、同步链路(Synchlink)DRAM(SLDRAM)、存储器总线(Rambus)直接RAM(RDRAM)、直接存储器总线动态RAM(DRDRAM)、以及存储器总线动态RAM(RDRAM)等。
根据本公开的另一方面，本发明还提出了一种存储介质，其上存储有程序，所述程序被执行时实现如下步骤：
获取原始物种分布数据；
确定物种分布显示的地图网格尺度，并获取各个网格范围内的原始物种分布数据；
针对每个网格，以其作为中心网格，利用其周围设定范围内的多个其他网格的原始物种分布数据对该中心网格进行数据增强处理，从而获取每个网格的物种分布数据聚合结果。
本发明实施方式的计算机可读存储介质，可以采用一个或多个计算机可读的介质的任意组合。计算机可读介质可以是计算机可读信号介质或者计算机可读存储介质。计算机可读存储介质例如可以是但不限于电、磁、光、电磁、红外线或半导体的系统、装置或器件，或者任意以上的组合。计算机可 读存储介质的更具体的例子(非穷举的列表)包括：具有一个或多个导线的电连接、便携式计算机硬盘、硬盘、随机存取存储器(RAM)、只读存储器(ROM)、可擦式可编程只读存储器(EPROM或闪存)、光纤、便携式紧凑磁盘只读存储器(CD-ROM)、光存储器件、磁存储器件、或者上述的任意合适的组合。在本文中，计算机可读存储介质可以是任何包含或存储程序的有形介质，该程序可以被指令执行系统、装置或者器件使用或者与其组合使用。
计算机可读的信号介质可以包括在基带中或者作为载波一部分传播的数据信号，其中承载了计算机可读的程序代码。这种传播的数据信号可以采用多种形式，包括但不限于电磁信号、光信号或上述的任意合适的组合。计算机可读的信号介质还可以是计算机可读存储介质以外的任何计算机可读介质，该计算机可读介质可以发送、传播或者传输用于由指令执行系统、装置或者器件使用或者与其结合使用的程序。
可以以一种或多种程序设计语言或其组合来编写用于执行本发明操作的计算机程序代码，所述程序设计语言包括面向对象的程序设计语言-诸如Java、Smalltalk、C++，还包括常规的过程式程序设计语言-诸如“C”语言或类似的程序设计语言。程序代码可以完全地在用户计算机上执行、部分地在用户计算机上执行、作为一个独立的软件包执行、部分在用户计算机上部分在远程计算机上执行、或者完全在远程计算机或服务器上执行。在涉及远程计算机的情形中，远程计算机可以通过任意种类的网络——包括局域网(LAN)或广域网(WAN)连接到用户计算机，或者可以连接到外部计算机(例如利用因特网服务提供商来通过因特网连接)。
应当注意的是，在本文的实施方式中所揭露的装置和方法，也可以通过其他的方式实现。以上所描述的装置实施方式仅仅是示意性的，例如，附图中的流程图和框图显示了根据本文的多个实施方式的装置、方法和计算机程序产品的可能实现的体系架构、功能和操作。在这点上，流程图或框图中的每个方框可以代表一个模块、程序或代码的一部分，所述模块、程序段或代码的一部分包含一个或多个用于实现规定的逻辑功能的可执行指令，所述模块、程序段或代码的一部分包含一个或多个用于实现规定的逻辑功能的可执 行指令。也应当注意，在有些作为替换的实现方式中，方框中所标注的功能也可以以不同于附图中所标注的顺序发生。例如，两个连续的方框实际上可以基本并行地执行，它们有时也可以按相反的顺序执行，这依所涉及的功能而定。也要注意的是，框图和/或流程图中的每个方框、以及框图和/或流程图中的方框的组合，可以用于执行规定的功能或动作的专用的基于硬件的系统来实现，或者可以用专用硬件与计算机指令的组合来实现。
另外，在本文各个实施方式中的各功能模块可以集成在一起形成一个独立的部分，也可以是各个模块单独存在，也可以两个或两个以上模块集成形成一个独立的部分。
上述描述仅是对本发明较佳实施方式的描述，并非对本发明范围的任何限定，本发明领域的普通技术人员根据上述揭示内容做的任何变更、修饰，均属于权利要求书的保护范围。

Claims (12)

  1. 一种物种分布数据聚合方法，其特征在于，包括下列步骤：
    获取原始物种分布数据；
    确定物种分布显示的地图网格尺度，并获取各个网格范围内的原始物种分布数据；
    针对每个网格，以其作为中心网格，利用其周围设定范围内的多个其他网格的原始物种分布数据对该中心网格进行数据增强处理，从而获取每个网格的物种分布数据聚合结果。
  2. 根据权利要求1所述的物种分布数据聚合方法，其特征在于，所述原始物种分布数据通过物种分布数据源以及物种识别结果信息获取。
  3. 根据权利要求2所述的物种分布数据聚合方法，其特征在于，所述原始物种分布数据通过物种识别结果信息获取包括：获取用户的无线数据或移动数据并进行处理后得到原始物种分布数据。
  4. 根据权利要求1所述的物种分布数据聚合方法，其特征在于，该方法还包括：获取各个网格范围内的原始物种分布数据后根据物种常见度对所述原始物种分布数据进行处理以得到处理后的物种分布数据，并利用处理后的物种分布数据进行后续数据增强处理。
  5. 根据权利要求1所述的物种分布数据聚合方法，其特征在于，该方法还包括：获取每个网格的海拔数值，计算每个中心网格和周围设定范围内的多个其他网格之间的海拔数值差距，当任一其他网格和其中心网格的海拔数值差距超过设定阈值时，该其他网格的原始物种分布数据不参与所述中心网格的数据增强处理。
  6. 根据权利要求5所述的物种分布数据聚合方法，其特征在于，所述海拔数值差距的设定阈值根据不同区域分别设定调整。
  7. 根据权利要求1所述的物种分布数据聚合方法，其特征在于，所述地图网格尺度根据不同区域分别设定调整和/或对同一区域设定不同的地图网格尺度。
  8. 根据权利要求1所述的物种分布数据聚合方法，其特征在于，所述数据增强处理包括：根据设置的衰减系数得到所述中心网格周围设定范围内的每个其他网格的权重值，将其他网格的原始物种分布数据乘以权重值后累加到中心网格的数据中，最终得到所述中心网格的数据增强后的物种分布数据聚合结果。
  9. 根据权利要求8所述的物种分布数据聚合方法，其特征在于，所述衰减系数根据不同区域分别设定调整。
  10. 根据权利要求1所述的物种分布数据聚合方法，其特征在于，该方法还包括：根据时间维度对所述原始物种分布数据进行分类处理，获取各个网格范围内处于不同时间的原始物种分布数据，并根据不同时间的原始物种分布数据分别进行数据增强处理。
  11. 一种物种分布数据聚合系统，其特征在于，包括处理器和存储器，所述存储器上存储有程序，所述程序被所述处理器执行时，实现根据权利要求1～10中任一项所述的物种分布数据聚合方法。
  12. 一种存储介质，其上存储有程序，其特征在于，所述程序被执行时实现根据权利要求1～10中任一项所述的物种分布数据聚合方法。
PCT/CN2023/108385 2022-08-23 2023-07-20 物种分布数据聚合方法、系统及存储介质 WO2024041275A1 (zh)

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