A smart maintenance method, device, and equipment for large-scale forestry

Abstract

This invention discloses an intelligent forestry maintenance method, device, and equipment, relating to the technical field of forestry maintenance. The method includes: collecting multi-dimensional feature information of the target forest; filtering the forestry maintenance database using geographical feature constraints to obtain a target database containing first forestry maintenance features; vectorizing the target maintenance features and the maintenance features of the first forest; comparing the two feature vectors and calculating the distance value; if the distance value meets the standard, retrieving the first ecological feature information of the first forest; and using the first ecological feature information as the target ecological feature to analyze the target ecological capacity. This invention solves the technical problem that in large-scale forestry scenarios, the low efficiency and limited coverage of on-site collection of ecological feature information lead to a lag in ecological benefit assessment compared to the maintenance execution process. It achieves the technical effect of rapidly obtaining the ecological feature information of the target forest based on similarity matching, thereby improving the timeliness and accuracy of large-scale forestry maintenance.

Description

Technical Field

[0001] This application relates to the technical field of forestry maintenance, specifically to an intelligent maintenance method, device, and equipment for large-scale forestry. Background Technology

[0002] In large-scale forestry maintenance, traditional methods rely heavily on manual experience, resulting in low efficiency and difficulty adapting to complex and changing geographical environments and ecological conditions. In large-scale forestry areas, due to the vast territory, diverse tree species, and significant differences in site conditions, traditional one-size-fits-all maintenance strategies often fail to achieve ideal results and may even lead to ecological damage or resource waste due to inappropriate strategies. Meanwhile, with the rapid development of technologies such as the Internet of Things, big data, and artificial intelligence, intelligent forestry has become an inevitable trend for industry transformation and upgrading. However, the application of existing technological systems in forestry maintenance still faces many bottlenecks, such as incomplete multi-dimensional data collection, the lack of intelligent decision-making mechanisms under geographical constraints, and lagging ecological benefit assessment. Therefore, there is an urgent need for an intelligent maintenance method that can dynamically adapt to different geographical environments, accurately match maintenance needs, and quantify ecological benefits in real time, providing data support for precise maintenance and ecological capacity enhancement in large-scale forestry. Summary of the Invention

[0003] This application provides an intelligent maintenance method, device, and equipment for large-scale forestry, which solves the technical problem that in large-scale forestry scenarios, the efficiency of on-site collection of ecological characteristic information is high but the coverage is limited, resulting in the ecological benefit assessment lagging behind the maintenance execution process. It achieves the technical effect of quickly obtaining the ecological characteristic information of the target forestry based on similarity matching, thereby improving the timeliness and accuracy of large-scale forestry maintenance.

[0004] This application provides an intelligent forestry maintenance method for large-scale forestry. The method includes: collecting target feature information of a target forestry from multiple dimensions, wherein the target feature information includes target geographical feature information and target maintenance feature information; filtering a forestry maintenance database using the target geographical feature information as a constraint to obtain a target database, wherein the target database includes first maintenance feature information of a first forestry; sequentially vectorizing the target maintenance feature information and the first maintenance feature information to obtain a target feature vector and a first feature vector, respectively; comparing the target feature vector and the first feature vector to obtain a first vector distance value; if the first vector distance value reaches a predetermined constraint, retrieving the first ecological feature information of the first forestry; using the first ecological feature information as the target ecological feature information of the target forestry, and analyzing to obtain the target ecological capacity.

[0005] This application also provides an intelligent forestry maintenance device for large-scale forestry, comprising: an information acquisition module for acquiring target feature information of the target forestry from multiple dimensions, wherein the target feature information includes target geographical feature information and target maintenance feature information; a data filtering module for filtering a forestry maintenance database using the target geographical feature information as a constraint to obtain a target database, wherein the target database includes first maintenance feature information of a first forestry; a vectorization module for sequentially vectorizing the target maintenance feature information and the first maintenance feature information to obtain a target feature vector and a first feature vector, respectively; a distance calculation module for comparing the target feature vector and the first feature vector to obtain a first vector distance value; a distance judgment module for retrieving the first ecological feature information of the first forestry if the first vector distance value reaches a predetermined constraint; and an ecological strength analysis module for using the first ecological feature information as the target ecological feature information of the target forestry and analyzing it to obtain the target ecological strength.

[0006] This application also provides an electronic device, including: a memory for storing executable instructions; and a processor for implementing an intelligent maintenance method for large-scale forestry when executing the executable instructions stored in the memory.

[0007] This application proposes a method, device, and equipment for intelligent forestry maintenance on a large scale. The method collects target feature information of the target forestry from multiple dimensions, including target geographical feature information and target maintenance feature information. The forestry maintenance database is filtered using the target geographical feature information as a constraint to obtain a target database, which includes the first maintenance feature information of a first forestry. The target maintenance feature information and the first maintenance feature information are then vectorized sequentially to obtain target feature vectors and first feature vectors, respectively. A first vector distance value is obtained by comparing the target feature vector and the first feature vector. If the first vector distance value reaches a predetermined constraint, the first ecological feature information of the first forestry is retrieved. This first ecological feature information is used as the target ecological feature information of the target forestry, and the target ecological capacity is analyzed. This method solves the technical problem that in large-scale forestry scenarios, the high efficiency and limited coverage of on-site collection of ecological feature information lead to a lag in ecological benefit assessment compared to the maintenance execution process. It achieves the technical effect of rapidly obtaining the ecological feature information of the target forestry based on similarity matching, thereby improving the timeliness and accuracy of large-scale forestry maintenance. Attached Figure Description

[0008] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the accompanying drawings of the embodiments of this disclosure will be briefly described below. Flowcharts are used in this application to illustrate the operations performed by the apparatus according to the embodiments of this application. It should be understood that the preceding or following operations are not necessarily performed precisely in sequence. Instead, various steps can be processed in reverse order or simultaneously as needed. Furthermore, other operations can be added to these processes, or one or more steps can be removed from these processes.

[0009] Figure 1 This is a schematic diagram of a smart maintenance method for large-scale forestry, provided as an embodiment of this application.

[0010] Figure 2 This is a schematic diagram of an intelligent maintenance device for large-scale forestry, provided as an embodiment of this application.

[0011] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0012] Explanation of reference numerals in the attached figures: Information acquisition module 11, data filtering module 12, vectorization module 13, distance calculation module 14, distance judgment module 15, ecological force analysis module 16, memory 21, processor 22, input device 23, output device 24. Detailed Implementation

[0013] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application.

[0014] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description of this application will be provided in conjunction with the accompanying drawings. The described embodiments should not be considered as limitations on this application. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0015] In the following description, references to "some embodiments" describe a subset of all possible embodiments. However, it is understood that "some embodiments" can be the same or different subsets of all possible embodiments and can be combined with each other without conflict. The terms "first" and "second" are used merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, apparatus, product, or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules not explicitly listed or inherent to these processes, methods, products, or apparatuses. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only.

[0016] This application provides an intelligent forestry maintenance method for large-scale forestry, such as... Figure 1 As shown, the method includes:

[0017] The target forestry target feature information is collected from multiple dimensions, including target geographical feature information and target maintenance feature information.

[0018] Specifically, the first step is to collect geographical characteristic data of the target forestry, such as the specific location, total area, and latitude and longitude of the forest land. This geographical data helps determine the natural conditions of the area, influencing the formulation of tree growth and maintenance strategies. Simultaneously, information related to target maintenance is collected, including tree species and distribution. This maintenance characteristic data reflects the actual needs of forestry management and helps in developing personalized maintenance plans. By integrating this multi-dimensional data, a set of target characteristic information for the target forestry can be constructed, providing a data basis for subsequent maintenance decisions.

[0019] Furthermore, multi-dimensional target feature information of the target forestry is collected, wherein the target feature information includes target geographical feature information and target maintenance feature information, including:

[0020] Obtain the target total area of ​​the target forestry; obtain the target latitude and longitude of the target forestry; collect the target site feature parameter set of the target forestry based on predetermined site condition indicators; the target total area, the target latitude and longitude, and the target site feature parameter set constitute the target geographical feature information.

[0021] Preferably, the total target area of ​​the target forestry, i.e., the forest coverage area, is obtained through Geographic Information System (GIS) or other positioning technologies. This data reflects the scale of the entire forestry area. Then, the latitude and longitude coordinates of the target forestry are obtained through Global Positioning System (GPS) or other positioning technologies. This coordinate information provides a precise geographical location for subsequent analysis, ensuring that the analysis and management of the area can be based on accurate location data. Subsequently, the specific data items to be collected are determined according to predetermined site condition indicators. Site condition indicators typically include climate conditions (such as temperature, humidity, and precipitation), soil type (such as soil pH, organic matter content, and permeability), topographic features (such as slope and altitude), and other environmental factors (such as sunlight and wind speed). These indicators play a crucial role in the ecological environment of the forest land, directly affecting the growth and maintenance needs of trees. Afterward, various sensors, remote sensing technologies, weather stations, and soil testing tools are used to collect on-site data from the target forestry area. For example, meteorological data can be collected through weather station equipment, soil data can be analyzed through soil sampling, and topographic data can be obtained through remote sensing technology or GIS. Then, based on predetermined site condition indicators, the collected data is filtered and organized to generate a set of site characteristic parameters for the target forestry. This parameter set describes the environmental characteristics of the target forest land, providing a basis for the rational selection of tree species and the formulation of appropriate maintenance strategies. Finally, the total target area, target latitude and longitude, and target site characteristic parameter set obtained above are combined to form target geographic feature information. This target geographic feature information integrates the spatial location, area, and environmental characteristics of the target forestry, providing comprehensive geographic data support for the optimization and implementation of subsequent maintenance plans.

[0022] Furthermore, target feature information of the target forestry is collected from multiple dimensions, wherein the target feature information includes target geographical feature information and target maintenance feature information, and the method includes:

[0023] A target tree species set for the target forestry is constructed, and the target tree species set includes multiple tree species with predetermined characteristics; based on the multiple tree species with predetermined characteristics, the target maintenance characteristic information is formed; wherein, the predetermined characteristics include at least the planting quantity and the distribution uniformity.

[0024] Optionally, firstly, based on the geographical location, site conditions, and maintenance needs of the target forestry, suitable tree species are selected to form a target tree species set. This target tree species set includes a variety of tree species with specific identifiers. Each tree species has a clear identifier within the set to distinguish it from other species. The selection of these tree species is usually based on the climate, soil type, environmental conditions, and expected ecological goals of the target forest land. For example, drought-resistant tree species may be selected in arid regions, while tree species with higher water requirements may be selected in humid regions. Subsequently, based on these identified tree species, relevant maintenance characteristic information is collected and organized, and combined with the predetermined characteristics of the identifiers to form target maintenance characteristic information. This target maintenance characteristic information includes the planting quantity, distribution uniformity, suitable soil conditions, light requirements, and suitable temperature range for each tree species. This characteristic information will provide detailed parameters for the growth and maintenance of each tree species, ensuring that the specific needs of the tree species can be met during the maintenance process.

[0025] Table 1: Examples of Tree Species

[0026]

[0027] As shown in Table 1, the tree species example table displays the core parameters for tree species configuration, covering tree species identification, planting density, distribution uniformity, soil compatibility, light requirements, and suitable annual average temperature range, providing execution parameters for intelligent maintenance.

[0028] Furthermore, after collecting target feature information of the target forestry from multiple dimensions, it also includes:

[0029] Extract any tree species from the plurality of tree species with predetermined characteristics; analyze and construct an arbitrary site requirement feature parameter set for the arbitrary tree species; compare the arbitrary site requirement feature parameter set with the target site feature parameter set to obtain the arbitrary matching degree; if the arbitrary matching degree does not reach the predetermined matching degree threshold, then perform growth monitoring on the arbitrary tree species.

[0030] Optionally, a tree species with predetermined characteristics is first extracted from the target forestry's tree species set. These species are selected based on their adaptability, ecological requirements, and growth characteristics, possessing certain biological characteristics (such as drought resistance and cold resistance). One randomly selected species serves as the basis for subsequent analysis. Next, for the selected species, its site requirement characteristics are analyzed, and a site requirement characteristic parameter set for that species is constructed. These requirements include the species' growth requirements under different environmental conditions, such as soil type, temperature range, and light conditions. This data can be obtained from existing tree species growth records, climate adaptability studies, and other materials. Through this data, a comprehensive understanding of the tree species' ecological requirements and site conditions can be obtained. Finally, the extracted tree species site requirement characteristic parameter set is compared with the target forestry's site characteristic parameter set, and the matching degree between the two parameter sets is quantified using Euclidean distance. If the matching degree reaches or exceeds the preset matching degree threshold, it indicates that the tree species is adapted to the site conditions of the target forest and maintenance management can continue. If the matching degree does not reach the preset threshold, it indicates that the tree species may not be adapted to the current site conditions, and the next step of growth monitoring will be initiated. Growth monitoring includes real-time tracking of the tree species' growth and health status, as well as whether growth problems caused by environmental maladaptation (such as wilting, pests, etc.) occur. Data (such as tree height, leaf quantity, tree health status, etc.) is collected regularly and uploaded to the monitoring system for analysis. If the growth status of the tree species is found to be unsatisfactory, the maintenance plan can be further adjusted or the tree species can be replaced. Through the above steps, it is possible to accurately assess whether each tree species is adapted to the conditions of the target forest and take targeted monitoring measures to ensure that forestry maintenance decisions are more refined and personalized, achieving the best ecological benefits.

[0031] The forestry maintenance database is filtered using the target geographical feature information as a constraint to obtain the target database, and the target database includes the first maintenance feature information of the first forestry.

[0032] Specifically, firstly, the target geographic feature information is used as a constraint to filter the existing forestry maintenance database. During this process, Euclidean distance is used to calculate the similarity distance between the target geographic feature information and the geographic features of each forestry area in the database, resulting in multiple similarity distances. Then, these similarity distances are compared with a distance threshold, and all geographic feature markers with similarity distances less than or equal to that threshold are selected. Maintenance feature information marked with these geographic features is then extracted from the forestry maintenance database as reference samples. Finally, the extracted forestry and maintenance feature information is used as the first forestry and first maintenance feature information and added to an empty database to form the target database, providing more accurate and personalized data support for the maintenance of the target forestry.

[0033] The target maintenance feature information and the first maintenance feature information are vectorized sequentially to obtain the target feature vector and the first feature vector, respectively.

[0034] Specifically, after constructing the target database, the target maintenance feature information and the first maintenance feature information in the target database are sequentially vectorized. Vectorization transforms this information into a numerical form that can be used for calculation and comparison. In this process, one-hot encoding is used to convert non-numerical data in the feature information into multiple binary variables, for example, pine tree = [1,0,0], cypress tree = [0,1,0]. These converted binary variables are then concatenated with the numerical data in the feature information, thereby transforming the target maintenance feature information into a target feature vector and the first maintenance feature information in the target database into a corresponding first feature vector. This vectorized feature information provides the foundation for subsequent comparison and analysis, ensuring that the similarity or difference between vectors can be determined by calculating the distance between them.

[0035] By comparing the target feature vector with the first feature vector, a first vector distance value is obtained.

[0036] Specifically, after obtaining the target feature vector and the first feature vector, a preliminary distance assessment and an advanced distance assessment are performed on the target feature vector and each first feature vector according to the vector distance assessment mechanism to quantify their similarity, thereby obtaining the first vector distance value. Through this multi-level assessment, the accuracy of vector comparison can be improved, helping to determine the similarity of maintenance characteristics between the target forestry and the first forestry, thus providing support for selecting the most suitable maintenance plan and improving maintenance effectiveness.

[0037] Further, by comparing the target feature vector with the first feature vector to obtain a first vector distance value, including:

[0038] Read a predetermined distance table; construct a first feature point pair, the first feature point pair including a first target feature point of the target feature vector and a first forestry feature point of the first feature vector; extract an initial judgment filling plan from the vector distance judgment mechanism; according to the initial judgment filling plan, fill the predetermined distance table with the first distance value obtained from judging the first target feature point and the first forestry feature point to obtain an initial judgment filling table; extract an advanced judgment filling scheme from the vector distance judgment mechanism; according to the advanced judgment filling scheme, perform advanced judgment filling on the initial judgment filling table to obtain a target judgment filling table; take the filling distance value of a predetermined unit in the target judgment filling table as the first vector distance value.

[0039] Preferably, during vector comparison, a predetermined distance table is first read. This predetermined distance table is a blank two-dimensional data table, providing a basic data framework for subsequent calculations. Then, feature points at each location are extracted from the target feature vector and the first feature vector, forming multiple first feature point pairs. These first feature point pairs include the first target feature point in the target feature vector and the first forestry feature point in the first feature vector, used to calculate the distance between them. Next, an initial judgment filling plan is extracted from the vector distance judgment mechanism. This initial judgment filling plan contains preliminary processing rules and calculation formulas for distance calculation, ensuring that the initial distance value calculations meet the requirements. Based on the extracted initial judgment filling plan, distance judgment is performed on each first feature point pair; that is, the first distance value of each feature point pair is calculated using the Euclidean distance calculation formula built into the initial judgment filling plan, and these first distance values ​​are then filled into the corresponding positions of the feature point pairs in the predetermined distance table. After filling, an initial judgment filling table is generated as the basis for further analysis. Then, an advanced judgment and filling scheme is extracted from the vector distance judgment mechanism. This scheme includes further refined calculation rules, enabling more detailed adjustments to the initial judgment and filling table. Based on the extracted advanced judgment and filling scheme, the initial judgment and filling table is divided into multiple blocks according to the position of the data in the initial judgment and filling table. The first distance value is then recalculated based on these blocks to update the initial judgment and filling table. After the advanced judgment and filling of the initial judgment and filling table is completed, the target judgment and filling table is formed. Through this process, the filling result is more accurate and can better reflect the actual difference between the target feature vector and the first feature vector. Finally, the filling distance value of the predetermined unit (set by business requirements) is extracted from the target judgment and filling table. This value is the first vector distance value. This final distance value can accurately reflect the degree of difference between the target feature vector and the first feature vector for subsequent decision analysis.

[0040] Furthermore, according to the advanced judgment and filling scheme, the initial judgment and filling table is subjected to advanced judgment and filling to obtain the target judgment and filling table, including:

[0041] The initial judgment filling table is partitioned according to the advanced judgment filling scheme to obtain the partitioning result; advanced blocks are obtained from the partitioning result, and arbitrary units in the advanced blocks are extracted, wherein the arbitrary unit corresponds to an arbitrary initial value; an arbitrary reference unit set of the arbitrary unit is constructed, and the arbitrary reference unit set is filtered to obtain an arbitrary maximum unit value, wherein the arbitrary reference unit set includes the left neighbor unit, the upper neighbor unit, and the upper left neighbor unit of the arbitrary unit; the sum of the arbitrary maximum unit value and the arbitrary initial value is used to replace the arbitrary initial value as the arbitrary advanced value of the arbitrary unit; the target judgment filling table is formed based on the arbitrary advanced value.

[0042] Optionally, when performing advanced analysis and filling of the initial analysis and filling table, the initial analysis and filling table is first partitioned according to the advanced analysis and filling scheme. This partitioning aims to divide the initial analysis and filling table into different regions based on the data's position within the table, including initial blocks and advanced blocks. Partitioning allows for clearer identification of feature differences within each region and enables more precise adjustments. Subsequently, advanced blocks are extracted from the partitioning results, and any single unit is extracted from each advanced block as the unit to be processed. The first distance value of this unit in the initial analysis and filling table is used as the initial value of the unit to be processed. Next, a reference unit set is constructed for the unit to be processed, containing its left neighbor, top neighbor, and top-left neighbor. This reference unit set is then filtered to find the largest unit value as a reference value. Finally, the largest selected unit value is added to the initial value of the unit to be processed to obtain an advanced value. This advanced value replaces the original initial value and serves as the new first distance value for the unit to be processed. Finally, based on the advanced values ​​of any unit, the initial judgment filling table is filled to form the target judgment filling table. This target judgment filling table contains all unit values ​​after advanced optimization, which can more accurately reflect the difference between the target feature vector and the first feature vector, and provide more accurate data support for subsequent vector distance calculation.

[0043] Furthermore, the partitioning result includes an initial block and an advanced block, wherein the initial block refers to the first row and first column of cells in the predetermined distance table, and the advanced block refers to the cell set in the predetermined distance table other than the initial block.

[0044] Optionally, according to the advanced analysis and filling scheme, the initial analysis and filling table is divided into initial blocks and advanced blocks. The initial block consists of the first row and first column of cells in the predetermined distance table. In other words, the initial block contains all cells in the first row and first column of the table. This data is usually used for preliminary distance calculation and analysis, serving as the starting point for subsequent processing. The advanced block refers to all other cell sets in the predetermined distance table excluding the initial block, i.e., cells not in the first row and first column. This data usually needs to undergo advanced analysis for further refinement of distance calculation and analysis. This partitioning method allows for the separate processing of preliminary data and data requiring more in-depth optimization, thereby improving the efficiency and accuracy of distance comparison.

[0045] If the first vector distance value reaches the predetermined constraint, then the first ecological feature information of the first forestry is retrieved.

[0046] Specifically, if the calculated first vector distance value is less than or equal to the predetermined constraint, it indicates that the characteristics of the target forestry are sufficiently similar to those of the first forestry. In this case, the first ecological characteristic information related to the first forestry will be retrieved. This ecological characteristic information includes the ecological environment indicators of the forest land, such as soil quality, vegetation coverage, water resource status, and climate adaptability. This ecological characteristic information is crucial for subsequent maintenance decisions and can help to better select the most suitable ecological maintenance plan for the target forestry, ensuring the accuracy of subsequent maintenance.

[0047] The first ecological characteristic information is used as the target ecological characteristic information of the target forestry, and the target ecological capacity is obtained through analysis.

[0048] Specifically, after obtaining the primary ecological characteristic information, this information is directly applied to the target forestry as its target ecological characteristic information. Subsequently, this target ecological characteristic information is analyzed to obtain the target ecological capacity through the primary ecological characteristic information. This target ecological capacity is a comprehensive indicator that reflects the ecological health status and sustainable development capacity of the target forest land under its specific ecological environment. It can ensure that the conservation strategies are compatible with the ecological environment of the target forestry and promote its sustainable development.

[0049] Furthermore, the first ecological characteristic information is used as the target ecological characteristic information of the target forestry, and the target ecological capacity is analyzed to obtain the following:

[0050] Read the predetermined ecological indicators; extract the target ecological feature information based on the predetermined ecological indicators to obtain the target indicator parameter set; perform variation weighted analysis on the target indicator parameter set to obtain the target ecological force; wherein, the predetermined ecological indicators include wind speed reduction index, fixed sand index and soil improvement index.

[0051] Preferably, firstly, a set of predetermined ecological indicators is read. These indicators are key parameters used to assess the ecological status of the target forestry. The predetermined ecological indicators include wind speed reduction index, sand fixation index, and soil improvement index. These indicators reflect different aspects of ecological functions, such as wind speed reduction capacity, sand fixation capacity, and soil improvement, all of which play a crucial role in the ecological benefits of the forest land. Subsequently, based on the predetermined ecological indicators, the target ecological characteristic information is traversed. This target ecological characteristic information includes various ecological data of the forest land (such as vegetation cover, soil condition, and climate data). By comparing the traversed data with the predetermined ecological indicators, data related to the predetermined ecological indicators are extracted to form a target indicator parameter set. This parameter set includes specific data on wind speed reduction, sand fixation, and soil improvement, serving as the basis for analyzing the target ecological capacity. Once the target indicator parameter set is extracted, variation-weighted analysis is performed on these data. Specifically, starting with the target indicator parameters at the current time, historical indicator parameters within a preset time window are obtained, and the standard deviation and mean of each indicator parameter are calculated. The time-series coefficient of variation for each indicator is obtained by dividing the standard deviation by the mean, adding the quotient to 1, and then dividing 1 by the sum. Subsequently, the time-series coefficient of variation for each indicator is weighted and calculated with the corresponding target indicator parameters in the target indicator parameter set to obtain the target ecological strength. This target ecological strength is a comprehensive assessment result, reflecting the overall ecological function and benefits of the target forest land in terms of wind speed reduction, sand fixation, and soil improvement. This ecological strength value can help assess the ecological health of the target forestry and its role in environmental protection and soil improvement, thereby providing support for the selection of further conservation plans and management decisions.

[0052] In the above text, refer to Figure 1 A smart forestry maintenance method for large-scale forestry according to an embodiment of the present invention is described in detail. Next, reference will be made to... Figure 2 This invention describes an intelligent maintenance device for large-scale forestry according to an embodiment of the present invention.

[0053] According to an embodiment of the present invention, an intelligent forestry maintenance device for large-scale forestry addresses the technical problem that, in large-scale forestry scenarios, the high efficiency and limited coverage of on-site collection of ecological characteristic information lead to a lag in ecological benefit assessment compared to the maintenance execution process. The device achieves the technical effect of rapidly acquiring ecological characteristic information of the target forestry based on similarity matching, thereby improving the timeliness and accuracy of large-scale forestry maintenance. The intelligent forestry maintenance device for large-scale forestry includes: an information acquisition module 11, a data filtering module 12, a vectorization module 13, a distance calculation module 14, a distance judgment module 15, and an ecological force analysis module 16.

[0054] Information acquisition module 11: Collects target feature information of the target forestry from multiple dimensions, wherein the target feature information includes target geographical feature information and target maintenance feature information; Data filtering module 12: Filters the forestry maintenance database using the target geographical feature information as a constraint to obtain a target database, wherein the target database includes the first maintenance feature information of the first forestry; Vectorization module 13: Performs vectorization processing on the target maintenance feature information and the first maintenance feature information in sequence to obtain the target feature vector and the first feature vector, respectively; Distance calculation module 14: Compares the target feature vector and the first feature vector to obtain a first vector distance value; Distance judgment module 15: If the first vector distance value reaches a predetermined constraint, retrieves the first ecological feature information of the first forestry; Ecological strength analysis module 16: Uses the first ecological feature information as the target ecological feature information of the target forestry and analyzes to obtain the target ecological strength.

[0055] The specific configuration of the information acquisition module 11 will be described in detail below. The information acquisition module 11 may further include: obtaining the target total area of ​​the target forestry; obtaining the target latitude and longitude of the target forestry; collecting a target site feature parameter set of the target forestry based on predetermined site condition indicators; the target total area, the target latitude and longitude, and the target site feature parameter set constitute the target geographical feature information.

[0056] The specific configuration of the information collection module 11 will be described in detail below. The information collection module 11 further includes: constructing a target tree species set for the target forestry, wherein the target tree species set includes multiple tree species with predetermined characteristics; forming the target maintenance characteristic information based on the multiple tree species with predetermined characteristics; wherein the predetermined characteristics include at least the planting quantity and the distribution uniformity.

[0057] The specific configuration of the information acquisition module 11 will be described in detail below. The information acquisition module 11 further includes: extracting any tree species from the plurality of tree species with predetermined characteristics; analyzing and constructing an arbitrary site requirement characteristic parameter set for the arbitrary tree species; comparing the arbitrary site requirement characteristic parameter set with the target site characteristic parameter set to obtain an arbitrary matching degree; if the arbitrary matching degree does not reach a predetermined matching degree threshold, then performing growth monitoring on the arbitrary tree species.

[0058] The specific configuration of the distance calculation module 14 will be described in detail below. The distance calculation module 14 may further include: reading a predetermined distance table; constructing a first feature point pair, the first feature point pair including a first target feature point of the target feature vector and a first forestry feature point of the first feature vector; extracting an initial judgment filling plan from the vector distance judgment mechanism; filling the predetermined distance table with the first distance value obtained by judging the first target feature point and the first forestry feature point according to the initial judgment filling plan, to obtain an initial judgment filling table; extracting an advanced judgment filling scheme from the vector distance judgment mechanism; performing advanced judgment filling on the initial judgment filling table according to the advanced judgment filling scheme, to obtain a target judgment filling table; and taking the filling distance value of a predetermined unit in the target judgment filling table as the first vector distance value.

[0059] The specific configuration of the distance calculation module 14 will be described in detail below. The distance calculation module 14 may further include: partitioning the initial judgment filling table according to the advanced judgment filling scheme to obtain the partitioning result; obtaining the advanced blocks in the partitioning result and extracting any unit in the advanced blocks, wherein the arbitrary unit corresponds to an arbitrary initial value; constructing an arbitrary reference unit set for the arbitrary unit and filtering the arbitrary reference unit set to obtain an arbitrary maximum unit value, wherein the arbitrary reference unit set includes the left neighbor unit, the upper neighbor unit, and the upper left neighbor unit of the arbitrary unit; replacing the arbitrary initial value with the sum of the arbitrary maximum unit value and the arbitrary initial value as the arbitrary advanced value of the arbitrary unit; and forming the target judgment filling table based on the arbitrary advanced value.

[0060] The specific configuration of the distance calculation module 14 will be described in detail below. The distance calculation module 14 may further include: the partitioning result includes an initial block and the advanced block, wherein the initial block refers to the first row and first column cell sets in the predetermined distance table, and the advanced block refers to the cell sets in the predetermined distance table other than the initial block.

[0061] The specific configuration of the ecological force analysis module 16 will be described in detail below. The ecological force analysis module 16 may further include: reading predetermined ecological indicators; extracting target ecological feature information based on the predetermined ecological indicators to obtain a target indicator parameter set; performing variation weighted analysis on the target indicator parameter set to obtain the target ecological force; wherein, the predetermined ecological indicators include wind speed reduction index, fixed sand index, and soil improvement index.

[0062] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention, showing a block diagram of an exemplary electronic device suitable for implementing the embodiments of the present invention. Figure 3The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments of the present invention. This electronic device is in the form of a general-purpose computing device, and its components may include, but are not limited to, a memory 21, a processor 22, an input device 23, and an output device 24. The processor 22 may be one or more; the memory 21 may include a computer-readable medium and at least one program product having a set (at least one) of program modules configured to perform the functions of the embodiments of this application.

[0063] The memory 21 shown in this embodiment of the invention can be any combination of one or more computer-readable media; the computer-readable storage medium can be, but is not limited to, an infrared, semiconductor device, apparatus or device, or any combination thereof, for storing software programs, computer-executable programs and modules, such as the program instructions / modules corresponding to a smart maintenance method for large-scale forestry in this embodiment of the invention. The processor 22 executes various functional applications and data processing of the computer device by running the software programs, instructions and modules stored in the memory 21, thereby realizing the aforementioned smart maintenance method for large-scale forestry.

[0064] The intelligent forestry maintenance device provided in this embodiment of the invention can execute the intelligent forestry maintenance method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0065] Although this application makes various references to certain modules in the apparatus according to the embodiments of this application, any number of different modules can be used and run on user terminals and / or servers. The various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional unit are only for easy distinction between each other and are not intended to limit the scope of protection of this invention.

[0066] The specific embodiments described above do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A smart maintenance method for large-scale forestry, characterized in that, include: Target feature information of the target forestry is collected from multiple dimensions, wherein the target feature information includes target geographical feature information and target maintenance feature information; The forestry maintenance database is filtered using the target geographic feature information as a constraint to obtain the target database, and the target database includes the first maintenance feature information of the first forestry. The target maintenance feature information and the first maintenance feature information are sequentially vectorized to obtain the target feature vector and the first feature vector, respectively. By comparing the target feature vector with the first feature vector, a first vector distance value is obtained; If the first vector distance value reaches the predetermined constraint, then the first ecological feature information of the first forestry is retrieved; The first ecological characteristic information is used as the target ecological characteristic information of the target forestry, and the target ecological capacity is obtained through analysis; The intelligent forestry maintenance method for large-scale forestry is characterized by acquiring target feature information of the target forestry from multiple dimensions, wherein the target feature information includes target geographical feature information and target maintenance feature information, including: Obtain the total target area of ​​the target forestry; Obtain the target latitude and longitude of the target forest; The target site characteristic parameter set of the target forestry is obtained by collecting predetermined site condition indicators; The total area of ​​the target, the latitude and longitude of the target, and the set of site feature parameters of the target constitute the geographical feature information of the target; The intelligent forestry maintenance method for large-scale forestry is characterized by acquiring target feature information of the target forestry from multiple dimensions, wherein the target feature information includes target geographical feature information and target maintenance feature information, including: Construct a target tree species set for the target forestry, wherein the target tree species set includes multiple tree species with predetermined characteristics; Based on the tree species identified by the multiple identifiers with predetermined characteristics, the target maintenance feature information is formed; The predetermined characteristics include at least the planting quantity and the uniformity of distribution.

2. The intelligent forestry maintenance method according to claim 1, characterized in that, By comparing the target feature vector with the first feature vector, a first vector distance value is obtained, including: Read the predetermined distance table; A first feature point pair is formed, the first feature point pair including the first target feature point of the target feature vector and the first forestry feature point of the first feature vector; Extract the initial judgment and filling plan from the vector distance judgment mechanism; According to the initial judgment filling plan, the first distance value obtained by judging the first target feature point and the first forestry feature point is filled into the predetermined distance table to obtain the initial judgment filling table; Extract the advanced judgment and filling scheme from the vector distance judgment mechanism; According to the advanced judgment filling scheme, the initial judgment filling table is subjected to advanced judgment filling to obtain the target judgment filling table; The filling distance value of the predetermined unit in the target judgment filling table is taken as the first vector distance value.

3. The intelligent forestry maintenance method according to claim 2, characterized in that, According to the advanced judgment and filling scheme, the initial judgment and filling table is subjected to advanced judgment and filling to obtain the target judgment and filling table, including: The initial judgment and filling table is partitioned according to the advanced judgment and filling scheme to obtain the partitioning results; Obtain the advanced blocks in the partitioning results, and extract any unit from the advanced blocks, wherein the arbitrary unit corresponds to any initial value; Construct an arbitrary reference unit set for the arbitrary unit, and filter the arbitrary reference unit set to obtain an arbitrary maximum unit value, wherein the arbitrary reference unit set includes the left neighbor unit, the top neighbor unit, and the top-left neighbor unit of the arbitrary unit; The sum of the arbitrary maximum unit value and the arbitrary initial value is used to replace the arbitrary initial value, and this sum is used as the arbitrary progression value of the arbitrary unit. The target assessment filling table is formed based on the arbitrary progression value.

4. The intelligent forestry maintenance method according to claim 3, characterized in that, The partitioning result includes an initial block and the advanced block, wherein the initial block refers to the first row and first column of the cell set in the predetermined distance table, and the advanced block refers to the cell set in the predetermined distance table other than the initial block.

5. The intelligent forestry maintenance method according to claim 1, characterized in that, The first ecological characteristic information is used as the target ecological characteristic information of the target forestry, and the target ecological capacity is analyzed to obtain the following: Read the predetermined ecological indicators; Based on the predetermined ecological indicators, the target ecological feature information is extracted through a traversal process to obtain the target indicator parameter set; The target ecological capacity is obtained by performing a variation-weighted analysis on the target indicator parameter set; The predetermined ecological indicators include the wind speed reduction index, the sand fixation index, and the soil improvement index.

6. The intelligent forestry maintenance method according to claim 1, characterized in that, After obtaining the target feature information of the target forestry from multiple dimensions, the process also includes: Extract any tree species from the plurality of tree species identified by predetermined features; Analyze and construct a set of arbitrary site requirement characteristic parameters for the arbitrary tree species; The degree of matching between the arbitrary site requirement feature parameter set and the target site feature parameter set is obtained by comparison; If the matching degree of any tree species does not reach the predetermined matching degree threshold, then growth monitoring is performed on the tree species.

7. An intelligent maintenance device for large-scale forestry, characterized in that, The device is used to implement the intelligent forestry maintenance method for large-scale forestry as described in any one of claims 1 to 6, comprising: Information acquisition module: Collects target feature information of the target forestry from multiple dimensions, wherein the target feature information includes target geographical feature information and target maintenance feature information; Data filtering module: The forestry maintenance database is filtered based on the target geographic feature information to obtain the target database, and the target database includes the first maintenance feature information of the first forestry. Vectorization module: The target maintenance feature information and the first maintenance feature information are vectorized sequentially to obtain the target feature vector and the first feature vector, respectively; Distance calculation module: compares the target feature vector with the first feature vector to obtain the first vector distance value; Distance determination module: If the distance value of the first vector reaches the predetermined constraint, then retrieve the first ecological feature information of the first forestry. Ecological force analysis module: The first ecological characteristic information is used as the target ecological characteristic information of the target forestry, and the target ecological force is obtained through analysis.

8. An electronic device, characterized in that, The electronic device includes: Memory, used to store executable instructions; The processor, when executing executable instructions stored in the memory, implements the intelligent maintenance method for large-scale forestry as described in any one of claims 1-6.

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