Method for predicting ecological environment changes based on human activity influences

Abstract

The application relates to the technical field of data prediction, and proposes an ecological environment change prediction method based on human activity influence, which comprises the following steps: acquiring environment monitoring data of a forest protection area, acquiring a poaching monitoring sequence of each monitoring point in the forest protection area according to the environment monitoring data, acquiring an ecological monitoring coordination coefficient according to the poaching monitoring sequence, acquiring an environment dynamic monitoring sample set according to the ecological monitoring coordination coefficient and wild animal quantity monitoring result data, constructing an environment transient influence coefficient sequence according to the environment dynamic monitoring sample set, acquiring a prediction result of an environment transient influence coefficient of the forest protection area in a future period of time based on the environment transient influence coefficient sequence by using an autoregressive moving average model, and predicting and analyzing ecological environment change based on the prediction result of the environment transient influence coefficient. The application predicts the change of the ecological environment by constructing the environment transient influence coefficient, and improves the accuracy of the prediction result of the ecological environment change.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data prediction, in particular to an ecological environment change prediction method based on human activity influence. BACKGROUND

[0002] Forests are one of the most biologically diverse ecosystems on Earth. In recent years, with the increasing influence of human activities on the ecological environment, the ecological environment has been polluted and damaged to some extent. Human activities such as poaching not only destroy biodiversity but also deepen the impact on the ecological system. Therefore, ecological environment monitoring is an important means of ecological environment protection. The prediction analysis of ecological environment change based on the influence of human activities on the ecological environment is an effective means to avoid overdevelopment and prevent environmental damage. Predicting ecological environment change helps to identify weak links and key nodes in the ecological system, and promotes biodiversity protection and maintenance of ecological balance.

[0003] Nowadays, the main technologies for monitoring forest poaching activities include remote sensing-based monitoring, GPS positioning and tracking technology-based monitoring, and intelligent monitoring system-based monitoring. By monitoring the impact of human activities on the environment in forests, the change of the ecological environment is predicted to achieve the purpose of protecting the environment. However, the accuracy of traditional data feature extraction algorithms and data prediction algorithms for ecological environment analysis is low. For example, the distribution characteristics of data affect the results of linear regression prediction fitting. When new regression prediction analysis is performed based on data, the fitting effect is poor, which affects the prediction results of ecological environment change and further affects the protection and restoration of the ecological environment. SUMMARY

[0004] The present application provides an ecological environment change prediction method based on human activity influence to solve the problem of low prediction accuracy of ecological environment change based on human activity influence. The technical solution adopted is as follows:

[0005] One embodiment of the present application provides an ecological environment change prediction method based on human activity influence, which comprises the following steps:

[0006] Obtain environment monitoring data of a forest protection area, wherein the environment monitoring data includes poaching activity monitoring result data and wild animal quantity monitoring result data;

[0007] Obtain a poaching monitoring sequence of each monitoring point in the forest protection area according to the poaching activity monitoring result data in the forest protection area, obtain an ecological monitoring coordination coefficient according to the poaching monitoring sequence of each monitoring point in the forest protection area, obtain an environment dynamic monitoring sample set according to the ecological monitoring coordination coefficient corresponding to the monitoring data of the forest protection area and the wild animal quantity monitoring result data, and obtain an environment transient influence coefficient according to the local feature analysis result of the environment dynamic monitoring sample set.

[0008] Based on the environmental transient impact coefficient, a prediction algorithm is used to obtain the prediction results of the environmental transient impact coefficient, and based on the prediction results of the environmental transient impact coefficient, the prediction results of the ecological environment changes in the forest protection area are obtained.

[0009] Preferably, the method for obtaining the poaching monitoring sequence for each monitoring point within the forest protection area based on poaching activity monitoring data is as follows:

[0010] The poaching activity monitoring data obtained from each monitoring point within the forest protection area within a preset time period are evenly divided. The collection time corresponding to each group of poaching activity monitoring data obtained after division is taken as a monitoring cycle for each monitoring point within the forest protection area. The data of one monitoring cycle for each monitoring point is encoded, and the sequence composed of the encoded results is taken as the poaching monitoring sequence for each monitoring point within the forest protection area.

[0011] Preferably, the specific method for encoding the data of one monitoring cycle for each monitoring point is as follows:

[0012] For each monitoring point within a monitoring period, if any abnormal poaching monitoring data appears in the daily poaching activity monitoring data for each monitoring point within a monitoring period, all poaching monitoring data for that day in that monitoring period for that monitoring point will be coded as 1; if no abnormal poaching monitoring data appears in the daily poaching activity monitoring data for each monitoring point within a monitoring period, all poaching monitoring data for that day in that monitoring period for that monitoring point will be coded as 0.

[0013] Preferably, the method for obtaining the ecological monitoring coordination coefficient based on the poaching monitoring sequence of each monitoring point within the forest protection area is as follows:

[0014] The first overall coordination coefficient is the sum of the similarity judgment results of the poaching monitoring sequences corresponding to any two monitoring points in the same monitoring period within the forest protection area and the preset parameters. The second overall coordination coefficient is the Euclidean distance between any two monitoring points in the forest protection area. The product of the first and second overall coordination coefficients is the denominator. The number of elements with 1 in the poaching monitoring sequences corresponding to any two monitoring points in the same monitoring period within the forest protection area is the numerator. The ratio of the numerator to the denominator is the third overall coordination coefficient between any two monitoring points in the forest protection area. The average of the cumulative results of the third overall coordination coefficients at all monitoring points in the forest protection area is the ecological monitoring overall coordination coefficient within one monitoring period of the forest protection area.

[0015] Preferably, the method for obtaining the environmental dynamic monitoring sample set based on the ecological monitoring coordination coefficient corresponding to the monitoring data of the forest protection area and the wildlife population monitoring results is as follows:

[0016] One monitoring cycle of the forest protection area is taken as an environmental dynamic monitoring sample. The ecological monitoring coordination coefficient calculated within one monitoring cycle of the forest protection area is taken as the horizontal axis, and the monitoring results of the number of all wild animals within one monitoring cycle of the forest protection area are taken as the vertical axis. All environmental dynamic monitoring samples are mapped to a two-dimensional rectangular coordinate system composed of the horizontal and vertical axes, and the mapping results of all environmental dynamic monitoring samples are taken as the environmental dynamic monitoring sample set.

[0017] Preferably, the method for obtaining the environmental transient impact coefficient based on the local feature scores of the environmental dynamic monitoring sample set is as follows:

[0018] Clustering algorithms are used to obtain the clustering results of the environmental dynamic monitoring sample set. For each cluster in the clustering results, the sequence of the vertical coordinates corresponding to all environmental dynamic monitoring samples in each cluster in ascending order is used as the monitoring quantity feature sequence of each cluster. The sequence of the ecological monitoring coordination coefficients corresponding to each element in the monitoring quantity feature sequence is used as the environmental transient change sequence of each cluster. Normalization algorithms are used to obtain the normalization results of the monitoring quantity feature sequence and the environmental transient change sequence of each cluster.

[0019] The relative coefficient of poaching environment change for each cluster is obtained based on the normalized results of the monitoring quantity characteristic sequence and the environmental transient change sequence for each cluster; the environmental transient impact coefficient is constructed based on the relative coefficients of poaching environment change for different clusters in the clustering results of the environmental dynamic monitoring sample set.

[0020] Preferably, the specific method for obtaining the relative coefficient of poaching environment change for each cluster based on the normalized results of the monitoring quantity feature sequence and the environmental transient change sequence for each cluster is as follows:

[0021] The absolute value of the difference between the standard deviation of all elements in the normalized result of the monitoring quantity feature sequence of each cluster and the standard deviation of all elements in the normalized result of the environmental transient change sequence is used as the first relative coefficient; the Euclidean distance between any two environmental dynamic monitoring samples in each cluster is used as the second relative coefficient; the mean of the cumulative results of the second relative coefficient in each cluster is used as the third relative coefficient; and the product of the first relative coefficient and the third relative coefficient is used as the poaching environmental change relative coefficient of each cluster.

[0022] Preferably, the specific method for constructing the environmental transient impact coefficient based on the relative coefficients of poaching environment changes in different clusters in the clustering results of the environmental dynamic monitoring sample set is as follows:

[0023] The absolute value of the difference between the means of all elements in the normalized results of the quantitative feature sequences corresponding to any two clusters in the environmental dynamic monitoring sample set is taken as the first transient coefficient. The absolute value of the difference between the means of all elements in the normalized results of the environmental transient change sequences corresponding to any two clusters in the environmental dynamic monitoring sample set is taken as the second transient coefficient. The sum of the first and second transient coefficients is taken as the numerator. The absolute value of the difference between the relative coefficients of poaching environmental change corresponding to any two clusters in the environmental dynamic monitoring sample set and the sum of the preset parameters are taken as the denominator. The ratio of the numerator to the denominator is taken as the third transient coefficient. The mean of the cumulative results of the third transient coefficients over all clusters in the environmental dynamic monitoring sample set is taken as the environmental transient influence coefficient.

[0024] Preferably, the method for obtaining the prediction results of the environmental transient impact coefficient of the forest protection area over a future period using a prediction algorithm based on the environmental transient impact coefficient is as follows:

[0025] Environmental monitoring data for a predetermined time period within a forest protected area is acquired. The environmental monitoring data is then uniformly divided, and each group of environmental monitoring data is taken as a set of environmental monitoring data for a predetermined time period within the forest protected area. The environmental transient impact coefficient corresponding to each set of environmental monitoring data for a predetermined time period is calculated. The sequence of all environmental transient impact coefficients corresponding to the environmental monitoring data for a predetermined time period within the forest protected area is taken as the environmental transient impact coefficient sequence. A prediction algorithm is used to obtain the prediction result of the environmental transient impact coefficient sequence. The normalized result of the prediction result of the environmental transient impact coefficient sequence is taken as the prediction result of the environmental transient impact coefficient of the forest protected area in the future predetermined time period.

[0026] Preferably, the method for obtaining the prediction results of ecological environment changes in forest protection areas based on the prediction results of environmental transient impact coefficients is as follows:

[0027] The system obtains vegetation coverage prediction data and environmental pollution prediction data through the ecosystem large-screen visualization management platform. The vegetation coverage prediction data and environmental pollution prediction data are positively normalized and normalized. The weighted sum of the positively normalized and normalized vegetation coverage prediction data and the prediction results of environmental transient impact coefficient is used as the ecological environment change prediction coefficient. The comparison between the ecological environment change prediction coefficient and the preset prediction interval is used as the ecological environment change prediction result.

[0028] The beneficial effects of this application are as follows: It compares and analyzes the monitoring results of forest poaching at different monitoring points within a forest protection area and the monitoring results of the number of wild animals in the area. An ecological monitoring coordination coefficient is constructed based on the summarized analysis of the forest poaching monitoring results from different monitoring points within the forest protection area. This coefficient reflects the degree of impact of human activities within the forest protection area over a period of time. An environmental dynamic monitoring sample set is obtained based on the ecological monitoring coordination coefficient and the monitoring data of the number of wild flora and fauna in the area. An environmental transient impact coefficient for the forest protection area is constructed based on the differences in local human activity impacts and environmental dynamic changes within the environmental dynamic monitoring sample set. This coefficient reflects the trend of ecological environment changes affected by human activities within the area, avoiding the problem of large errors in data fitting and prediction analysis due to the discrete distribution characteristics of the data. The prediction results of ecological environment changes are obtained based on the environmental transient impact coefficient. Its beneficial effect lies in considering the local dynamic impact relationship between human activities and ecological environment changes, thereby improving the accuracy of ecological environment change prediction and analysis. Attached Figure Description

[0029] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0030] Figure 1 A flowchart illustrating a method for predicting ecological and environmental changes based on the impact of human activities, provided in one embodiment of this application;

[0031] Figure 2 This is a schematic diagram of an environmental dynamic monitoring sample set provided in one embodiment of this application. Detailed Implementation

[0032] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0033] Please see Figure 1 The diagram illustrates a flowchart of a method for predicting ecological and environmental changes based on the impact of human activities, according to an embodiment of this application. The method includes the following steps:

[0034] Step S001: Obtain environmental monitoring data for the forest protection area.

[0035] Taking a specific forest protection area as an example, the following steps are taken to set up a forest protection area: (A size of 100 is used as an empirical value) monitoring points. Audio signals from each monitoring point in the forest protection area are acquired using AI voiceprint monitoring equipment. The collected audio signals from the forest protection area are transmitted to the ecosystem large-screen visualization management platform. The input to the ecosystem large-screen visualization management platform is the audio signals collected from each monitoring point in the forest protection area. The poaching activity monitoring results data for each monitoring point are obtained by processing the data using a recurrent neural network. The optimization algorithm is the Adam (Adaptive Moment Estimation) optimization algorithm, and the loss function is the cross-entropy loss function. The poaching activity monitoring results data include two results: normal monitoring and abnormal monitoring. The specific training process of the recurrent neural network is a well-known technology and will not be described in detail here.

[0036] Furthermore, by installing locators on wild animals within the forest protection area, data on the population of wild animals within the forest protection area can be obtained through a large-screen visualization management platform for the ecosystem. Therefore, all environmental monitoring data within the forest protection area over a three-month period can be acquired. This environmental monitoring data includes data on poaching activities and wild animal populations, and this monitoring data is used as the environmental monitoring data for the forest protection area.

[0037] Thus, environmental monitoring data for the forest protection area was obtained.

[0038] Step S002: Obtain poaching monitoring sequences for each monitoring point in the forest protection area based on environmental monitoring data; obtain ecological monitoring coordination coefficients based on poaching monitoring sequences; and obtain an environmental dynamic monitoring sample set based on ecological monitoring coordination coefficients and wildlife population monitoring results for a period of time in the forest protection area.

[0039] This analysis examines the impact of human activities on ecological environment changes within forest protected areas, based on monitoring results from all poaching monitoring points. Since ecological environment changes caused by human activities are a long-term process, the analysis considers long-term environmental monitoring results for forest protected areas. The analysis reveals a close relationship between the number of wild animals and poaching activities within these areas. Therefore, the relationship between monitoring results of poaching activities and the number of wild animals in forest protected areas is used to predict ecological environment changes.

[0040] Furthermore, for example, for forest protected areas Analyze environmental monitoring data over the past month, and The environmental monitoring data within the past month will be divided into categories. Specifically, it will be... Using a monitoring cycle of 1 day, the monitoring results within that cycle are summarized, and a poaching monitoring sequence for each monitoring point is constructed based on the poaching activity monitoring data from monitoring points within the forest protected area during that cycle. Specifically, for example, the first day of a monitoring point within a monitoring cycle... , , The data from the monitoring of poaching activities over the past day, if a monitoring point within the forest protected area is on the [day]... , , If an abnormal monitoring result occurs within a day, then the [number]th [day] will be [determined]. , All poaching activity monitoring data for the day are coded as follows: If the first , , If no abnormal test results are found within 1 day, then the results will be... , , All poaching activity monitoring data for the day are coded as follows: Therefore, the poaching monitoring sequence at this monitoring point is: .

[0041] Furthermore, based on the distance between different monitoring points within the forest protection area and the differences in forest poaching monitoring results, the ecological monitoring coordination coefficient for each monitoring period is calculated. This coefficient reflects the overall impact of forest poaching activities within the forest protection area. Monitoring points that are closer together demonstrate a higher reliability in reflecting the impact of forest poaching activities. The specific calculation formula for the ecological monitoring coordination coefficient for each monitoring period is as follows:

[0042]

[0043] In the formula, This represents the ecological monitoring coordination coefficient for each monitoring cycle in a forest protection area. and These represent the first [number] within the forest protection area. The and the first Poaching monitoring sequence at each monitoring point express and Hamming distance between them; Indicates the first The and the first The total number of abnormal forest poaching monitoring results at each monitoring point; Indicates the first The and the first Euclidean distance between monitoring points; This indicates the number of monitoring points set up within the forest protection area; To adjust the parameters, the value is taken as an empirical value of 0.01.

[0044] If the first [item] within the forest protection area The and the first If two monitoring points are close to each other and the monitoring results of forest poaching between the two monitoring points are highly similar, then the calculated... The smaller the value, the higher the first overall planning coefficient. The smaller the value, and the more abnormal monitoring times of forest poaching at both monitoring points, the greater the likelihood that the area is a frequent site of forest poaching. The calculated second coordination coefficient... The larger the value, the higher the calculated third coordination coefficient. The larger the value, the greater the impact of poaching activities on any two monitoring points within the forest protection area. The higher the value, the greater the ecological monitoring coordination coefficient for each monitoring cycle in the forest protection area. The larger the value, the greater the likelihood of frequent poaching activities in the area, indicating a greater potential impact of human activities on the ecological environment.

[0045] Furthermore, a relative impact analysis is conducted on human activities and changes in the ecological environment within the forest protection area, and the correlation coefficients of transient environmental impacts are obtained based on the analysis results. Specifically, such as... Figure 2 As shown, a monitoring cycle of a forest protected area is used as an environmental dynamic monitoring sample. The ecological monitoring coordination coefficient calculated within a monitoring cycle of the forest protected area is used as the abscissa, and the wildlife population monitoring data within a monitoring cycle of the forest protected area is used as the ordinate. All environmental dynamic monitoring samples are mapped to a two-dimensional rectangular coordinate system composed of the abscissa and ordinate. The mapping results of all environmental dynamic monitoring samples are used as the environmental dynamic monitoring sample set. The environmental transient impact coefficient is obtained based on the local dynamic difference characteristics of the environmental dynamic monitoring sample set. The environmental transient impact coefficient reflects the degree of impact of poaching activities on ecological environment changes. Its beneficial effect is to avoid the large error caused by direct linear fitting analysis of data due to direct local distribution differences, which affects the accuracy of the analysis results of ecological environment changes.

[0046] Thus, a dynamic environmental monitoring sample set was obtained.

[0047] Step S003: Construct the relative coefficient of poaching environment and the transient environmental impact coefficient based on the environmental dynamic monitoring sample set. Based on the transient environmental impact coefficient, use the autoregressive moving average model to obtain the prediction results of the transient environmental impact coefficient of the forest protection area in the future.

[0048] This paper analyzes the local differences in the relationship between ecological environment changes and the impact of human activities within forest protection areas based on a dynamic environmental monitoring sample set. Specifically, the input is the dynamic environmental monitoring sample set. The K-means clustering algorithm is used to obtain the clustering results of all dynamic environmental monitoring samples. The transient impact coefficient of environmental impact is calculated based on the clustering results of all dynamic environmental monitoring samples. The specific implementation process of the K-means clustering algorithm is a well-known technique and will not be elaborated further. Under normal circumstances, the number of wild animals will decrease with the occurrence of forest poaching. However, changes in the ecological environment within forest areas are the result of long-term impacts, and the number of wild animals within forest areas is also a dynamic process. The decrease in the number of wild animals is the result of long-term monitoring of human activities such as forest poaching. Therefore, this paper analyzes the relationship between the ecological environment and human activities in different time periods, reflecting the impact of human activities on ecological environment changes through local change characteristics.

[0049] Furthermore, the sequence of wild animal numbers corresponding to all environmental dynamic monitoring samples within each cluster, arranged in ascending order, is used as the monitoring quantity feature sequence for each cluster. The sequence of ecological monitoring coordination coefficients corresponding to each element in the monitoring quantity feature sequence is used as the environmental transient change sequence for each cluster. The inputs are the quantity feature sequence and the environmental transient change sequence for each cluster, respectively. The normalization results of the quantity feature sequence and the environmental transient change sequence are obtained using the max-min normalization algorithm. The specific calculation process of the max-min normalization algorithm is a well-known technique and will not be elaborated further. The environmental transient impact coefficient is calculated based on the clustering results of the environmental dynamic monitoring samples. The specific calculation formula is as follows:

[0050]

[0051]

[0052] In the formula, This represents the environmental transient impact coefficient of the forest protection area; and They represent the first The and the first The relative coefficient of poaching environment change for each cluster; and They represent the first The standard deviation of all elements in the normalized result of the environmental transient change sequence of each cluster, and the standard deviation of all elements in the normalized result of the quantitative feature sequence. and They represent the first The mean of all elements in the normalized result of the environmental transient change sequence of each cluster, and the mean of all elements in the normalized result of the quantitative feature sequence. Indicates the first In the cluster, the th The and the first Euclidean distance between environmental dynamic monitoring samples Indicates the first The number of samples in each cluster; and They represent the first The mean of all elements in the normalized result of the environmental transient change sequence of a cluster and the mean of all elements in the normalized result of the quantitative feature sequence. Indicates the number of clusters; This indicates the adjustment parameter, and its value is taken from an empirical value. .

[0053] If the fluctuation characteristics of the ecological environment coordination coefficient and the fluctuation characteristics of the wild animal population differ significantly in each cluster of environmental dynamic monitoring samples, and the discrete characteristics of the environmental dynamic monitoring samples within the clusters formed by the ecological environment coordination coefficient and the wild animal population are significant, then the first relative coefficient is calculated. Second relative coefficient The larger the value, the higher the third relative coefficient. The larger the value, the higher the calculated relative coefficient of poaching environment change. The larger the value, the better; if the relative coefficients of poaching environment change between any two clusters are close, then the calculated value is higher. The smaller the value, and the greater the difference in local data between any two clusters, the better the calculated first transient coefficient. Second transient coefficient The larger the value, the higher the third transient coefficient. The larger the value, the higher the calculated environmental transient impact coefficient of the forest protection area. The larger the value, the greater the trend of ecological environment change in the forest protection area, as indicated by the analysis of local control changes and data differences in the forest protection area over a 3-month monitoring period.

[0054] Furthermore, the ecological environment is predicted and analyzed based on the changing relationship between forest poaching activities and wildlife in the ecosystem. Specifically, if the environmental transient impact coefficient of the forest protected area is large, obtained from the analysis of poaching activity monitoring data and wildlife population monitoring data over a period of time, then the ecological environment changes within the forest protected area may be severely affected by human activities, and the ecological balance within the forest protected area is likely to be disrupted.

[0055] Furthermore, environmental monitoring data within a preset time period (i.e., within 36 months) of the forest protection area is divided into 3-month intervals. Environmental transient impact coefficients are calculated based on the environmental monitoring data within each 3-month interval. The sequence of all environmental transient impact coefficients corresponding to the environmental monitoring data within the 36-month period, arranged in ascending order of time, is used as the input. The normalized result of the environmental transient impact coefficient sequence is obtained using the maximum-minimum normalization algorithm. The normalized result of the environmental transient impact coefficient sequence of the 3-year environmental monitoring data of the forest protection area is then used. The predicted result of the environmental transient impact coefficients is obtained using the ARIMA (Autoregressive Integrated Moving Average) prediction algorithm. Based on the predicted result of the environmental transient impact coefficients, the possibility of damage to the ecological balance of the forest protection area within the next 3 months is predicted. The specific calculation processes of the ARIMA algorithm and the maximum-minimum normalization algorithm are well-known techniques and will not be elaborated further.

[0056] Thus, the predicted results of the environmental transient impact coefficient of the forest protection area over the next three months have been obtained.

[0057] Step S004: Obtain the prediction results of ecological environment changes based on the prediction results of the environmental transient impact coefficient of the forest protection area within the next 3 months.

[0058] The predicted results of the environmental transient impact coefficient are transmitted to the ecosystem visualization management platform. This platform then obtains the predicted vegetation cover and environmental pollution data for the next three months in the forest protection area. The predicted vegetation cover and environmental pollution results undergo positive normalization and normalization processes; the specific calculation procedures for these processes are well-known and will not be elaborated upon. Specifically, the predicted data of the environmental transient impact coefficient... The results of positive transformation and normalization of vegetation cover prediction data The results of the forward normalization and normalization of environmental pollution prediction data The weighted summation result is used as the prediction result of ecological environment change in forest protection areas. ,in , , The weights are respectively , , The formula for calculating the predicted ecological environment changes in forest protected areas is as follows: The results of the ecological environment change prediction include the degree of severe ecological environment damage ( In the interval Internal), moderate degree of ecological environment damage ( In the interval (Internal), low degree of ecological environment damage ( In the interval Inside).

[0059] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. The above descriptions are merely preferred embodiments of this application and are not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the principles of this application should be included within the protection scope of this application.

Claims

1. A method for predicting eco-environmental changes based on human activity impacts, characterized by, The method comprises the following steps: obtaining environmental monitoring data of the forest protection area, wherein the environmental monitoring data comprises poaching activity monitoring result data and wild animal quantity monitoring result data; obtaining a poaching monitoring sequence of each monitoring point in the forest protection area according to the poaching activity monitoring result data of the forest protection area, obtaining an ecological monitoring coordination coefficient according to the poaching monitoring sequence of each monitoring point in the forest protection area, obtaining an environmental dynamic monitoring sample set according to the ecological monitoring coordination coefficient corresponding to the monitoring data of the forest protection area and the wild animal quantity monitoring result data, and obtaining an environmental transient influence coefficient according to a local feature analysis result of the environmental dynamic monitoring sample set; obtaining a prediction result of the environmental transient influence coefficient in a future period of time of the forest protection area by using a prediction algorithm based on the environmental transient influence coefficient, and obtaining a prediction result of the ecological environment change of the forest protection area according to the prediction result of the environmental transient influence coefficient; the method for obtaining the ecological monitoring coordination coefficient according to the poaching monitoring sequence of each monitoring point in the forest protection area comprises the following steps: taking a sum of a preset parameter and a similarity determination result of the poaching monitoring sequence corresponding to any two monitoring points in the forest protection area in a same monitoring period as a first coordination coefficient, taking an Euclidean distance between any two monitoring points in the forest protection area as a second coordination coefficient, taking a product of the first coordination coefficient and the second coordination coefficient as a denominator, taking a number of elements with a value of 1 in the poaching monitoring sequence corresponding to any two monitoring points in the forest protection area in the same monitoring period as a numerator, taking a ratio of the numerator to the denominator as a third coordination coefficient between any two monitoring points in the forest protection area, and taking an average of an accumulation result of the third coordination coefficient on all monitoring points in the forest protection area as the ecological monitoring coordination coefficient in a monitoring period of the forest protection area; the method for obtaining the environmental transient influence coefficient according to the local feature analysis result of the environmental dynamic monitoring sample set comprises the following steps: obtaining a clustering result of the environmental dynamic monitoring sample set by using a clustering algorithm, taking a sequence composed of all environmental dynamic monitoring samples corresponding to a longitudinal coordinate in each clustering cluster in a descending order as a monitoring quantity feature sequence of each clustering cluster, taking a sequence composed of ecological monitoring coordination coefficients corresponding to each element in the monitoring quantity feature sequence as an environmental transient change sequence of each clustering cluster, and obtaining a normalization result of the monitoring quantity feature sequence and the environmental transient change sequence of each clustering cluster by using a normalization algorithm respectively; obtaining a poaching environmental change relative coefficient of each clustering cluster according to the normalization result of the monitoring quantity feature sequence and the environmental transient change sequence of each clustering cluster, and constructing the environmental transient influence coefficient according to the poaching environmental change relative coefficients of different clustering clusters in the clustering result of the environmental dynamic monitoring sample set.

2. The method of claim 1, wherein the human activity affecting the eco-environmental change is determined by using a human activity affecting the eco-environmental change database. the method for obtaining the poaching monitoring sequence of each monitoring point in the forest protection area according to the poaching activity monitoring result data of the forest protection area comprises the following steps: The monitoring result data of poaching activities of each monitoring point in the forest protection area within a preset time is uniformly divided, the collection time length of each group of poaching activity monitoring result data obtained after the division is taken as a monitoring period of each monitoring point in the forest protection area, the data of the monitoring period of each monitoring point is encoded, and a sequence composed of the encoding result is taken as a poaching monitoring sequence of each monitoring point in the forest protection area.

3. The method of claim 2, wherein the human activity affecting the ecological environment is determined by using a human activity affecting the ecological environment database. The specific method of encoding the data of the monitoring period of each monitoring point is: For the data of the monitoring period of each monitoring point, when the poaching activity monitoring result data of each day in the monitoring period of each monitoring point contains poaching monitoring abnormal result data, all the poaching monitoring result data corresponding to each day in the monitoring period of each monitoring point is encoded as 1; when the poaching activity monitoring result data of each day in the monitoring period of each monitoring point does not contain poaching monitoring abnormal result data, all the poaching monitoring result data corresponding to each day in the monitoring period of each monitoring point is encoded as 0.

4. The method of claim 1, wherein the human activity affecting the eco-environmental change is determined by using a human activity affecting the eco-environmental change database. The method for obtaining the environmental dynamic monitoring sample set according to the ecological monitoring overall coefficient and the wild animal quantity monitoring result data corresponding to the monitoring data of the forest protection area is: A monitoring period of the forest protection area is taken as an environmental dynamic monitoring sample, the ecological monitoring overall coefficient calculated in the monitoring period of the forest protection area is taken as the abscissa, all the wild animal quantity monitoring result data in the monitoring period of the forest protection area is taken as the ordinate, all the environmental dynamic monitoring samples are mapped into a two-dimensional rectangular coordinate system composed of the abscissa and the ordinate, and the mapping result of all the environmental dynamic monitoring samples is taken as the environmental dynamic monitoring sample set.

5. The method of claim 1, wherein the human activity affecting the eco-environmental change is determined by using a human activity affecting the eco-environmental change database. The specific method for obtaining the poaching environmental change relative coefficient of each clustering cluster according to the normalized result of the monitoring quantity feature sequence and the environmental transient change sequence of each clustering cluster is: The absolute value of the difference between the standard deviation of all elements in the normalized result of the monitoring quantity feature sequence of each clustering cluster and the standard deviation of all elements in the normalized result of the environmental transient change sequence is taken as a first relative coefficient; The Euclidean distance of any two environmental dynamic monitoring samples in each clustering cluster is taken as a second relative coefficient, the mean of the accumulation result of the second relative coefficient in each clustering cluster is taken as a third relative coefficient, and the product of the first relative coefficient and the third relative coefficient is taken as the poaching environmental change relative coefficient of each clustering cluster. 6.The method of predicting eco-environmental changes based on human activity impacts according to claim 1, characterized in that, The specific method for constructing the environmental transient influence coefficient according to the poaching environmental change relative coefficients of different clustering clusters in the clustering result of the environmental dynamic monitoring sample set is: The absolute value of the difference between the mean of all elements in the normalized result of the quantity feature sequence corresponding to any two clustering clusters in the environmental dynamic monitoring sample set and the mean of all elements in the normalized result of the environmental transient change sequence corresponding to the two clustering clusters is taken as a first transient coefficient, the absolute value of the difference between the mean of all elements in the normalized result of the quantity feature sequence corresponding to any two clustering clusters in the environmental dynamic monitoring sample set and the mean of all elements in the normalized result of the environmental transient change sequence corresponding to the two clustering clusters is taken as a second transient coefficient, and the sum of the first transient coefficient and the second transient coefficient is taken as the numerator. The sum of the absolute value of the difference between the poaching environment change relative coefficients corresponding to any two clustering clusters in the environment dynamic monitoring sample set and the preset parameter is taken as the denominator, the ratio of the numerator to the denominator is taken as the third transient coefficient, and the mean of the cumulative results of the third transient coefficient on all clustering clusters in the environment dynamic monitoring sample set is taken as the environmental transient influence coefficient.

7. The method of claim 1, wherein the human activity affecting the eco-environmental change is determined by using a human activity affecting the eco-environmental change database. The method for obtaining the prediction result of the environmental transient influence coefficient of the forest protection area in the future period of time based on the environmental transient influence coefficient is: Obtain the environmental monitoring data in the preset time period of the forest protection area, uniformly divide the environmental monitoring data, take each group of divided environmental monitoring data as the environmental monitoring data in a preset time of the forest protection area, calculate the environmental transient influence coefficient corresponding to each group of environmental monitoring data in the preset time, take the sequence composed of all environmental transient influence coefficients corresponding to the environmental monitoring data in the preset time period in the forest protection area in time sequence as the environmental transient influence coefficient sequence, obtain the prediction result of the environmental transient influence coefficient sequence by using a prediction algorithm, and take the normalized result of the prediction result of the environmental transient influence coefficient sequence as the prediction result of the environmental transient influence coefficient of the forest protection area in the future preset time. 8.The method of claim 1, wherein, The method for obtaining the prediction result of the ecological environment change of the forest protection area according to the prediction result of the environmental transient influence coefficient is: Obtain the vegetation coverage prediction result data and the environmental pollution prediction result data through the ecological system large-screen visual management platform, perform positive and normalization processing on the vegetation coverage prediction result data and the environmental pollution prediction result data, take the weighted sum result of the positive and normalization processing result data of the vegetation coverage prediction result data and the environmental pollution prediction result data and the prediction result of the environmental transient influence coefficient as the ecological environment change prediction coefficient, and take the comparison result of the ecological environment change prediction coefficient and the preset prediction interval as the prediction result of the ecological environment change.

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