An insect adaptability evaluation method based on ecological environment monitoring data

Abstract

The present application relates to the technical field of data analysis, and more particularly to an insect adaptability evaluation method based on ecological environment monitoring data, which comprises the following steps: acquiring micro-habitat environment element time series, insect activity time series and most suitable habitat distribution center benchmark value; obtaining heterogeneous tortuosity decay factor through local trajectory tortuosity difference analysis; obtaining bounded damping factor through physiological tolerance damping analysis; obtaining global optimal regular cumulative cost by jointly reconstructing the heterogeneous tortuosity decay factor, the bounded damping factor and the basic Euclidean space cost; and obtaining insect environmental adaptability dynamic evaluation results by adaptively normalizing the global optimal regular cumulative cost, thereby solving the problem that the existing DTW algorithm only relies on absolute numerical difference for time series alignment, cannot adapt to insect smooth physiological response under micro-habitat high-frequency disturbance and insect tolerance static mechanism under extreme stress, and leads to distorted insect environmental adaptability evaluation results.

Description

Technical Field

[0001] This invention relates to the field of data analysis technology, and in particular to a method for assessing insect adaptability based on ecological environment monitoring data. Background Technology

[0002] The activity status of insect populations is closely related to changes in environmental factors such as temperature, light, and humidity in their microhabitats. In applications such as agricultural pest and disease monitoring, forest understory ecological surveys, biodiversity conservation, and early warning of target insect population dynamics, it is typically necessary to simultaneously collect data on microhabitat environmental factors and insect activity status using IoT microclimate sensors, image trapping devices, or acoustic trapping devices. The response relationship between insect activity rhythms and environmental changes is then analyzed based on continuous time series data. Existing methods often employ dynamic time warping algorithms to align microhabitat environmental factor time series and insect activity time series to quantify the dynamic matching degree between two heterogeneous time series. The optimal cumulative warping distance between the two time series is used to evaluate the synchronicity between changes in insect activity and microhabitat environment, thereby further determining the insect's adaptability to its current habitat.

[0003] However, existing insect fitness assessment methods based on dynamic time warping algorithms typically use the absolute numerical difference or Euclidean space cost between pairwise data points as the underlying point-to-point distance metric, and accumulate this distance through dynamic programming to obtain the global warped distance. While this approach can align time series of different lengths or with time lags, its underlying distance metric still primarily reflects numerical proximity and lacks adaptation to the physiological response mechanisms of insects as poikilothermic animals. In actual microhabitats, factors such as canopy shading, micro-airflow disturbances, or surface soil heat exchange can easily cause high-frequency sawtooth fluctuations in environmental factors such as surface illuminance and microenvironmental temperature within a short period. Insects, due to their thermal inertia, metabolic regulation, and delayed endocrine responses, often do not oscillate synchronously with these short-term high-frequency fluctuations. In such cases, existing dynamic time warping algorithms can easily force the normal high-frequency physical disturbances in the environment to align onto the smooth activity response curve of the insect, resulting in false local warping penalties and erroneously lowering the insect fitness assessment results.

[0004] Meanwhile, under extreme habitat conditions, when microhabitat environmental factors continuously deviate from the suitable range and approach or exceed the physiological tolerance range of insects, insects may adopt protective adaptations by reducing activity frequency, entering dormancy, or remaining still. This low-activity, low-fluctuation state does not necessarily indicate a failure of environmental adaptation in insects, but may be a tolerance strategy developed by insects in the face of extreme stress. However, existing dynamic time warping algorithms still continuously accumulate penalties based on the absolute difference between environmental factor values ​​and insect activity values. When environmental data continuously deviates while insect activity data is legally close to the low baseline, unreasonable distance amplification can easily occur, causing protective stillness to be misjudged as a severe mismatch. Therefore, how to ensure that the time-aligned distance simultaneously adapts to the smooth physiological response of insects under high-frequency microhabitat disturbances and the insect tolerance stillness mechanism under extreme stress in insect adaptation assessment, and reduce the accumulation of false penalties by traditional dynamic time warping algorithms, is a problem that urgently needs to be solved. Summary of the Invention

[0005] In view of this, the present invention aims to propose an insect adaptability assessment method based on ecological environment monitoring data, in order to solve the problem that the existing DTW algorithm only relies on absolute numerical difference for time alignment, which cannot adapt to the smooth physiological response of insects under high frequency disturbance of microenvironment and the tolerance quiescence mechanism of insects under extreme stress, resulting in the distortion of insect environmental adaptability assessment results.

[0006] To achieve the above objectives, the technical solution of the present invention is implemented as follows:

[0007] A method for assessing insect adaptability based on ecological environment monitoring data, the method comprising:

[0008] Step S1: Collect and preprocess microhabitat environmental element data and insect activity status data to obtain time series of microhabitat environmental elements, time series of insect activities, and baseline values ​​of the most suitable habitat distribution center;

[0009] Step S2: Obtain the heterogeneous tortuosity attenuation factor by performing local trajectory tortuosity difference analysis on the time series of microhabitat environmental elements and insect activity time series;

[0010] Step S3: Obtain the bounded damping factor by performing physiological tolerance damping analysis on the time series of microhabitat environmental elements, the time series of insect activity, and the baseline values ​​of the distribution center of the most suitable habitat;

[0011] Step S4: Obtain the globally optimal regular cumulative cost by jointly reconstructing the heterogeneous tortuosity attenuation factor, bounded damping factor and basic Euclidean space cost;

[0012] Step S5: Obtain the dynamic assessment results of insect environmental adaptability by adaptively normalizing the cumulative cost of global optimal regularization.

[0013] Furthermore, the process of collecting and preprocessing microhabitat environmental element data and insect activity status data to obtain time series data of microhabitat environmental elements, insect activity time series data, and baseline values ​​of the most suitable habitat distribution center includes:

[0014] For the monitoring habitat and target insect population to be evaluated, IoT microclimate sensors and image trapping devices or acoustic trapping devices are deployed in the monitoring habitat to be evaluated. The IoT microclimate sensors are set in the canopy or soil surface to collect micro-habitat environmental data, and the image trapping devices or acoustic trapping devices are used to collect insect activity data.

[0015] Set a continuous monitoring cycle and sampling time interval. Within the continuous monitoring cycle, collect microhabitat environmental element data and insect activity status data synchronously according to the sampling time interval. Among them, the microhabitat environmental element data includes at least surface light intensity data or microenvironment temperature data, and the insect activity status data includes at least the number of insects passing by or the number of insects emerging per unit time.

[0016] The microhabitat environmental element data are arranged in chronological order to obtain a time series of microhabitat environmental elements. Insect activity status data are then arranged according to the time nodes corresponding to the time series of microhabitat environmental elements to obtain an insect activity time series.

[0017] The time series of microhabitat environmental elements and insect activity were formatted and outlier removed, and missing timestamps were filled by linear interpolation to obtain time series of microhabitat environmental elements and insect activity.

[0018] Timestamp alignment processing is performed on time series of microhabitat environmental elements and time series of insect activities that are continuous in time.

[0019] The time series of microhabitat environmental elements and the time series of insect activities after timestamp alignment are subjected to maximum and minimum value normalization. The values ​​of each environmental element in the time series of microhabitat environmental elements and the values ​​of each insect activity in the time series of insect activities are linearly mapped to the dimensionless interval of zero to one, so as to obtain the normalized time series of microhabitat environmental elements and the normalized time series of insect activities.

[0020] Set the sliding time window size and perform Gaussian kernel density estimation based on the normalized microhabitat environmental element time series to obtain the global probability density distribution curve corresponding to the microhabitat environmental element time series. Use the environmental element value corresponding to the maximum peak point in the global probability density distribution curve as the benchmark value of the most suitable habitat distribution center.

[0021] Furthermore, the method of obtaining the heterogeneous tortuosity attenuation factor by performing local trajectory tortuosity difference analysis on the time series of microhabitat environmental elements and the time series of insect activities includes:

[0022] By characterizing the local trajectory tortuosity of the time series of microhabitat environmental elements and the time series of insect activities, local trajectory tortuosity data of the environment and local trajectory tortuosity data of insects are obtained.

[0023] Heterogeneous tortuosity attenuation factor is obtained by performing heterogeneous tortuosity overflow attenuation processing on local environmental trajectory tortuosity data and local insect trajectory tortuosity data.

[0024] Furthermore, the step of characterizing the local trajectory tortuosity of the time series of microhabitat environmental elements and the time series of insect activities to obtain local trajectory tortuosity data of the environment and the insects includes:

[0025] For any target environmental time node in the time series of microhabitat environmental elements and any target insect time node in the time series of insect activity, based on the set sliding time window size, a local sliding time window of the environment centered on the target environmental time node and a local sliding time window of the insect centered on the target insect time node are obtained respectively.

[0026] Extract multiple environmental element values ​​arranged in chronological order from the local sliding time window of the environment, and accumulate the absolute values ​​of the differences between adjacent environmental element values ​​within the local sliding time window to obtain the total displacement of the local true trajectory of the environment corresponding to the target environmental time node; take the absolute value of the difference between the end environmental element value and the beginning environmental element value within the local sliding time window to obtain the local macroscopic span of the environment corresponding to the target environmental time node.

[0027] Add the local macroscopic span of the environment corresponding to the target environmental time node to a preset small positive constant to obtain the normalized denominator of the environmental tortuosity; remove the total position of the local true trajectory of the environment corresponding to the target environmental time node and use the result of the calculation of the normalized denominator of the environmental tortuosity as the tortuosity data of the local trajectory of the target environmental time node.

[0028] Extract multiple insect activity values ​​arranged in chronological order from the local sliding time window of the insect, and accumulate the absolute values ​​of the differences between adjacent insect activity values ​​within the local sliding time window to obtain the total displacement of the insect's local true trajectory corresponding to the target insect time node; take the absolute value of the difference between the end insect activity value and the beginning insect activity value within the local sliding time window to obtain the insect's local macroscopic span corresponding to the target insect time node.

[0029] Add the local macroscopic span of the insect corresponding to the target insect time node to a preset small positive constant to obtain the normalized denominator of the insect tortuosity; remove the total position of the insect's local true trajectory corresponding to the target insect time node from the calculation result of the insect tortuosity normalized denominator, and use it as the tortuosity data of the insect's local trajectory corresponding to the target insect time node.

[0030] Furthermore, the process of obtaining a heterogeneous tortuosity attenuation factor by performing heterogeneous tortuosity overflow attenuation processing on environmental local trajectory tortuosity data and insect local trajectory tortuosity data includes:

[0031] For any target environmental time node in the time series of microhabitat environmental elements and any target insect time node in the time series of insect activity, obtain the local trajectory tortuosity data of the target environmental time node and the local trajectory tortuosity data of the insect corresponding to the target insect time node;

[0032] The difference between the local trajectory tortuosity data of the target environment at the time node and the local trajectory tortuosity data of the target insect at the time node is used as the initial tortuosity spillover difference assessment.

[0033] When the initial tortuosity spillover difference assessment is less than a constant zero, the tortuosity spillover difference assessment corresponding to the target environment time node and the target insect time node is set to a constant zero; when the initial tortuosity spillover difference assessment is greater than or equal to a constant zero, the initial tortuosity spillover difference assessment is used as the tortuosity spillover difference assessment corresponding to the target environment time node and the target insect time node.

[0034] The tortuosity spillover difference assessment is squared to obtain the tortuosity spillover enhancement assessment corresponding to the target environment time node and the target insect time node;

[0035] Add the insect local trajectory tortuosity data corresponding to the target insect time node to the preset smoothing adjustment constant to obtain the insect tortuosity adaptive basis corresponding to the target environment time node and the target insect time node;

[0036] Divide the tortuosity spillover enhancement assessment by the calculation result of the insect tortuosity adaptive basis as the heterogeneous tortuosity normalization penalty assessment corresponding to the target environment time node and the target insect time node;

[0037] The negative number of the heterogeneous tortuosity normalization penalty assessment is subjected to exponential mapping with the natural constant as the base, and the corresponding exponential mapping result is used as the heterogeneous tortuosity attenuation factor for the target environment time node and the target insect time node.

[0038] Furthermore, the method of obtaining bounded damping factors by performing physiological tolerance damping analysis on the time series of microhabitat environmental elements, the time series of insect activity, and the baseline values ​​of the most suitable habitat distribution center includes:

[0039] By performing deviation potential energy characterization on the time series of microhabitat environmental elements and the baseline values ​​of the most suitable habitat distribution center, environmental deviation potential energy data is obtained.

[0040] By jointly characterizing the current activity intensity and local fluctuation kinetic energy of insect activity time series, we can obtain insect dual stagnation characterization data.

[0041] By performing soft-saturation damping mapping on environmental deviation potential energy data and insect dual stagnation characterization data, a bounded damping factor was obtained.

[0042] Furthermore, the step of obtaining environmental deviation potential energy data by characterizing the deviation potential energy between the time series of microhabitat environmental elements and the baseline values ​​of the most suitable habitat distribution center includes:

[0043] For any target environmental time node in the time series of microhabitat environmental elements, extract the environmental element values ​​corresponding to the target environmental time node from the time series of microhabitat environmental elements, and obtain the baseline values ​​of the most suitable habitat distribution center.

[0044] The difference between the environmental element values ​​corresponding to the target environmental time point and the baseline values ​​of the most suitable habitat distribution center is calculated to obtain the environmental center deviation difference assessment corresponding to the target environmental time point.

[0045] The environmental center deviation difference assessment corresponding to the target environmental time node is squared to obtain the environmental deviation potential energy data corresponding to the target environmental time node.

[0046] Furthermore, the process of jointly characterizing the current activity intensity and local fluctuation kinetic energy of insect activity time series to obtain insect dual stagnation characterization data includes:

[0047] For any target insect time node in the insect activity time series, extract the insect activity value corresponding to the target insect time node from the insect activity time series, and obtain the insect backward sliding time window with the target insect time node as the endpoint based on the set sliding time window size;

[0048] The insect activity values ​​corresponding to the target insect time points are squared to obtain the current activity intensity assessment of the target insect at the corresponding time points.

[0049] Extract multiple insect activity values ​​arranged in chronological order from the backward sliding time window of the insects, and square the difference between adjacent insect activity values ​​within the backward sliding time window to obtain multiple squared evaluations of adjacent activity differences;

[0050] The summation of the squared differences between multiple adjacent activities is calculated, and the summation result is divided by the size of the sliding time window to obtain the local fluctuation kinetic energy assessment corresponding to the time node of the target insect.

[0051] The current activity intensity assessment corresponding to the target insect time node is added to the local fluctuation kinetic energy assessment corresponding to the target insect time node to obtain the insect dual stagnation characterization data corresponding to the target insect time node.

[0052] Furthermore, the process of obtaining a bounded damping factor by performing soft-saturation damping mapping on environmental deviation potential energy data and insect dual stagnation characterization data includes:

[0053] For any target environmental time node in the time series of microhabitat environmental elements and any target insect time node in the time series of insect activity, obtain the environmental deviation potential energy data corresponding to the target environmental time node and the insect dual stagnation characterization data corresponding to the target insect time node;

[0054] The environmental deviation potential energy data corresponding to the target environment time node is used as the numerator, and the calculation result of adding the environmental deviation potential energy data corresponding to the target environment time node, the insect double stagnation characterization data corresponding to the target insect time node, and the preset small positive constant is used as the denominator. The corresponding fraction is used as the deviation stagnation coupling ratio between the target environment time node and the target insect time node.

[0055] The negative numbers of the insect double stagnation characterization data corresponding to the target insect time node are subjected to exponential mapping with the natural constant as the base to obtain the stagnation index mapping results corresponding to the target environment time node and the target insect time node.

[0056] Multiply the deviation-stagnation coupling ratio corresponding to the target environment time node and the target insect time node by the stagnation index mapping result to obtain the damping deduction term corresponding to the target environment time node and the target insect time node;

[0057] Subtract the constant 1 from the damping deduction terms corresponding to the target environment time node and the target insect time node to obtain the bounded damping factor corresponding to the target environment time node and the target insect time node.

[0058] Furthermore, the method of obtaining the globally optimal regularized cumulative cost by jointly reconstructing the heterogeneous tortuosity attenuation factor, the bounded damping factor, and the basic Euclidean space cost includes:

[0059] For any target environmental time node in the microhabitat environmental element time series and any target insect time node in the insect activity time series, extract the environmental element values ​​corresponding to the target environmental time node from the microhabitat environmental element time series, and extract the insect activity values ​​corresponding to the target insect time node from the insect activity time series;

[0060] The difference between the environmental element values ​​corresponding to the target environmental time node and the insect activity values ​​corresponding to the target insect time node is squared to obtain the basic Euclidean space cost corresponding to the target environmental time node and the target insect time node.

[0061] Obtain the heterogeneous tortuosity attenuation factor and the bounded damping factor corresponding to the target environment time node and the target insect time node. Multiply the heterogeneous tortuosity attenuation factor and the bounded damping factor with the basic Euclidean space cost to obtain the normalized distance of the reconstructed basic point pair corresponding to the target environment time node and the target insect time node.

[0062] Based on the sequence length of the time series of microhabitat environmental elements and the sequence length of the time series of insect activity, a cumulative cost matrix is ​​constructed. Each element of the cumulative cost matrix is ​​used to store the cumulative regularization cost between the corresponding environmental time node and the corresponding insect time node.

[0063] Set the value of the initial matrix element in the cumulative cost matrix to the normalized distance of the reconstruction base point pair between the corresponding initial environmental time node and the initial insect time node; for any non-initial matrix element in the first column of the cumulative cost matrix, add the cumulative normalized cost of the matrix element in the row above the non-initial matrix element to the normalized distance of the reconstruction base point pair corresponding to the non-initial matrix element to obtain the cumulative normalized cost of the non-initial matrix element; for any non-initial matrix element in the first row of the cumulative cost matrix, add the cumulative normalized cost of the matrix element in the row above the non-initial matrix element to the normalized distance of the reconstruction base point pair corresponding to the non-initial matrix element to obtain the cumulative normalized cost of the non-initial matrix element.

[0064] For any target matrix element other than the first row and first column in the cumulative cost matrix, obtain the three cumulative regularization costs corresponding to the matrix element in the row above, the matrix element in the column before, and the matrix element in the upper left diagonal. Take the minimum of the three cumulative regularization costs as the predecessor minimum cumulative cost corresponding to the target matrix element. Add the regularization distance of the reconstruction base point pair corresponding to the target matrix element to the predecessor minimum cumulative cost to obtain the cumulative regularization cost corresponding to the target matrix element.

[0065] The cumulative normalization cost is calculated sequentially for each element in the cumulative cost matrix according to the row and column order of the matrix until the cumulative normalization cost of the endpoint matrix element in the cumulative cost matrix is ​​calculated. The cumulative normalization cost corresponding to the endpoint matrix element is then taken as the global optimal normalized cumulative cost.

[0066] Compared with the prior art, the present invention has the following advantages:

[0067] This invention discloses an insect adaptability assessment method based on ecological environment monitoring data. By transforming the traditional absolute numerical difference alignment of the matching relationship between the time series of microhabitat environmental elements and the time series of insect activity into a dynamic regularization evaluation that conforms to the physiological response patterns of insects, it effectively reduces the impact of high-frequency physical disturbances in the microhabitat, such as canopy shading, micro-airflow disturbances, and short-term temperature jumps in the soil surface, on the assessment results. Through difference analysis between local environmental trajectory tortuosity data and insect local trajectory tortuosity data, it can identify reasonable ecological phenomena where short-term, drastic sawtooth fluctuations in the environment are accompanied by a smooth response in the insect. Furthermore, it utilizes a heterogeneous tortuosity attenuation factor to suppress the false Euclidean penalty caused by such phenomena, thereby avoiding misjudging the low-pass physiological response exhibited by poikilothermic insects due to thermal inertia and metabolic lag as activity rhythm mismatch, and improving the accuracy of insect adaptability assessment in representing the true biological response process. Furthermore, this technical solution also identifies the protective tolerance mechanism of insects entering a low-activity, low-fluctuation state under extreme habitat deviation conditions through coupled analysis of environmental deviation potential energy data and insect dual stagnation characterization data. It utilizes a bounded damping factor to apply soft saturation constraints to the basic Euclidean space cost within the continuous deviation interval, preventing unreasonable cumulative cost amplification when environmental data is continuously stretched while insect activity legally approaches the baseline. The resulting globally optimal regularized cumulative cost is no longer a simple accumulation of mathematical distances, but rather an ecologically adaptive distance that takes into account microhabitat disturbance characteristics, insect thermal inertia response, and extreme stress dormancy mechanisms. This provides a more stable, objective, and cross-monitoring applicable quantitative basis for early warning of agricultural pest and disease occurrence risks, determination of target insect population activity rhythms, and microhabitat protection management. Attached Figure Description

[0068] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings:

[0069] Figure 1 This is a flowchart illustrating an insect adaptability assessment method based on ecological environment monitoring data, as described in an embodiment of the present invention. Detailed Implementation

[0070] The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0071] See Figure 1 This is a flowchart of an insect adaptability assessment method based on ecological environment monitoring data provided in Embodiment 1 of the present invention. Figure 1 As shown, an insect adaptability assessment method based on ecological environment monitoring data may include:

[0072] Step S1 involves collecting and preprocessing microhabitat environmental element data and insect activity status data to obtain time series of microhabitat environmental elements, time series of insect activities, and baseline values ​​of the most suitable habitat distribution center.

[0073] First, for the monitoring habitat and target insect population to be evaluated, IoT microclimate sensors and image trapping devices or acoustic trapping devices are deployed in the monitoring habitat to be evaluated. The IoT microclimate sensors are set in the canopy or soil surface to collect microhabitat environmental data, and the image trapping devices or acoustic trapping devices are used to collect insect activity data.

[0074] A continuous monitoring period and sampling time interval are set. In this embodiment of the invention, the continuous monitoring period is set to 72 hours and the sampling time interval is 10 minutes. During the continuous monitoring period, microhabitat environmental element data and insect activity status data are collected synchronously according to the sampling time interval. The microhabitat environmental element data includes at least surface light intensity data or microenvironment temperature data, and the insect activity status data includes at least the number of insects passing by or the number of insects emerging per unit time.

[0075] The microhabitat environmental element data are arranged chronologically to obtain a time series of microhabitat environmental elements. Insect activity data are then arranged according to the corresponding time nodes to obtain a corresponding insect activity time series. The time series of microhabitat environmental elements and insect activity are cleaned and outlier removal is performed, and missing timestamps are filled using linear interpolation to obtain continuous time series of microhabitat environmental elements and insect activity. Timestamp alignment is then performed on both the continuous time series of microhabitat environmental elements and insect activity. Max-min value normalization is then applied to both the timestamp-aligned time series of microhabitat environmental elements and insect activity, linearly mapping the values ​​of each environmental element in the time series of microhabitat environmental elements and the values ​​of each insect activity in the time series of insect activity to a dimensionless interval of zero to one, resulting in normalized time series of microhabitat environmental elements and insect activity.

[0076] The sliding time window size is set. In this embodiment of the invention, the sliding time window is set to a length of 6 time steps before and after. Gaussian kernel density estimation is performed based on the normalized microhabitat environmental element time series to obtain the global probability density distribution curve corresponding to the microhabitat environmental element time series. The environmental element value corresponding to the maximum peak point in the global probability density distribution curve is used as the benchmark value of the most suitable habitat distribution center.

[0077] Thus far, the acquisition of time series data of microhabitat environmental elements, time series data of insect activity, and baseline values ​​of the most suitable habitat distribution center has been completed through the collection and preprocessing of microhabitat environmental element data and insect activity status data.

[0078] Step S2: Obtain the heterogeneous tortuosity attenuation factor by performing local trajectory tortuosity difference analysis on the time series of microhabitat environmental elements and the time series of insect activities.

[0079] In ecological monitoring practices of natural habitats, due to airflow disturbances or alternating shading from forest gaps, environmental factors in local microhabitats (such as surface illuminance and microenvironmental temperature) can experience frequent, jagged fluctuations within a short period, forming unique secondary high-frequency fluctuations in physical noise specific to microhabitats. However, because poikilothermic insects possess inherent physical thermal inertia in their exoskeleton and internal body fluids, coupled with the biochemical lag in transmembrane conduction of their endocrine system in response to external stimuli, their physiological rhythm system naturally exhibits a low-pass filtering mechanism when facing such short-term high-frequency physical noise. Their overall macroscopic activity response curve remains smooth and does not exhibit high-frequency synchronous oscillations. Existing dynamic time warping algorithms rely purely on rigid local Euclidean space differences for path optimization when constructing the point-to-point cumulative cost matrix. This mechanical alignment mechanism fails to recognize the low-pass filtering characteristics at the biological level, forcibly mapping the high-frequency jagged peaks and valleys generated on the environmental curve to the smooth low-pass physiological curve of the insect. This adds a significant amount of false local matching penalty cost to this ecologically reasonable and harmless environmental disturbance. To address this problem of false distance accumulation caused by the mismatch between physical noise and physiological thermal inertia at the algorithmic level, simply performing full-cycle mean smoothing on the environmental data is insufficient; otherwise, it would obliterate genuine mutation warning signals in the environmental sequence. This invention abandons traditional numerical distribution considerations and instead focuses on the geometric heterogeneity of temporal evolution trajectories. By capturing the tortuosity differences in the sequence data morphology within local windows, a heterogeneous attenuation mechanism is designed. When the underlying metric algorithm identifies high-frequency nonlinear oscillations purely caused by the environment while the biological end maintains a steady-state mapping, it can autonomously release the rigid constraint of the Euclidean distance, achieving active attenuation of the false penalty cost without altering the original spatiotemporal structure of the data.

[0080] In summary, this invention first performs local trajectory tortuosity characterization processing on the time series of microhabitat environmental elements and the time series of insect activity to obtain local trajectory tortuosity data for the environment and insects. Specifically, for any target environmental time node in the time series of microhabitat environmental elements and any target insect time node in the time series of insect activity, based on a set sliding time window size, local sliding time windows centered on the target environmental time node and local sliding time windows centered on the target insect time node are obtained respectively. Multiple environmental element values ​​arranged in chronological order are extracted from the local sliding time window, and the absolute values ​​of the differences between adjacent environmental element values ​​within the local sliding time window are accumulated to obtain the total displacement of the local true trajectory corresponding to the target environmental time node. The absolute value of the difference between the end environmental element value and the beginning environmental element value within the local sliding time window is taken to obtain the local macroscopic span of the environment corresponding to the target environmental time node. The environmental local macroscopic span corresponding to the target environmental time node is added to a preset small positive constant to obtain the normalized denominator of the environmental tortuosity. The total displacement of the actual environmental local trajectory corresponding to the target environmental time node is removed from the calculation result calculated using the normalized denominator of the environmental tortuosity, and this result is used as the tortuosity data of the local trajectory corresponding to the target environmental time node. Multiple insect activity values ​​arranged in chronological order are extracted from the insect local sliding time window, and the absolute values ​​of the differences between adjacent insect activity values ​​within the insect local sliding time window are accumulated to obtain the total displacement of the insect local actual trajectory corresponding to the target insect time node. The absolute value of the difference between the end insect activity value and the beginning insect activity value within the insect local sliding time window is taken to obtain the insect local macroscopic span corresponding to the target insect time node.

[0081] Add the local macroscopic span of the insect corresponding to the target insect time node to a preset small positive constant to obtain the normalized denominator of the insect tortuosity; remove the total position of the insect's local true trajectory corresponding to the target insect time node from the calculation result of the insect tortuosity normalized denominator, and use it as the tortuosity data of the insect's local trajectory corresponding to the target insect time node.

[0082] After obtaining the insect local trajectory tortuosity data, heterogeneous tortuosity spillover attenuation processing is performed on the environmental local trajectory tortuosity data and the insect local trajectory tortuosity data to obtain a heterogeneous tortuosity attenuation factor. Specifically, for any target environmental time node in the microhabitat environmental element time series and any target insect time node in the insect activity time series, the environmental local trajectory tortuosity data corresponding to the target environmental time node and the insect local trajectory tortuosity data corresponding to the target insect time node are obtained. The difference between the environmental local trajectory tortuosity data corresponding to the target environmental time node and the insect local trajectory tortuosity data corresponding to the target insect time node is used as the initial tortuosity spillover difference assessment. When the initial tortuosity spillover difference assessment is less than a constant zero, the tortuosity spillover difference assessment corresponding to the target environmental time node and the target insect time node is set to a constant zero; when the initial tortuosity spillover difference assessment is greater than or equal to a constant zero, the initial tortuosity spillover difference assessment is used as the tortuosity spillover difference assessment corresponding to the target environmental time node and the target insect time node. The tortuosity spillover difference assessment is squared to obtain the tortuosity spillover enhancement assessment corresponding to the target environmental time node and the target insect time node. The insect local trajectory tortuosity data corresponding to the target insect time node is added to a preset smoothing adjustment constant to obtain the insect tortuosity adaptive basis corresponding to the target environment time node and the target insect time node. In this embodiment of the invention, the smoothing adjustment constant is set to 0.1. The tortuosity overflow enhancement evaluation divided by the calculation result of the insect tortuosity adaptive basis is used as the heterogeneous tortuosity normalization penalty evaluation corresponding to the target environment time node and the target insect time node. The negative number of the heterogeneous tortuosity normalization penalty evaluation is subjected to exponential mapping with the natural constant as the base, and the corresponding exponential mapping result is used as the heterogeneous tortuosity attenuation factor corresponding to the target environment time node and the target insect time node.

[0083] In one implementation, assume the sliding time window length is ;No. Time series data of microhabitat environmental elements at each environmental time point are as follows: ;No. The time series data of insect activity at each insect time point are as follows: The smoothing adjustment constant is ; a small positive constant is Then the first The environmental sequence point and the first The expression for calculating the heterotonicity attenuation factor corresponding to each insect sequence point is:

[0084]

[0085] in, Indicates the first The environmental sequence point and the first The heterogeneity tortuosity attenuation factor corresponding to each insect sequence point; This represents an exponential function with the natural constant e as the base. Represents the maximum value function; Indicates the length of the sliding time window; Indicates the first Time series data of microhabitat environmental elements at each environmental time point; Indicates the first Time series data of insect activity at individual insect time points; Indicates the first The total displacement of the local true trajectory of the environment corresponding to each environmental time node; Indicates the first The local macroscopic span of the environment corresponding to each environmental time point; Indicates the first The total displacement of the local true trajectory of an insect at each time point; Indicates the first The local macroscopic span of insects corresponding to each insect time node; This represents a small positive constant, which is set in the embodiments of the present invention. ; This represents the smoothing adjustment constant.

[0086] It should be noted that existing statistical algorithms often use local variance to measure volatility, but variance cannot distinguish whether the data has generated a valid unidirectional rapid increase or fallen into meaningless high-frequency sawtooth jumps. Therefore, this formula uses the total displacement of the true trajectory of the sequence within a local time window (trajectory arc length) divided by the macroscopic span of the beginning and end points (trajectory chord length) to describe the nonlinear tortuosity rate in the core ratio term. The logic is as follows: if the environment only experiences high-frequency secondary jumps, the sum of its true displacement due to back-and-forth oscillations will be much greater than the span of its macroscopic beginning and end points, causing the ratio to surge; conversely, if the environmental sequence experiences a genuine and valid unidirectional rapid increase, the trajectory arc length will be approximately equal to the chord length, causing the ratio to converge to 1. This structure accurately and independently extracts the secondary high-frequency volatility characteristics of the environment that need to be denoised through nonlinear tortuosity rate extraction.

[0087] Based on this, since the thermal inertia of insects filters out the aforementioned environmental physical noise, the matching cost between the two should not increase. Therefore, the difference operation in the numerator of the formula extracts the overflow net difference between the environmental tortuosity rate and the insect's own tortuosity rate, and a maximum value function mechanism is added to the outer layer. This design ensures that only when the environment experiences high-frequency sawtooth fluctuations while the insect maintains a smooth low-pass response due to physiological homeostasis, i.e., the environmental tortuosity rate is significantly greater than the insect's tortuosity rate, will a positive difference be released for subsequent attenuation intervention. If the environment itself evolves smoothly in one direction, or if the organism's own pathological abnormalities trigger high-frequency agitation behavior independent of the environment, the maximum value function will immediately truncate it to zero, maintaining the normal Euclidean penalty evaluation of the algorithm. Furthermore, considering the huge differences in the frequency of basic activities among different insect species in nature, using a rigid global threshold as a judgment benchmark would lead to model failure. The denominator of the formula extracts the current local insect tortuosity rate plus a smoothing constant as an adaptive reference benchmark. This allows the sensitivity of this factor to dynamically and adaptively scale with the real-time activity benchmark of the individual organism. Finally, the squared relative high-frequency differences extracted from molecules are normalized by this adaptive basis and then input into the outer natural negative exponential function structure. In scenarios involving high-frequency disturbances in the microenvironment and normal, stable biological responses, this linkage mechanism will nonlinearly output a continuously decaying product factor that rapidly approaches zero, directly acting on the spatial distance between two points in the underlying layer. This eliminates the spurious penalty cost accumulated by the underlying algorithm due to the forced alignment of secondary noise without disrupting the time scale of the original sequence.

[0088] Thus, the heterogeneous tortuosity attenuation factor was obtained by analyzing the difference in local trajectory tortuosity between the time series of microhabitat environmental elements and the time series of insect activities.

[0089] Step S3: Obtain the bounded damping factor by performing physiological tolerance damping analysis on the time series of microhabitat environmental elements, the time series of insect activity, and the baseline values ​​of the most suitable habitat distribution center.

[0090] After removing the spurious alignment cost caused by secondary high-frequency fluctuations in the microenvironment through the heterogeneous tortuosity attenuation factor in step S2, the algorithm faces another error penalty scenario. In the evolution of natural habitats, when changes in environmental factors are not secondary fluctuations in place, but rather smooth, unidirectional, and continuous extreme deterioration (taking a continuous drop in temperature below the critical thermal lower limit as an example), the actual trajectory displacement of environmental data within the local window will be approximately equal to the macroscopic span, causing the net difference in tortuosity constructed in step S2 to become zero, and the tortuosity attenuation mechanism will no longer be triggered according to law. At this time, according to the evolutionary law of the thermal performance curve of poikilothermic animals, when faced with environmental stress exceeding the physiological tolerance limit, insects will activate behavioral avoidance and hibernation blocking mechanisms. Their overall activity will rapidly decrease and enter the tolerance saturation zone with an extremely low threshold, which is represented on the time series curve as an absolutely static state close to the base zero point and completely ceasing fluctuations. Existing Euclidean distance metrics, when dealing with data in this extreme habitat deviation range, lack a damping convergence mechanism to account for the dual stagnation phenomenon of biological extreme tolerance. The environmental curve continuously stretches towards extreme values, while the biological curve legally stagnates at the baseline. The absolute difference between the two expands uncontrollably, leading to a quadratic exponential increase in the cumulative regularization cost. From a biological adaptation perspective, maintaining absolute stillness in extremely lethal environments is the highest level of adaptation and survival strategy for insects. The algorithm's accumulation of a huge penalty distance in this range will cause the final evaluation result to contradict true ecological laws. To address this macroscopic assessment deficiency exposed under smooth unidirectional evolution, a bounded damping tensor factor targeting the physiological tolerance saturation state must be constructed in the underlying metric model. By quantifying the coupling activation relationship between the environmental deviation potential energy and the insect's dynamic stagnation level, the mathematical penalty divergence under extreme habitats can be actively blocked.

[0091] In summary, this invention first obtains environmental deviation potential energy data by characterizing the deviation potential energy between the time series of microhabitat environmental elements and the baseline values ​​of the most suitable habitat distribution center. Specifically, for any target environmental time node in the time series of microhabitat environmental elements, the environmental element values ​​corresponding to the target environmental time node are extracted from the time series of microhabitat environmental elements, and the baseline values ​​of the most suitable habitat distribution center are obtained. The difference between the environmental element values ​​corresponding to the target environmental time node and the baseline values ​​of the most suitable habitat distribution center is calculated to obtain the environmental center deviation difference assessment corresponding to the target environmental time node. The environmental center deviation difference assessment corresponding to the target environmental time node is squared to obtain the environmental deviation potential energy data corresponding to the target environmental time node.

[0092] After obtaining the environmental deviation potential energy data, the insect activity time series is further processed by jointly characterizing the current activity intensity and local fluctuation kinetic energy to obtain insect dual stagnation characterization data. Specifically, for any target insect time node in the insect activity time series, the insect activity value corresponding to the target insect time node is extracted from the insect activity time series, and a backward sliding time window is obtained with the target insect time node as the endpoint, based on a set sliding time window size. The insect activity value corresponding to the target insect time node is squared to obtain the current activity intensity assessment corresponding to the target insect time node. Multiple insect activity values ​​arranged in chronological order are extracted from the insect backward sliding time window, and the differences between adjacent insect activity values ​​within the backward sliding time window are squared to obtain multiple adjacent activity difference square assessments. The multiple adjacent activity difference square assessments are accumulated, and the accumulated result is divided by the sliding time window size to obtain the local fluctuation kinetic energy assessment corresponding to the target insect time node. The current activity intensity assessment corresponding to the target insect time node is added to the local fluctuation kinetic energy assessment corresponding to the target insect time node to obtain the insect dual stagnation characterization data corresponding to the target insect time node.

[0093] After obtaining the insect dual stagnation characterization data, a bounded damping factor was obtained by performing soft-saturation damping mapping on the environmental deviation potential energy data and the insect dual stagnation characterization data. Specifically, for any target environmental time node in the microhabitat environmental element time series and any target insect time node in the insect activity time series, the environmental deviation potential energy data and the insect dual stagnation characterization data corresponding to the target environmental time node were obtained. The environmental deviation potential energy data corresponding to the target environmental time node was used as the numerator, and the result of adding the environmental deviation potential energy data, the insect dual stagnation characterization data, and a preset small positive constant was used as the denominator. The resulting fraction was used as the deviation-stagnation coupling ratio between the target environmental time node and the target insect time node. The negative of the insect dual stagnation characterization data corresponding to the target insect time node was subjected to exponential mapping with a natural constant as the base to obtain the stagnation exponential mapping result between the target environmental time node and the target insect time node. The deviation-stagnation coupling ratio between the target environmental time node and the target insect time node was multiplied by the stagnation exponential mapping result to obtain the damping deduction term between the target environmental time node and the target insect time node. Subtract the constant 1 from the damping deduction terms corresponding to the target environment time node and the target insect time node to obtain the bounded damping factor corresponding to the target environment time node and the target insect time node.

[0094] In one implementation, it is assumed that the baseline value for the optimal habitat distribution center is... Then the first The environmental time node and the first The formula for calculating the bounded damping factor corresponding to each insect time point is:

[0095]

[0096] in, Indicates the first The environmental time node and the first Bounded damping factor corresponding to each insect time point; Indicates the first Time series data of microhabitat environmental elements at each environmental time point; Indicates the first Time series data of insect activity at individual insect time points; This represents the baseline value for the center of distribution of the most suitable habitat; Indicates the first Environmental deviation potential energy data corresponding to each environmental time point; Indicates the first Assessment of local fluctuation kinetic energy corresponding to each insect time point; This represents an exponential function with the natural constant e as the base.

[0097] It should be noted that, in order to suppress distance divergence under extreme environments without thresholding normal habitat data, this application achieves dynamic damping by constructing a soft saturation constraint tensor of the data itself. The minuend structure in the formula is designed as a feature multiplicative coupling term, the core of which is to simultaneously verify the degree of environmental extremism and the degree of insect stagnation. First, the squared difference between the current environmental value and the baseline value of the optimal habitat center is extracted as the deviation potential energy characterizing the intensity of extreme stress. Simultaneously, abandoning the evaluation method that solely relies on instantaneous values, the square of the insect's current absolute activity value is added to the first-order fluctuation kinetic energy within the local window, constructing a composite quantitative index reflecting the insect's dual stagnation state. The formula uses the environmental deviation potential energy as the numerator, the sum of the environmental deviation potential energy and the insect's dual stagnation index as the denominator, and multiplies it by a natural negative exponential function with the added insect dual stagnation index as the independent variable. The logic is as follows: Only when a sustained deviation from environmental conditions, such as extreme cold or heat, causes a sharp increase in the deviation potential energy, and simultaneously, the insect strictly adheres to its thermal performance curve, triggering complete dormancy—that is, when the absolute value approaches zero and no further local fluctuations occur—leading the double stagnation index to approach zero, will the fractional part of the formula infinitely approach 1, and the natural negative exponent term at the tail will also approach 1 as the double stagnation index reaches zero. In this state, the entire minuend approaches 1, resulting in the final damping tensor factor. The convergence occurs and approaches zero. This allows the algorithm to actively eliminate spurious distance amplification caused by extreme environmental degradation and the pull of the zero-base when it identifies a legitimate biological protective dormancy state. Conversely, if the habitat only undergoes normal evolution within a suitable range (with small deviations in potential energy), or if insects fail to effectively establish dormancy defenses and instead struggle and become agitated when the habitat deteriorates (leading to a surge in the double stagnation index, reducing the fractional term and decaying the negative exponent term at the tail to zero), the entire minuend structure will rapidly degenerate towards zero, making... The factor remained close to The normal value of . Finally, subsequent steps can use the reconstructed point-pair regularization formula to . and Coupled to the native Euclidean distance in a multiplicative manner, the entire dynamic alignment process can maintain a keen adaptive synchronous difference assessment in the normal range, and can automatically trigger the underlying feature denoising and damping protection in two types of boundary scenarios that are prone to misjudgment: extremely high frequency physical noise and extreme unidirectional stress, thus restoring the true ecological evaluation attributes of habitat temporal alignment.

[0098] Thus far, the bounded damping factor has been obtained by conducting physiological tolerance damping analysis on the time series of microhabitat environmental elements, the time series of insect activity, and the baseline values ​​of the most suitable habitat distribution center.

[0099] Step S4: Obtain the globally optimal regular cumulative cost by jointly reconstructing the heterogeneous tortuosity attenuation factor, the bounded damping factor, and the basic Euclidean space cost.

[0100] After obtaining the heterogeneous tortuosity attenuation factor and the bounded damping factor, this invention further optimizes the basic Euclidean space cost using these factors. This yields the normalized distance of the basic point pairs used to fill the dynamic programming cost matrix. Specifically, for any target environmental time node in the microhabitat environmental element time series and any target insect time node in the insect activity time series, the environmental element values ​​corresponding to the target environmental time node are extracted from the microhabitat environmental element time series, and the insect activity values ​​corresponding to the target insect time node are extracted from the insect activity time series. The difference between the environmental element values ​​corresponding to the target environmental time node and the insect activity values ​​corresponding to the target insect time node is squared to obtain the basic Euclidean space cost corresponding to the target environmental time node and the target insect time node. The heterogeneous tortuosity attenuation factor and the bounded damping factor corresponding to the target environmental time node and the target insect time node are obtained. The heterogeneous tortuosity attenuation factor and the bounded damping factor are multiplied by the basic Euclidean space cost to obtain the normalized distance of the reconstructed basic point pairs corresponding to the target environmental time node and the target insect time node.

[0101] After obtaining the normalized distance between the reconstructed base point pairs corresponding to the target environment time node and the target insect time node, a cumulative cost matrix is ​​constructed based on the sequence length of the microhabitat environmental element time series and the sequence length of the insect activity time series. Each matrix element in the cumulative cost matrix is ​​used to store the cumulative normalized cost between the corresponding environmental time node and the corresponding insect time node.

[0102] The values ​​of the initial matrix elements in the cumulative cost matrix are set to the normalized distance of the reconstruction base point pair between the corresponding initial environmental time node and the initial insect time node. For any non-initial matrix element in the first column of the cumulative cost matrix, the cumulative normalized cost of the matrix element in the row preceding the non-initial matrix element is added to the normalized distance of the reconstruction base point pair corresponding to the non-initial matrix element to obtain the cumulative normalized cost of the non-initial matrix element. For any non-initial matrix element in the first row of the cumulative cost matrix, the cumulative normalized cost of the matrix element in the row preceding the non-initial matrix element is added to the normalized distance of the reconstruction base point pair corresponding to the non-initial matrix element to obtain the cumulative normalized cost of the non-initial matrix element.

[0103] For any target matrix element in the cumulative cost matrix other than the first row and first column, obtain the three cumulative normalization costs corresponding to the previous row, the previous column, and the top-left diagonal matrix element of the target matrix element. Take the minimum of these three cumulative normalization costs as the predecessor minimum cumulative cost corresponding to the target matrix element. Add the normalization distance of the reconstruction base point pair corresponding to the target matrix element to the predecessor minimum cumulative cost to obtain the cumulative normalization cost corresponding to the target matrix element.

[0104] The cumulative normalization cost is calculated sequentially for each element in the cumulative cost matrix according to the row and column order of the matrix until the cumulative normalization cost of the endpoint matrix element in the cumulative cost matrix is ​​calculated. The cumulative normalization cost corresponding to the endpoint matrix element is then taken as the global optimal normalized cumulative cost.

[0105] Thus, the global optimal regular cumulative cost was obtained by jointly reconstructing the heterogeneous tortuosity attenuation factor, the bounded damping factor, and the basic Euclidean space cost.

[0106] Step S5: Obtain the dynamic assessment results of insect environmental adaptability by adaptively normalizing the cumulative cost of global optimal regularization.

[0107] After calculating the global optimal regularization cumulative cost, the global optimal regularization cumulative cost corresponding to the endpoint element of the cumulative cost matrix is ​​extracted. Since the global optimal regularization cumulative cost is obtained after being jointly corrected by the heterogeneous tortuosity attenuation factor and the bounded damping factor, it no longer only represents the accumulation of absolute numerical errors between the time series of microhabitat environmental elements and the time series of insect activity, but can reflect the true dynamic cooperative matching degree between insect activity rhythms and microhabitat environmental evolution after the high-frequency disturbances of the microhabitat are attenuated and the insect's tolerance to quiescence under extreme stress is damped and protected.

[0108] To avoid inconsistencies in scoring scales between different monitoring habitats due to the use of a fixed scaling constant, this step further constructs an adaptive normalized scoring benchmark based on the global dispersion of the time series of microhabitat environmental elements and insect activity. Specifically, the mean of all environmental element values ​​in the microhabitat environmental element time series is calculated, and the squared differences between each environmental element value and this mean are summed to obtain the global discrete fluctuation energy of the environment. Simultaneously, the mean of all insect activity values ​​in the insect activity time series is calculated, and the squared differences between each insect activity value and this mean are summed to obtain the global discrete fluctuation energy of the insects. The sum of the global discrete fluctuation energy of the environment and the global discrete fluctuation energy of the insects is used as the system background evolution kinetic energy of the current monitoring habitat and the target insect population within the continuous monitoring period.

[0109] Subsequently, the ratio of the global optimal regularization cumulative cost to the system's baseline evolutionary kinetic energy is calculated to obtain the normalized regularization residual assessment. The negative of this assessment is then subjected to exponential mapping with a natural constant as the base, yielding the dynamic assessment result of insect environmental adaptability. When the global optimal regularization cumulative cost is relatively small compared to the system's baseline evolutionary kinetic energy, it indicates a high degree of synergy between insect activity rhythms and microhabitat environmental evolution, and the dynamic assessment result approaches a constant of 1. Conversely, when the global optimal regularization cumulative cost is relatively large compared to the system's baseline evolutionary kinetic energy, it indicates a significant mismatch between insect activity rhythms and microhabitat environmental evolution, and the dynamic assessment result approaches a constant of 0. Therefore, the dynamic assessment result of insect environmental adaptability can provide a quantitative basis for determining the timing of agricultural pest and disease early warnings, analyzing the activity status of target insect populations, and formulating biodiversity conservation strategies under specific microhabitats.

[0110] Thus, the dynamic assessment results of insect environmental adaptability were obtained by adaptively normalizing the scoring of the global optimal regularization cumulative cost.

[0111] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for assessing insect adaptability based on ecological environment monitoring data, characterized in that, The method includes: Step S1: Collect and preprocess microhabitat environmental element data and insect activity status data to obtain time series of microhabitat environmental elements, time series of insect activities, and baseline values ​​of the most suitable habitat distribution center; Step S2: Obtain the heterogeneous tortuosity attenuation factor by performing local trajectory tortuosity difference analysis on the time series of microhabitat environmental elements and insect activity time series; Step S3: Obtain the bounded damping factor by performing physiological tolerance damping analysis on the time series of microhabitat environmental elements, the time series of insect activity, and the baseline values ​​of the distribution center of the most suitable habitat; Step S4: Obtain the globally optimal regular cumulative cost by jointly reconstructing the heterogeneous tortuosity attenuation factor, bounded damping factor and basic Euclidean space cost; Step S5: Obtain the dynamic assessment results of insect environmental adaptability by adaptively normalizing the cumulative cost of global optimal regularization.

2. The method for assessing insect adaptability based on ecological environment monitoring data according to claim 1, characterized in that, The process of collecting and preprocessing microhabitat environmental element data and insect activity status data to obtain time series of microhabitat environmental elements, insect activity time series, and baseline values ​​of the most suitable habitat distribution center includes: For the monitoring habitat and target insect population to be evaluated, IoT microclimate sensors and image trapping devices or acoustic trapping devices are deployed in the monitoring habitat to be evaluated. The IoT microclimate sensors are set in the canopy or soil surface to collect micro-habitat environmental data, and the image trapping devices or acoustic trapping devices are used to collect insect activity data. Set a continuous monitoring cycle and sampling time interval. Within the continuous monitoring cycle, collect microhabitat environmental element data and insect activity status data synchronously according to the sampling time interval. Among them, the microhabitat environmental element data includes at least surface light intensity data or microenvironment temperature data, and the insect activity status data includes at least the number of insects passing by or the number of insects emerging per unit time. The microhabitat environmental element data are arranged in chronological order to obtain a time series of microhabitat environmental elements. Insect activity status data are then arranged according to the time nodes corresponding to the time series of microhabitat environmental elements to obtain an insect activity time series. The time series of microhabitat environmental elements and insect activity were formatted and outlier removed, and missing timestamps were filled by linear interpolation to obtain time series of microhabitat environmental elements and insect activity. Timestamp alignment processing is performed on time series of microhabitat environmental elements and time series of insect activities that are continuous in time. The time series of microhabitat environmental elements and the time series of insect activities after timestamp alignment are subjected to maximum and minimum value normalization. The values ​​of each environmental element in the time series of microhabitat environmental elements and the values ​​of each insect activity in the time series of insect activities are linearly mapped to the dimensionless interval of zero to one, so as to obtain the normalized time series of microhabitat environmental elements and the normalized time series of insect activities. Set the sliding time window size and perform Gaussian kernel density estimation based on the normalized microhabitat environmental element time series to obtain the global probability density distribution curve corresponding to the microhabitat environmental element time series. Use the environmental element value corresponding to the maximum peak point in the global probability density distribution curve as the benchmark value of the most suitable habitat distribution center.

3. The method for assessing insect adaptability based on ecological environment monitoring data according to claim 1, characterized in that, The method of obtaining heterogeneous tortuosity attenuation factor by performing local trajectory tortuosity difference analysis on the time series of microhabitat environmental elements and insect activity time series includes: By characterizing the local trajectory tortuosity of the time series of microhabitat environmental elements and the time series of insect activities, local trajectory tortuosity data of the environment and local trajectory tortuosity data of insects are obtained. Heterogeneous tortuosity attenuation factor is obtained by performing heterogeneous tortuosity overflow attenuation processing on local environmental trajectory tortuosity data and local insect trajectory tortuosity data.

4. The insect adaptability assessment method based on ecological environment monitoring data according to claim 3, characterized in that, The process involves characterizing the local trajectory tortuosity of time series of microhabitat environmental elements and insect activity time series to obtain local trajectory tortuosity data of the environment and insects, including: For any target environmental time node in the time series of microhabitat environmental elements and any target insect time node in the time series of insect activity, based on the set sliding time window size, a local sliding time window of the environment centered on the target environmental time node and a local sliding time window of the insect centered on the target insect time node are obtained respectively. Extract multiple environmental element values ​​arranged in chronological order from the local sliding time window of the environment, and accumulate the absolute values ​​of the differences between adjacent environmental element values ​​within the local sliding time window to obtain the total displacement of the local true trajectory of the environment corresponding to the target environmental time node; take the absolute value of the difference between the end environmental element value and the beginning environmental element value within the local sliding time window to obtain the local macroscopic span of the environment corresponding to the target environmental time node. Add the local macroscopic span of the environment corresponding to the target environmental time node to a preset small positive constant to obtain the normalized denominator of the environmental tortuosity; remove the total position of the local true trajectory of the environment corresponding to the target environmental time node and use the result of the calculation of the normalized denominator of the environmental tortuosity as the tortuosity data of the local trajectory of the target environmental time node. Extract multiple insect activity values ​​arranged in chronological order from the local sliding time window of the insect, and accumulate the absolute values ​​of the differences between adjacent insect activity values ​​within the local sliding time window to obtain the total displacement of the insect's local true trajectory corresponding to the target insect time node; take the absolute value of the difference between the end insect activity value and the beginning insect activity value within the local sliding time window to obtain the insect's local macroscopic span corresponding to the target insect time node. Add the local macroscopic span of the insect corresponding to the target insect time node to a preset small positive constant to obtain the normalized denominator of the insect tortuosity; remove the total position of the insect's local true trajectory corresponding to the target insect time node from the calculation result of the insect tortuosity normalized denominator, and use it as the tortuosity data of the insect's local trajectory corresponding to the target insect time node.

5. The method for assessing insect adaptability based on ecological environment monitoring data according to claim 3, characterized in that, The process involves performing heterogeneous tortuosity overflow attenuation processing on local environmental trajectory tortuosity data and local insect trajectory tortuosity data to obtain a heterogeneous tortuosity attenuation factor, including: For any target environmental time node in the time series of microhabitat environmental elements and any target insect time node in the time series of insect activity, obtain the local trajectory tortuosity data of the target environmental time node and the local trajectory tortuosity data of the insect corresponding to the target insect time node; The difference between the local trajectory tortuosity data of the target environment at the time node and the local trajectory tortuosity data of the target insect at the time node is used as the initial tortuosity spillover difference assessment. When the initial tortuosity spillover difference assessment is less than a constant zero, the tortuosity spillover difference assessment corresponding to the target environment time node and the target insect time node is set to a constant zero; when the initial tortuosity spillover difference assessment is greater than or equal to a constant zero, the initial tortuosity spillover difference assessment is used as the tortuosity spillover difference assessment corresponding to the target environment time node and the target insect time node. The tortuosity spillover difference assessment is squared to obtain the tortuosity spillover enhancement assessment corresponding to the target environment time node and the target insect time node; Add the insect local trajectory tortuosity data corresponding to the target insect time node to the preset smoothing adjustment constant to obtain the insect tortuosity adaptive basis corresponding to the target environment time node and the target insect time node; Divide the tortuosity spillover enhancement assessment by the calculation result of the insect tortuosity adaptive basis as the heterogeneous tortuosity normalization penalty assessment corresponding to the target environment time node and the target insect time node; The negative number of the heterogeneous tortuosity normalization penalty assessment is subjected to exponential mapping with the natural constant as the base, and the corresponding exponential mapping result is used as the heterogeneous tortuosity attenuation factor for the target environment time node and the target insect time node.

6. The method for assessing insect adaptability based on ecological environment monitoring data according to claim 1, characterized in that, The method involves obtaining bounded damping factors through physiological tolerance damping analysis of time series of microhabitat environmental elements, insect activity time series, and baseline values ​​of the most suitable habitat distribution center, including: By performing deviation potential energy characterization on the time series of microhabitat environmental elements and the baseline values ​​of the most suitable habitat distribution center, environmental deviation potential energy data is obtained. By jointly characterizing the current activity intensity and local fluctuation kinetic energy of insect activity time series, we can obtain insect dual stagnation characterization data. By performing soft-saturation damping mapping on environmental deviation potential energy data and insect dual stagnation characterization data, a bounded damping factor was obtained.

7. The method for assessing insect adaptability based on ecological environment monitoring data according to claim 6, characterized in that, The process involves characterizing the deviation potential energy between the time series of microhabitat environmental elements and the baseline values ​​of the most suitable habitat distribution center to obtain environmental deviation potential energy data, including: For any target environmental time node in the time series of microhabitat environmental elements, extract the environmental element values ​​corresponding to the target environmental time node from the time series of microhabitat environmental elements, and obtain the baseline values ​​of the most suitable habitat distribution center. The difference between the environmental element values ​​corresponding to the target environmental time point and the baseline values ​​of the most suitable habitat distribution center is calculated to obtain the environmental center deviation difference assessment corresponding to the target environmental time point. The environmental center deviation difference assessment corresponding to the target environmental time node is squared to obtain the environmental deviation potential energy data corresponding to the target environmental time node.

8. The method for assessing insect adaptability based on ecological environment monitoring data according to claim 6, characterized in that, The process involves jointly characterizing the current activity intensity and local fluctuation kinetic energy of insect activity time series to obtain insect dual stagnation characterization data, including: For any target insect time node in the insect activity time series, extract the insect activity value corresponding to the target insect time node from the insect activity time series, and obtain the insect backward sliding time window with the target insect time node as the endpoint based on the set sliding time window size; The insect activity values ​​corresponding to the target insect time points are squared to obtain the current activity intensity assessment of the target insect at the corresponding time points. Extract multiple insect activity values ​​arranged in chronological order from the backward sliding time window of the insects, and square the difference between adjacent insect activity values ​​within the backward sliding time window to obtain multiple squared evaluations of adjacent activity differences; The summation of the squared differences between multiple adjacent activities is calculated, and the summation result is divided by the size of the sliding time window to obtain the local fluctuation kinetic energy assessment corresponding to the time node of the target insect. The current activity intensity assessment corresponding to the target insect time node is added to the local fluctuation kinetic energy assessment corresponding to the target insect time node to obtain the insect dual stagnation characterization data corresponding to the target insect time node.

9. The method for assessing insect adaptability based on ecological environment monitoring data according to claim 6, characterized in that, The process of obtaining a bounded damping factor by performing soft-saturation damping mapping on environmental deviation potential energy data and insect dual stagnation characterization data includes: For any target environmental time node in the time series of microhabitat environmental elements and any target insect time node in the time series of insect activity, obtain the environmental deviation potential energy data corresponding to the target environmental time node and the insect dual stagnation characterization data corresponding to the target insect time node; The environmental deviation potential energy data corresponding to the target environment time node is used as the numerator, and the calculation result of adding the environmental deviation potential energy data corresponding to the target environment time node, the insect double stagnation characterization data corresponding to the target insect time node, and the preset small positive constant is used as the denominator. The corresponding fraction is used as the deviation stagnation coupling ratio between the target environment time node and the target insect time node. The negative numbers of the insect double stagnation characterization data corresponding to the target insect time node are subjected to exponential mapping with the natural constant as the base to obtain the stagnation index mapping results corresponding to the target environment time node and the target insect time node. Multiply the deviation-stagnation coupling ratio corresponding to the target environment time node and the target insect time node by the stagnation index mapping result to obtain the damping deduction term corresponding to the target environment time node and the target insect time node; Subtract the constant 1 from the damping deduction terms corresponding to the target environment time node and the target insect time node to obtain the bounded damping factor corresponding to the target environment time node and the target insect time node.

10. The method for assessing insect adaptability based on ecological environment monitoring data according to claim 1, characterized in that, The method of obtaining the globally optimal regularized cumulative cost by jointly reconstructing the heterogeneous tortuosity attenuation factor, bounded damping factor, and basic Euclidean space cost includes: For any target environmental time node in the microhabitat environmental element time series and any target insect time node in the insect activity time series, extract the environmental element values ​​corresponding to the target environmental time node from the microhabitat environmental element time series, and extract the insect activity values ​​corresponding to the target insect time node from the insect activity time series; The difference between the environmental element values ​​corresponding to the target environmental time node and the insect activity values ​​corresponding to the target insect time node is squared to obtain the basic Euclidean space cost corresponding to the target environmental time node and the target insect time node. Obtain the heterogeneous tortuosity attenuation factor and the bounded damping factor corresponding to the target environment time node and the target insect time node. Multiply the heterogeneous tortuosity attenuation factor and the bounded damping factor with the basic Euclidean space cost to obtain the normalized distance of the reconstructed basic point pair corresponding to the target environment time node and the target insect time node. Based on the sequence length of the time series of microhabitat environmental elements and the sequence length of the time series of insect activity, a cumulative cost matrix is ​​constructed. Each element of the cumulative cost matrix is ​​used to store the cumulative regularization cost between the corresponding environmental time node and the corresponding insect time node. Set the value of the initial matrix element in the cumulative cost matrix to the normalized distance of the reconstruction base point pair between the corresponding initial environmental time node and the initial insect time node; for any non-initial matrix element in the first column of the cumulative cost matrix, add the cumulative normalized cost of the matrix element in the row above the non-initial matrix element to the normalized distance of the reconstruction base point pair corresponding to the non-initial matrix element to obtain the cumulative normalized cost of the non-initial matrix element; for any non-initial matrix element in the first row of the cumulative cost matrix, add the cumulative normalized cost of the matrix element in the row above the non-initial matrix element to the normalized distance of the reconstruction base point pair corresponding to the non-initial matrix element to obtain the cumulative normalized cost of the non-initial matrix element. For any target matrix element other than the first row and first column in the cumulative cost matrix, obtain the three cumulative regularization costs corresponding to the matrix element in the row above, the matrix element in the column before, and the matrix element in the upper left diagonal. Take the minimum of the three cumulative regularization costs as the predecessor minimum cumulative cost corresponding to the target matrix element. Add the regularization distance of the reconstruction base point pair corresponding to the target matrix element to the predecessor minimum cumulative cost to obtain the cumulative regularization cost corresponding to the target matrix element. The cumulative normalization cost is calculated sequentially for each element in the cumulative cost matrix according to the row and column order of the matrix until the cumulative normalization cost of the endpoint matrix element in the cumulative cost matrix is ​​calculated. The cumulative normalization cost corresponding to the endpoint matrix element is then taken as the global optimal normalized cumulative cost.

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