Method and device for evaluating forestry yield, electronic equipment and storage medium

Abstract

This invention discloses a method, apparatus, electronic device, and storage medium for assessing forestry yield, relating to the field of financial technology. The method includes: acquiring remote sensing images of a target forest area, wherein the target forest area is a pre-selected region planted with N types of plants; identifying the plant species and age groups within the target forest area based on the remote sensing images, wherein the plant age group refers to the age group to which the plants belong; dividing the target forest area displayed in the remote sensing images into M sub-regions based on the plant species and age groups; confirming the surface distribution information and yield information of plants within each sub-region; and assessing the total plant yield of the target forest area based on the surface distribution information and yield information. This invention solves the technical problem that existing forestry yield assessment methods cannot accurately assess the plant yield of a target forest area.

Description

Technical Field

[0001] This invention relates to the field of financial technology and other related technical fields. Specifically, it relates to a method and apparatus for evaluating forestry yield, electronic equipment, and storage medium. Background Technology

[0002] Forestry plays a vital role in promoting socio-economic development, protecting the natural environment, and maintaining stable livelihoods. To accelerate the high-quality development of the forestry industry, forestry output assessment is particularly important in various fields, such as forestry credit and forestry auditing.

[0003] In related technologies, there are three main methods for assessing forestry yield: First, manual estimation based on experience; however, manual assessment has low accuracy and requires highly skilled personnel. Second, based on the Weibull distribution function, a parametric estimation method is used to establish a forestry yield prediction model. The drawback of this method is that it is only effective for certain plant species, limiting its applicability. Third, based on sample data, the influence of growth factors on individual trees and stand fruit set is analyzed. Logistic regression and nonlinear regression models are used to construct yield prediction models, and stepwise linear regression is used to establish a stand fruit set prediction model to assess forestry yield. This method is also only applicable to specific plants, limiting its applicability. In summary, the forestry yield prediction strategies in related technologies require significant human resources, can only predict yield, and are not applicable to the prediction of all forest yields.

[0004] There is currently no effective solution to the above problems. Summary of the Invention

[0005] This invention provides a method and apparatus for assessing forestry yield, an electronic device, and a storage medium, to at least solve the technical problem that forestry yield assessment methods in related technologies cannot accurately assess plant yield in target forest areas.

[0006] According to one aspect of the present invention, a method for evaluating forestry yield is provided, comprising: acquiring remote sensing images of a target forest area, wherein the target forest area is a pre-selected area for planting N types of plants, where N is a positive integer greater than or equal to 1; identifying plant species and plant age groups within the target forest area based on the remote sensing images, wherein the plant age group refers to the age group to which the plant belongs; dividing the target forest area displayed in the remote sensing images into M sub-regions based on the plant species and the plant age group, and confirming the surface distribution information and yield information of plants within each sub-region, wherein M is a positive integer greater than or equal to 2; and evaluating the total plant yield of the target forest area based on the surface distribution information and the yield information.

[0007] Optionally, before acquiring remote sensing images of the target forest area, the process includes: providing a forest area monitoring service page to a user's client, wherein the forest area monitoring service page includes basic information related to various plants, a forest area location button, and a forest area selection button; after registering on the forest area monitoring service page, the user selects the planting area of ​​the plants using the forest area location button and the forest area selection button to determine the target forest area; receiving plant information input by the user after selecting the target forest area, wherein the plant information includes at least: plant species; and configuring a corresponding forest area monitoring strategy for the target user based on the target forest area and the plant information, wherein the forest area monitoring strategy is used to instruct satellite remote sensing monitoring of the target forest area to assess the total plant yield of the target forest area.

[0008] Optionally, the step of configuring a corresponding forest area monitoring strategy for the target user based on the target forest area and the plant information includes: querying a plant characteristic mapping table based on the plant species to obtain the growth characteristics of the plants planted in the target forest area, wherein the plant characteristic mapping table stores the mapping relationship between plant species and plant growth characteristics; configuring a monitoring frequency and a preset yield threshold for the target forest area based on the plant growth characteristics, wherein the monitoring frequency is used to control the frequency at which remote sensing equipment collects remote sensing images of the target forest area, and the preset yield threshold represents the minimum yield of the plant under normal growth conditions.

[0009] Optionally, the step of identifying plant species and age groups within the target forest area based on the remote sensing image includes: preprocessing and pre-processing the remote sensing image, wherein the preprocessing includes: performing atmospheric refraction correction and cloud / fog removal on the remote sensing image, and the preprocessing includes: image denoising and image enhancement; extracting the spectral reflectance features and temporal features of the remote sensing image, querying a plant remote sensing information mapping table to determine the plant species, wherein the plant remote sensing information mapping table stores the mapping relationship between the plant species and the spectral reflectance features, the temporal features, and the plant age group and the proportion of reflected light in the reflected bands; extracting the proportion of reflected light in the reflected bands of the remote sensing image, querying the plant remote sensing information mapping table to determine the plant age group.

[0010] Optionally, the step of identifying the plant age group of the target forest area based on the remote sensing image further includes: obtaining historical growth information of the plants in the target forest area, wherein the historical growth information includes: the mapping relationship between the average growth cycle of the plants and their growth height and canopy coverage area; comparing the current remote sensing image of the target forest area with remote sensing images of historical time periods to obtain remote sensing comparison information, wherein the remote sensing comparison information includes at least: the difference in plant growth height and the difference in canopy coverage area; and determining the plant age group of the plants in the target forest area based on the historical growth information and the remote sensing comparison information.

[0011] Optionally, the step of dividing the target forest area displayed in the remote sensing image into M sub-regions based on the plant species and the plant age group, and confirming the surface distribution information and yield information of plants in each sub-region, includes: dividing the target forest area displayed in the remote sensing image into N sub-regions based on the identified N plant species of the target forest area; dividing the N sub-regions into M sub-regions based on the identified plant age group; performing clustering processing on the plants planted in each of the M sub-regions to obtain the surface distribution information, wherein the surface distribution information includes at least: plant coverage area; and counting the number of plants planted in each of the M sub-regions to obtain the yield information.

[0012] Optionally, after calculating the total output of the target forest area based on the surface distribution information and the output information, the process includes: comparing the total output of the target forest area with a preset output threshold; if the total output of the target forest area is less than the preset output threshold, generating an output warning report and sending the output warning report to a management terminal, wherein the management terminal is a terminal held by a business personnel of a financial institution responsible for credit business.

[0013] Optionally, after calculating the total yield of the target forest area based on the surface distribution information and the yield information, the method further includes: extracting abnormal images from the remote sensing images of the target forest area, comparing the abnormal images with a preset disaster image set, wherein the abnormal images refer to images that show abnormal changes compared to normal images of the target forest area associated with them in a standard image library, and the preset disaster image set contains mapping relationships between various types of disaster risks and disaster images and disaster information; if the similarity between the abnormal images and the preset disaster images is greater than a similarity threshold, it is determined that the target forest area has the disaster risk, and disaster early warning information is generated.

[0014] According to another aspect of the present invention, a forestry yield assessment apparatus is also provided, comprising: an acquisition unit for acquiring remote sensing images of a target forest area, wherein the target forest area is a pre-selected area for planting N types of plants, where N is a positive integer greater than or equal to 1; an identification unit for identifying the plant species and plant age groups within the target forest area based on the remote sensing images, wherein the plant age group refers to the age group to which the plant belongs; a division unit for dividing the target forest area displayed in the remote sensing images into M sub-regions based on the plant species and plant age groups, and confirming the surface distribution information and yield information of plants within each sub-region, wherein M is a positive integer greater than or equal to 2; and an assessment unit for assessing the total plant yield of the target forest area based on the surface distribution information and the yield information.

[0015] Optionally, the forestry yield assessment device further includes: a first providing module, used to provide a forest area monitoring service page to a user terminal held by a target user, wherein the forest area monitoring service page includes basic information related to various plants, a forest area positioning button, and a forest area selection button; after registering on the forest area monitoring service page, the user terminal selects the planting area of ​​the plants using the forest area positioning button and the forest area selection button to determine the target forest area; a first receiving module, used to receive plant information input by the user terminal after selecting the target forest area, wherein the plant information includes at least: plant species; and a first configuration module, used to configure a corresponding forest area monitoring strategy for the target user based on the target forest area and the plant information, wherein the forest area monitoring strategy is used to instruct satellite remote sensing monitoring of the target forest area to assess the total plant yield of the target forest area.

[0016] Optionally, the first configuration module includes: a first query submodule, used to query a plant characteristic mapping table based on the plant species to obtain the growth characteristics of the plants planted in the target forest area, wherein the plant characteristic mapping table stores the mapping relationship between plant species and plant growth characteristics; and a first configuration submodule, used to configure a monitoring frequency and a preset yield threshold for the target forest area based on the plant growth characteristics, wherein the monitoring frequency is used to control the frequency at which remote sensing equipment acquires remote sensing images of the target forest area, and the preset yield threshold represents the minimum yield of the plant under normal growth conditions.

[0017] Optionally, the identification unit includes: a first processing module, used to preprocess and pre-process the remote sensing image, wherein the preprocessing includes: performing atmospheric refraction correction and cloud / fog removal on the remote sensing image, and the preprocessing includes: image denoising and image enhancement processing; a first determining module, used to extract the spectral reflectance features and temporal features of the remote sensing image, query a plant remote sensing information mapping table, and determine the plant species, wherein the plant remote sensing information mapping table stores the mapping relationship between the plant species and the spectral reflectance features, the temporal features, and the plant age group and the proportion of reflected light in the reflected band; and a second determining module, used to extract the proportion of reflected light in the reflected band of the remote sensing image, query the plant remote sensing information mapping table, and determine the plant age group.

[0018] Optionally, the identification unit further includes: a first acquisition module, used to acquire historical growth information of plants in the target forest area, wherein the historical growth information includes: the mapping relationship between the average growth cycle of the plants and their growth height and coverage area; a first comparison module, used to compare the current remote sensing image of the target forest area with remote sensing images of historical time periods to obtain remote sensing comparison information, wherein the remote sensing comparison information includes at least: the difference in plant growth height and the difference in canopy coverage area; and a third determination module, used to determine the plant age group of the plants in the target forest area based on the historical growth information and the remote sensing comparison information.

[0019] Optionally, the partitioning unit includes: a first partitioning module, used to partition the target forest area displayed in the remote sensing image of the target forest area into N sub-regions based on the identified N plant species of the target forest area; a second partitioning module, used to partition the N sub-regions into M sub-regions based on the identified plant age groups; a first processing module, used to perform clustering processing on the plants planted in each of the M sub-regions to obtain the surface distribution information, wherein the surface distribution information includes at least: plant coverage area; and a first statistical module, used to count the number of plants planted in each of the M sub-regions to obtain the yield information.

[0020] Optionally, the forestry output assessment device further includes: a first comparison module for comparing the total output of the target forest area with a preset output threshold; and a first generation module for generating an output warning report when the total output of the target forest area is less than the preset output threshold, and sending the output warning report to a management terminal, wherein the management terminal is a terminal held by a business personnel of a financial institution responsible for credit business.

[0021] Optionally, the forestry yield assessment device further includes: a first analysis module, used to analyze whether the target forest area has disaster risks listed in a preset disaster table based on remote sensing images of the target forest area, wherein the preset disaster table contains mapping relationships between various types of disaster risks and disaster images and disaster information; and a second generation module, used to generate disaster early warning information when the target forest area has the disaster risks.

[0022] According to another aspect of the present invention, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform any of the above-described forestry yield assessment methods.

[0023] According to another aspect of the present invention, an electronic device is also provided, including one or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement any of the above-described methods for evaluating forestry yield.

[0024] In this disclosure, the following steps are taken: First, remote sensing images of the target forest area are acquired, wherein the target forest area is a pre-selected area for planting N kinds of plants. Then, based on the remote sensing images, the plant species and plant age groups in the target forest area are identified, wherein the plant age group refers to the group to which the age of the plant belongs. Then, based on the plant species and plant age groups, the target forest area shown in the remote sensing images is divided into M sub-regions, and the surface distribution information and yield information of the plants in each sub-region are confirmed. Finally, the total plant yield of the target forest area is evaluated based on the surface distribution information and yield information.

[0025] In this disclosure, based on remote sensing images of the target forest area, the growth and distribution of plants in the target forest area are analyzed in real time. The target forest area shown in the remote sensing images is divided into multiple sub-regions based on plant species and plant age groups. By analyzing the surface distribution information and yield information of each sub-region, the total yield of planted plants in the target forest area is evaluated, which improves the accuracy of yield calculation and can more accurately assess the plant yield of the target forest area. This solves the technical problem that forestry yield assessment methods in related technologies cannot accurately assess the plant yield of the target forest area. Attached Figure Description

[0026] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0027] Figure 1 This is a flowchart of an optional method for evaluating forestry yield according to an embodiment of the present invention;

[0028] Figure 2 This is a flowchart of another optional method for evaluating forestry yield according to an embodiment of the present invention;

[0029] Figure 3 This is a schematic diagram of an optional forestry yield assessment device according to an embodiment of the present invention;

[0030] Figure 4 This is a hardware structure block diagram of an electronic device (or mobile device) for a forestry yield evaluation method according to an embodiment of the present invention. Detailed Implementation

[0031] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0032] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be used interchangeably with appropriate information so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0033] It should be noted that the forestry yield assessment method and apparatus disclosed herein can be used in the financial technology field to assess the plant yield of a target forest area, and can also be used in any field other than the financial technology field to assess the plant yield of a target forest area. The application field of the forestry yield assessment method and apparatus disclosed herein is not limited.

[0034] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, geolocation data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of the relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and corresponding operation portals are provided for users to choose to authorize or refuse.

[0035] This invention can be applied to various yield assessment devices / appliances / products (e.g., various mobile terminals and PCs), combining digital technology with satellite remote sensing technology. By acquiring satellite remote sensing images of the target forest area, the plant growth in the target forest area can be monitored in real time. The target forest area displayed in the remote sensing images is divided into multiple sub-regions based on plant species and plant age groups. By analyzing the surface distribution information and yield information of each sub-region, the total yield of the planted plants in the target forest area can be assessed, improving the accuracy of yield calculation and enabling a more accurate assessment of the plant yield of the target forest area.

[0036] This invention can also be applied to various agricultural and forestry risk assessment equipment / devices / products. Forest areas are relatively large and have complex terrain. Satellite remote sensing can obtain ground data information within a large field of view, dynamically and in real time capture remote sensing images of forest areas, and promptly detect disaster situations in forest areas.

[0037] This invention can be applied to risk assessment systems / devices / products based on agricultural and forestry loans in the field of financial technology. By monitoring the plants in the target forest area in real time, the plant growth status can be obtained, providing a reference for the risk of agricultural and forestry loans. For example, the amount of agricultural and forestry loans can be assessed based on forest yield monitoring and yield forecasting, or the post-loan risk of agricultural and forestry loans can be assessed based on the risk of forest diseases and forest growth.

[0038] The present invention will now be described in detail with reference to various embodiments.

[0039] Example 1

[0040] According to an embodiment of the present invention, a method for evaluating forestry yield is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, under certain circumstances, the steps shown or described may be executed in a different order than that shown here.

[0041] Figure 1 This is a flowchart of an optional forestry yield assessment method according to an embodiment of the present invention, such as... Figure 1 As shown, the evaluation method includes the following steps:

[0042] Step S101: Obtain remote sensing images of the target forest area, where the target forest area is a pre-selected area planted with N kinds of plants, and N is a positive integer greater than or equal to 1;

[0043] Step S102: Identify the plant species and plant age groups in the target forest area based on remote sensing images, where the plant age group refers to the group to which the plant's age belongs.

[0044] Step S103: Based on plant species and plant age groups, the target forest area displayed in the remote sensing image is divided into M sub-regions, and the surface distribution information and yield information of plants in each sub-region are confirmed, where M is a positive integer greater than or equal to 2.

[0045] Step S104: Assess the total plant yield of the target forest area based on surface distribution information and yield information.

[0046] Through the above steps, firstly, remote sensing images of the target forest area are acquired. The target forest area is a pre-selected region where N types of plants are planted. Then, based on the remote sensing images, the plant species and age groups of the plants in the target forest area are identified. The plant age group refers to the group to which the age of the plant belongs. Then, based on the plant species and age groups, the target forest area shown in the remote sensing images is divided into M sub-regions. The surface distribution information and yield information of the plants in each sub-region are confirmed. Finally, the total plant yield of the target forest area is evaluated based on the surface distribution information and yield information.

[0047] In this embodiment, based on remote sensing images of the target forest area, the growth and distribution of plants in the target forest area are analyzed in real time. The target forest area shown in the remote sensing images is divided into multiple sub-regions based on plant species and plant age groups. By analyzing the surface distribution information and yield information of each sub-region, the total yield of the planted plants in the target forest area is evaluated, which improves the accuracy of yield calculation and can more accurately evaluate the plant yield of the target forest area. This solves the technical problem that forestry yield evaluation methods in related technologies cannot accurately evaluate the plant yield of the target forest area.

[0048] The following section will explain in detail each of the above implementation steps.

[0049] The implementation subject of this invention is a forest plant yield assessment system. Users can use this system to customize monitoring strategies and conduct real-time assessments of forest plants.

[0050] Step S101: Obtain remote sensing images of the target forest area, where the target forest area is a pre-selected area planted with N kinds of plants, and N is a positive integer greater than or equal to 1.

[0051] It should be noted that the target forest area is a forest area with crops planted in advance selected by the target user. Users register in the forest area plant yield assessment system and can view the growth, flowering and fruiting status of the plants planted in the selected forest area and the surrounding environment in real time through user terminals (such as mobile phones, tablets, computers, etc.). The forest area plant yield assessment system can generate a real-time report on the yield of the plants planted in the forest area for users to view.

[0052] Optionally, before acquiring remote sensing images of the target forest area, the process includes: providing a forest area monitoring service page to the user's client, wherein the forest area monitoring service page contains basic information related to various plants, a forest area location button, and a forest area selection button; after registering on the forest area monitoring service page, the user selects the planting area of ​​the plants using the forest area location button and the forest area selection button to determine the target forest area; receiving plant information input by the user after selecting the target forest area, wherein the plant information includes at least: plant species; and configuring a corresponding forest area monitoring strategy for the target user based on the target forest area and plant information, wherein the forest area monitoring strategy is used to instruct satellite remote sensing monitoring of the target forest area to assess the total plant yield of the target forest area.

[0053] It should be noted that before monitoring and assessing the target forest area, it is necessary to establish contact with the target forest area through the user terminal. The user terminal can directly log in to the forest area plant yield assessment system. First, fill in basic information on the forest area monitoring service page to register. After registration, there is a virtual forest area selection button on the forest area monitoring service page. After clicking / double-clicking / long-pressing, based on the map service, the user can select the forest area in their own region, and then select the specific forest area through the touch screen / pointing tool, etc. The selected forest area is marked with color or special markers. Forest area selection includes selecting tree planting areas and forest area boundaries, etc., or The system provides a forest area number for selection. Each forest area number has a pre-established mapping relationship with the actual forest area information. The forest area information records the location, area, adjacent forest area information, boundary information, and graphic information of the forest area. Alternatively, based on a preset virtual forest area line, the forest area boundary is color-coded. After the target forest area is selected, the server responds to the user's corresponding operation, determining the forest area's location information, scope information, and area information based on the selected forest area map information. At the same time, the user also needs to submit information on the plants they have planted. Based on the above information, a forest area monitoring task is generated, and corresponding forest area monitoring strategies are configured for registered users.

[0054] Optionally, the step of configuring a corresponding forest area monitoring strategy for the target user based on the target forest area and the plant information includes: querying a plant characteristic mapping table based on the plant species to obtain the growth characteristics of the plants planted in the target forest area, wherein the plant characteristic mapping table stores the mapping relationship between plant species and plant growth characteristics; configuring a monitoring frequency and a preset yield threshold for the target forest area based on the plant growth characteristics, wherein the monitoring frequency is used to control the frequency at which remote sensing equipment collects remote sensing images of the target forest area, and the preset yield threshold represents the minimum yield of the plant under normal growth conditions.

[0055] It should be noted that the situation in forest areas is quite complex. Large-scale tree planting areas often involve a variety of plant species, each with different growth characteristics. Therefore, different monitoring strategies need to be developed for different types of plants, including different monitoring frequencies, monitoring angles, and different preset yield thresholds.

[0056] Optionally, the preset yield threshold can represent the minimum yield of the plant under normal growth conditions, or it can represent the historical average yield of the target plant in the region.

[0057] Taking Korean pine forests as an example: Korean pine bears fruit very late. Under natural forest conditions, it takes 80-140 years to start bearing fruit, while under plantation conditions, it can bear fruit in 15-20 years. Its cones are large and heavy, 9-14 cm long and 6-8 cm in diameter, and are conical-ovoid, conical-oblong, or ovoid-oblong in shape. The flowering period of Korean pine forests is in June, and the cones mature in September-October of the following year. They are easily blown away by the wind before and after maturity. Therefore, for the monitoring of Korean pine forests, a high-frequency monitoring strategy is needed, focusing on the flowering period in June and the fruit ripening period in September-October of the following year.

[0058] Step S102: Identify the plant species and plant age groups in the target forest area based on remote sensing images, where the plant age group refers to the group to which the plant's age belongs.

[0059] It should be noted that different plants have different growth cycles, therefore, it is necessary to construct an age group mapping table for different plants. The age group mapping table includes the mapping relationship between the age and age group of different plant species. For example, under the conditions of Korean pine berry plantation, it can bear fruit in 15-20 years. For Korean pine berry, it can be divided into age groups according to age: 0-3 years old is the juvenile group.

[0060] Optionally, step S102 includes: preprocessing and pre-processing the remote sensing image, wherein the preprocessing includes: performing atmospheric refraction correction and cloud / fog removal on the remote sensing image, and the preprocessing includes: image denoising and image enhancement processing; extracting the spectral reflectance features and temporal features of the remote sensing image, querying the plant remote sensing information mapping table to determine the plant species, wherein the plant remote sensing information mapping table stores the mapping relationship between the plant species and the spectral reflectance features, the temporal features, and the plant age group and the proportion of reflected light in the reflected band; extracting the proportion of reflected light in the reflected band of the remote sensing image, querying the plant remote sensing information mapping table to determine the plant age group.

[0061] Optionally, plant age groups include, but are not limited to: juvenile group, middle-aged group, near-mature group, mature group, and over-mature group. Taking Korean pine as an example, under artificial forest conditions, Korean pine can bear fruit in 15-20 years. Korean pine can be divided into age groups according to age: 0-3 years old is the juvenile group, 3-10 years old is the middle-aged group, 10-14 years old is the near-mature group, 14-21 years old is the mature group, and over 20 years old is the over-mature group.

[0062] It should be noted that after acquiring remote sensing images, they need to be pre-processed to obtain clear and noise-free forest area images. When the target forest area is planted with a variety of plants, the species of plants need to be determined by the spectral reflectance characteristics and temporal characteristics in the remote sensing images. When analyzing plant age groups, the age of plants can also be determined by the proportion of reflected light bands in the remote sensing images, and the plant age groups can be obtained by looking up the age group mapping table.

[0063] Optionally, step S102 further includes: acquiring historical growth information of plants in the target forest area, wherein the historical growth information includes: the mapping relationship between the average growth cycle of the plants and their growth height and coverage area; comparing the current remote sensing image of the target forest area with remote sensing images of historical time periods to obtain remote sensing comparison information, wherein the remote sensing comparison information includes at least: the difference in plant growth height and the difference in canopy coverage area; and determining the plant age group of the plants in the target forest area based on the historical growth information and the remote sensing comparison information.

[0064] It should be noted that when obtaining plant age groups, remote sensing images of the same plant at different times can be compared. Based on the plant's growth information, multiple current remote sensing images of the plant can be compared with multiple remote sensing images from historical time periods to obtain the plant's average growth cycle. The growth height and canopy coverage area within the preset growth cycle can be determined by combining the plant's historical growth information.

[0065] Step S103: Based on plant species and plant age groups, the target forest area displayed in the remote sensing image is divided into M sub-regions, and the surface distribution information and yield information of plants in each sub-region are confirmed, where M is a positive integer greater than or equal to 2.

[0066] Optionally, step S103 includes: dividing the target forest area displayed in the remote sensing image into N sub-regions based on the identified N plant species of the target forest area; dividing the N sub-regions into M sub-regions based on the identified plant age groups; performing clustering processing on the plants planted in each of the M sub-regions to obtain the surface distribution information, wherein the surface distribution information includes at least: plant coverage area; and counting the number of plants planted in each of the M sub-regions to obtain the yield information.

[0067] It should be noted that when dividing the target forest area, it is first classified based on plant species to obtain N sub-regions. Then, these N sub-regions are further divided based on plant age groups to obtain M sub-regions. Dividing the target area into multiple sub-regions and calculating the plant yield in each sub-region can improve the calculation accuracy.

[0068] Step S104: Assess the total plant yield of the target forest area based on surface distribution information and yield information.

[0069] It should be noted that after obtaining the surface distribution information and yield information of multiple sub-regions, the surface distribution information and yield information of each sub-region are multiplied together to obtain the total plant yield of the target forest area. Then, the yields of each sub-region are summed up to obtain the total plant yield of the target forest area.

[0070] Optionally, after calculating the total yield of the target forest area based on the surface distribution information and yield information, the process includes: comparing the total yield of the target forest area with a preset yield threshold; if the total yield of the target forest area is less than the preset yield threshold, generating a yield warning report and sending the yield warning report to a management terminal, wherein the management terminal is a terminal held by the business personnel of the financial institution responsible for credit business.

[0071] It should be noted that in the process of monitoring forestry planting yields, yield assessments can help credit financial institutions predict post-loan risks and take necessary measures for forestry loan projects with risks, such as stopping lending, early loan recovery, or artificially intervening in their growth environment, thereby reducing unnecessary losses.

[0072] Optionally, after calculating the total yield of the target forest area based on surface distribution information and yield information, the method further includes: extracting abnormal images from the remote sensing images of the target forest area, comparing the abnormal images with a preset disaster image set, wherein the abnormal images refer to images that show abnormal changes compared to normal images of the target forest area associated with them in a standard image library, and the preset disaster image set contains mapping relationships between various types of disaster risks and disaster images and disaster information; if the similarity between the abnormal images and the preset disaster images is greater than a similarity threshold, it is determined that the target forest area has the disaster risk, and disaster early warning information is generated.

[0073] It should be noted that abnormal images include, but are not limited to: images of human activity, pests, fires, windstorms, landslides, mudslides, and blizzards.

[0074] It should be noted that, based on remote sensing images of forest areas, the disaster situation in forest areas can also be monitored. Forest areas are affected by various disasters at all times, such as mudslides, insect infestations, floods, fires, plant diseases, and illegal logging. By using satellite remote sensing images, the actual situation of forest areas can be identified and analyzed dynamically in real time, and the disaster situation in forest areas can be detected in a timely manner.

[0075] The present invention will now be described with reference to a more specific embodiment.

[0076] Figure 2 This is a flowchart of another optional method for evaluating forestry yield according to an embodiment of the present invention, such as... Figure 2 As shown, the forestry assessment method of this invention includes:

[0077] Step 1: Begin;

[0078] Target users register in the forest area plant yield assessment system, select the target forest area where the plants are planted, and submit the plant information, thereby generating a forest area monitoring task and configuring corresponding forest area monitoring strategies for registered users.

[0079] Step 2: Acquire remote sensing images of the target forest area;

[0080] Satellite remote sensing technology is used to acquire remote sensing images of forest areas. This relies on the electromagnetic waves emitted or reflected by various plants on the surface of a designated target forest area, which are received by satellites. These electromagnetic waves are then used to generate spectral data according to their different wavelengths, and subsequently converted into remote sensing images.

[0081] Step 3: Preprocess and pre-process the remote sensing images;

[0082] Remote sensing images are processed using multi-band, hyperspectral, and radar image processing technologies. Pre-processing operations such as atmospheric refraction correction and cloud / fog removal are performed on the remote sensing images, while noise reduction and enhancement are also carried out.

[0083] Step 4: Identify the species and age groups of plants planted in the target forest area;

[0084] When a variety of plants are planted in the target forest area, it is necessary to determine the species of plants by using the spectral reflectance characteristics and temporal characteristics in the remote sensing image. Similarly, when analyzing the plant age group, the age of the plant can be determined by the proportion of reflected light bands in the remote sensing image, and the plant age group can be obtained by querying the age group mapping table.

[0085] Alternatively, by comparing remote sensing images of the same plant at different times, and making a comprehensive judgment based on the plant's growth information, multiple current remote sensing images of the plant can be compared with multiple remote sensing images from historical time periods to obtain the plant's average growth cycle. The growth height and canopy coverage within the preset growth cycle can be determined by combining the plant's historical growth information and making a comprehensive judgment to determine the plant's age group.

[0086] Step 5: Divide the target forest area into multiple sub-regions based on plant species and age group;

[0087] When dividing the target forest area, it is first classified based on plant species to obtain N sub-regions. Then, these N sub-regions are further divided based on plant age groups to obtain M sub-regions. Dividing the target area into multiple sub-regions and calculating the plant yield in each sub-region can improve the calculation accuracy.

[0088] When dividing the target forest area, the vegetation region of the forest area is first defined as Area, with an area of ​​S. n different plant species are identified as a1, a2, a3, ..., an, where n>=1. Each plant species is further divided into five age groups: juvenile, middle-aged, near-mature, mature, and over-mature. The surface distribution is then statistically analyzed according to these age groups. Assume the area of ​​the juvenile forest of the nth plant species is S. n1 The area of ​​the middle-aged forest is S n2 The area of ​​near-mature forest is S n3 The area of ​​mature forest is S n4 The area of ​​overripe forest is S n5 The table shows the surface distribution of different types of resources in different age groups. Here, n is an integer greater than 1, and the area S is... n1 S n2 S n3 S n4 S n5The value range is greater than or equal to 0, resulting in the surface distribution information of different plant species in the target forest area, as shown in Table 1.

[0089] Table 1. Surface distribution information of different plant species in the target forest area

[0090] Plant species young forest area Middle-aged forest area Near-mature forest area mature forest area over-mature forest area <![CDATA[a1]]> <![CDATA[S 11 ]]> <![CDATA[S 12 ]]> <![CDATA[S 13 ]]> <![CDATA[S 14 ]]> <![CDATA[S 15 ]]> <![CDATA[a2]]> <![CDATA[S 21 ]]> <![CDATA[S 22 ]]> <![CDATA[S 23 ]]> <![CDATA[S 24 ]]> <![CDATA[S 25 <!-- 9 -->]]> <![CDATA[a3]]> <![CDATA[S 31 ]]> <![CDATA[S 32 ]]> <![CDATA[S 33 ]]> <![CDATA[S 34 ]]> <![CDATA[S 35 ]]> ... ... ... ... ... ... <![CDATA[a n ]]> <![CDATA[S n1 ]]> <![CDATA[S n2 ]]> <![CDATA[S n3 ]]> <![CDATA[S n4 ]]> <![CDATA[S n5 ]]>

[0091] Next, obtain the unit yield information of plants in each age group, such as... Figure 2 :

[0092] Table 2. Yield per unit area for different plant species in different age groups (unit: catties / mu)

[0093]

[0094] Step Six: Assess the total plant yield of the target forest area based on the plant yield of each sub-region;

[0095] Based on the known statistical table of surface distribution of different species and age groups, and combined with the unit yield of different species and age groups, the total plant yield Sum of the target forest area is calculated:

[0096] Sum = (b 11 S 11 +b 12 S 12 +b 13 S 13 +b 14 S 14 +b 15 S 15 )+(b 21 S 21 +b 22 S 22 +b 23 S 23 +b 24 S 24 +b 25 S 25 )+(b 31 S 31 +b 32 S 32 +b 33 S 33 +b 34 3+b 35 S 35 )+...+(b n1 S n1 +b n2 S n2 +b n3 S n3 +b n4 S n4 +bn5 S n5 ).

[0097] Step 7: End.

[0098] Specifically, taking the yield assessment of poplar trees as an example, a grove of poplar trees is planted in forest area D. Remote sensing imagery shows that the planting area is 100,000 mu (approximately 6,667 hectares). The maximum growth range of the poplar trees in forest area D is 7-8 meters. It takes five years from planting to reaching the maximum height. Currently, satellite remote sensing is used to photograph the growth of the 100,000 mu of poplar trees in this forest area every month. Assume that the photography has been tracked for one year. 10,000 mu (approximately 667 hectares) of poplar trees were found to be relatively short, with thin trunks and slow growth, indicating they are in their juvenile stage. 40,000 mu (approximately 2,667 hectares) of poplar trees were found to be growing relatively quickly, averaging 3 meters in height over the year of tracking and filming, indicating they are in their middle-aged stage. 20,000 mu (approximately 1,333 hectares) of poplar trees were found to have thick trunks, averaging 5-6 meters in height, but growing slowly, indicating they are nearing maturity. Another 20,000 mu (approximately 1,333 hectares) of poplar trees were found to have thick trunks, averaging 7-8 meters in height, but showing little growth in the past year, indicating they are in their mature stage. Finally, 10,000 mu (approximately 667 hectares) of poplar trees were found to have thick trunks, averaging 7-8 meters in height, but less lush foliage, indicating they are over-ripe. Combining this with historical yield statistics for the forest area, we analyzed the average yield per unit area of ​​poplar trees at different stages. Assuming the yield per unit area of ​​young poplar trees is approximately 100 tons per mu (0.067 hectares), middle-aged poplar trees approximately 200 tons per mu, near-mature poplar trees approximately 300 tons per mu, mature poplar trees approximately 400 tons per mu, and over-mature poplar trees approximately 390 tons per mu, we can calculate the total yield of poplar trees in the forest area as 10,000 mu × (100 tons / mu) + 40,000 mu × (200 tons / mu) + 20,000 mu × (300 tons / mu) + 20,000 mu × (400 tons / mu) + 10,000 mu × (390 tons / mu) = 2690 tons. This is an estimated total yield of 2690 tons of poplar trees in the forest area. As time progresses, maintenance will be carried out on the forest area. Overripe poplar trees are cut down, and juvenile poplars are replanted. These juvenile poplars will gradually grow, and the middle-aged poplars will eventually reach near maturity. Therefore, the geographical extent of poplar trees in the entire forest area changes dynamically at different stages. Satellite remote sensing can be used to periodically predict the growth and yield of poplar trees. If a natural disaster destroys the poplar trees in the forest area, satellite remote sensing can also be used to analyze the area of ​​loss, thus providing a good understanding of the poplar yield and its real-time fluctuations.

[0099] The yield estimation method of this invention can also be applied to the risk monitoring of loans by financial institutions. In the process of monitoring forestry yields, it can help credit financial institutions predict risks and take necessary measures, such as stopping lending, recalling loans in advance, or artificially intervening in the growth environment, thereby reducing unnecessary losses.

[0100] Taking Korean pine forests as an example, let's first analyze their growth characteristics: Korean pine bears fruit very late. Under natural forest conditions, it takes 80-140 years to begin bearing fruit, while under plantation conditions, it can bear fruit in 15-20 years. Its cones are large and heavy, 9-14 cm long and 6-8 cm in diameter, with a conical-ovoid, conical-oblong, or ovoid-oblong shape. The flowering period is in June, and the cones mature in September-October of the following year. They are easily blown away by the wind before and after maturity.

[0101] When monitoring Korean pine forests, the first step is to delineate the monitoring area. Based on the growth characteristics of Korean pine forests, continuous and irregular photography is taken of the monitored area starting from the month the pine trees are planted. Images from different dates are compared, and combined with the growth characteristics of Korean pine forests, the changes in growth status are compared across different months, especially during the flowering period in June, continuing until the fruit ripening period in September and October. During the monitoring process, if color changes in the images of Korean pine forests at different stages indicate a decrease in planted area or poor growth conditions (e.g., delayed flowering, fewer flowers, smaller or fewer fruits, or fruits being blown away by the wind), a future reduction in Korean pine production can be predicted. Related financial institutions can then reduce subsequent capital investment or recover loans earlier, minimizing unnecessary losses.

[0102] The above embodiments combine digital technology with satellite remote sensing technology. By acquiring satellite remote sensing images of the target forest area, the plant growth in the target forest area can be monitored in real time. The target forest area shown in the remote sensing images is divided into multiple sub-regions based on plant species and plant age groups. By analyzing the surface distribution information and yield information of each sub-region, the total yield of the planted plants in the target forest area can be evaluated, which improves the accuracy of yield calculation and enables a more accurate assessment of the plant yield in the target forest area.

[0103] The invention will now be described in conjunction with another alternative embodiment.

[0104] Example 2

[0105] This embodiment provides a forestry yield assessment device, wherein each implementation unit included in the forestry yield assessment device corresponds to each implementation step in the above embodiment one.

[0106] Figure 3 This is a schematic diagram of an optional forestry yield assessment device according to an embodiment of the present invention, such as... Figure 3As shown, the forestry yield assessment device includes: an acquisition unit 31, an identification unit 32, a division unit 33, and an assessment unit 34, wherein,

[0107] The acquisition unit 31 is used to acquire remote sensing images of the target forest area, wherein the target forest area is a pre-selected area planted with N kinds of plants, and N is a positive integer greater than or equal to 1;

[0108] The identification unit 32 is used to identify the plant species and plant age group of plants in the target forest area based on remote sensing images, wherein the plant age group refers to the group to which the age of the plant belongs;

[0109] Division unit 33 is used to divide the target forest area displayed in the remote sensing image into M sub-regions based on plant species and plant age group, and to confirm the surface distribution information and yield information of plants in each sub-region, where M is a positive integer greater than or equal to 2;

[0110] Assessment unit 34 is used to assess the total plant yield of the target forest area based on surface distribution information and yield information.

[0111] The aforementioned forestry yield assessment device acquires remote sensing images of a target forest area through acquisition unit 31, where the target forest area is a pre-selected region planted with N types of plants, and N is a positive integer greater than or equal to 1; identifies the plant species and age groups of the plants in the target forest area based on the remote sensing images through identification unit 32, where the plant age group refers to the age group to which the plants belong; divides the target forest area displayed in the remote sensing images into M sub-regions based on the plant species and age groups through division unit 33, and confirms the surface distribution information and yield information of the plants in each sub-region, where M is a positive integer greater than or equal to 2; and assesses the total plant yield of the target forest area based on the surface distribution information and yield information through assessment unit 34.

[0112] In this embodiment, based on remote sensing images of the target forest area, the growth and distribution of plants in the target forest area are analyzed in real time. The target forest area shown in the remote sensing images is divided into multiple sub-regions based on plant species and plant age groups. By analyzing the surface distribution information and yield information of each sub-region, the total yield of the planted plants in the target forest area is evaluated, which improves the accuracy of yield calculation and can more accurately evaluate the plant yield of the target forest area. This solves the technical problem that forestry yield evaluation methods in related technologies cannot accurately evaluate the plant yield of the target forest area.

[0113] Optionally, the forestry yield assessment device further includes: a first providing module, used to provide a forest area monitoring service page to a user terminal held by a target user, wherein the forest area monitoring service page includes basic information related to various plants, a forest area positioning button, and a forest area selection button; after registering on the forest area monitoring service page, the user terminal selects the planting area of ​​the plants using the forest area positioning button and the forest area selection button to determine the target forest area; a first receiving module, used to receive plant information input by the user terminal after selecting the target forest area, wherein the plant information includes at least: plant species; and a first configuration module, used to configure a corresponding forest area monitoring strategy for the target user based on the target forest area and the plant information, wherein the forest area monitoring strategy is used to instruct satellite remote sensing monitoring of the target forest area to assess the total plant yield of the target forest area.

[0114] Optionally, the first configuration module includes: a first query submodule, used to query a plant characteristic mapping table based on the plant species to obtain the growth characteristics of the plants planted in the target forest area, wherein the plant characteristic mapping table stores the mapping relationship between plant species and plant growth characteristics; and a first configuration submodule, used to configure a monitoring frequency and a preset yield threshold for the target forest area based on the plant growth characteristics, wherein the monitoring frequency is used to control the frequency at which remote sensing equipment acquires remote sensing images of the target forest area, and the preset yield threshold represents the minimum yield of the plant under normal growth conditions.

[0115] Optionally, the identification unit includes: a first processing module, used to preprocess and pre-process the remote sensing image, wherein the preprocessing includes: performing atmospheric refraction correction and cloud / fog removal on the remote sensing image, and the preprocessing includes: image denoising and image enhancement processing; a first determining module, used to extract the spectral reflectance features and temporal features of the remote sensing image, query a plant remote sensing information mapping table, and determine the plant species, wherein the plant remote sensing information mapping table stores the mapping relationship between the plant species and the spectral reflectance features, the temporal features, and the plant age group and the proportion of reflected light in the reflected band; and a second determining module, used to extract the proportion of reflected light in the reflected band of the remote sensing image, query the plant remote sensing information mapping table, and determine the plant age group.

[0116] Optionally, the identification unit further includes: a first acquisition module, used to acquire historical growth information of plants in the target forest area, wherein the historical growth information includes: the mapping relationship between the average growth cycle of the plants and their growth height and coverage area; a first comparison module, used to compare the current remote sensing image of the target forest area with remote sensing images of historical time periods to obtain remote sensing comparison information, wherein the remote sensing comparison information includes at least: the difference in plant growth height and the difference in canopy coverage area; and a third determination module, used to determine the plant age group of the plants in the target forest area based on the historical growth information and the remote sensing comparison information.

[0117] Optionally, the partitioning unit includes: a first partitioning module, used to partition the target forest area displayed in the remote sensing image of the target forest area into N sub-regions based on the identified N plant species of the target forest area; a second partitioning module, used to partition the N sub-regions into M sub-regions based on the identified plant age groups; a first processing module, used to perform clustering processing on the plants planted in each of the M sub-regions to obtain the surface distribution information, wherein the surface distribution information includes at least: plant coverage area; and a first statistical module, used to count the number of plants planted in each of the M sub-regions to obtain the yield information.

[0118] Optionally, the forestry output assessment device further includes: a first comparison module for comparing the total output of the target forest area with a preset output threshold; and a first generation module for generating an output warning report when the total output of the target forest area is less than the preset output threshold, and sending the output warning report to a management terminal, wherein the management terminal is a terminal held by a business personnel of a financial institution responsible for credit business.

[0119] Optionally, the forestry yield assessment device further includes: a first analysis module, used to analyze whether the target forest area has disaster risks listed in a preset disaster table based on remote sensing images of the target forest area, wherein the preset disaster table contains mapping relationships between various types of disaster risks and disaster images and disaster information; and a second generation module, used to generate disaster early warning information when the target forest area has the disaster risks.

[0120] The aforementioned forestry yield assessment device includes a processor and a memory. The acquisition unit 31, identification unit 32, division unit 33, assessment unit 34, etc., are all stored in the memory as program units. The processor executes the aforementioned program units stored in the memory to realize the corresponding functions.

[0121] The processor contains a kernel, which retrieves the corresponding program units from memory. One or more kernels can be configured, and the yield of plants planted in the target forest area can be evaluated by adjusting kernel parameters.

[0122] The memory may include non-permanent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0123] According to another aspect of the present invention, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform any of the above-described forestry yield assessment methods.

[0124] According to another aspect of the present invention, an electronic device is also provided, characterized in that it includes one or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement any of the above-described methods for evaluating forestry yield.

[0125] This application also provides a computer program product, which, when executed on a data processing device, is suitable for executing an initialization program with the following method steps: first, acquiring remote sensing images of a target forest area, wherein the target forest area is a pre-selected area for planting N types of plants; then, identifying the plant species and age groups of the plants in the target forest area based on the remote sensing images, wherein the plant age group refers to the group to which the plant's age belongs; then, dividing the target forest area displayed in the remote sensing images into M sub-regions based on the plant species and plant age groups; confirming the surface distribution information and yield information of the plants in each sub-region; and finally, evaluating the total plant yield of the target forest area based on the surface distribution information and yield information.

[0126] Figure 4 This is a hardware structure block diagram of an electronic device (or mobile device) for a forestry yield assessment method according to an embodiment of the present invention. Figure 4 As shown, the electronic device may include one or more processors 402 (shown as 402a, 402b, ..., 402n in the figure) 402 (processor 402 may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.) and a memory 404 for storing data. In addition, it may include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the I / O interface), a network interface, a keyboard, a power supply, and / or a camera. Those skilled in the art will understand that... Figure 4The structure shown is for illustrative purposes only and does not limit the structure of the electronic device described above. For example, the electronic device may also include components that are more... Figure 4 The more or fewer components shown, or having the same Figure 4 The different configurations shown.

[0127] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0128] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0129] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some interfaces; indirect couplings or communication connections between units or modules may be electrical or other forms.

[0130] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0131] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0132] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0133] The above are merely preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for evaluating forestry yield, characterized in that, include: Acquire remote sensing images of the target forest area, wherein the target forest area is a pre-selected area planted with N kinds of plants, and N is a positive integer greater than or equal to 1; Based on the remote sensing image, the plant species and plant age groups of the plants in the target forest area are identified, wherein the plant age group refers to the group to which the age of the plant belongs; The steps of identifying plant species and age groups within the target forest area based on the remote sensing imagery include: The remote sensing image is preprocessed and pre-processed, wherein the preprocessing includes: atmospheric refraction correction and cloud / fog removal of the remote sensing image, and the preprocessing includes: image denoising and image enhancement. Extract the spectral reflectance features and temporal features of the remote sensing image, query the plant remote sensing information mapping table, and determine the plant species. The plant remote sensing information mapping table stores the mapping relationship between plant species and spectral reflectance features, temporal features, and plant age group and the proportion of reflected light in the waveband. Extract the proportion of reflected light in the remote sensing image, query the plant remote sensing information mapping table, and determine the plant age group; Based on the plant species and the plant age group, the target forest area displayed in the remote sensing image is divided into M sub-regions, and the surface distribution information and yield information of plants in each sub-region are confirmed, where M is a positive integer greater than or equal to 2; The steps of dividing the target forest area displayed in the remote sensing image into M sub-regions based on the plant species and the plant age group, and confirming the surface distribution information and yield information of plants in each sub-region, include: Based on the N plant species identified in the target forest area, the target forest area displayed in the remote sensing image is divided into N sub-regions; Based on the identified plant age groups, the N sub-regions are divided into M sub-regions; Clustering is performed on the plants planted in each of the M sub-regions to obtain the surface distribution information, wherein the surface distribution information includes at least: plant coverage area; The yield information is obtained by counting the number of plants planted in each of the M sub-regions. The total plant yield of the target forest area is assessed based on the surface distribution information and the yield information.

2. The evaluation method according to claim 1, characterized in that, Before acquiring remote sensing imagery of the target forest area, the following steps are included: A forest area monitoring service page is provided to the user terminal held by the target user. The forest area monitoring service page includes basic information related to various plants, a forest area location button, and a forest area selection button. After registering on the forest area monitoring service page, the user terminal selects the planting area of ​​the plants through the forest area location button and the forest area selection button to determine the target forest area. The system receives plant information input by the user after selecting the target forest area, wherein the plant information includes at least: plant species; Based on the target forest area and the plant information, a corresponding forest area monitoring strategy is configured for the target user. The forest area monitoring strategy is used to instruct satellite remote sensing monitoring of the target forest area in order to assess the total plant yield of the target forest area.

3. The method according to claim 2, characterized in that, The steps of configuring a corresponding forest area monitoring strategy for the target user based on the target forest area and the plant information include: Based on the plant species, the plant characteristic mapping table is queried to obtain the growth characteristics of the plants planted in the target forest area, wherein the plant characteristic mapping table stores the mapping relationship between plant species and plant growth characteristics. Based on the growth characteristics of the plant, a monitoring frequency and a preset yield threshold are configured for the target forest area. The monitoring frequency is used to control the frequency at which remote sensing equipment collects remote sensing images of the target forest area, and the preset yield threshold represents the minimum yield of the plant under normal growth conditions.

4. The evaluation method according to claim 1, characterized in that, The step of identifying the plant age group of the target forest area based on the remote sensing image also includes: Obtain historical growth information of plants in the target forest area, wherein the historical growth information includes: the mapping relationship between the average growth cycle of the plants and their growth height and coverage area; The current remote sensing image of the target forest area is compared with the remote sensing image of the historical time period to obtain remote sensing comparison information, wherein the remote sensing comparison information includes at least: the difference in plant growth height and the difference in canopy coverage area. Based on the historical growth information and the remote sensing comparison information, the plant age group of the plants in the target forest area is determined.

5. The evaluation method according to claim 1, characterized in that, After calculating the total yield of the target forest area based on the surface distribution information and the yield information, the process includes: The total yield of the target forest area is compared with a preset yield threshold. If the total yield of the target forest area is less than a preset yield threshold, a yield warning report is generated and sent to a management terminal, which is a terminal held by a business personnel of a financial institution responsible for credit business.

6. The evaluation method according to claim 1, characterized in that, After calculating the total yield of the target forest area based on the surface distribution information and the yield information, the method further includes: Abnormal images are extracted from the remote sensing images of the target forest area, and the abnormal images are compared with a preset disaster image set. The abnormal images refer to images that show abnormal changes compared with normal images of the target forest area associated with them in the standard image library. The preset disaster image set contains various types of mapping relationships between disaster risks and disaster images and disaster information. If the similarity between the abnormal image and the preset disaster image is greater than a similarity threshold, it is determined that the target forest area has the disaster risk, and disaster early warning information is generated.

7. A forestry yield assessment device, characterized in that, include: The acquisition unit is used to acquire remote sensing images of the target forest area, wherein the target forest area is a pre-selected area planted with N kinds of plants, and N is a positive integer greater than or equal to 1; The identification unit is used to identify the plant species and plant age group of the plants in the target forest area based on the remote sensing image, wherein the plant age group refers to the group to which the age of the plant belongs; The identification unit includes: a first processing module for preprocessing the remote sensing image, wherein the preprocessing includes atmospheric refraction correction and cloud / fog removal of the remote sensing image, and the preprocessing includes image denoising and image enhancement; a first determining module for extracting the spectral reflectance features and temporal features of the remote sensing image, querying a plant remote sensing information mapping table, and determining the plant species, wherein the plant remote sensing information mapping table stores the mapping relationship between plant species and spectral reflectance features, temporal features, and plant age group and reflectance band light ratio; and a second determining module for extracting the reflectance band light ratio of the remote sensing image, querying the plant remote sensing information mapping table, and determining the plant age group. A division unit is used to divide the target forest area displayed in the remote sensing image into M sub-regions based on the plant species and the plant age group, and to confirm the surface distribution information and yield information of plants in each sub-region, where M is a positive integer greater than or equal to 2; The partitioning unit includes: a first partitioning module, used to partition the target forest area displayed in the remote sensing image of the target forest area into N sub-regions based on the identified N plant species of the target forest area; a second partitioning module, used to partition the N sub-regions into M sub-regions based on the identified plant age groups; a first processing module, used to perform clustering processing on the plants planted in each of the M sub-regions to obtain the surface distribution information, wherein the surface distribution information includes at least: plant coverage area; and a first statistical module, used to count the number of plants planted in each of the M sub-regions to obtain the yield information. An evaluation unit is used to evaluate the total plant yield of the target forest area based on the surface distribution information and the yield information.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device on which the computer-readable storage medium is located to perform the forestry yield assessment method according to any one of claims 1 to 6.

9. An electronic device, characterized in that, It includes one or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement the forestry yield assessment method according to any one of claims 1 to 6.

Images