Forest grass ecological environment intelligent monitoring method, system and equipment

Abstract

The invention relates to the technical field of forest grass ecological environment monitoring, in particular to a forest grass ecological environment intelligent monitoring method, system and equipment. The method comprises the following steps: acquiring various ecological index data of a specified monitoring sample plot of the forest grass ecological environment in a preset historical time period; according to the distribution condition of any two kinds of ecological index data along with the time change, obtaining an ecological index association pair; according to the ecological index data corresponding to the ecological index association pair, obtaining a standard range of each ecological index in the ecological index association pair; and judging whether the current sample plot is a boundary sample plot or not according to the distribution condition of the ecological index data corresponding to the ecological index association pair in the current sample plot in the corresponding standard range. By analyzing the distribution condition of the ecological index data in the current sample plot in the corresponding standard range, whether the current sample plot is the junction sample plot or not can be quickly judged, the junction sample plot judgment efficiency is improved, and resource waste is effectively reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of forest and grassland ecological environment monitoring, and in particular to an intelligent monitoring method, system and equipment for forest and grassland ecological environment. Background Art

[0002] Existing forest and grassland ecological environment monitoring methods usually install professional monitoring equipment in selected sample plots to monitor various ecological indicators of the sample plots in real time, and then evaluate the status of forest and grassland resources in real time. However, due to external factors such as human interference, the selected standard sample plots may evolve into transitional zones between forest and grassland ecosystems, namely boundary sample plots. As "ongoing" recorders of ecological processes, the ecological parameters of boundary sample plots fluctuate frequently, which makes it impossible to accurately evaluate the status of forest and grassland resources. Therefore, boundary sample plots are usually not suitable as standard sample plots for monitoring and evaluating forest and grassland ecological environments. In the existing methods, a large amount of data analysis is required to evaluate the boundary sample plots, which is inefficient and will cause a lot of waste of resources. Summary of the invention

[0003] In order to solve the technical problem of low efficiency in boundary plot assessment, the purpose of the present invention is to provide a method, system and equipment for intelligent monitoring of forest and grassland ecological environment. The technical scheme adopted is as follows: In a first aspect, an embodiment of the present invention provides a method for intelligently monitoring a forest and grassland ecological environment, the method comprising the following steps: Obtain data on various ecological indicators of designated monitoring plots of forest and grassland ecological environment within a preset historical time period; According to the distribution of any two ecological indicator data over time in a preset historical period, obtain the ecological indicator association pair; According to the ecological indicator data in the preset historical time period corresponding to each ecological indicator association pair, the standard range of each ecological indicator in each ecological indicator association pair is obtained; According to the distribution of ecological indicator data corresponding to each ecological indicator association pair in the current sample plot of the forest and grassland ecological environment in the corresponding standard range, it is judged whether the current sample plot is a boundary sample plot.

[0004] Furthermore, the method for obtaining the ecological indicator association pair is: Obtain the correlation degree of any two ecological indicator data according to the distribution of any two ecological indicator data changing over time in a preset historical time period; When the correlation degree is greater than a preset correlation degree threshold, the ecological indicators corresponding to the two ecological indicator data are taken as an ecological indicator correlation pair.

[0005] Furthermore, the method for obtaining the degree of association is: Divide the preset historical time period into local time periods of preset duration, and sequentially construct a first preset number of local time periods into a sample set in chronological order; For any sample set and any ecological indicator data, the ecological indicator data of each local time period in the sample set are clustered by a hierarchical clustering algorithm to obtain a reference cluster of the ecological indicator data of each local time period in the sample set; Obtain the range of the ecological indicator data in each reference cluster as the reference range of the corresponding reference cluster; Based on the range of the ecological indicator data in a preset historical time period, the ecological indicator data is divided into a second preset number of interval segments, and the interval points corresponding to each interval segment are obtained; For any interval point, obtain the number of all reference clusters whose reference range includes the interval point as the distribution number of the interval point corresponding to the ecological indicator data in the sample set; Obtaining a reference fluctuation degree of the interval point corresponding to the ecological indicator data in the sample set according to a difference between the distribution frequency and the distribution frequency of the interval point corresponding to the ecological indicator data in a preset neighborhood sample set of the sample set; The mean of the reference fluctuation degree of all interval points corresponding to the ecological indicator data in the sample set is taken as the overall fluctuation degree of the ecological indicator data in the sample set; According to the difference in the overall fluctuation degree of any two ecological indicator data in each sample set, the correlation degree of the two ecological indicator data is obtained.

[0006] Furthermore, the method for obtaining the reference fluctuation degree is: Obtain the difference between the distribution frequency of the interval point corresponding to the ecological indicator data in the sample set and the distribution frequency of the interval point corresponding to the ecological indicator data in each preset neighborhood sample set of the sample set, and take both as the first difference; The mean of the first difference is taken as the reference fluctuation degree of the interval point corresponding to the ecological indicator data in the sample set.

[0007] Furthermore, the method for obtaining the degree of association is: For any two ecological indicator data, the difference in the overall fluctuation degree of the two ecological indicator data in each sample set is obtained as the second difference; The result of negatively correlating and normalizing the variance of the second difference is taken as the correlation degree of the two ecological indicator data.

[0008] Furthermore, the method for obtaining the standard range is: For any ecological indicator association pair, the two ecological indicator data corresponding to the ecological indicator association pair within a preset historical time period are mapped to a two-dimensional spatial coordinate system; wherein the horizontal coordinate and the vertical coordinate of the two-dimensional spatial coordinate system correspond to the two ecological indicator data corresponding to the ecological indicator association pair; According to the distance between the corresponding coordinate points in the two-dimensional space coordinate system, the coordinate points in the two-dimensional space coordinate system are clustered by a hierarchical clustering algorithm to obtain analysis cluster clusters; The range of each ecological indicator data in each analysis cluster is used as the standard range of each ecological indicator in the ecological indicator association pair; wherein, one analysis cluster includes the ranges of two ecological indicator data and the ranges of the two ecological indicator data in one analysis cluster remain corresponding.

[0009] Furthermore, the method for obtaining the boundary sample plot is: According to the distribution of ecological indicator data corresponding to each ecological indicator association pair in the current sample plot of the forest and grassland ecological environment in the corresponding standard range, the degree of disorder of the current sample plot is obtained; When the disorder degree is greater than the preset disorder degree threshold, the current sample plot is judged to be a boundary sample plot; When the disorder degree is less than or equal to the preset disorder degree threshold, it is determined that the current sample plot is not a boundary sample plot.

[0010] Furthermore, the method for obtaining the degree of confusion is: For any ecological indicator association pair, when the two ecological indicator data corresponding to the ecological indicator association pair in the current sample plot are within the standard range of the two ecological indicators in the ecological indicator association pair, the confusion index of the ecological indicator association pair is marked as 0; When the two ecological indicator data corresponding to the ecological indicator association pair in the current sample plot are not within the standard range of the two ecological indicators in the ecological indicator association pair, the confusion index of the ecological indicator association pair is marked as 1; The sum of the chaos indices of all ecological indicator association pairs of the current sample plot is taken as the chaos degree of the current sample plot.

[0011] In a second aspect, another embodiment of the present invention provides an intelligent monitoring system for forest and grassland ecological environment, the system comprising: The data acquisition module is used to obtain various ecological indicator data of the designated monitoring plots of the forest and grassland ecological environment within a preset historical time period; The ecological indicator association pair acquisition module is used to obtain the ecological indicator association pair according to the distribution of any two ecological indicator data over time in a preset historical time period; A standard range acquisition module, used to obtain the standard range of each ecological indicator in each ecological indicator association pair according to the ecological indicator data in the preset historical time period corresponding to each ecological indicator association pair; The boundary plot judgment module is used to judge whether the current plot is a boundary plot according to the distribution of ecological indicator data corresponding to each ecological indicator association pair in the current plot of the forest and grassland ecological environment in the corresponding standard range.

[0012] In the third aspect, another embodiment of the present invention provides an intelligent monitoring device for forest and grassland ecological environment, which includes: a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of any one of the above methods are implemented.

[0013] The present invention has the following beneficial effects: The present invention first obtains ecological indicator association pairs according to the distribution of any two ecological indicator data changing with time within a preset historical time period, accurately determines the ecological indicators with association, avoids the interference of subsequent ecological indicators with weak association in the analysis of the boundary sample plot, and excludes the analysis of ecological indicators with weak association, so as to improve the efficiency of judging whether the current sample plot is reasonable; in order to accurately and efficiently analyze whether the current sample plot is a boundary sample plot in the subsequent time period, the standard range of each ecological indicator in each ecological indicator association pair is obtained according to the ecological indicator data within the preset historical time period corresponding to each ecological indicator association pair, and accurately reflects the standard range corresponding to each ecological indicator corresponding to each ecological indicator association pair in the standard sample plot; further, according to the distribution of ecological indicator data corresponding to each ecological indicator association pair in the current sample plot of the forest and grassland ecological environment in the corresponding standard range, accurately judges whether the current sample plot is a boundary sample plot, effectively improves the efficiency of judging whether the current sample plot is a boundary sample plot, and avoids the acquisition and analysis of more data on the current sample plot, effectively reducing the waste of a large amount of resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0015] Figure 1 A schematic flow chart of an intelligent monitoring method for forest and grassland ecological environment provided by an embodiment of the present invention; Figure 2A flow chart of a method for obtaining an ecological indicator association pair provided by an embodiment of the present invention; Figure 3 A flow chart of a method for obtaining a boundary sample plot provided by an embodiment of the present invention; Figure 4 A structural diagram of an intelligent monitoring system for forest and grassland ecological environment provided by an embodiment of the present invention; Figure 5 A schematic diagram of an intelligent monitoring device for forest and grassland ecological environment provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0016] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the intelligent monitoring method, system and equipment for forest and grassland ecological environment proposed by the present invention, its specific implementation method, structure, characteristics and effects in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0017] Unless defined otherwise, 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 invention belongs.

[0018] The specific scheme of the intelligent monitoring method, system and equipment for forest and grassland ecological environment provided by the present invention is described in detail below with reference to the accompanying drawings.

[0019] Embodiment 1: The specific implementation scenario of this embodiment is: in the existing method, sample plots are selected in forests and grasslands to obtain various ecological indicator data, and then the current status of forest and grassland resources is evaluated in real time. In order to accurately evaluate forest and grassland resources, it is necessary to reasonably select samples to avoid the selected sample plots from causing confusion in the ecological environment of the sample plots due to interference from external factors, that is, the boundary sample plots of ecosystems such as forests and grasslands, and thus cannot accurately reflect the situation of forest and grassland resources. Therefore, it is necessary to accurately select samples. In the existing method, when analyzing the chaotic situation of the ecological environment of the selected sample plots, various ecological indicator data will be obtained for observation for a period of time, resulting in a large waste of resources and low efficiency, and it is impossible to efficiently judge whether the selected sample plot is a boundary sample plot. In order to efficiently analyze whether the current sample plot is a boundary sample plot, this embodiment first analyzes the various ecological indicator data of the designated monitoring sample plot of the forest and grassland ecological environment within a preset historical time period, obtains the ecological indicator association pairs that are related to each other, and further obtains the standard range of each ecological indicator in each ecological indicator association pair, and then analyzes whether the ecological indicator data corresponding to each ecological indicator association pair in the current sample plot is within the standard range of the corresponding ecological indicator, indirectly reflecting the deviation of the ecological environment in the current sample plot, thereby efficiently judging whether the current sample plot is a boundary sample plot, avoiding the acquisition of more data for the current sample plot, and effectively avoiding the waste of resources while ensuring accuracy.

[0020] This invention proposes an intelligent monitoring method for forest and grassland ecological environment. Figure 1 , which shows a schematic flow chart of a method for intelligent monitoring of forest and grassland ecological environment provided by an embodiment of the present invention, the method comprising the following steps: Step S1: Obtain various ecological indicator data of designated monitoring plots of forest and grassland ecological environment within a preset historical time period.

[0021] Specifically, in order to efficiently analyze whether the sample plot selected for the forest, forest and grassland ecological environment is reasonable, this embodiment needs to first obtain various ecological indicator data of the designated monitoring sample plot of the forest, forest and grassland ecological environment within the preset historical time period, and prepare for the subsequent acquisition of the standard range corresponding to each ecological indicator data, so as to accurately analyze whether the current sample plot selected for the forest, forest and grassland ecological environment is reasonable. Among them, this embodiment sets the preset historical time period to 5 years and the time interval between two adjacent moments of the collected data to 1 hour. The implementer can set the size of the preset historical time period and the time interval between two adjacent moments of the collected data according to the actual situation, which is not limited here. It should be noted that there are multiple designated monitoring sample plots, which are standard sample plots corresponding to forests and grasslands, respectively, and there is no interference from external uncertain factors. This embodiment sets the designated monitoring sample plots to 100, of which 50 designated monitoring sample plots are standard sample plots for forests, and 50 designated monitoring sample plots are standard sample plots for grasslands. The implementer can set the number of designated monitoring sample plots and the number of standard sample plots for forests and grasslands in the designated monitoring sample plots according to the actual situation, which is not limited here.

[0022] Among them, this embodiment obtains environment-related index data such as temperature index data, light index data, wind speed index data, etc. for each designated monitoring plot at each time through a geographic surveying instrument; obtains vegetation-related index data such as vegetation coverage index data and species richness index data for each designated monitoring plot at each time through a multi-spectral camera carried by an unmanned aerial vehicle; obtains soil-related index data such as Ph index data and organic carbon content index data for each designated monitoring plot at each time through a soil system; therefore, ecological index data include temperature index data, light index data, wind speed index data, vegetation coverage index data, species richness index data, Ph index data, organic carbon content index data, etc.

[0023] Step S2: Obtain an ecological indicator association pair according to the distribution of any two ecological indicator data over time within a preset historical time period.

[0024] It is known that under normal circumstances, there is a certain correspondence between different types of ecological indicator data in the standard sample plot. For example, when the organic carbon content indicator data in the standard sample plot is large, the vegetation coverage indicator data is also relatively large; and for the boundary sample plot, the relationship between different types of ecological indicator data will be significantly different from the performance relationship in the standard sample plot. For example, when the organic carbon content indicator data in the boundary sample plot is small, the vegetation coverage indicator data may also be relatively large. Therefore, this embodiment can construct a standard range comparison table corresponding to different types of ecological indicators in the standard sample plot, analyze whether the ecological indicator data in the current sample plot exists in the relationship model corresponding to the standard range comparison table, and then efficiently determine whether the current sample plot belongs to the boundary sample plot.

[0025] In actual situations, not all types of ecological indicators are associated. For example, the size of biomass indicator data depends on the volume and density of plants, while vegetation height indicator data only reflects the vertical growth of plants. There is no obvious correlation between biomass indicators and vegetation height indicators. In the process of constructing the standard range comparison table corresponding to ecological indicators, if irrelevant ecological indicators are included, noise will be introduced to interfere with the judgment of the correlation of ecological indicator data corresponding to a single ecological environment, resulting in errors in the analysis of sample site chaos. Therefore, before constructing the standard range comparison table, it is necessary to first determine the ecological indicator pairs that are associated and exclude the ecological indicators that are irrelevant to each other to improve the accuracy of calculating the sample site chaos, thereby increasing the correctness of the judgment of the boundary sample site.

[0026] Among them, judging whether two ecological indicators are related needs to be combined with the data characteristics of specific ecological processes. If ecological indicator A and ecological indicator B have no direct driving relationship in ecological function, then when the data of ecological indicator A has a distribution pattern, the data of ecological indicator B always presents an irregular random distribution; if ecological indicator A and ecological indicator B are related, then when the data of ecological indicator A is always distributed within a fixed range, the data of ecological indicator B also satisfies a certain regular interval range distribution; furthermore, this embodiment obtains ecological indicator association pairs according to the distribution of any two ecological indicator data over time within a preset historical time period, accurately determines the associated ecological indicators, and is conducive to the subsequent accurate construction of the standard range comparison table corresponding to the ecological indicators.

[0027] Preferably, in one possible implementation of this embodiment, the method for obtaining the ecological indicator association pair can be found in Figure 2 , which shows a flow chart of a method for obtaining an ecological indicator association pair provided in this embodiment, the method comprising the following steps: Step S201: Obtain the correlation degree between any two ecological indicator data according to the distribution of any two ecological indicator data changing with time in a preset historical time period.

[0028] The distribution of two related ecological indicators in standard plots must have a stable trend. For example, for the carbon content index and vegetation coverage index in the forest ecological model, the distribution range of the carbon content index data in the standard forest plot is usually The distribution range of vegetation coverage index data is usually ; For the carbon content index and vegetation coverage index in the grassland ecological model, the distribution range of carbon content index data in the standard grassland plot is usually The distribution range of vegetation coverage index data is usually ; Therefore, the distribution intervals of the data corresponding to the carbon content index and the vegetation coverage index maintain the same overall trend in the time series, and the carbon content index and the vegetation coverage index are two related ecological indicators. For two unrelated ecological indicators, the interval ranges of their corresponding data do not have a consistent trend in the time series and are disordered. Therefore, this embodiment obtains the degree of correlation between any two ecological indicator data based on the distribution of any two ecological indicator data over time within a preset historical time period. The greater the degree of correlation, the more related the corresponding two ecological indicators are.

[0029] In one possible implementation of this embodiment, the method for obtaining the degree of association is as follows: in order to accurately analyze the distribution of various ecological indicator data within a preset historical time period, this embodiment divides the preset historical time period into local time periods of preset duration, and sequentially constructs a first preset number of local time periods into a sample set in chronological order; this embodiment sets the preset duration to 1 week and the first preset number to 10, and the implementer can set the preset duration and the first preset number according to actual conditions, which are not limited here. It should be noted that if the remaining local time periods are less than 10, the remaining local time periods will still be divided into a sample set. For any sample set and any kind of ecological indicator data, the ecological indicator data in each local time period in the sample set are clustered using a hierarchical clustering algorithm to obtain a reference clustering cluster of the ecological indicator data in each local time period in the sample set; wherein, the hierarchical clustering algorithm is a well-known technology and will not be described in detail; In order to determine the distribution of the ecological indicator data, and then obtain the range of the ecological indicator data in each reference cluster as the reference range of the corresponding reference cluster; based on the range of the ecological indicator data in the preset historical time period, the ecological indicator data is divided into a second preset number of interval segments, and the interval points corresponding to each interval segment are obtained; in this embodiment, the second preset number is set to 6, and the implementer can set the size of the second preset number according to the actual situation, which is not limited here. For example, assuming that the value range of the carbon content indicator in the preset historical time period is , then the divided intervals are , , , , and , the interval points are 10, 20, 30, 40, 50, 60 and 70 respectively. For any interval point, the number of all reference clusters whose reference range contains the interval point is obtained as the distribution frequency of the interval point corresponding to the ecological indicator data in the sample set; the larger the distribution frequency, the more frequently the ecological indicator data is distributed at the interval point in the sample set; In order to analyze the stability of the distribution of the ecological indicator data in the interval points in the sample set, this embodiment obtains the reference fluctuation degree of the interval points corresponding to the ecological indicator data in the sample set according to the difference between the distribution frequency and the distribution frequency of the interval points corresponding to the ecological indicator data in the preset neighborhood sample set of the sample set; wherein, this embodiment sets the preset neighborhood sample set of the sample set to 6 sample sets adjacent to the sample set, for example, if the sample set is the kth sample set, then the preset neighborhood sample sets of the sample set are the k-3th sample set, the k-2th sample set, the k-1th sample set, the k+1th sample set, the k+2th sample set and the k+3th sample set, and the implementer can set the number and position of the preset neighborhood sample sets according to the actual situation, which is not limited here. It should be noted that if the sample set is a boundary sample set, only the preset neighborhood sample set existing in the sample set is analyzed; The greater the reference fluctuation degree, the more unstable the distribution of the ecological indicator data in the interval point in the sample set. The method for obtaining the reference fluctuation degree is as follows: obtain the absolute value of the difference between the distribution times of the interval point corresponding to the ecological indicator data in the sample set and the distribution times of the interval point corresponding to the ecological indicator data in each preset neighborhood sample set of the sample set, and use them as the first difference; the greater the first difference, the more unstable the distribution of the ecological indicator data in the interval point. In order to accurately reflect the distribution of the ecological indicator data in the interval point in the sample set, the mean value of the first difference is used as the reference fluctuation degree of the interval point corresponding to the ecological indicator data in the sample set; In order to reflect the distribution fluctuation of the ecological indicator data in the sample set as a whole, the mean of the reference fluctuation degree of all interval points corresponding to the ecological indicator data in the sample set is taken as the overall fluctuation degree of the ecological indicator data in the sample set; Similarly, the overall fluctuation degree of each ecological indicator data in each sample set is obtained. When the differences in the overall fluctuation degree of two ecological indicator data in each sample set are similar, it means that the more consistent the overall distribution trend of the two ecological indicator data in the preset time period, the stronger the correlation between the two ecological indicator data; and thus, this embodiment obtains the correlation degree of the two ecological indicator data according to the difference in the overall fluctuation degree of any two ecological indicator data in each sample set. Among them, the method for obtaining the correlation degree is: for any two ecological indicator data, the difference in the overall fluctuation degree of the two ecological indicator data in each sample set is obtained, and both are used as the second difference; the variance of the second difference is negatively correlated and normalized, and the result is used as the correlation degree of the two ecological indicator data. It should be noted that, in the process of obtaining the second difference, the types of ecological indicators corresponding to the minuend and the subtrahend must always remain consistent. For example, if the two ecological indicator data are the x-th ecological indicator data and the y-th ecological indicator data, respectively, then when obtaining the second difference corresponding to the two ecological indicator data in each sample set, it must be unified as the difference between the overall fluctuation degree of the x-th ecological indicator data and the overall fluctuation degree of the y-th ecological indicator data, or unified as the difference between the overall fluctuation degree of the y-th ecological indicator data and the overall fluctuation degree of the x-th ecological indicator data. No limitation is made here.

[0030] The calculation formula of the correlation degree is: ; In the formula, is the correlation degree between the xth ecological indicator data and the yth ecological indicator data; is the variance of the second difference between the x-th ecological indicator data and the y-th ecological indicator data; exp is an exponential function with a natural constant as the base.

[0031] At this point, the correlation degree between any two ecological indicator data is obtained.

[0032] Step S202: When the correlation degree is greater than a preset correlation degree threshold, the ecological indicators corresponding to the two ecological indicator data are regarded as an ecological indicator correlation pair.

[0033] It is known that the greater the correlation degree, the greater the correlation between the ecological indicators corresponding to the two ecological indicator data. Therefore, this embodiment sets the preset correlation degree threshold to 0.5. The implementer can set the value of the preset correlation degree threshold according to the actual situation, which is not limited here. When the correlation degree is greater than the preset correlation degree threshold, the ecological indicators corresponding to the two ecological indicator data are regarded as an ecological indicator correlation pair.

[0034] At this point, all ecological indicator association pairs in the forest and grassland ecological environment are obtained.

[0035] Step S3: According to the ecological indicator data in the preset historical time period corresponding to each ecological indicator association pair, the standard range of each ecological indicator in each ecological indicator association pair is obtained.

[0036] It is known that the ecological indicator data within the preset historical time period are all ecological indicator data in the standard sample plots. In order to determine the standard range of each ecological indicator corresponding to each ecological indicator association pair, this embodiment obtains the standard range of each ecological indicator in each ecological indicator association pair based on the ecological indicator data within the preset historical time period corresponding to each ecological indicator association pair.

[0037] Preferably, in a method that can be implemented in this embodiment, the method for obtaining the standard range is: for any ecological indicator association pair, the two ecological indicator data corresponding to the ecological indicator association pair within a preset historical time period are mapped into a two-dimensional space coordinate system; wherein the abscissa and ordinate of the two-dimensional space coordinate system correspond to the two ecological indicator data corresponding to the ecological indicator association pair; for example, if the two ecological indicators corresponding to the ecological indicator association pair are ecological indicator A and ecological indicator B, respectively, then the abscissa and ordinate of the two-dimensional space coordinate system are ecological indicator A data and ecological indicator B data, respectively. It should be noted that if the abscissa of the two-dimensional space coordinate system is ecological indicator A data, then the ordinate of the two-dimensional space coordinate system is ecological indicator B data; if the abscissa of the two-dimensional space coordinate system is ecological indicator B data, then the ordinate of the two-dimensional space coordinate system is ecological indicator A data, and this is not limited here; According to the Euclidean distance between the corresponding coordinate points in the two-dimensional space coordinate system, the coordinate points in the two-dimensional space coordinate system are clustered by a hierarchical clustering algorithm to obtain analysis clusters; wherein, the method for obtaining the Euclidean distance is a well-known technology and will not be described in detail. Then, the range of each ecological indicator data in each analysis cluster is used as the standard range of each ecological indicator in the ecological indicator association pair; wherein, an analysis cluster includes the range of two ecological indicator data and the ranges of the two ecological indicator data in an analysis cluster remain corresponding. For example, assuming that the two ecological indicators corresponding to the ecological indicator association pair are the carbon content indicator and the vegetation coverage indicator, if two analysis clusters are finally obtained, wherein the standard range corresponding to the carbon content indicator data in one analysis cluster is , the standard range of vegetation coverage index data is , then the standard range of one of the ecological indicator association pairs is: 30≤Carbon content (g / kg) indicator data≤50, 70≤Vegetation coverage (%) indicator data≤90; the standard range corresponding to the carbon content indicator data in the other analysis cluster is , the standard range of vegetation coverage index data is , then the other standard range of the ecological indicator association pair is: 10≤carbon content (g / kg) indicator data≤20, 50≤vegetation coverage (%) indicator data≤60.

[0038] At this point, the standard range of each ecological indicator in each ecological indicator association pair is obtained.

[0039] Step S4: Determine whether the current sample plot is a boundary sample plot according to the distribution of ecological indicator data corresponding to each ecological indicator association pair in the current sample plot of the forest and grassland ecological environment in the corresponding standard range.

[0040] Specifically, when the ecological indicator data corresponding to a certain ecological indicator association pair in the current sample plot is within the corresponding standard range in the standard range comparison table, it means that the ecological indicator association pair in the current sample plot meets the typical characteristics of the ecosystem; when the ecological indicator data corresponding to the ecological indicator association pair in the current sample plot is not within the corresponding standard range in the standard range comparison table, it means that the relationship pattern between the ecological indicator association pair in the current sample plot deviates from the typical characteristics of a single ecosystem, and the current sample plot is more likely to be a boundary sample plot. Furthermore, this embodiment determines whether the current sample plot is a boundary sample plot according to the distribution of the ecological indicator data corresponding to each ecological indicator association pair in the current sample plot of the forest and grassland ecological environment in the corresponding standard range.

[0041] Preferably, in one possible implementation of this embodiment, the method for obtaining the boundary plot is as follows: Figure 3 , which shows a flow chart of a method for obtaining a boundary plot provided in this embodiment, the method comprising the following steps: Step S301: Obtain the degree of chaos of the current sample plot according to the distribution of ecological indicator data corresponding to each ecological indicator association pair in the current sample plot of the forest and grassland ecological environment in the corresponding standard range.

[0042] The more ecological indicator data corresponding to the ecological indicator association pairs in the current sample plot are not distributed in the corresponding standard range, the more complex the ecological environment of the current sample plot is. In this embodiment, the degree of confusion of the current sample plot is obtained according to the distribution of ecological indicator data corresponding to each ecological indicator association pair in the current sample plot of the forest and grass ecological environment in the corresponding standard range. The greater the degree of confusion, the more likely the current sample is a boundary sample plot.

[0043] In one possible implementation of the present embodiment, the method for obtaining the degree of confusion is as follows: for any ecological indicator association pair, when the two ecological indicator data corresponding to the ecological indicator association pair in the current sample plot are within the standard range of the two ecological indicators in the ecological indicator association pair, the confusion index of the ecological indicator association pair is marked as 0; when the two ecological indicator data corresponding to the ecological indicator association pair in the current sample plot are not within the standard range of the two ecological indicators in the ecological indicator association pair, the confusion index of the ecological indicator association pair is marked as 1; for example, the two ecological indicators corresponding to the ecological indicator association pair are the carbon content index and the vegetation coverage index, if the carbon content index data in the current sample plot is 40 and the vegetation coverage index is 80, then the confusion index of the ecological indicator association pair is marked as 0; if the carbon content index data in the current sample plot is 40 and the vegetation coverage index is 50, then the confusion index of the ecological indicator association pair is marked as 1. The sum of the confusion indexes of all ecological indicator association pairs in the current sample plot is used as the degree of confusion of the current sample plot.

[0044] Step S302: Determine whether the current plot is a boundary plot based on the degree of confusion.

[0045] It is known that the greater the degree of chaos, the more likely the current sample is a boundary plot, and thus the preset chaos threshold is set to 5 in this embodiment. The implementer can set the value of the preset chaos threshold according to the actual situation, and it is not limited here. When the degree of chaos is greater than the preset chaos threshold, the current plot is judged to be a boundary plot; when the degree of chaos is less than or equal to the preset chaos threshold, the current plot is judged not to be a boundary plot.

[0046] At this point, it is possible to accurately and efficiently determine whether the current sample plot is a boundary sample plot, providing a topological optimization basis for the subsequent establishment of a regional forest and grassland ecological intelligent monitoring network, ensuring that the selected sample plots meet the requirements of spatial representativeness and ecological integrity at the same time, so as to enable efficient analysis of the forest and grassland ecological environment.

[0047] In summary, this embodiment obtains various ecological indicator data of a designated monitoring plot of a forest and grass ecological environment within a preset historical time period; obtains an ecological indicator association pair based on the distribution of any two ecological indicator data over time; obtains the standard range of each ecological indicator in the ecological indicator association pair based on the ecological indicator data corresponding to the ecological indicator association pair; and determines whether the current plot is a boundary plot based on the distribution of the ecological indicator data corresponding to the ecological indicator association pair in the current plot in the corresponding standard range. The present invention can quickly determine whether the current plot is a boundary plot by analyzing the distribution of the ecological indicator data in the current plot in the corresponding standard range, thereby improving the efficiency of judging the boundary plot and effectively reducing the waste of resources.

[0048] Embodiment 2: The present invention also proposes an intelligent monitoring system for forest and grass ecological environment, please refer to Figure 4 , which shows a structural diagram of an intelligent monitoring system for forest and grassland ecological environment provided by an embodiment of the present invention. The system includes: a data acquisition module 10, an ecological indicator association pair acquisition module 20, a standard range acquisition module 30 and a boundary sample site judgment module 40.

[0049] The data acquisition module 10 is used to obtain various ecological indicator data of the designated monitoring sample plot of the forest and grassland ecological environment within a preset historical time period.

[0050] The ecological indicator association pair acquisition module 20 is used to acquire an ecological indicator association pair according to the distribution of any two ecological indicator data over time in a preset historical time period.

[0051] The standard range acquisition module 30 is used to acquire the standard range of each ecological indicator in each ecological indicator association pair according to the ecological indicator data in the preset historical time period corresponding to each ecological indicator association pair.

[0052] The boundary plot judgment module 40 is used to judge whether the current plot is a boundary plot according to the distribution of ecological indicator data corresponding to each ecological indicator association pair in the current plot of the forest and grassland ecological environment in the corresponding standard range.

[0053] It should be noted that the system provided in the above embodiment is only illustrated by the division of the above functional modules. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the computer device can be divided into different functional modules to complete all or part of the functions described above. In addition, the forest, grass and ecological environment intelligent monitoring system and the forest, grass and ecological environment intelligent monitoring method embodiment provided in the above embodiment belong to the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0054] Embodiment 3: The present invention also proposes an intelligent monitoring device for forest and grass ecological environment, please refer to Figure 5 The device includes a memory 401, a processor 402, and a computer program 403 stored in the memory 401 and running on the processor 402, wherein when the processor 402 executes the computer program 403, the device can execute any one of the intelligent monitoring methods for forest and grassland ecological environment introduced above.

[0055] In addition, the present invention also proposes an intelligent monitoring device for forest and grassland ecological environment, which includes a memory and a processor, wherein the memory stores an executable program code, and the processor is used to call and execute the executable program code to execute an intelligent monitoring method for forest and grassland ecological environment provided in the embodiment of the present application. The device can be a chip, a component or a module, and the chip may include a connected processor and a memory; wherein the memory is used to store instructions, and when the processor calls and executes the instructions, the chip can execute an intelligent monitoring method for forest and grassland ecological environment provided in the above embodiment.

[0056] Embodiment 4: The present invention also provides a computer-readable storage medium, which stores computer program code. When the computer program code runs on a computer, the computer executes the above-mentioned related method steps to implement an intelligent monitoring method for forest and grassland ecological environment provided in the above-mentioned embodiment.

[0057] Embodiment 5: The present invention also provides a computer program product. When the computer program product runs on a computer, it enables the computer to execute the above-mentioned related steps to implement an intelligent monitoring method for the forest and grassland ecological environment provided in the above-mentioned embodiment.

[0058] Among them, the device, computer-readable storage medium, computer program product or chip provided by the present invention are all used to execute the corresponding methods provided above. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding methods provided above, and will not be repeated here.

[0059] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0060] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A method for intelligent monitoring of forest and grassland ecological environment, characterized in that: The method comprises the following steps: Obtain data on various ecological indicators of designated monitoring plots of forest and grassland ecological environment within a preset historical time period; According to the distribution of any two ecological indicator data over time in a preset historical period, obtain the ecological indicator association pair; According to the ecological indicator data in the preset historical time period corresponding to each ecological indicator association pair, the standard range of each ecological indicator in each ecological indicator association pair is obtained; According to the distribution of ecological indicator data corresponding to each ecological indicator association pair in the current sample plot of the forest and grassland ecological environment in the corresponding standard range, it is judged whether the current sample plot is a boundary sample plot.

2. The method for intelligent monitoring of forest and grassland ecological environment according to claim 1, characterized in that: The method for obtaining the ecological indicator association pair is: Obtain the correlation degree of any two ecological indicator data according to the distribution of any two ecological indicator data changing over time in a preset historical time period; When the correlation degree is greater than a preset correlation degree threshold, the ecological indicators corresponding to the two ecological indicator data are taken as an ecological indicator correlation pair.

3. The method for intelligent monitoring of forest and grassland ecological environment as claimed in claim 2, characterized in that: The method for obtaining the degree of association is: Divide the preset historical time period into local time periods of preset duration, and sequentially construct a first preset number of local time periods into a sample set in chronological order; For any sample set and any ecological indicator data, the ecological indicator data of each local time period in the sample set are clustered by a hierarchical clustering algorithm to obtain a reference cluster of the ecological indicator data of each local time period in the sample set; Obtain the range of the ecological indicator data in each reference cluster as the reference range of the corresponding reference cluster; Based on the range of the ecological indicator data in a preset historical time period, the ecological indicator data is divided into a second preset number of interval segments, and the interval points corresponding to each interval segment are obtained; For any interval point, obtain the number of all reference clusters whose reference range includes the interval point as the distribution number of the interval point corresponding to the ecological indicator data in the sample set; Obtaining a reference fluctuation degree of the interval point corresponding to the ecological indicator data in the sample set according to a difference between the distribution frequency and the distribution frequency of the interval point corresponding to the ecological indicator data in a preset neighborhood sample set of the sample set; The mean of the reference fluctuation degree of all interval points corresponding to the ecological indicator data in the sample set is taken as the overall fluctuation degree of the ecological indicator data in the sample set; According to the difference in the overall fluctuation degree of any two ecological indicator data in each sample set, the correlation degree of the two ecological indicator data is obtained.

4. The method for intelligent monitoring of forest and grassland ecological environment as claimed in claim 3, characterized in that: The method for obtaining the reference fluctuation degree is: Obtain the difference between the distribution frequency of the interval point corresponding to the ecological indicator data in the sample set and the distribution frequency of the interval point corresponding to the ecological indicator data in each preset neighborhood sample set of the sample set, and take both as the first difference; The mean of the first difference is taken as the reference fluctuation degree of the interval point corresponding to the ecological indicator data in the sample set.

5. The method for intelligent monitoring of forest and grassland ecological environment as claimed in claim 3, characterized in that: The method for obtaining the degree of association is: For any two ecological indicator data, the difference in the overall fluctuation degree of the two ecological indicator data in each sample set is obtained as the second difference; The result of negatively correlating and normalizing the variance of the second difference is taken as the correlation degree of the two ecological indicator data.

6. The method for intelligent monitoring of forest and grassland ecological environment according to claim 1, characterized in that: The method for obtaining the standard range is: For any ecological indicator association pair, the two ecological indicator data corresponding to the ecological indicator association pair within a preset historical time period are mapped to a two-dimensional spatial coordinate system; wherein the horizontal coordinate and the vertical coordinate of the two-dimensional spatial coordinate system correspond to the two ecological indicator data corresponding to the ecological indicator association pair; According to the distance between the corresponding coordinate points in the two-dimensional space coordinate system, the coordinate points in the two-dimensional space coordinate system are clustered by a hierarchical clustering algorithm to obtain analysis cluster clusters; The range of each ecological indicator data in each analysis cluster is used as the standard range of each ecological indicator in the ecological indicator association pair; wherein, one analysis cluster includes the ranges of two ecological indicator data and the ranges of the two ecological indicator data in one analysis cluster remain corresponding.

7. The method for intelligent monitoring of forest and grassland ecological environment according to claim 1, characterized in that: The method for obtaining the boundary sample plot is as follows: According to the distribution of ecological indicator data corresponding to each ecological indicator association pair in the current sample plot of the forest and grassland ecological environment in the corresponding standard range, the degree of disorder of the current sample plot is obtained; When the disorder degree is greater than the preset disorder degree threshold, the current sample plot is judged to be a boundary sample plot; When the disorder degree is less than or equal to the preset disorder degree threshold, it is determined that the current sample plot is not a boundary sample plot.

8. The method for intelligent monitoring of forest and grassland ecological environment as claimed in claim 7, characterized in that: The method for obtaining the degree of chaos is: For any ecological indicator association pair, when the two ecological indicator data corresponding to the ecological indicator association pair in the current sample plot are within the standard range of the two ecological indicators in the ecological indicator association pair, the confusion index of the ecological indicator association pair is marked as 0; When the two ecological indicator data corresponding to the ecological indicator association pair in the current sample plot are not within the standard range of the two ecological indicators in the ecological indicator association pair, the confusion index of the ecological indicator association pair is marked as 1; The sum of the chaos indices of all ecological indicator association pairs of the current sample plot is taken as the chaos degree of the current sample plot.

9. An intelligent monitoring system for forest and grassland ecological environment, characterized in that: The system comprises: A data acquisition module is used to obtain data on various ecological indicators of designated monitoring plots of forest and grassland ecological environment within a preset historical time period; The ecological indicator association pair acquisition module is used to obtain the ecological indicator association pair according to the distribution of any two ecological indicator data over time in a preset historical time period; A standard range acquisition module, used to obtain the standard range of each ecological indicator in each ecological indicator association pair according to the ecological indicator data in the preset historical time period corresponding to each ecological indicator association pair; The boundary plot judgment module is used to judge whether the current plot is a boundary plot according to the distribution of ecological indicator data corresponding to each ecological indicator association pair in the current plot of the forest and grassland ecological environment in the corresponding standard range.

10. An intelligent monitoring device for forest and grassland ecological environment, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When executing the computer program, the processor implements the steps of the intelligent monitoring method of forest and grassland ecological environment described in any one of claims 1 to 8.

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