Intelligent maintenance method, device and equipment for large-scale forestry

Abstract

The invention discloses an intelligent maintenance method, device and equipment for large-scale forestry, and relates to the related technical field of forestry maintenance, and the method comprises the steps: collecting the multi-dimensional feature information of target forestry; a forestry maintenance database is screened according to geographic feature constraints, and a target database containing the first forestry maintenance features is obtained; performing vectorization processing on the target maintenance characteristics and the maintenance characteristics of the first forestry; comparing the two feature vectors, and calculating a distance value; if the distance value reaches the standard, calling first ecological characteristic information of the first forestry; and taking the first ecological feature information as a target ecological feature, and analyzing the target ecological force. The technical problem that in a large-scale forestry scene, due to the fact that the field collection efficiency of ecological characteristic information is low and the coverage range is limited, ecological benefit evaluation lags behind the maintenance execution process is solved, and the purposes of rapidly obtaining the ecological characteristic information of the target forestry based on similarity matching and improving the ecological benefit evaluation efficiency are achieved. And the timeliness and accuracy of large-scale forestry maintenance are improved.

Description

Technical Field

[0001] The present application relates to the technical field related to forestry maintenance, and specifically to an intelligent maintenance method, device and equipment for large-scale forestry. Background Art

[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 vastness of the region, the diversity of tree species, and the 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 and resource waste due to inappropriate strategies. Simultaneously, with the rapid development of technologies such as the Internet of Things, big data, and artificial intelligence, intelligent forestry has become an inevitable trend in the industry's transformation and upgrading. However, the application of existing technological systems in forestry maintenance still faces numerous bottlenecks, such as incomplete multi-dimensional data collection, the lack of intelligent decision-making mechanisms under geographical constraints, and delayed ecological benefit assessment. Therefore, there is an urgent need for intelligent maintenance methods 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 resilience enhancement in large-scale forestry. Summary of the Invention

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

[0004] The present application provides an intelligent maintenance method for large-scale forestry, which includes: multi-dimensional collection to obtain target feature information of a target forestry, wherein the target feature information includes target geographic feature information and target maintenance feature information; screening a forestry maintenance database with the target geographic feature information as a constraint to obtain a target database, and the target database includes first maintenance feature information of a first forestry; vectorizing the target maintenance feature information and the first maintenance feature information in sequence to obtain a target feature vector and a first feature vector, respectively; comparing the target feature vector with the first feature vector to obtain a first vector distance value; if the first vector distance value reaches a predetermined constraint, retrieving 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 target ecological force.

[0005] The present application also provides an intelligent maintenance device for large-scale forestry, including: an information acquisition module: multi-dimensional acquisition to obtain target feature information of the target forestry, wherein the target feature information includes target geographic feature information and target maintenance feature information; a data screening module: using the target geographic feature information as a constraint to screen the forestry maintenance database to obtain a target database, and the target database includes first maintenance feature information of a first forestry; a vectorization module: 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: comparing the target feature vector with the first feature vector to obtain a first vector distance value; a distance judgment module: if the first vector distance value reaches a predetermined constraint, retrieving the first ecological feature information of the first forestry; an ecological force analysis module: using the first ecological feature information as the target ecological feature information of the target forestry, and analyzing to obtain the target ecological force.

[0006] The present application also provides an electronic device, comprising: 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 an intelligent large-scale forestry maintenance method, device, and equipment. This method collects target characteristic information of a target forest across multiple dimensions, including target geographic characteristic information and target maintenance characteristic information. A forestry maintenance database is then screened using the target geographic characteristic information as a constraint to obtain a target database, which includes first maintenance characteristic information for a first forest. The target maintenance characteristic information and the first maintenance characteristic information are then vectorized to obtain a target characteristic vector and a first characteristic vector, respectively. The target characteristic vector and the first characteristic vector are then compared to obtain a first vector distance value. If the first vector distance value meets a predetermined constraint, first ecological characteristic information of the first forest is retrieved. The first ecological characteristic information is used as the target ecological characteristic information for the target forest, and analyzed to obtain the target ecological force. This method addresses the technical issue of high efficiency and limited coverage of field collection of ecological characteristic information in large-scale forestry scenarios, resulting in a lag in ecological benefit assessment behind maintenance execution. This method achieves the technical effect of rapidly acquiring ecological characteristic information of the target forest based on similarity matching, improving the timeliness and accuracy of large-scale forestry maintenance. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0009] Figure 1 A flow chart of an intelligent maintenance method for large-scale forestry provided in an embodiment of the present application.

[0010] Figure 2 A schematic structural diagram of an intelligent maintenance device for large-scale forestry provided in an embodiment of the present application.

[0011] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0012] Explanation of the accompanying symbols: information collection module 11, data screening 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 DESCRIPTION

[0013] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0014] In order to make the purpose, technical solutions and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limiting this application. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0015] In the following description, reference is made to “some embodiments”, which describes a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict, and the terms “first\second” involved are merely used to distinguish similar objects and do not represent a specific ordering of the objects. The terms “including” and “having” and any variations are intended to cover non-exclusive inclusions. For example, a process, method, device, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs. The terms used herein are for the purpose of describing the embodiments of this application only.

[0016] The present application embodiment provides an intelligent maintenance method for large-scale forestry, such as Figure 1 As shown, the method includes: Target feature information of the target forestry is obtained through multi-dimensional collection, wherein the target feature information includes target geographic feature information and target maintenance feature information.

[0017] Specifically, we first collect geographic characteristic data for the target forestry, such as the specific location, total area, and longitude and latitude of the forest. This geographic data helps determine the natural conditions of the area, influencing tree growth and maintenance strategies. We also collect information related to targeted maintenance, including tree species and distribution. This maintenance characteristic data reflects the actual needs of forestry management and helps develop personalized maintenance plans. By integrating this multi-dimensional data, we can construct a target characteristic information set for the target forestry, providing a data basis for subsequent maintenance decisions.

[0018] Furthermore, target characteristic information of the target forestry is obtained through multi-dimensional collection, wherein the target characteristic information includes target geographical characteristic information and target maintenance characteristic information, including: Obtain the target total area of ​​the target forestry; obtain the target longitude and latitude of the target forestry; obtain the target site characteristic parameter set of the target forestry based on predetermined site condition indicators; the target total area, the target longitude and latitude and the target site characteristic parameter set constitute the target geographic feature information.

[0019] Preferably, the total target area of ​​the target forestry, i.e., the forest coverage area, is obtained using a geographic information system (GIS) or other positioning technology. This data can reflect the scale of the entire forestry area. The latitude and longitude coordinates of the target forestry area are then obtained using a global positioning system (GPS) or other positioning technology. These coordinates provide a precise geographic location for subsequent analysis, ensuring that analysis and management of the area are based on accurate location data. Subsequently, specific data items to be collected are determined based on predetermined site condition indicators. Site condition indicators typically include climate conditions (such as temperature, humidity, and precipitation), soil type (such as soil acidity, organic matter content, and water permeability), topographic characteristics (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 forestland and directly affect tree growth and maintenance needs. Subsequently, on-site data collection is conducted on the target forestry area using various sensors, remote sensing technology, weather stations, and soil testing tools. For example, meteorological data can be collected using weather station equipment, soil data can be obtained through soil sampling and analysis, and topographic data can be obtained through remote sensing technology or a geographic information system. Then, based on predetermined site condition indicators, the collected data is screened and organized to generate a set of site characteristic parameters for the target forest. This parameter set describes the environmental characteristics of the target forest, providing a basis for rationally selecting tree species and formulating appropriate maintenance strategies. Finally, the target total area, target longitude and latitude, and target site characteristic parameter set obtained above are combined to form target geographic characteristic information. This target geographic characteristic information integrates the spatial location, area, and environmental characteristics of the target forest, providing comprehensive geographic data support for the optimization and implementation of subsequent maintenance plans.

[0020] Furthermore, target characteristic information of the target forestry is obtained by multi-dimensional collection, wherein the target characteristic information includes target geographical characteristic information and target maintenance characteristic information, and the method includes: A target tree species set for the target forestry is formed, and the target tree species set includes a plurality of identified tree species with predetermined characteristics; based on the plurality of identified tree species with predetermined characteristics, the target maintenance characteristic information is formed; wherein the predetermined characteristics include at least the planting quantity and distribution uniformity.

[0021] Optionally, first, 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 contains a variety of tree species with specific identifications. Each tree species has a clear identification in the set to distinguish different tree 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. For example, drought-resistant tree species may be selected in arid areas, and tree species with higher water requirements may be selected in humid areas. Subsequently, based on these identified tree species, relevant maintenance characteristic information is collected and sorted, and combined with the predetermined characteristics of the identification to form target maintenance characteristic information. This target maintenance characteristic information includes the planting quantity of each tree species, distribution uniformity, suitable soil conditions, light requirements, adapted temperature range, etc. These characteristic information will provide detailed parameters for the growth and maintenance of each tree species to ensure that the specific needs of the tree species can be met during the maintenance process.

[0022] Table 1: Example of tree species As shown in Table 1, the tree species example table shows the core parameters of tree species configuration, covering tree species identification, planting density, distribution uniformity, soil adaptability, light requirements and suitable annual average temperature range, providing execution parameters for intelligent maintenance.

[0023] Furthermore, after obtaining target characteristic information of the target forestry through multi-dimensional collection, the following is also included: Extract any tree species from the multiple identified tree species with predetermined characteristics; analyze and construct any site requirement characteristic parameter set of the any tree species; compare and obtain any matching degree between the any site requirement characteristic parameter set and the target site characteristic parameter set; if the any matching degree does not reach a predetermined matching degree threshold, perform growth monitoring on the any tree species.

[0024] Optionally, a tree species with predetermined characteristics is first extracted from the target forestry's tree species collection. These species are selected based on their adaptability, ecological needs, and growth characteristics, possessing certain biological properties (such as drought tolerance and cold tolerance). A randomly selected species serves as the basis for subsequent analysis. Subsequently, the site requirements of the selected species are analyzed, and a set of site requirement characteristic parameters 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 derived from existing tree species growth records, climate adaptability studies, and other data. This data provides a comprehensive understanding of the species' ecological needs and site conditions. The extracted set of site requirement characteristic parameters is then compared with the target forest's site characteristic parameter set, and the degree of match between the two parameter sets is quantified using Euclidean distance. If the resulting match reaches or exceeds the preset match threshold, it indicates that the tree species is adapted to the site conditions of the target forest and maintenance management can continue. If the match does not reach the predetermined 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 any growth problems (such as wilting, pests and diseases) caused by environmental maladaptation. By regularly collecting data (such as tree height, number of leaves, tree health status, etc.) and uploading the data to the monitoring system for analysis, if the growth 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 can be more refined and personalized to achieve the best ecological benefits.

[0025] The forestry maintenance database is screened with the target geographic feature information as a constraint to obtain a target database, and the target database includes first maintenance feature information of a first forestry.

[0026] Specifically, the target geographic feature information is first used as a constraint to screen the existing forestry maintenance database. During this process, the target geographic feature information is calculated using Euclidean distance to calculate the similarity distance between the target geographic feature information and each forestry geographic feature in the forestry maintenance database, resulting in multiple similarity distances. Subsequently, these multiple similarity distances are compared with a distance threshold to filter out all geographic feature markers less than or equal to this similarity distance. The maintenance feature information marked by these geographic features is then extracted from the forestry maintenance database as a reference sample. Finally, the extracted forestry and maintenance feature information is added as the first forestry and first maintenance feature information to an empty database to form the target database, providing more accurate and personalized data support for the maintenance of the target forestry.

[0027] The target maintenance feature information and the first maintenance feature information are vectorized in sequence to obtain a target feature vector and a first feature vector, respectively.

[0028] 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 converts this information into numerical form that can be used for calculation and comparison. During this process, one-hot encoding is used to convert the non-numeric data in the feature information into multiple binary variables (for example, pine = [1,0,0], cypress = [0,1,0]). These converted binary variables are then concatenated with the numerical data in the feature information, converting the target maintenance feature information into a target feature vector and the first maintenance feature information in the target database into a one-to-one corresponding first feature vector. This vectorized feature information provides the basis for subsequent comparison and analysis, ensuring that similarities or differences can be determined by calculating the distance between vectors.

[0029] The target feature vector is compared with the first feature vector to obtain a first vector distance value.

[0030] Specifically, after obtaining the target eigenvector and the first eigenvector, the system performs preliminary and advanced distance analysis on the target eigenvector and each first eigenvector based on the vector distance analysis mechanism to quantify their similarity, thereby obtaining the first vector distance value. This multi-level analysis improves the accuracy of vector comparisons and helps determine the similarity in maintenance characteristics between the target forest and the first forest, thus supporting the selection of the most appropriate maintenance plan and improving maintenance effectiveness.

[0031] Furthermore, comparing the target feature vector with the first feature vector to obtain a first vector distance value includes: Read the predetermined distance table; form a first feature point pair, 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 analysis and filling plan in the vector distance analysis mechanism; according to the initial analysis and filling plan, fill the first distance value obtained by analyzing the first target feature point and the first forestry feature point into the predetermined distance table to obtain an initial analysis and filling table; extract the advanced analysis and filling scheme in the vector distance analysis mechanism; according to the advanced analysis and filling scheme, perform advanced analysis and filling on the initial analysis and filling table to obtain a target analysis and filling table; take the filling distance value of the predetermined unit in the target analysis and filling table as the first vector distance value.

[0032] Preferably, when performing vector comparison, a predetermined distance table is first read. This predetermined distance table is a blank two-dimensional data table, which provides a basic data framework for subsequent calculations. Subsequently, the feature points of each position are extracted from the target feature vector and the first feature vector, and multiple first feature point pairs are formed. 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, which are used to calculate the distance between them. Afterwards, the initial analysis and filling plan is extracted from the vector distance analysis mechanism. This initial analysis and filling plan contains the preliminary processing rules and calculation formulas for distance calculation to ensure that the preliminary distance value calculation meets the requirements. Based on the extracted initial analysis and filling plan, each first feature point pair will be subjected to distance analysis, that is, the first distance value of each feature point pair is calculated by the Euclidean distance calculation formula built into the initial analysis and filling plan, and then these first distance values ​​are filled into the corresponding positions of the feature point pairs in the predetermined distance table. After filling, an initial analysis and filling table will be generated as the basis for further analysis. Then, an advanced analysis and filling scheme is extracted from the vector distance analysis mechanism. This scheme includes further refined calculation rules and can make more detailed adjustments to the initial analysis and filling table. Based on the extracted advanced analysis and filling scheme, the initial analysis and filling table will be divided into multiple blocks according to the position of the data in the initial analysis and filling table, and the first distance value will be recalculated based on these blocks to update the initial analysis and filling table. After the advanced analysis and filling of the initial analysis and filling table is completed, the target analysis 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 needs) is extracted from the target analysis 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.

[0033] Furthermore, according to the advanced analysis and filling scheme, the initial analysis and filling table is subjected to advanced analysis and filling to obtain a target analysis and filling table, including: Partition the initial analysis and filling table according to the advanced analysis and filling scheme to obtain a partition result; obtain the advanced block in the partition result, and extract any unit in the advanced block, wherein the arbitrary unit corresponds to an arbitrary initial value; form an arbitrary reference unit set of the arbitrary unit, and screen the arbitrary reference unit set to obtain an arbitrary maximum unit value, wherein the arbitrary reference unit set includes the left adjacent unit, the upper adjacent unit and the upper-left adjacent unit of the arbitrary unit; replace 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; form the target analysis and filling table based on the arbitrary advanced value.

[0034] Optionally, when performing advanced evaluation and filling of the initial evaluation and filling table, the initial evaluation and filling table is first partitioned according to the advanced evaluation and filling scheme. This partitioning operation aims to divide the initial evaluation and filling table into different regions, including initial blocks and advanced blocks, based on the position of the data in the initial evaluation and filling table. Partitioning enables more clear identification of feature differences within each region and allows for more precise adjustments. Subsequently, the advanced blocks are extracted from the partitioning results, and any cell from the advanced blocks is extracted as the cell to be processed. The first distance value of the cell in the initial evaluation and filling table is used as the initial value of the cell to be processed. A reference cell set is then constructed for the cell to be processed. This reference cell set includes the cell to the left, upper, and left-upper neighbor of the cell to be processed. This constructed reference cell set is then filtered to find the maximum cell value, which serves as the reference value. The maximum cell value is then added to the initial value of the cell to be processed to obtain an advanced value. This advanced value replaces the original initial value and serves as the new first distance value of the cell to be processed. Finally, based on the advanced value 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 eigenvector and the first eigenvector, and provide more accurate data support for subsequent vector distance calculations.

[0035] Furthermore, the partitioning result includes an initial block and the advanced block, wherein the initial block refers to the first row unit set and the first column unit set in the predetermined distance table, and the advanced block refers to the unit set in the predetermined distance table except the initial block.

[0036] Optionally, the initial analysis and filling table will be divided into initial blocks and advanced blocks according to the advanced analysis and filling scheme, where the initial block is composed of the first row cell set and the first column cell set in the predetermined distance table, that is, the initial block contains all the cells in the first row and the first column of the table. This part of the data is usually used for preliminary distance calculation and analysis as the starting point for subsequent processing; the advanced block refers to all other cell sets in the predetermined distance table except the initial block, that is, the cells that are not in the first row and the first column. This part of the data usually needs to undergo advanced analysis to further refine the distance calculation and analysis. Through this partitioning method, preliminary data and data that requires more in-depth optimization can be processed separately, thereby improving the efficiency and accuracy of distance comparison.

[0037] If the first vector distance value reaches a predetermined constraint, the first ecological characteristic information of the first forestry is retrieved.

[0038] Specifically, if the calculated first vector distance value is less than or equal to the predetermined constraint, it means that the characteristics of the target forestry are sufficiently similar to the characteristics of the first forestry. At this time, the first ecological characteristic information related to the first forestry will be retrieved. These ecological characteristic information include the ecological environment indicators of the forest land, such as soil quality, vegetation coverage, water resources, climate adaptability and other data. These ecological characteristic information are very critical to subsequent maintenance decisions, and can help better select the most appropriate ecological maintenance plan for the target forestry and ensure the accuracy of subsequent maintenance.

[0039] 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.

[0040] Specifically, after obtaining the first ecological characteristic information, this information is directly applied to the target forestry sector as its target ecological characteristic information. Subsequently, this target ecological characteristic information is analyzed to derive the target ecological strength. This target ecological strength is a comprehensive indicator that reflects the ecological health and sustainable development capacity of the target forestland within its specific ecological environment. This ensures that maintenance strategies are aligned with the ecological environment of the target forestry sector, promoting its sustainable development.

[0041] Furthermore, the first ecological characteristic information is used as the target ecological characteristic information of the target forestry, and the target ecological force is obtained by analysis, including: Read predetermined ecological indicators; traverse and extract the target ecological characteristic information based on the predetermined ecological indicators to obtain a target indicator parameter set; perform a variation weighted analysis on the target indicator parameter set to obtain the target ecological force; wherein the predetermined ecological indicators include a wind speed reduction index, a fixed sand index, and a soil improvement index.

[0042] Preferably, a set of predetermined ecological indicators is first retrieved. These indicators are key parameters used to assess the ecological status of the target forest. These include the wind speed reduction index, the sand fixation index, and the soil improvement index. These indicators reflect different aspects of ecological function, such as wind speed reduction, sand fixation, and soil improvement, and they play a significant role in the ecological benefits of the forest. Subsequently, the target ecological characteristic information is traversed based on the predetermined ecological indicators. The target ecological characteristic information includes various ecological data of the forest (such as vegetation cover, soil conditions, and climate data). By comparing the traversed data with the predetermined ecological indicators, data related to the predetermined ecological indicators is extracted to form a target indicator parameter set. This parameter set includes specific data on wind speed reduction, sand fixation, and soil improvement, which serves as the basis for analyzing the target ecological capacity. Once the target indicator parameter set is extracted, a weighted variance analysis is performed on this data. Specifically, starting with the current target indicator parameter, the historical indicator parameters within the 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 calculated quotient to 1, and dividing the calculated sum by 1. Subsequently, the time-series coefficient of variation for each indicator is weighted and added to the corresponding target indicator parameter in the target indicator parameter set to obtain the target ecological force. This target ecological force is a comprehensive assessment result that reflects the overall ecological function and benefits of the target forest in terms of wind speed reduction, sand fixation, and soil improvement. This ecological force 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 maintenance plans and management decisions.

[0043] In the above, refer to Figure 1 A method for intelligent maintenance of large-scale forestry according to an embodiment of the present invention is described in detail. Figure 2 An intelligent maintenance device for large-scale forestry according to an embodiment of the present invention is described.

[0044] According to an embodiment of the present invention, an intelligent maintenance device for large-scale forestry is designed to address the technical issue of high efficiency but limited coverage of on-site ecological characteristic information collection in large-scale forestry scenarios, resulting in ecological benefit assessment lagging behind maintenance execution. This device achieves the technical effect of rapidly acquiring ecological characteristic information of target forests based on similarity matching, improving the timeliness and accuracy of large-scale forestry maintenance. The intelligent maintenance device for large-scale forestry includes: an information collection module 11, a data screening module 12, a vectorization module 13, a distance calculation module 14, a distance determination module 15, and an ecological force analysis module 16.

[0045] Information collection module 11: multi-dimensional collection to obtain target feature information of the target forestry, wherein the target feature information includes target geographic feature information and target maintenance feature information; data screening module 12: using the target geographic feature information as a constraint to screen the forestry maintenance database to obtain a target database, and the target database includes the first maintenance feature information of the first forestry; vectorization module 13: 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; distance calculation module 14: comparing the target feature vector with 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, retrieving the first ecological feature information of the first forestry; ecological force analysis module 16: using the first ecological feature information as the target ecological feature information of the target forestry, and analyzing to obtain the target ecological force.

[0046] The specific configuration of the information collection module 11 will be described in detail below. The information collection module 11 may further include: obtaining a target total area of ​​the target forestry; obtaining target longitude and latitude of the target forestry; collecting a target site characteristic parameter set for the target forestry based on predetermined site condition indicators; the target total area, the target longitude and latitude, and the target site characteristic parameter set forming the target geographic characteristic information.

[0047] The specific configuration of information collection module 11 will be described in detail below. Information collection module 11 further includes: establishing a target tree species set for the target forestry, wherein the target tree species set includes multiple identified tree species with predetermined characteristics; and generating target maintenance characteristic information based on the multiple identified tree species with predetermined characteristics; wherein the predetermined characteristics include at least the number of plantations and distribution uniformity.

[0048] The specific configuration of the information collection module 11 will be described in detail below. The information collection module 11 further includes: extracting any tree species from the plurality of identified tree species having predetermined characteristics; analyzing and assembling any site requirement characteristic parameter set for the any tree species; comparing the any site requirement characteristic parameter set with the target site characteristic parameter set to obtain any matching degree; and, if the matching degree does not reach a predetermined matching degree threshold, performing growth monitoring on the any tree species.

[0049] 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; forming a first feature point pair, 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; extracting an initial judgment filling plan in the vector distance judgment mechanism; according to the initial judgment filling plan, filling the first distance value obtained by judging the first target feature point and the first forestry feature point into the predetermined distance table to obtain an initial judgment filling table; extracting an advanced judgment filling scheme in the vector distance judgment mechanism; according to the advanced judgment filling scheme, performing advanced judgment filling on the initial judgment filling table to obtain a target judgment filling table; taking the filling distance value of a predetermined unit in the target judgment filling table as the first vector distance value.

[0050] 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 a partition result; obtaining an advanced block in the partition result, and extracting any unit in the advanced block, wherein the arbitrary unit corresponds to an arbitrary initial value; forming an arbitrary reference unit set of the arbitrary unit, and screening the arbitrary reference unit set to obtain an arbitrary maximum unit value, wherein the arbitrary reference unit set includes the left adjacent unit, the upper adjacent unit and the upper left adjacent 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; forming the target judgment filling table based on the arbitrary advanced value.

[0051] The specific configuration of the distance calculation module 14 will be described in detail below. The distance calculation module 14 may further include: the partition result includes an initial block and the advanced block, wherein the initial block refers to the first row cell set and the first column 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.

[0052] The specific configuration of ecological force analysis module 16 will be described in detail below. Ecological force analysis module 16 may further include: reading predetermined ecological indicators; traversing and extracting target ecological characteristic information based on the predetermined ecological indicators to obtain a target indicator parameter set; and performing a weighted variance analysis on the target indicator parameter set to obtain the target ecological force. The predetermined ecological indicators include a wind speed reduction index, a sand fixation index, and a soil improvement index.

[0053] Figure 3 1 is a schematic structural diagram of an electronic device provided by an embodiment of the present invention, showing a block diagram of an exemplary electronic device suitable for implementing an embodiment of the present invention. Figure 3The electronic device shown is merely an example and should not limit the functionality and scope of use of the embodiments of the present invention. The electronic device is implemented as 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, which has a set (at least one) of program modules configured to perform the functions of the various embodiments of the present application.

[0054] The memory 21 shown in the embodiment of the present invention may adopt any combination of one or more computer-readable media; the computer-readable storage medium may be, but is not limited to, an infrared, semiconductor device, apparatus or component, or any combination thereof, for storing software programs, computer executable programs and modules, such as the program instructions / modules corresponding to an intelligent maintenance method for large-scale forestry in an embodiment of the present 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 above-mentioned intelligent maintenance method for large-scale forestry.

[0055] An intelligent maintenance device for large-scale forestry provided by an embodiment of the present invention can execute an intelligent maintenance method for large-scale forestry provided by any embodiment of the present invention, and has functional modules and beneficial effects corresponding to the execution method.

[0056] Although the present application makes various references to certain modules in the apparatus according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and 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 the functional units are only for the convenience of distinguishing each other and are not used to limit the scope of protection of the present invention.

[0057] The above specific embodiments 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 may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application shall be included within the scope of protection of this application.

Claims

1. An intelligent maintenance method for large-scale forestry, characterized in that: include: Multi-dimensional collection obtains target characteristic information of the target forestry, wherein the target characteristic information includes target geographical characteristic information and target maintenance characteristic information; The forestry maintenance database is screened using the target geographic feature information as a constraint to obtain a target database, wherein the target database includes first maintenance feature information of a first forestry; performing vectorization processing on the target maintenance feature information and the first maintenance feature information in sequence to obtain a target feature vector and a first feature vector, respectively; Comparing the target feature vector with 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 characteristic information of the first forestry; 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.

2. The intelligent maintenance method for large-scale forestry according to claim 1, characterized in that: The target characteristic information of the target forestry is obtained by multi-dimensional collection, wherein the target characteristic information includes target geographical characteristic information and target maintenance characteristic information, including: Obtaining the target total area of ​​the target forestry; Obtaining the target latitude and longitude of the target forestry; Obtaining a target site characteristic parameter set for the target forestry based on predetermined site condition indicators; The target total area, the target longitude and latitude, and the target site feature parameter set constitute the target geographic feature information.

3. The intelligent maintenance method for large-scale forestry according to claim 2, characterized in that: The target characteristic information of the target forestry is obtained by multi-dimensional collection, wherein the target characteristic information includes target geographical characteristic information and target maintenance characteristic information, including: Establishing a target tree species set for the target forestry, wherein the target tree species set includes a plurality of identified tree species with predetermined characteristics; forming the target maintenance characteristic information based on the plurality of identified tree species having predetermined characteristics; The predetermined characteristics include at least the number of plantings and the distribution uniformity.

4. The intelligent maintenance method for large-scale forestry according to claim 1, characterized in that: Comparing the target feature vector with the first feature vector to obtain a first vector distance value includes: Reading a predetermined distance table; forming 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 the initial assessment and filling plan in the vector distance assessment mechanism; According to the initial analysis and filling plan, the first distance value obtained by analyzing the first target feature point and the first forestry feature point is filled into the predetermined distance table to obtain an initial analysis and filling table; Extracting the advanced analysis and filling scheme in the vector distance analysis mechanism; Performing advanced analysis and filling on the initial analysis and filling table according to the advanced analysis and filling scheme to obtain a target analysis and filling table; The filling distance value of the predetermined unit in the target analysis and filling table is taken as the first vector distance value.

5. The intelligent maintenance method for large-scale forestry according to claim 4, characterized in that: According to the advanced analysis and filling scheme, the initial analysis and filling table is subjected to advanced analysis and filling to obtain a target analysis and filling table, including: Partitioning the initial analysis and filling table according to the advanced analysis and filling scheme to obtain a partition result; Obtaining an advanced block in the partitioning result, and extracting an arbitrary unit in the advanced block, wherein the arbitrary unit corresponds to an arbitrary initial value; Establishing an arbitrary reference unit set of the arbitrary unit, and screening the arbitrary reference unit set to obtain an arbitrary maximum unit value, wherein the arbitrary reference unit set includes the left adjacent unit, the upper adjacent unit, and the upper-left adjacent 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; The target analysis and filling table is formed based on the arbitrary advanced value.

6. The intelligent maintenance method for large-scale forestry according to claim 5, characterized in that: The partition result includes an initial block and the advanced block, wherein the initial block refers to the first row unit set and the first column unit set in the predetermined distance table, and the advanced block refers to the unit set in the predetermined distance table except the initial block.

7. The intelligent maintenance method for large-scale forestry 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 force is obtained by analysis, including: Read predetermined ecological indicators; Traversing and extracting the target ecological characteristic information based on the predetermined ecological indicator to obtain a target indicator parameter set; Performing a weighted variation analysis on the target indicator parameter set to obtain the target ecological force; Among them, the predetermined ecological indicators include wind speed reduction index, fixed sand index and soil improvement index.

8. The intelligent maintenance method for large-scale forestry according to claim 3, characterized in that: After obtaining the target characteristic information of the target forestry through multi-dimensional collection, it also includes: Extracting any tree species from the plurality of identified tree species having predetermined characteristics; Analyzing and establishing any site requirement characteristic parameter set of any tree species; Comparing and obtaining any matching degree between the arbitrary site requirement characteristic parameter set and the target site characteristic parameter set; If the arbitrary matching degree does not reach a predetermined matching degree threshold, growth monitoring is performed on the arbitrary tree species.

9. An intelligent maintenance device for large-scale forestry, characterized in that: The device is used to implement the intelligent maintenance method for large-scale forestry according to any one of claims 1 to 8, comprising: Information collection module: multi-dimensional collection to obtain target characteristic information of target forestry, wherein the target characteristic information includes target geographical characteristic information and target maintenance characteristic information; A data screening module: screening the forestry maintenance database based on the target geographic feature information as a constraint to obtain a target database, wherein the target database includes first maintenance feature information of a first forestry; Vectorization module: performing vectorization processing on the target maintenance feature information and the first maintenance feature information in sequence to obtain a target feature vector and a first feature vector respectively; Distance calculation module: compares the target feature vector with the first feature vector to obtain a first vector distance value; Distance judgment module: if the first vector distance value reaches a predetermined constraint, retrieve the first ecological characteristic information of the first forest; Ecological force analysis module: takes the first ecological characteristic information as the target ecological characteristic information of the target forestry, and analyzes it to obtain the target ecological force.

10. An electronic device, characterized in that: The electronic device comprises: a memory for storing executable instructions; The processor is configured to implement the intelligent maintenance method for large-scale forestry as described in any one of claims 1 to 8 when executing the executable instructions stored in the memory.

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