CN-120930005-B - Forest resource investigation sampling device and method

CN120930005BCN 120930005 BCN120930005 BCN 120930005BCN-120930005-B

Abstract

The application provides forest resource investigation sampling equipment and a method, which are used for determining space-time consistency deviation of an insect sound spectrum at a sampling node in a current period based on a frequency spectrum change difference value of an insect sound spectrum signal at the sampling node in a time dimension and a distribution change characteristic of insects at the sampling node in the space dimension, carrying out deviation exception analysis on the space-time consistency deviation of the insect sound spectrum at the sampling node in the current period by combining influence association relations between different environmental parameters and outbreaks of diseases and insect pests and environmental parameter data of the current period to obtain an exception confidence value of the insects at the sampling node, clustering the sampling node with the exception confidence value exceeding an exception threshold according to all exception confidence values and a spatial clustering algorithm, and further dividing sampling priority areas of a plurality of diseases and insect pests in a target forest region. By adopting the scheme of the application, the sampling area of the target forest can be optimized by combining the multidimensional abnormal analysis of the monitoring data.

Inventors

GONG YANPING
HAN XINXIN
Lv Changxiao
ZHANG FEN
YANG CHUANQIANG
ZHANG JING
WU KE
GAO JINGHUA
QIN XIAORUI
SUN JINGFEI

Assignees

山东省国土空间规划院(山东省自然资源和不动产登记中心)

Dates

Publication Date: 20260505
Application Date: 20250722

Claims (10)

1. The sampling area optimization method is used for optimizing the sampling area of a target forest zone by forest resource investigation sampling equipment and is characterized by comprising the following steps: Monitoring and collecting insect sound spectrum signals at all sampling nodes in a target forest zone through an intelligent sensor, wherein the insect sound spectrum signals consist of characteristic frequency bands corresponding to target insects; For each sampling node, determining a spectrum variation difference value of an insect sound spectrum signal at the sampling node in a time dimension based on spectrum variation characteristics of an insect sound spectrum between the sampling node and an adjacent sampling node in a current period, determining a distribution variation characteristic of insects at the sampling node in a space dimension according to the insect sound spectrum signal at the sampling node in the current period, and determining a space-time consistency deviation of the insect sound spectrum at the sampling node in the current period according to the spectrum variation difference value and the distribution variation characteristic; Acquiring historical environmental parameter data and historical pest marking data at sampling nodes, and constructing influence association relations between different environmental parameters and pest outbreaks based on the historical environmental parameter data and the historical pest marking data; carrying out deviation exception analysis on space-time consistency deviation of insect sound spectra at sampling nodes in the current time period by combining each influence association relationship with environmental parameter data of the current time period acquired by the intelligent sensor to obtain exception confidence values of insects at the sampling nodes, and further obtaining exception confidence values of the insects at each sampling node; And clustering sampling nodes with abnormal confidence values exceeding an abnormal threshold value according to the combination of all the abnormal confidence values and a spatial clustering algorithm, so as to divide sampling priority areas of a plurality of diseases and insect pests in the target forest region.
2. The method of claim 1, wherein determining a spectral variation difference value of the insect sound spectrum signal at the sampling node in the time dimension based on the spectral variation characteristics of the insect sound spectrum between the sampling node and the adjacent sampling node in the current period of time comprises: Acquiring insect sound spectrum signals of sampling nodes and each adjacent sampling node in a current time period; Carrying out spectrum analysis on each insect sound spectrum signal, and extracting to obtain frequency distribution, energy density and peak frequency of a sampling node and each adjacent sampling node; calculating through all frequency distribution, energy density and peak frequency to obtain the spectrum change characteristics of insect sound spectrum between the sampling node and each adjacent sampling node in the current period; And determining the spectrum variation difference value of the insect sound spectrum signal at the sampling node in the time dimension based on all the spectrum variation characteristics.
3. The method of claim 1, wherein determining a distribution variation characteristic of insects at the sampling node in a spatial dimension from the insect spectrogram signal at the sampling node over the current time period comprises: acquiring insect sound spectrum signals of a plurality of continuous time points in a current period of time at a sampling node; carrying out spectrum analysis on insect sound spectrum signals at each time point so as to construct a time sequence track reflecting insect sound spectrum activity; And extracting distribution change characteristics of insects at the characteristic sampling nodes in the space dimension based on the time sequence track.
4. The method of claim 1, wherein determining a spatiotemporal consistency deviation of the insect spectrum at the sampling node over the current period of time from the spectral variation variance value and the distribution variation characteristic comprises: acquiring a spectrum change difference value and a corresponding distribution change characteristic at a sampling node in a current period; Normalizing the distribution change characteristics to obtain distribution change characteristic values; determining the weighting coefficients of the spectrum variation difference value and the distribution variation characteristic value; and carrying out weighted fusion according to the spectrum change difference value, the distribution change characteristic value and the corresponding weighting coefficient to obtain the space-time consistency deviation of the insect sound spectrum at the sampling node in the current period.
5. The method of claim 1, wherein constructing an impact association between different environmental parameters and pest outbreaks based on the historical environmental parameter data and the historical pest annotation data specifically comprises: constructing a data alignment sample set of the historical environmental parameter data and the historical pest and disease damage labeling data; Labeling the disease and pest outbreak state label of each data alignment sample in the data alignment sample set according to the historical disease and pest labeling data; extracting a plurality of key environmental factors affecting pest outbreaks from the data alignment sample set; And (3) establishing a correlation model of the key environmental factors and the disease and insect outbreak state by combining all the data alignment samples marked by the disease and insect outbreak state labels and all the key environmental factors, and further outputting influence correlation relations between different environmental parameters and disease and insect outbreaks.
6. The method of claim 1, wherein performing bias anomaly analysis on the space-time consistency bias of the insect sound spectrum at the sampling node in the current period by combining each influence association relationship with the environmental parameter data of the current period acquired by the intelligent sensor, and obtaining the anomaly confidence value of the insect at the sampling node specifically comprises: Constructing an environmental response sensitive weight set for representing the contribution degree of each key environmental factor according to all the influence association relations; Acquiring real-time environmental parameter data corresponding to sampling nodes in a current period, and mapping the real-time environmental parameter data into environmental parameter vectors according to a preset sequence; Calculating an environmental response factor value of the current period according to the environmental response sensitive weight set and the environmental parameter vector; And multiplying the environmental response factor value by the insect sound spectrum space-time consistency deviation at the sampling node in the current period to obtain an abnormal confidence value of the insect at the sampling node.
7. The method of claim 1, wherein clustering the sampling nodes with abnormal confidence values exceeding the abnormal threshold according to all abnormal confidence values combined with a spatial clustering algorithm, and further dividing sampling priority areas of a plurality of plant diseases and insect pests in the target forest zone specifically comprises: Acquiring a preset abnormal threshold value; Screening sampling nodes corresponding to the abnormal confidence value exceeding the abnormal threshold value from all the sampling nodes to obtain a plurality of abnormal sampling nodes; and clustering all abnormal sampling nodes by using a spatial clustering algorithm, and planning sampling priority areas of a plurality of plant diseases and insect pests in the target forest region through a clustering result.
8. A forest resource investigation sampling device, the forest resource investigation sampling device comprising a sampling area optimization unit, characterized in that the sampling area optimization unit comprises: the acquisition module is used for monitoring and acquiring insect sound spectrum signals at each sampling node in a target forest zone through the intelligent sensor, wherein the insect sound spectrum signals consist of characteristic frequency bands corresponding to target insects; The processing module is used for determining a spectrum variation difference value of an insect sound spectrum signal at the sampling node in the time dimension based on the spectrum variation characteristics of the insect sound spectrum between the sampling node and the adjacent sampling node in the current period, determining a distribution variation characteristic of insects at the sampling node in the space dimension according to the insect sound spectrum signal at the sampling node in the current period, and determining a space-time consistency deviation of the insect sound spectrum at the sampling node in the current period according to the spectrum variation difference value and the distribution variation characteristic; the processing module is further used for acquiring historical environmental parameter data and historical pest marking data at the sampling node and constructing influence association relations between different environmental parameters and pest outbreaks based on the historical environmental parameter data and the historical pest marking data; The processing module is further used for carrying out deviation exception analysis on space-time consistency deviation of the insect sound spectrum at the sampling node in the current time period by combining each influence association relation with the environmental parameter data of the current time period acquired by the intelligent sensor to obtain an exception confidence value of the insect at the sampling node, and further obtaining the exception confidence value of the insect at each sampling node; The sampling area dividing module is used for clustering sampling nodes with abnormal confidence values exceeding an abnormal threshold value according to the combination of all the abnormal confidence values and a spatial clustering algorithm, so that sampling priority areas of a plurality of plant diseases and insect pests in the target forest area are divided.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the sampling region optimization method of any one of claims 1 to 7 when the computer program is executed.
10. A computer readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the steps of the sampling region optimization method according to any one of claims 1 to 7.

Description

Forest resource investigation sampling device and method Technical Field The application relates to the technical field of forest resource investigation and sampling, in particular to forest resource investigation and sampling equipment and a method. Background The forest resource investigation sampling is divided into basic investigation and special investigation, wherein the basic investigation aims at core elements of forest resources, such as forest species, accumulation, breast diameter, tree height, forest land area, soil physicochemical properties and the like, so as to grasp the basic current situation of the forest resources, and the special investigation mainly aims at investigation of specific questions, such as forest diseases and insect pests, forest fire sites, rare animal and plant distribution, carbon reserves and the like, so as to solve specific resource management or ecological problems. For forest resource investigation sampling of forest diseases and insect pests, a large amount of manpower is needed to go deep into a forest area in the traditional technology, the working environment is hard, the large-area forest investigation period is long, the requirement of quickly acquiring information is difficult to meet, the manpower cost and the logistic guarantee cost are too high, the technology such as an Internet of things sensor and an unmanned aerial vehicle can be combined to monitor a target forest in the prior art, then a data processing center processes data obtained by monitoring and analyzes an area needing to be sampled, but the influence of environmental factors and the sensor can cause inaccurate data obtained by monitoring, for example, originally normal monitoring data are shifted to be abnormal monitoring data due to the influence of the environmental factors or the originally abnormal monitoring data are shifted to be normal data due to the influence of the environmental factors, so that the sampling area is not representative enough, and therefore, how to optimize the sampling area of the target forest by combining multi-dimensional abnormal analysis of the monitoring data becomes a problem faced in the industry. Disclosure of Invention Based on the above, the application provides a forest resource investigation sampling device and a forest resource investigation sampling method for optimizing a sampling area of a target forest in combination with multidimensional anomaly analysis of monitoring data. In a first aspect, the present application provides a sampling area optimization method for optimizing a sampling area of a target forest area by using a forest resource investigation sampling device, the method comprising the following steps: Monitoring and collecting insect sound spectrum signals at all sampling nodes in a target forest zone through an intelligent sensor, wherein the insect sound spectrum signals consist of characteristic frequency bands corresponding to target insects; For each sampling node, determining a spectrum variation difference value of an insect sound spectrum signal at the sampling node in a time dimension based on spectrum variation characteristics of an insect sound spectrum between the sampling node and an adjacent sampling node in a current period, determining a distribution variation characteristic of insects at the sampling node in a space dimension according to the insect sound spectrum signal at the sampling node in the current period, and determining a space-time consistency deviation of the insect sound spectrum at the sampling node in the current period according to the spectrum variation difference value and the distribution variation characteristic; Acquiring historical environmental parameter data and historical pest marking data at sampling nodes, and constructing influence association relations between different environmental parameters and pest outbreaks based on the historical environmental parameter data and the historical pest marking data; carrying out deviation exception analysis on space-time consistency deviation of insect sound spectra at sampling nodes in the current time period by combining each influence association relationship with environmental parameter data of the current time period acquired by the intelligent sensor to obtain exception confidence values of insects at the sampling nodes, and further obtaining exception confidence values of the insects at each sampling node; And clustering sampling nodes with abnormal confidence values exceeding an abnormal threshold value according to the combination of all the abnormal confidence values and a spatial clustering algorithm, so as to divide sampling priority areas of a plurality of diseases and insect pests in the target forest region. In some embodiments, determining the spectral variation difference value of the insect sound spectrum signal at the sampling node in the time dimension based on the spectral variation characteristics of the insect sound spectrum betwee