Pollen classification method and device, electronic equipment and readable storage medium
By constructing a pollen morphological parameter database and adopting a multi-level weighted matching strategy, the problems of low efficiency and insufficient accuracy of pollen classification are solved, and an efficient and low-cost pollen classification method is realized, which is suitable for conventional laboratories.
Patent Information
- Application Number
- CN202510292479.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-03-12
- Publication Date
- 2025-08-01
AI Technical Summary
The existing pollen classification methods are inefficient, rely on labor, lack standardization and accuracy, and are highly dependent on professional equipment, making it difficult to widely promote.
A characteristic database was constructed containing standard values of various plant pollen morphological parameters and their allowable error ranges, and parameters were obtained through optical microscopy and quickly classified using a multi-level weighted matching strategy.
It improves the efficiency and accuracy of pollen classification, reduces costs, and standardizes the classification process, making it easier to be popularized in conventional laboratories.
Smart Images
Figure CN120408354A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of data classification, and particularly to a pollen classification method, apparatus, electronic device, and readable storage medium. Background Art
[0002] In the field of pollen classification, traditional methods mainly rely on the experience of experts. By observing through a microscope and manually recording the morphological characteristics of pollen, such as the polar axis length and the type of germ pore, etc. However, this method has significant limitations: First, its efficiency is low, and it is necessary to compare item by item with a retrieval table, consuming a lot of time; Second, the traditional method lacks a fast classification method that can integrate various parameters of qualitative and quantitative characteristics, restricting the standardization and accuracy of classification. In addition, although solutions based on electron microscopes or image recognition have emerged in the prior art, these methods are often costly and highly dependent on professional equipment, making it difficult to be widely promoted and applied in conventional laboratories. Therefore, developing a pollen classification method that is both efficient and accurate and easy to popularize has become an urgent problem in this field. Summary of the Invention
[0003] The present invention provides a pollen classification method, apparatus, electronic device, and readable storage medium, aiming to solve the problem that the existing pollen classification technology is highly dependent on professional equipment or relies on manual classification, and to achieve an efficient, accurate, and easy-to-popularize pollen classification.
[0004] The present invention provides a pollen classification method, including: constructing a feature database containing the standard values of morphological parameters of various plant pollens and their allowable error ranges; acquiring the morphological parameters of the pollen of the plant to be classified; matching the morphological parameters of the pollen of the plant to be classified with the standard values of the morphological parameters and their allowable error ranges in the feature database to obtain a matching result; obtaining the classification result of the pollen of the plant to be classified based on the matching result.
[0005] Optionally, the morphological parameters of the pollen of the plant to be classified include qualitative characteristics; The qualitative characteristics include any one or combination of the following: Type of germ pore, polar view contour, and exine ornamentation.
[0006] Optionally, the morphological parameters of the pollen of the plant to be classified include quantitative characteristics; The quantitative characteristics include any one or combination of the following: Polar axis length, equatorial axis width, ratio of the polar axis length to the equatorial axis width, and germ pore diameter.
[0007] Optionally, the matching of the morphological parameters of the pollen of the plant to be classified with the standard values of the morphological parameters and their allowable error ranges in the feature database to obtain a matching result includes: Adopting a multi-level weighted matching strategy; The multi-level weighted matching strategy includes: Performing forced matching on qualitative features to screen out pollen that conforms to specific qualitative features; Performing interval matching on quantitative features to screen out pollen that conforms to a specific quantitative feature range; Obtaining the artificial review result to match fuzzy features.
[0008] Optionally, the allowable error range for the interval matching of the quantitative features is ±5%.
[0009] Optionally, the obtaining of the morphological parameters of the pollen of the plant to be classified includes: Obtaining the morphological parameters of the pollen of the plant to be classified through an optical microscope and a supporting micrometer.
[0010] Optionally, the construction of the feature database including the standard values of the morphological parameters of various plant pollens and their allowable error ranges includes: Responding to a user-defined extension instruction to add regional endemic species data to the feature database.
[0011] The present invention also provides a pollen classification device, including the following modules: A construction module for constructing a feature database including the standard values of the morphological parameters of various plant pollens and their allowable error ranges; An acquisition module for acquiring the morphological parameters of the pollen of the plant to be classified; A matching module for matching the morphological parameters of the pollen of the plant to be classified with the standard values of the morphological parameters and their allowable error ranges in the feature database to obtain a matching result; A classification module for obtaining the classification result of the pollen of the plant to be classified based on the matching result.
[0012] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the pollen classification method described in any one of the above is implemented.
[0013] The present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the pollen classification method described in any one of the above is implemented.
[0014] The present invention also provides a computer program product, including a computer program, which implements the pollen classification method as described in any one of the above when executed by a processor.
[0015] The pollen classification method, device, electronic device and readable storage medium provided by the present invention construct a feature database and match the morphological parameters of the pollen of the plant to be classified with the standard values and allowable error ranges in the database. Compared with the traditional method that relies on expert experience and compares item by item with a retrieval table, there is no need for manual comparison one by one, and the matching result can be quickly obtained, and then the classification result can be obtained, greatly improving the efficiency of pollen classification and saving a lot of time; when classifying, various morphological parameters are comprehensively considered, making the classification process more standardized, reducing the errors caused by human factors, and improving the accuracy of pollen classification; it does not rely on expensive professional equipment, and only needs to obtain the morphological parameters of the pollen of the plant to be classified and match them with the database to complete the classification, which is easy to implement in a conventional laboratory, reducing the cost of pollen classification, and is conducive to popularization and application in a wider range of scenarios. BRIEF DESCRIPTION OF THE DRAWINGS
[0016] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained according to these drawings without creative efforts.
[0017] Figure 1 It is a flowchart of a pollen classification method provided by the present invention.
[0018] Figure 2 It is a schematic diagram of the equatorial axial plane of pollen under an optical microscope provided by the present invention.
[0019] Figure 3 It is a schematic diagram of the polar axial plane of pollen under an optical microscope provided by the present invention.
[0020] Figure 4 It is a schematic structural diagram of a pollen classification device provided by the present invention.
[0021] Figure 5 It exemplifies a schematic diagram of the physical structure of an electronic device.
[0022] Reference Signs: Pollen classification device 40; construction module 401; acquisition module 402; matching module 403; classification module 404; processor 510; communication interface 520; memory 530; communication bus 540. DETAILED DESCRIPTION OF THE EMBODIMENTS
[0023] To make the objectives, technical solutions, and advantages of the present invention clearer, the following will clearly and completely describe the technical solutions in the present invention in conjunction with the accompanying drawings in the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present invention without creative efforts fall within the scope of protection of the present invention.
[0024] The morphological parameters of pollen refer to a series of quantitative and qualitative indicators used to describe and distinguish the morphological characteristics of pollen grains. These parameters are usually based on the external morphology, structural characteristics, and surface ornamentation of pollen, and specifically include qualitative and quantitative characteristics.
[0025] The quantitative characteristics include the following categories: Polar Axis length (P): The longest distance of the pollen grain from pole to pole.
[0026] Equatorial Axis length (E): The maximum diameter of the pollen grain in the equatorial plane.
[0027] P / E ratio: The ratio of the polar axis length to the equatorial axis length, used to describe the shape of pollen, such as spherical, prolate, oblate, etc.
[0028] Germination pore diameter: The opening size of the germination pore.
[0029] Number of germination pores: The total number of germination pores on the surface of the pollen grain.
[0030] Exine thickness: The thickness of the pollen exine, usually measured at different positions and averaged.
[0031] Granule density: The distribution density of pollen surface ornamentation (such as granules, spines, etc.).
[0032] Ornamentation height: The protrusion height of the pollen surface ornamentation.
[0033] The qualitative characteristics include the following categories: Germination pore type: The morphological characteristics of the germination pore, such as monoporate, triporate, compound pore, sulcate pore, etc.
[0034] Polar view contour: The external contour shape of the pollen grain from the polar view, such as circular, triangular, polygonal, elliptical, irregular, etc.
[0035] Exine ornamentation type: The ornamentation morphology on the pollen surface, such as granular, striate, reticulate, spiny, etc.
[0036] Pollen shape: Shape classification based on the P / E ratio, such as spherical (P / E≈1), prolate (P / E>1), oblate (P / E<1), etc.
[0037] Germination pore position: The distribution position of the germination pore on the pollen grain, such as the polar region, equatorial region, etc.
[0038] Symmetry: The symmetry type of the pollen, such as radial symmetry, bilateral symmetry, etc.
[0039] Figure 1 is a flowchart of a pollen classification method provided by the present invention. As Figure 1 shown, this pollen classification method is used in devices such as servers, desktops, laptops, etc., and includes the following steps.
[0040] In step 101, a characteristic database is constructed that includes the standard values of the morphological parameters of various plant pollens and their allowable error ranges.
[0041] Among them, the characteristic database includes the standard values of the morphological parameters of various common plant pollens and their allowable error ranges, such as pollen of the genus Betula, pollen of the genus Quercus, pollen of the Gramineae family, pollen of the genus Pinus, pollen of the genus Artemisia, etc.
[0042] The morphological parameters include the above-mentioned quantitative characteristics and qualitative characteristics; for example, the quantitative characteristics include any one or combination of the following: polar axis length, equatorial axis width, P / E ratio, germination pore diameter, number of germination pores, exine thickness, particle density, and ornamentation height; the qualitative characteristics include any one or combination of the following: germination pore type, polar view contour, exine ornamentation type, pollen shape, germination pore position, and symmetry. As Figure 2-3 shown, Figure 2 is a schematic diagram of the equatorial axial plane of pollen under an optical microscope provided by the present invention. Figure 3 is a schematic diagram of the polar axial plane of pollen under an optical microscope provided by the present invention.
[0043] In one embodiment, the allowable error range of the quantitative characteristics is ±5%.
[0044] By constructing a characteristic database that includes the standard values of the morphological parameters of various plant pollens and their allowable error ranges, and matching the morphological parameters of the subsequent pollen to be classified, a variety of parameters are comprehensively considered, making the classification process more standardized, reducing the errors caused by human factors, and improving the accuracy of pollen classification.
[0045] In step 102, the morphological parameters of the pollen of the plant to be classified are obtained.
[0046] The present invention obtains the morphological parameters of the pollen of plants to be classified through an optical microscope and a supporting micrometer, such as a 100× oil immersion objective lens; exemplarily, the pollen sample is decomposed by acetic anhydride and prepared with glycerin jelly, and the P value and E value of the pollen sample are measured with an ocular micrometer under a 100× oil immersion objective lens. The obtained morphological parameters of the pollen of plants to be classified are input into a computer for subsequent matching.
[0047] Existing methods for obtaining the morphological parameters of plant pollen based on electron microscopes or image recognition are costly and highly dependent on professional equipment, making it difficult to widely promote. The present invention obtains the morphological parameters of plant pollen through an optical microscope and a supporting micrometer, without relying on expensive professional equipment, and is easy to implement in a conventional laboratory, reducing the cost of pollen classification and facilitating popularization and application in a wider range of scenarios.
[0048] In step 103, the morphological parameters of the pollen of plants to be classified are matched with the standard values and their allowable error ranges of the morphological parameters in the feature database to obtain a matching result.
[0049] The present invention adopts a multi-level weighted matching strategy; this multi-level weighted matching strategy includes: The first level: First, perform a forced match on qualitative features to screen out pollen that meets specific qualitative features, such as screening all pollen with "three germ pores"; The second level: Then perform an interval match on quantitative features to screen out pollen that meets a specific quantitative feature range, such as P = 30 - 35 μm and P / E = 1.0 - 1.5; After completing the first-level and second-level matches, determine whether there are fuzzy features that the computer cannot judge. If there are no fuzzy features, directly output the matching result; if there are fuzzy features, perform a third-level match; The third level: Obtain the manual review result to match the fuzzy features; then output the matching result.
[0050] Exemplarily, the priority of individual morphological parameters can be dynamically adjusted during matching. For example, higher weights are assigned to key discriminant features, such as the type of germ pore.
[0051] Exemplarily, the user inputs parameters through a table or software interface, such as: P = 32 μm, E = 28 μm, three germ pores, and striated outer wall; the system screens from the feature database and outputs a list of matching species, such as "*Betula pendula*, matching degree 90%, *Quercus robur*, matching degree 75%". At this time, the species with the highest matching degree can be used as the final matching result.
[0052] By constructing a feature database and performing a multi-level weighted matching strategy on the morphological parameters of the pollen of the plants to be classified with the standard values and allowable error range in the database, compared with the traditional method of relying on expert experience and comparing the retrieval table item by item, a variety of parameters are comprehensively considered, making the classification process more standardized, and being able to quickly obtain matching results, and then obtain classification results, which greatly improves the efficiency of pollen classification and saves a lot of time.
[0053] In step 104, a classification result of the plant pollen to be classified is obtained based on the matching result.
[0054] For example, a list of pollen species sorted by matching degree and key identification criteria is output. Results with matching degree > 85% can be directly adopted, while results with matching degree < 85% are prompted to manually review key features.
[0055] The following uses five matching cases to illustrate this.
[0056] Case 1: Betula pollen identification Sample processing: Pollen samples were decomposed with acetic anhydride, prepared into glycerol gel slides, and observed under a 40× oil microscope.
[0057] Parameter measurement: Qualitative characteristics: The germination pore type is "three-pore", the polar surface outline is "nearly circular", and the outer wall pattern is "granular"; Quantitative characteristics: P=25μm, E=20μm, P / E=1.25.
[0058] Feature database matching process: The first level (qualitative feature screening): screening all pollen with “three germination holes”; Level 2 (quantitative feature screening): screening for species with P = 23-27 μm and P / E = 1.2-1.3; Output matching results: Betula pendula, 95% match; Alnus glutinosa, 78% match, excluded due to differences in outer wall ornamentation.
[0059] Case 2: Quercus pollen identification Sample processing: Pollen samples were dried and stored and directly prepared for observation.
[0060] Parameter measurement: Qualitative characteristics: The germination pore type is "three-pore", the polar surface outline is "triangular", and the outer wall pattern is "net-like"; Quantitative characteristics: P=35μm, E=28μm, P / E=1.25.
[0061] Feature database matching process: First level (qualitative feature screening): Screen the combined feature of "three germ pores + polar surface triangle". Second level (quantitative feature screening): Screen species with P = 33 - 37μm and the outer wall ornamentation being "reticulate". Output matching results: English oak (*Quercus robur*), matching degree 92%; Cork oak (*Quercus suber*), matching degree 88%, and it is sorted in descending order due to slightly lower P / E.
[0062] Case 3: Identification of pollen of Gramineae Sample processing: The pollen sample was freshly collected and observed directly without staining.
[0063] Parameter measurement: Qualitative features: The type of germ pore is "single pore", the polar outline is "circular", and the outer wall ornamentation is "smooth". Quantitative features: P = 40μm, E = 30μm, P / E = 1.33.
[0064] Feature database matching process: First level (qualitative feature screening): Screen the combined feature of "single germ pore + smooth outer wall". Second level (quantitative feature screening): Screen species with P = 38 - 42μm and P / E = 1.3 - 1.4. Output matching results: Rice (*Oryza sativa*), matching degree 90%; Wheat (*Triticum aestivum*), matching degree 82%, and it is excluded due to the P value deviating from the standard range.
[0065] Case 4: Identification of pollen of Pinus Sample processing: The pollen sample was treated with potassium hydroxide to remove impurities and then made into slides.
[0066] Parameter measurement: Qualitative features: The type of germ pore is "single pore", the polar outline is "with 2 air sacs", and the outer wall ornamentation is "grooved". Quantitative features: P = 45μm, E = 30μm, P / E = 1.5.
[0067] Feature database matching process: First level (qualitative feature screening): Screen the combined feature of "with air sacs + granular outer wall". Second level (quantitative feature screening): Screen species with P = 43 - 47μm and P / E = 1.4 - 1.6. Output matching results: Scots pine (*Pinus sylvestris*), matching degree 96%; Black pine (*Pinus nigra*), matching degree 85%, sorted in descending order due to the difference in the density of outer wall particles.
[0068] Case 5: Identification of Artemisia pollen Sample processing: The pollen sample was treated with hydrochloric acid to remove pigments and then observed; Parameter measurement: Qualitative characteristics: The type of germ pore is "compound pore", the polar outline is "polygonal", and the outer wall ornamentation is "striated"; Quantitative characteristics: P = 18μm, E = 15μm, P / E = 1.2.
[0069] Process of matching with the feature database: First level (qualitative feature screening): Screen the combined feature of "compound germ pore + polygonal polar surface"; Second level (quantitative feature screening): Screen species with P = 16 - 20μm and outer wall ornamentation of "striated"; Output matching results: Sweet wormwood (*Artemisia annua*), matching degree 93%; Chinese mugwort (*Artemisia argyi*), matching degree 80%, marked as "to be rechecked" due to the deviation of the P / E value from the standard.
[0070] The above cases cover representative pollen types of 5 families including Betulaceae, Fagaceae, Poaceae, Pinaceae, and Asteraceae, demonstrating the applicability of the pollen classification method of the present invention in multiple classification scenarios: In scenarios with high matching degrees, such as Pinus and Betula, reliable results can be directly output when the quantitative and qualitative characteristics highly coincide; in fuzzy matching scenarios, such as Artemisia, an artificial recheck mechanism can be obtained to avoid misjudgment; in cross - family identification scenarios, such as Poaceae and Pinaceae, relying on the key distinctions of the type of germ pore and outer wall ornamentation improves the accuracy and flexibility of classification.
[0071] The pollen classification method, device, electronic device and readable storage medium provided by the present invention construct a feature database and match the morphological parameters of the pollen of the plant to be classified with the standard values and allowable error ranges in the database. Compared with the traditional method that relies on expert experience and compares item by item with a retrieval table, it does not require manual comparison one by one, can quickly obtain the matching result, and then obtain the classification result, greatly improving the efficiency of pollen classification and saving a lot of time. When classifying, various morphological parameters are comprehensively considered, making the classification process more standardized, reducing the errors caused by human factors, and improving the accuracy of pollen classification. It does not rely on expensive professional equipment, and only needs to obtain the morphological parameters of the pollen of the plant to be classified and match them with the database to complete the classification. It is easy to implement in a conventional laboratory, reducing the cost of pollen classification and facilitating popularization and application in a wider range of scenarios.
[0072] The pollen classification device provided by the present invention will be described below. The pollen classification device described below can be correspondingly referred to the pollen classification method described above.
[0073] Figure 4 It is a schematic structural diagram of a pollen classification device provided by the present invention. This pollen classification device is applied to devices such as servers, desktop computers, and laptop computers. Referring to Figure 4 , the pollen classification device 40 includes a construction module 401, an acquisition module 402, a matching module 403, and a classification module 404.
[0074] The construction module 401 is configured to construct a feature database including standard values of morphological parameters of various plant pollens and their allowable error ranges; The acquisition module 402 is configured to acquire the morphological parameters of the pollen of the plant to be classified; The matching module 403 is configured to match the morphological parameters of the pollen of the plant to be classified with the standard values of the morphological parameters in the feature database and their allowable error ranges to obtain a matching result; The classification module 404 is configured to obtain the classification result of the pollen of the plant to be classified based on the matching result.
[0075] Figure 5 It exemplifies a schematic physical structure diagram of an electronic device, such as Figure 5As shown, the electronic device may include: a processor 510, a communications interface 520, a memory 530, and a communication bus 540. Among them, the processor 510, the communication interface 520, and the memory 530 complete communication with each other through the communication bus 540. The processor 510 may call logic instructions in the memory 530 to execute the pollen classification method, which includes: constructing a feature database containing the standard values of the morphological parameters of various plant pollens and their allowable error ranges; obtaining the morphological parameters of the plant pollen to be classified; matching the morphological parameters of the plant pollen to be classified with the standard values of the morphological parameters and their allowable error ranges in the feature database to obtain a matching result; and obtaining the classification result of the plant pollen to be classified based on the matching result.
[0076] In addition, when the logic instructions in the above-mentioned memory 530 are implemented in the form of software function units and sold or used as an independent product, they may be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, may be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.
[0077] On the other hand, the present invention also provides a computer program product. The computer program product includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the pollen classification method provided by the above-mentioned various methods. The method includes: constructing a feature database containing the standard values of the morphological parameters of various plant pollens and their allowable error ranges; obtaining the morphological parameters of the plant pollen to be classified; matching the morphological parameters of the plant pollen to be classified with the standard values of the morphological parameters and their allowable error ranges in the feature database to obtain a matching result; and obtaining the classification result of the plant pollen to be classified based on the matching result.
[0078] In another aspect, the present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the pollen classification method provided by the above-mentioned various methods. The method includes: constructing a feature database containing the standard values of morphological parameters of various plant pollens and their allowable error ranges; obtaining the morphological parameters of the plant pollen to be classified; matching the morphological parameters of the plant pollen to be classified with the standard values of the morphological parameters and their allowable error ranges in the feature database to obtain a matching result; and obtaining the classification result of the plant pollen to be classified based on the matching result.
[0079] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement it without creative work.
[0080] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the essence of the above technical solution, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disc, etc., and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.
[0081] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments or perform equivalent replacements for some of the technical features. These modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims
1. A method for pollen classification, characterized in that, Including: Constructing a feature database containing the standard values of the morphological parameters of various plant pollens and their allowable error ranges; Obtaining the morphological parameters of the plant pollen to be classified; Matching the morphological parameters of the plant pollen to be classified with the standard values of the morphological parameters in the feature database and their allowable error ranges to obtain a matching result; Obtaining the classification result of the plant pollen to be classified based on the matching result.
2. The method according to claim 1, characterized in that, The morphological parameters of the plant pollen to be classified include qualitative characteristics; The qualitative characteristics include any one or combination of the following: Type of germ pore, polar outline and exine ornamentation.
3. The method according to claim 1, wherein The morphological parameters of the plant pollen to be classified include quantitative characteristics; The quantitative characteristics include any one or combination of the following: Polar axis length, equatorial axis width, the ratio of the polar axis length to the equatorial axis width, and germ pore diameter.
4. The method according to claim 1, characterized in that, The matching the morphological parameters of the plant pollen to be classified with the standard values of the morphological parameters in the feature database and their allowable error ranges to obtain a matching result includes: Adopting a multi-level weighted matching strategy; The multi-level weighted matching strategy includes: Performing forced matching on qualitative characteristics to screen out pollens that meet specific qualitative characteristics; Performing interval matching on quantitative characteristics to screen out pollens that meet specific quantitative characteristic ranges; Obtaining the artificial review result to match fuzzy characteristics.
5. The method according to claim 1, wherein The allowable error range for the interval matching of the quantitative characteristics is ±5%.
6. The method according to claim 1, wherein The obtaining the morphological parameters of the plant pollen to be classified includes: Obtaining the morphological parameters of the plant pollen to be classified through an optical microscope and a supporting micrometer.
7. The method according to claim 1, characterized in that The constructing a feature database containing the standard values of the morphological parameters of various plant pollens and their allowable error ranges includes: Responding to a user-defined extension instruction to add regional endemic species data to the feature database.
8. A pollen classification device, characterized in that, Including: A construction module for constructing a feature database containing the standard values of the morphological parameters of various plant pollens and their allowable error ranges; An obtaining module for obtaining the morphological parameters of the plant pollen to be classified; A matching module for matching the morphological parameters of the plant pollen to be classified with the standard values of the morphological parameters in the feature database and their allowable error ranges to obtain a matching result; A classification module for obtaining the classification result of the plant pollen to be classified based on the matching result.
9. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the pollen classification method according to any one of claims 1 to 7.
10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the pollen classification method according to any one of claims 1 to 7.