CN116502912A - Method and device for detecting potential distribution of medicinal plants, storage medium and electronic equipment - Google Patents
Method and device for detecting potential distribution of medicinal plants, storage medium and electronic equipment Download PDFInfo
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Abstract
The application provides a method and a device for detecting potential distribution of medicinal plants, a storage medium and electronic equipment, and relates to the field of medicinal plant resource development. The electronic equipment determines a plurality of sensitive environmental factors of the target medicinal plant according to the environmental information of a plurality of sample points in the target area, wherein the target medicinal plant grows at the position of each sample point, and each sensitive environmental factor represents the environmental factor which can influence the quality of the target medicinal plant; then, determining potential distribution areas of the target medicinal plants from the target areas; and performing similarity calculation on all sample points and the potential distribution area according to various sensitive environmental factors to obtain the suitability index of the target medicinal plant in the potential distribution area. Therefore, compared with the adoption of a species distribution model, the method and the device can quickly, efficiently and universally detect the suitability distribution pattern of the target medicinal plants in a specific area without respectively carrying out parameter setting on different target medicinal plants.
Description
Technical Field
The application relates to the field of medicinal plant resource development, in particular to a medicinal plant potential distribution detection method, a medicinal plant potential distribution detection device, a storage medium and electronic equipment.
Background
The accurate mastering of the space-time distribution of wild medicinal plants has important practical value and scientific significance for effective development and utilization of Chinese herbal medicines, resource cataloging and artificial cultivation. Most of the current research uses species distribution models (also called environmental niche models). The species distribution model estimates geographic distribution under specific space-time conditions based on known distribution points and environmental factor data thereof; however, species distribution models have inherent problems with applicability and are prone to misuse if one lacks an understanding of the background knowledge of the model. Furthermore, in practical applications, severe dataforms, complex parameter settings, lengthy computational processes, etc. are often faced, especially when analyzing for large areas, where spatio-temporal heterogeneity often leads to uncertainty in simulation results.
Disclosure of Invention
In order to overcome at least one defect in the prior art, the application provides a method, a device, a storage medium and electronic equipment for detecting potential distribution of medicinal plants, which specifically comprise the following steps:
in a first aspect, the present application provides a method for detecting potential distribution of a medicinal plant, the method comprising:
determining a plurality of sensitive environmental factors of a target medicinal plant according to environmental information of a plurality of sample points in a target area, wherein the target medicinal plant grows at the position of each sample point, and each sensitive environmental factor represents an environmental factor capable of affecting the quality of the target medicinal plant;
determining a potential distribution area of the target medicinal plant from the target area;
and respectively carrying out similarity calculation on all sample points and the potential distribution area according to the plurality of sensitive environmental factors to obtain the suitability index of the target medicinal plant in the potential distribution area.
With reference to the optional implementation manner of the first aspect, the environmental information of each sample point includes a plurality of preset environmental factors, and the determining a plurality of sensitive environmental factors of the target medicinal plant according to the environmental information of a plurality of sample points in the target area includes:
for each preset environmental factor, acquiring a first distribution of the preset environmental factors in the target area;
acquiring a second distribution of the preset environmental factors among the plurality of samples;
and if the duty ratio of the second distribution in the first distribution is lower than a value range coverage threshold, taking the preset environmental factor as a sensitive environmental factor.
With reference to the optional implementation manner of the first aspect, the determining the potential distribution area of the target medicinal plant from the target area includes:
dividing the target area into grid cells;
acquiring attribute values of various sensitive environment factors of the grid unit;
and if the attribute values of the plurality of sensitive environment factors of the grid unit are all in the respective potential ranges, the grid unit is used as a potential distribution area, wherein the potential range of each sensitive environment factor is determined by the attribute values of the sensitive environment factors of the plurality of sample points.
With reference to the optional implementation manner of the first aspect, the dividing the target area into grid cells includes:
selecting the smallest grid size from the distances among the plurality of sample points;
and dividing the target area into grid cells according to the grid size.
With reference to the optional implementation manner of the first aspect, the method further includes:
for each sensitive environment factor, acquiring attribute values of the sensitive environment factors of the plurality of sample points;
and taking the distribution range of the attribute values of the sensitive environment factors as the potential range of the sensitive environment factors.
With reference to the optional implementation manner of the first aspect, the calculating similarity between all sample points and the multiple sensitive environmental factors of the potential distribution area to obtain a suitability index of the target medicinal plant in the potential distribution area includes:
respectively carrying out similarity calculation on all sample points and the potential distribution area according to the plurality of sensitive environment factors, and determining a target sample point which is most similar to the potential distribution area from the plurality of sample points;
and taking the similarity between the potential distribution area and the target sample point as a suitability index of the target medicinal plant in the potential distribution area.
With reference to the optional implementation manner of the first aspect, the method includes:
the potential distribution area is marked in the target area as a color corresponding to the suitability index according to the suitability index of the potential distribution area.
In a second aspect, the present application also provides a device for detecting potential distribution of a medicinal plant, the device comprising:
the environment factor module is used for determining various sensitive environment factors of a target medicinal plant according to environment information of a plurality of sample points in a target area, wherein the target medicinal plant grows at the position of each sample point, and each sensitive environment factor represents an environment factor capable of affecting the quality of the target medicinal plant;
the region screening module is used for determining potential distribution regions of the target medicinal plants from the target regions;
and the suitability index module is used for respectively carrying out similarity calculation on all sample points and the potential distribution area according to the plurality of sensitive environment factors to obtain the suitability index of the target medicinal plant in the potential distribution area.
With reference to the optional implementation manner of the second aspect, the environmental information of each sample point includes a plurality of preset environmental factors, and the environmental factor module is further configured to:
for each preset environmental factor, acquiring a first distribution of the preset environmental factors in the target area;
acquiring a second distribution of the preset environmental factors among the plurality of samples;
and if the duty ratio of the second distribution in the first distribution is lower than a value range coverage threshold, taking the preset environmental factor as a sensitive environmental factor.
With reference to the optional implementation manner of the second aspect, the area screening module is further configured to:
dividing the target area into grid cells;
acquiring attribute values of various sensitive environment factors of the grid unit;
and if the attribute values of the plurality of sensitive environment factors of the grid unit are all in the respective potential ranges, the grid unit is used as a potential distribution area, wherein the potential range of each sensitive environment factor is determined by the attribute values of the sensitive environment factors of the plurality of sample points.
With reference to the optional implementation manner of the second aspect, the area screening module is further configured to:
selecting the smallest grid size from the distances among the plurality of sample points;
and dividing the target area into grid cells according to the grid size.
With reference to the optional implementation manner of the second aspect, the area screening module is further configured to:
for each sensitive environment factor, acquiring attribute values of the sensitive environment factors of the plurality of sample points;
and taking the distribution range of the attribute values of the sensitive environment factors as the potential range of the sensitive environment factors.
With reference to an optional implementation manner of the second aspect, the suitability index module is further configured to:
respectively carrying out similarity calculation on all sample points and the potential distribution area according to the plurality of sensitive environment factors, and determining a target sample point which is most similar to the potential distribution area from the plurality of sample points;
and taking the similarity between the potential distribution area and the target sample point as a suitability index of the target medicinal plant in the potential distribution area.
With reference to an optional implementation manner of the second aspect, the suitability index module is further configured to:
the potential distribution area is marked in the target area as a color corresponding to the suitability index according to the suitability index of the potential distribution area.
In a third aspect, the present application provides a storage medium storing a computer program which, when executed by a processor, implements the method of detecting a potential distribution of a medicinal plant.
In a fourth aspect, the present application provides an electronic device comprising a processor and a memory, the memory storing a computer program which, when executed by the processor, implements the method of detecting a potential distribution of a medicinal plant.
Compared with the prior art, the application has the following beneficial effects:
according to the method, the device, the storage medium and the electronic equipment for detecting the potential distribution of the medicinal plants, the electronic equipment determines various sensitive environment factors of the target medicinal plants according to the environment information of a plurality of sample points in a target area, wherein the target medicinal plants grow at the position of each sample point, and each sensitive environment factor represents an environment factor capable of affecting the quality of the target medicinal plants; then, determining potential distribution areas of the target medicinal plants from the target areas; and performing similarity calculation on all sample points and the potential distribution area according to various sensitive environmental factors to obtain the suitability index of the target medicinal plant in the potential distribution area. Therefore, compared with the adoption of a species distribution model, the method and the device can quickly, efficiently and universally detect the suitability distribution pattern of the target medicinal plants in a specific area without respectively carrying out parameter setting on different target medicinal plants.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered limiting the scope, and that other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a method for detecting potential distribution of a medicinal plant according to an embodiment of the present application;
FIG. 2 is a schematic diagram of sample point distribution according to an embodiment of the present disclosure;
FIG. 3 is a schematic diagram of a cell grid provided in an embodiment of the present application;
FIG. 4 is a schematic diagram of a distribution of potential distribution areas according to an embodiment of the present disclosure;
fig. 5 is a schematic diagram of a correlation coefficient calculation principle provided in an embodiment of the present application;
fig. 6 is a schematic structural diagram of a device for detecting potential distribution of a medicinal plant according to an embodiment of the present application;
fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Icon: 101-an environmental factor module; 102-a region screening module; 103-suitability index module; 201-a memory; 202-a processor; 203-a communication unit; 204-system bus.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments. The components of the embodiments of the present application, which are generally described and illustrated in the figures herein, may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present application, as provided in the accompanying drawings, is not intended to limit the scope of the application, as claimed, but is merely representative of selected embodiments of the application. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, are intended to be within the scope of the present application.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures.
In the description of the present application, it should be noted that the terms "first," "second," "third," and the like are used merely to distinguish between descriptions and are not to be construed as indicating or implying relative importance. Furthermore, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
Based on the above statement, as introduced by the background technology, accurately mastering the space-time distribution of wild medicinal plants has important practical value and scientific significance for effective development and utilization of Chinese herbal medicines, resource cataloging and artificial cultivation.
For example, medicinal plants are always an important foundation for development and application of traditional Chinese medicine, and domestic medicinal plant resources are extremely abundant, so that a natural medicinal plant resource library exists on wide territories. Wherein, the growth environment has close relation to the content of the active ingredients of the medicinal plants, which is the basic condition for determining the medicinal value. Therefore, the accurate understanding of the distribution pattern of the high-quality wild medicinal plants has important practical value and scientific significance for effective development and utilization of Chinese herbal medicines, resource cataloging and artificial cultivation. However, the living environment of the medicinal plants is special and complex, and the environment of the medicinal plants is continuously evolved, so that the suitable living environment is changed, and extremely high time cost and economic cost are required for exploring the potential distribution area of the medicinal plants.
In the related art, a species distribution model (e.g., a MaxEnt model) is adopted to estimate the geographic distribution under specific space-time conditions based on known distribution points and environmental factor data thereof. However, the model has a problem of applicability, and misuse of the model is easily caused if understanding of background knowledge of the model is lacking. In addition, in practical applications, because the setting of the model parameters is global, spatial-temporal heterogeneity is not contraindicated, and severe data forms, complex parameter settings, lengthy calculation processes and the like are also faced, especially when analyzing large areas, the spatial-temporal heterogeneity often causes uncertainty of simulation results.
When the simulation result is poor, the model parameters often need to be continuously adjusted, which easily leads to overfitting and limits the speculation ability of the model. Therefore, when the species distribution model is adopted to carry out distribution simulation on the medicinal plants, the practical challenges that different types of medicinal plants need to select different models, set different parameters, prepare different data and the like are faced, so that the time cost and the economic cost of the general investigation and evaluation of medicinal plant resources are increased, and the continuous development of large-scale medicinal plant resource division drawing and cataloging work is difficult.
It should be noted that the above prior art solutions have all the drawbacks that the inventors have obtained after practice and careful study, and thus the discovery process of the above problems and the solutions to the problems that the embodiments of the present application hereinafter propose should not be construed as what the inventors have made in the invention creation process to the present application, but should not be construed as what is known to those skilled in the art.
In view of this, the present embodiment provides a method for detecting potential distribution of medicinal plants. According to the method, electronic equipment determines multiple sensitive environmental factors of target medicinal plants according to environmental information of multiple sample points in a target area, wherein the target medicinal plants grow at the position of each sample point, and each sensitive environmental factor represents an environmental factor capable of affecting the quality of the target medicinal plants; then, determining potential distribution areas of the target medicinal plants from the target areas; and performing similarity calculation on all sample points and the potential distribution area according to various sensitive environmental factors to obtain the suitability index of the target medicinal plant in the potential distribution area. Therefore, compared with the adoption of a species distribution model, the method and the device can quickly, efficiently and universally detect the suitability distribution pattern of the target medicinal plants in a specific area without respectively carrying out parameter setting on different target medicinal plants.
The electronic device implementing the method may be, but is not limited to, a mobile terminal, a desktop computer, a tablet computer, a laptop computer, a server, and the like. In the case of a server, the server may be a single server or a group of servers. The server farm may be centralized or distributed (e.g., the servers may be distributed systems). In some embodiments, the server may be local or remote to the user terminal. In some embodiments, the server may be implemented on a cloud platform; by way of example only, the Cloud platform may include a private Cloud, public Cloud, hybrid Cloud, community Cloud (Community Cloud), distributed Cloud, cross-Cloud (Inter-Cloud), multi-Cloud (Multi-Cloud), or the like, or any combination thereof. In some embodiments, the server may be implemented on an electronic device having one or more components.
Based on the above description, the steps of the method for detecting potential distribution of a medicinal plant according to the present embodiment will be described in detail with reference to fig. 1. It should be understood that the operations of the flow diagrams may be performed out of order and that steps that have no logical context may be performed in reverse order or concurrently. Moreover, one or more other operations may be added to the flow diagrams and one or more operations may be removed from the flow diagrams as directed by those skilled in the art. As shown in fig. 1, the method includes:
s101, determining various sensitive environmental factors of target medicinal plants according to environmental information of a plurality of sample points in a target area.
The target area can be any area with unknown distribution of target medicinal plants, and a plurality of sample points with typical significance can be determined from the target area through field investigation, literature data analysis and other modes; thus, the target medicinal plant grows at the position of each sample point, and each sensitive environmental factor in the present embodiment represents an environmental factor capable of affecting the quality of the target medicinal plant, wherein the higher the quality of the target medicinal plant, the higher the medicinal activity thereof.
In an alternative embodiment, the environmental information of each sample point includes a plurality of preset environmental factors, and in this case, the alternative embodiment of step S101 includes:
s101-1, for each preset environmental factor, acquiring a first distribution of the preset environmental factors in the target area.
S101-2, obtaining second distribution of preset environmental factors among a plurality of samples.
S101-3, if the duty ratio of the second distribution in the first distribution is lower than the value range coverage threshold, taking the preset environmental factor as the sensitive environmental factor.
With the above embodiment of step S101, it should be understood that, for a strange target medicinal plant, since the growth habit thereof is not known, it is necessary to screen out a sensitive environmental factor that is more significant to the quality of the target medicinal plant from among a plurality of preset environmental factors. Illustratively, the plurality of preset environmental factors in the present embodiment include 142 environmental factors including elevation, slope, grade, air temperature, precipitation, solar radiation, wind speed, water vapor pressure, distance from water source, soil type, surface coverage, etc. covering the topography, climate, surface characteristics.
The study also found that, for each preset environmental factor, the preset environmental factor is distributed among a plurality of selected sample points within a smaller threshold range, which means that the environmental factor has important significance on the quality of the target medicinal plant.
Therefore, in the present embodiment, the coverage RC (Rang Coverage) of each environmental factor calculated by the following expression is provided for each of the continuous-type environmental factor and the discrete-type environmental factor, respectively, for screening the sensitive environmental factor. Wherein, the coverage rate RC is in the range of 0-1, and the smaller the coverage rate RC of the environmental factor is, the larger the influence of the environmental factor on the quality of the target medicinal plant is.
For each continuous environmental factor, the coverage rate RC is calculated by the following steps:
in the formula, QV max Representing the maximum value, QV, of such environmental factors in the target region min Representing the minimum value of such environmental factors in the target region, and therefore, the QV max -QV min Representing a first distribution; SV (SV) max Representing the maximum value of the environmental factor, SV, in a plurality of samples min Representing the minimum value of the environmental factor in a plurality of samples, therefore, SV max -SV min Representing a second distribution.
For example, taking altitude as an example, assume that the altitude of the target area is distributed inThe elevation of a plurality of sample points is concentrated and distributed in 1390 m-4303 m, the coverage rate RC is 0.35 and is smaller than the set threshold value 0.53, and the elevation can be regarded as the sensitive environmental factor of the target medicinal plant.
For each discrete environmental factor, the coverage rate RC is calculated by:
in UT (wireless local area network) sample The number of categories, UT, in a plurality of sample points representing a plurality of attribute values of the environmental factor dispersion region A category number in the target area representing a plurality of attribute values of the environmental factor dispersion.
Illustratively, a slope direction is a discrete environmental factor that is divided from north clockwise every 45 ° into an attribute value, encoded sequentially as 1, 2, 3, 4, 5, 6, 7, and 8. Assuming that the above 8 directions of slope co-occur in a plurality of sample points, the coverage rate RC thereof is calculated to be 1, indicating that the inclusion of these 8 class values in the sample points, i.e., the slope has no significant effect on the quality of the target medicinal plant. Therefore, when the sensitive environment factors are screened, the method can fully utilize the existing data resource information from multi-source data fusion, and simultaneously consider two data forms of discrete and continuous data without considering the difference of spatial resolution and data redundancy, so that the method can adapt to the current big data analysis environment.
In the above embodiments, a screening manner of the sensitive environmental factors is described, and with continued reference to fig. 1, the method further includes:
s102, determining potential distribution areas of target medicinal plants from the target areas.
In an alternative embodiment, step S102 includes the following embodiments, for screening potential distribution areas from the target area:
s102-1, dividing the target area into grid cells.
The electronic device can select the smallest grid size from the distances among the plurality of sample points; the target area is divided into grid cells according to the grid size.
For example, the target medicinal plant is assumed to be swertia, has the effects of clearing heat, removing toxicity, clearing liver and promoting bile flow, resisting inflammation, reducing fever, reducing blood fat and the like, and is an important medicinal resource plant for treating diseases such as heat syndrome, liver and gall disease, hematopathy, malaria, common cold fever, poliomyelitis and the like. In order to continuously improve the medicinal value and the economic value of swertia and continuously develop and utilize high-quality wild swertia resources and improve the environmental protection, the distribution pattern of the high-quality wild swertia is required to be clarified, and therefore, five provinces of Gansu province, qinghai province, tibet autonomous region, sichuan province and Yunnan province are taken as target areas, and sample points of swertia in the wild are collected.
The distribution position of each sample point in the target area is shown in fig. 2, and the minimum distance between the sample points is found to be 1km by calculating the Euclidean distance between every two sample points in the target area, so as to ensure the maximum availability of the samples, the target area is divided into 1×1km 2 Grid cells of a size. As shown in fig. 3, each grid cell is a square with a radius of 0.5km centered on a point, and the distance between the center points of adjacent squares is 1km. Thus, the size of the target area investigation grid unit is set based on the density of the target medicinal plant sample points, and the application value of sample data can be furthest mined.
S102-2, obtaining attribute values of various sensitive environment factors of the grid unit.
S102-3, if the attribute values of the plurality of sensitive environment factors of the grid unit are all in the respective potential ranges, the grid unit is taken as a potential distribution area.
Wherein the potential range of each sensitive environment factor is determined by attribute values of the sensitive environment factors of a plurality of sample points. In an alternative embodiment, for each sensitive environment factor, the electronic device obtains attribute values of the sensitive environment factors of a plurality of sample points; the distribution range of the attribute values of the sensitive environment factors is used as the potential range of the sensitive environment factors.
By way of example, assume a variety of preset environmental factors including altitude, grade, slope, air temperature, precipitation, solar radiation, wind speed, water vapor pressure, distance from water source, soil type, surface coverage. The screened sensitive environmental factors comprise three kinds of factors including altitude, air temperature and precipitation. The electronic device then calculates the altitude of the plurality of sample points, with the lowest altitude and the highest altitude being the upper and lower limits of the potential range. Similarly, the respective potential ranges of air temperature and precipitation can be obtained.
Finally, comparing the altitude of each grid cell with the potential range of altitude, comparing the air temperature value of the grid cell with the potential range of air temperature, and comparing the precipitation amount of the grid cell with the potential range of precipitation; if all three attribute values fall within the respective corresponding potential ranges, the grid cell is considered as a potential distribution area.
Continuing to take the target area of the swertia as an example, screening the target area according to the screening mode of the potential distribution area, wherein the distribution positions of the screened potential distribution area in the target area are shown in fig. 4. Therefore, in a macroscopic view, the interval effect of the living environment factors of the target medicinal plants is considered, and potential distribution areas are determined through data fusion.
The above embodiment describes a screening manner of potential distribution areas, and with continued reference to fig. 1, the method further includes:
and S103, respectively carrying out similarity calculation on all sample points and the potential distribution area according to various sensitive environmental factors to obtain the suitability index of the target medicinal plant in the potential distribution area.
In an alternative embodiment of step S103, the electronic device may perform similarity calculation on all the sample points and the potential distribution area according to multiple sensitive environmental factors, and determine a target sample point most similar to the potential distribution area from the multiple sample points; and taking the similarity between the potential distribution area and the target sample point as the suitability index of the target medicinal plant in the potential distribution area.
In the embodiment, when the suitability index is calculated, a data organization mode and a distributed calculation mode of a data cube are adopted, so that efficient parallelization processing is facilitated, rapid detection in a large area range is facilitated, and time and economic cost are effectively reduced. For example, as shown in fig. 5, spearman correlation coefficients between the potential distribution area and each sample point may be selected as the similarity between the two, and the saliency level α=0.05 when Spearman correlation coefficients are calculated. In the specific calculation process, the electronic equipment normalizes attribute values of various sensitive environment factors of the potential distribution area to be expressed as x 1 ～x n The attribute value of at least the sensitive environmental factor of each sample electricity is normalized to be expressed as y 1 ～y n . In this way, the Spearman correlation coefficient between the f (u, v) potential distribution region and each sample point is calculated, and the correlation coefficient that passes the significance test and is the largest is selected as the suitability index. Therefore, on the microcosmic basis, quantitative suitability evaluation is carried out on the potential distribution area based on the principle of environmental similarity, so that more detailed classification research is conveniently carried out in the local area, the potential distribution of the high-quality medicinal plants is comprehensively carved, and the effectiveness and the accuracy of the method are ensured.
Further, the electronic device may further mark the potential distribution area as a color corresponding to the suitability index in the target area according to the suitability index of the potential distribution area. In this way, the user can conveniently see which positions in the target area are suitable for producing high quality target medicinal plants.
In summary, the embodiment starts from multi-source data fusion, and can fully utilize the existing data resource information, which is specifically expressed as follows: firstly, setting the size of a target area investigation grid unit based on the density degree of wild medicinal plant sample points, and mining the application value of sample data to the greatest extent; secondly, the environment space variable is divided into discrete data and continuous data, and all data forms can be used without considering the difference of space resolution and data redundancy, so that the system is suitable for the current big data analysis environment; finally, macroscopically, considering the interval effect of the living environment variable of the medicinal plant, and determining a potential distribution area through data fusion; on microcosmic basis, the potential distribution area is quantitatively and suitably evaluated based on the principle of environmental similarity, so that more detailed classification research can be conveniently carried out in a local area later, the potential distribution of high-quality medicinal plants is comprehensively carved out, and the effectiveness and the accuracy of the method are ensured.
In addition, when the suitability index is calculated, the data organization mode and the distributed calculation mode of the data cube are adopted, so that efficient parallelization processing is facilitated, rapid detection in a large area range is facilitated, and time and economic cost are effectively reduced. Because of less requirements on data and parameters, the method can be fully automatically processed, thereby conveniently realizing the general investigation and evaluation of medicinal plant resources and timely updating the understanding and understanding of the evolution of the distribution pattern of the medicinal plants.
Finally, the design concept of big data space-time mining adopted by the invention is also suitable for space-time speculation of other species and phenomena, and has wide application prospect.
Based on the same inventive concept as the method for detecting the potential distribution of the medicinal plant provided in this embodiment, this embodiment also provides a method for detecting the potential distribution of the medicinal plant, which includes at least one software functional module that can be stored in a memory or solidified in an electronic device in the form of software. The processor in the electronic device is configured to execute executable modules stored in the memory, such as software functional modules and computer programs included in the potential distribution area finder device. Referring to fig. 6, functionally divided, a device for detecting potential distribution of a medicinal plant may include:
an environmental factor module 101, configured to determine multiple sensitive environmental factors of a target medicinal plant according to environmental information of multiple sample points in a target area, where a target medicinal plant grows at a position of each sample point, and each sensitive environmental factor represents an environmental factor that can affect quality of the target medicinal plant;
the area screening module 102 is configured to determine a potential distribution area of the target medicinal plant from the target area;
and the suitability index module 103 is used for respectively carrying out similarity calculation on all sample points and the potential distribution area according to various sensitive environment factors to obtain the suitability index of the target medicinal plant in the potential distribution area.
In this embodiment, the environmental factor module 101 is used to implement step S101 in fig. 1, the region screening module 102 is used to implement step S102 in fig. 1, the suitability index module 103 is used to implement step S103 in fig. 1, and for the detailed description of each module above, reference may be made to the detailed description of each step of the method.
In addition, the functional modules in the embodiments of the present application may be integrated together to form a single part, or each module may exist alone, or two or more modules may be integrated to form a single part.
It should also be appreciated that the above embodiments, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored on a computer readable storage medium. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the embodiments of the present application.
Accordingly, the present embodiment also provides a computer-readable storage medium storing a computer program which, when executed by a processor, implements the medicinal plant potential distribution detection method provided by the present embodiment. The computer readable storage medium may be any of various media capable of storing a program code, such as a usb (universal serial bus), a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk.
Referring to fig. 7, fig. 7 is a schematic diagram of a hardware structure of an electronic device according to the present embodiment. The electronic device may include a processor 202 and a memory 201. The memory 201 stores a computer program, and the processor reads and executes the computer program corresponding to the above embodiment in the memory 201 to realize the method for detecting the potential distribution of the medicinal plant according to the present embodiment.
With continued reference to fig. 7, the electronic device further comprises a communication unit 203. The memory 201, the processor 202, and the communication unit 203 are electrically connected to each other directly or indirectly through a system bus 204 to achieve data transmission or interaction.
The memory 201 may be an information recording device based on any electronic, magnetic, optical or other physical principle for recording execution instructions, data, etc. In some embodiments, the memory 201 may be, but is not limited to, volatile memory, non-volatile memory, storage drives, and the like.
Therein, by way of example only, the volatile memory may be random access memory (Random Access Memory, RAM). The nonvolatile Memory may be Read Only Memory (ROM), programmable Read Only Memory (Programmable Read-Only Memory, PROM), erasable Read Only Memory (Erasable Programmable Read-Only Memory, EPROM), electrically erasable Read Only Memory (Electric Erasable Programmable Read-Only Memory, EEPROM), flash Memory, or the like; the storage drive may be a magnetic disk drive, a solid state disk, any type of storage disk (e.g., optical disk, DVD, etc.), or a similar storage medium, or a combination thereof, etc.
The communication unit 203 is used for transmitting and receiving data through a network. In some embodiments, the network may include a wired network, a wireless network, a fiber optic network, a telecommunications network, an intranet, the internet, a local area network (Local Area Network, LAN), a wide area network (Wide Area Network, WAN), a wireless local area network (Wireless Local Area Networks, WLAN), a metropolitan area network (Metropolitan Area Network, MAN), a wide area network (Wide Area Network, WAN), a public switched telephone network (Public Switched Telephone Network, PSTN), a bluetooth network, a ZigBee network, a near field communication (Near Field Communication, NFC) network, or the like, or any combination thereof. In some embodiments, the network may include one or more network access points. For example, the network may include wired or wireless network access points, such as base stations and/or network switching nodes, through which one or more components of the service request processing system may connect to the network to exchange data and/or information.
The processor 202 may be an integrated circuit chip with signal processing capabilities and may include one or more processing cores (e.g., a single-core processor or a multi-core processor). By way of example only, the processors may include a central processing unit (Central Processing Unit, CPU), an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), a special instruction set Processor (Application Specific Instruction-set Processor, ASIP), a graphics processing unit (Graphics Processing Unit, GPU), a physical processing unit (Physics Processing Unit, PPU), a digital signal Processor (Digital Signal Processor, DSP), a field programmable gate array (Field Programmable Gate Array, FPGA), a programmable logic device (Programmable Logic Device, PLD), a controller, a microcontroller unit, a reduced instruction set computer (Reduced Instruction Set Computing, RISC), a microprocessor, or the like, or any combination thereof.
It should be understood that the apparatus and method disclosed in the above embodiments may be implemented in other manners. The apparatus embodiments described above are merely illustrative, for example, flow diagrams and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The foregoing is merely various embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily think about changes or substitutions within the technical scope of the present application, and the changes and substitutions are intended to be covered in the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A method for detecting potential distribution of a medicinal plant, the method comprising:
determining a plurality of sensitive environmental factors of a target medicinal plant according to environmental information of a plurality of sample points in a target area, wherein the target medicinal plant grows at the position of each sample point, and each sensitive environmental factor represents an environmental factor capable of affecting the quality of the target medicinal plant;
determining a potential distribution area of the target medicinal plant from the target area;
and respectively carrying out similarity calculation on all sample points and the potential distribution area according to the plurality of sensitive environmental factors to obtain the suitability index of the target medicinal plant in the potential distribution area.
2. The method of claim 1, wherein the environmental information of each sample point includes a plurality of predetermined environmental factors, and wherein determining a plurality of sensitive environmental factors of the target medical plant based on the environmental information of the plurality of sample points in the target area comprises:
for each preset environmental factor, acquiring a first distribution of the preset environmental factors in the target area;
acquiring a second distribution of the preset environmental factors among the plurality of sample points;
and if the duty ratio of the second distribution in the first distribution is lower than a value range coverage threshold, taking the preset environmental factor as a sensitive environmental factor.
3. The method of claim 1, wherein said determining a potential distribution area of said target medical plant from said target area comprises:
dividing the target area into grid cells;
acquiring attribute values of various sensitive environment factors of the grid unit;
and if the attribute values of the plurality of sensitive environment factors of the grid unit are all in the respective potential ranges, the grid unit is used as a potential distribution area, wherein the potential range of each sensitive environment factor is determined by the attribute values of the sensitive environment factors of the plurality of sample points.
4. A method of detecting potential distribution of a medicinal plant according to claim 3, wherein the dividing the target area into grid cells comprises:
selecting the smallest grid size from the distances among the plurality of sample points;
and dividing the target area into grid cells according to the grid size.
5. A method of detecting potential distribution of a medicinal plant according to claim 3, wherein the method further comprises:
for each sensitive environment factor, acquiring attribute values of the sensitive environment factors of the plurality of sample points;
and taking the distribution range of the attribute values of the sensitive environment factors as the potential range of the sensitive environment factors.
6. The method for detecting potential distribution of a medicinal plant according to claim 1, wherein the calculating the similarity between all the sample points and the potential distribution area according to the plurality of sensitive environmental factors to obtain the suitability index of the target medicinal plant in the potential distribution area comprises:
respectively carrying out similarity calculation on all sample points and the potential distribution area according to the plurality of sensitive environment factors, and determining a target sample point which is most similar to the potential distribution area from the plurality of sample points;
and taking the similarity between the potential distribution area and the target sample point as a suitability index of the target medicinal plant in the potential distribution area.
7. The method for detecting potential distribution of a medicinal plant according to claim 1, wherein the method comprises:
the potential distribution area is marked in the target area as a color corresponding to the suitability index according to the suitability index of the potential distribution area.
8. A device for detecting potential distribution of a medicinal plant, the device comprising:
the environment factor module is used for determining various sensitive environment factors of a target medicinal plant according to environment information of a plurality of sample points in a target area, wherein the target medicinal plant grows at the position of each sample point, and each sensitive environment factor represents an environment factor capable of affecting the quality of the target medicinal plant;
the region screening module is used for determining potential distribution regions of the target medicinal plants from the target regions;
and the suitability index module is used for respectively carrying out similarity calculation on all sample points and the potential distribution area according to the plurality of sensitive environment factors to obtain the suitability index of the target medicinal plant in the potential distribution area.
9. A storage medium storing a computer program which, when executed by a processor, implements the method of detecting a potential distribution of a medicinal plant according to any one of claims 1 to 7.
10. An electronic device comprising a processor and a memory, the memory storing a computer program which, when executed by the processor, implements the method of detecting a potential distribution of a medicinal plant of any one of claims 1-7.
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