Natural enemy control effect intelligent simulation system for forestry pest control

Abstract

This invention relates to the field of forestry pest and disease control technology, specifically to an intelligent simulation system for the control effect of natural enemies in forestry pest and disease control. The system includes an ecological monitoring integration module, a behavior stage discrimination module, a predation rate inversion module, a stage index mapping module, and a parameter feedback correction module. Based on forest pest monitoring nodes, it analyzes the pest quantity information uploaded by the nodes and determines the sampling point location corresponding to signal changes. This invention integrates forest pest monitoring data, natural enemy activity, and environmental changes in a time series, and introduces behavior stage discrimination based on ecological state information. Combined with paired statistics of natural enemy behavior and pest quantity, it dynamically reveals the ecological response relationship under different behavior stages. The simulated data can automatically adjust the parameter set according to periodic monitoring differences, and the stage effects and trends are promptly mapped into management indicators, making the simulation of control effects more stage-discriminative and ecologically adaptable.

Description

Technical Field

[0001] This invention relates to the field of forestry pest and disease control technology, and in particular to an intelligent simulation system for the control effect of natural enemies in forestry pest and disease control. Background Technology

[0002] Forest pest and disease control involves the prevention, monitoring, and management of forest resources when they are attacked by pests and diseases. This includes the identification and classification of forest pests, the study of pest and disease occurrence patterns, the selection and application of control agents, the formulation of biological control strategies, and the evaluation of control effects. Among these, the traditional intelligent simulation system for the control effect of natural enemies in forest pest and disease control refers to a simulation and analysis system designed to assess the role of natural enemies in controlling forest pests. It typically employs static statistical methods combined with historical field survey data, relying on species relationship tables, human experience estimations, and pest and disease occurrence records for manual analysis. It primarily assesses the control potential of a particular natural enemy against a specific pest by establishing a basic population dynamics model and referencing fixed ecological parameters such as reproductive rate, predation rate, and environmental suitability index.

[0003] Existing technologies largely rely on historical survey data and fixed species relationship tables, with parameters set statically. They lack real-time acquisition and time-series analysis of information on the activities of forest pests and natural enemies, making it impossible to capture dynamic changes in ecological processes and subtle responses at different behavioral stages. In practical applications, it is difficult to subdivide different stages of natural enemy behavior, resulting in a coarse connection between changes in the number of natural enemies and pests. Simulation results are prone to deviating from reality, and strategy formulation is difficult to adjust in a timely manner according to real-time ecological evolution, affecting the precision and dynamic response capabilities of prevention and control work. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and propose an intelligent simulation system for the control effect of natural enemies in forest pest and disease control.

[0005] To achieve the above objectives, the present invention employs the following technical solution: an intelligent simulation system for the control effect of natural enemies in forestry pest and disease control, the system comprising: The ecological monitoring integration module is based on forest pest monitoring nodes, analyzes the number of pests uploaded, determines the sampling points corresponding to signal changes, compares the differences in signal duration by associating predation sensing records, and integrates meteorological and environmental information to obtain the forest ecological status sequence. The behavior stage discrimination module determines the frequency and duration of predation actions based on the ecological state sequence of the forest area, calculates the predation trend within the monitoring period, compares the predation trend with the intensity classification standard, and obtains the life cycle stage label set. The predation rate inversion module calculates the behavioral classification under the life cycle stage label set, analyzes the ecological monitoring data of the corresponding time period, compares the changes in pest density with the number of predatory actions of natural enemies, and statistically analyzes the behavioral differences of each stage to obtain stage predation response data. Based on the predation response data of the stage, the stage indicator mapping module filters the predation actions of the stage, associates them with the changes in the number of pests within the monitoring period, converts the ratio of predation to pest data, and summarizes the results by stage to obtain a set of behavioral response indicators. The parameter feedback correction module adjusts the data of each stage of the behavior response index set, acquires periodic predation frequency monitoring, analyzes data differences, corrects strategy parameters based on the changing trends, and obtains stage parameter update results.

[0006] The present invention is improved in that the forest ecological state sequence includes the spatial distribution of pests, natural enemy activity data and environmental monitoring information; the life cycle stage label set includes stage type, behavior cycle and label index number; the stage predation response data includes predation response rate, corresponding changes in behavior stage and interaction response factor; the behavior response index set includes inhibition effect parameter, stage response ratio and behavior effect index; and the stage parameter update result includes parameter adjustment item, feedback correction amount and adaptation factor.

[0007] The present invention is improved in that the ecological monitoring integration module includes: The data stream receiving submodule is based on forest pest monitoring nodes. It analyzes the pest quantity and insect density monitoring data uploaded by the nodes, compares the pest quantity change trends of each node, judges the change characteristics of sensor status and natural enemy activity signals, identifies the monitoring data that changes in continuous period, and obtains signal fluctuation spatial positioning information. The predation action analysis submodule analyzes predation action event data based on the spatial positioning information of the signal fluctuation, calculates the start and end time distribution and frequency of predation actions at different sampling points in each sampling period, compares the duration of actions with the interval of actions, judges the distribution characteristics of predation behavior among sampling points, and obtains a predation behavior distribution feature group. The time-label association submodule analyzes the timestamps of various monitoring data and periodic meteorological data based on the predation behavior distribution feature group, optimizes the time distribution relationship between the pest quantity change sequence and the predation action record of natural enemies, and filters the corresponding positions of multi-source data under a unified time scale to obtain the forest area ecological status sequence.

[0008] The present invention is improved in that the behavior stage discrimination module includes: The frequency change extraction submodule analyzes the predation action data of natural enemies in each monitoring period based on the ecological state sequence of the forest area, calculates the rate of change of the number of predation actions between each node, judges the activity continuity characteristics of each node within the period, filters data that meet the continuous activity state, and obtains the predation frequency continuity parameter. The action trend calculation submodule analyzes the combined performance of periodic action frequency and activity duration characteristics based on the predation frequency duration parameter, calculates the direction of change of action frequency in the periodic sequence, compares the continuity of action frequency changes in multiple periods, and statistically analyzes the behavior change trend of each node to obtain a predation behavior trend vector set. The stage attribution labeling submodule compares the trend change performance with the behavior intensity classification benchmark based on the predation behavior trend vector set, determines the behavior activity category corresponding to each cycle, adjusts the mapping relationship between monitoring data and stage labels, updates the label binding method, and obtains the life cycle stage label set.

[0009] The present invention is improved in that the predation rate inversion module includes: The time period data alignment submodule calculates the behavior classification under the life cycle stage label set, compares the ecological monitoring data segments of each behavior stage, analyzes the changes in the number of pests monitored by the pest monitoring nodes, calculates the number of predation actions recorded by the natural enemy activity monitoring device, and filters matching data of pest change rate and predation action frequency within the same period to obtain the behavior time period array. The linkage trend determination submodule, based on the behavior time period array, determines the changes in pest density and the number of predatory actions of natural enemies in each monitoring period, analyzes the periodic trends of pest density changes and predatory action frequency, compares the synchronous changes of each type of data in multiple periods, and obtains a behavior linkage comparison set. The predation response calculation submodule uses the following formula based on the aforementioned behavior linkage comparison set: ; The periodic response matching strength between predation activity and pest changes at each behavioral stage was obtained to acquire stage-specific predation response data. Indicates the first The intensity of the periodic response matching for each lifecycle behavior stage Indicates the first The rate of change in pest density during each stage of the life cycle behavior. Indicates the first The length of the time window corresponding to each lifecycle behavior stage. Indicates the first Frequency of predation actions within each life cycle behavioral phase Indicates the first Enemy behavior response parameters within each lifecycle behavior phase Indicates the first Pest disturbance parameters for each stage of the life cycle behavior. Indicates the first Fluctuation parameters of monitoring data for each stage of the life cycle behavior.

[0010] The present invention is improved in that the stage indicator mapping module includes: The behavior data filtering submodule compares the frequency and duration of each predation action based on the predation response data of the aforementioned stage, filters events whose frequency fluctuations and durations both conform to the characteristics of the behavior cycle, determines whether abnormal predation actions are excluded, calculates the frequency and average duration of predation actions within the monitoring period, and obtains the predation behavior statistics to be filtered. The predation ratio calculation submodule, based on the predation behavior statistics, judges the change in the number of pests within the monitoring period, calculates the ratio of the number of pests to the frequency of predation actions in each behavioral stage, compares the correspondence between the frequency deviation and the change in pests, and obtains the predation action ratio of each stage. The behavior tag aggregation submodule determines the distribution of predatory behavior across all monitoring periods under the same tag based on the predatory action ratio of the aforementioned stage, calculates the average ratio, frequency fluctuation range, and synchronization characteristics of pest numbers, and obtains a set of behavior response indicators.

[0011] The present invention is improved in that the parameter feedback correction module includes: The behavior data comparison submodule adjusts the data of each stage of the behavior response index set, analyzes the inhibition effect parameters and behavior action indicators of each stage, compares the predation frequency data with the stage performance of each index within the monitoring period, judges the difference distribution during each week, identifies the data of stage difference fluctuations, and obtains the stage difference trend sequence. The difference change analysis submodule analyzes the cumulative increase or decrease of difference changes between stages over multiple periods based on the stage difference trend sequence, calculates the offset range between behavioral response changes and stage difference changes, determines whether there are periodic concentrated changes, and obtains the offset relationship parameter set. Based on the offset relationship parameter group, the strategy parameter correction submodule corrects the behavior stage discrimination conditions and response ratio parameter settings according to the offset characteristics of predation frequency performance, adjusts the strategy control item configuration method, updates the parameter content of each behavior stage, and obtains the stage parameter update results.

[0012] The present invention is improved in that the change in predation frequency refers to the number of times the predator's predation behavior occurs in each monitoring period and its increasing or decreasing trend; the activity duration refers to the duration of a single or continuous predation behavior of the predator; and the behavior classification refers to the behavior stage category to which each set of monitoring data belongs.

[0013] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In this invention, by integrating forest pest monitoring data, natural enemy activity, and environmental changes over time, and introducing behavioral stage discrimination based on ecological status information, combined with the paired statistics of natural enemy behavior and pest numbers, the dynamic revelation of the ecological response relationship under behavioral stages is achieved. The simulated data can automatically adjust the parameter set according to periodic monitoring differences, and the stage effects and trends can be mapped into management indicators in a timely manner. This makes the simulation of control effects more stage-distinctive and ecologically adaptable, and assists control decisions to achieve dynamic, continuous optimization and precise adjustment. Attached Figure Description

[0014] Figure 1 This is a system flowchart of the present invention; Figure 2 This is a flowchart of the ecological monitoring integration module in this invention; Figure 3 This is a flowchart of the behavior stage discrimination module in this invention; Figure 4 This is a flowchart of the predation rate inversion module in this invention; Figure 5 This is a flowchart of the stage indicator mapping module in this invention; Figure 6 This is a flowchart of the parameter feedback correction module in this invention. Detailed Implementation

[0015] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0016] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, in the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0017] All user-related information involved in this invention (including but not limited to biometric information, identity verification information, behavioral data, device information, and other data that can be used for identity verification and personalized services) is collected and processed with the user's full knowledge and voluntary consent. The use of data is limited to purposes necessary for providing the technical services of this invention, and reasonable technical and management measures will be taken to ensure the security and confidentiality of users' personal information in terms of information protection and privacy.

[0018] Please see Figure 1 This invention provides a technical solution: an intelligent simulation system for the control effect of natural enemies in forestry pest and disease control, comprising: The ecological monitoring integration module is based on forest pest monitoring nodes. It analyzes the pest quantity information uploaded by the nodes, determines the sampling point location corresponding to the signal change, compares the continuous differences of each sensing record by associating with the predation action sensing record generated by the natural enemy activity monitoring device, and optimizes the correspondence of data under a unified time label by combining meteorological environmental information to obtain the forest ecological status sequence. The behavior stage discrimination module is based on the ecological state sequence of the forest area to judge the changes in the frequency of predation actions and the duration of activities, calculate the predation action trend in each monitoring period, compare the trend with the predation intensity classification standard, perform classification on each group of monitoring data, adjust the binding method of result labels and original data, and obtain the life cycle stage label set. The predation rate inversion module calculates the behavioral classification under the life cycle stage label set, analyzes the ecological monitoring data content corresponding to the time period of the behavioral classification, compares the changes in pest density reflected by pest monitoring nodes with the number of predatory actions of natural enemies, analyzes the linkage trend through one-to-one pairing, and statistically calculates the cumulative differences between behaviors in each stage to obtain stage predation response data. The phased indicator mapping module is based on phased predation response data, filters phased predation action data, correlates the changes in the number of pests within the monitoring period, converts the predation data and the changes in the number of pests proportionally, matches the corresponding behavioral tags, sorts out the natural enemy control performance of the behavioral phase, and summarizes the data results by phase to obtain a set of behavioral response indicators. The parameter feedback correction module adjusts the data of each stage of the behavior response index set, reviews the predation frequency data obtained from subsequent periodic monitoring, analyzes the differences between the monitoring data and the data, corrects the strategy parameter settings based on the trend of the difference, executes the linkage feedback of data of each behavior stage, and obtains the stage parameter update results.

[0019] The forest ecological status sequence includes the spatial distribution of pests, data on natural enemy activity, and environmental monitoring information. The life cycle stage label set includes stage type, behavior cycle, and label index number. The stage predation response data includes predation response rate, corresponding changes in behavior stages, and interactive response factors. The behavior response index set includes inhibition effect parameters, stage response ratios, and behavior effect indicators. The stage parameter update results include parameter adjustment items, feedback correction amounts, and adaptation factors.

[0020] In the integrated ecological monitoring module, pest quantity information refers to the raw observation data such as the individual pest count and insect density per unit area, which are periodically collected and uploaded through pest monitoring nodes (such as yellow sticky traps, traps, and automatic photography devices) deployed in the forest area; signal changes refer to the dynamic performance of monitoring signals such as pest quantity, sensor status, and natural enemy activity within a continuous sampling period, including fluctuations, increases, decreases, and anomalies; sampling point location refers to the specific geographical coordinates or numbered nodes. In the forest monitoring system, each monitoring terminal, sensor, and node has a clear spatial identifier, facilitating data attribution and spatial analysis; and natural enemy activity monitoring devices refer to those used to capture, sense, and record natural enemies (such as ladybugs and spiders). Sensing devices for behavioral activities commonly include infrared triggering devices, vibration sensors, and image recognition devices; predation action sensing records refer to the event sequence and duration of predation behaviors (such as biting, feeding, and movement) detected by the monitoring device, which are represented by digital records of the number of events, time sequence, and activity length; persistence difference refers to the differences in the duration and interval of predation behaviors between different time periods, locations, or individual predators, used to reflect changes in predator activity and predation efficiency; unified time labeling means that all monitoring data are synchronized through timestamps to ensure that multi-source data have temporal correspondence within the same sampling period, facilitating data integration and behavioral process reconstruction.

[0021] In the behavior stage discrimination module, the predation action frequency change refers to the number of predation behaviors of natural enemies in each monitoring period and their increasing or decreasing trend, reflecting the activity level and periodic dynamics of natural enemies; the activity duration refers to the duration of a single or continuous predation behavior of natural enemies, reflecting their activity state and energy consumption characteristics; the predation action trend refers to analyzing the overall change trajectory of the predation frequency and duration of natural enemies within a series of monitoring periods (such as continuous increase, stabilization, decrease, etc.); the predation intensity classification standard refers to the threshold or rule used to determine the behavior stage of natural enemies, such as dividing behavior into pre-development, active, and declining stages based on parameters such as frequency and duration; the classification refers to determining which behavior stage each group of monitoring data belongs to according to the above standards (such as classifying a certain period as the "active predation" stage); the result label refers to the stage category identifier assigned to each group or time period of monitoring data (such as the "pre-development" and "active predation" labels); the raw data refers to the basic monitoring data that has not undergone stage discrimination and classification processing, usually time series data directly collected by the sensor terminal.

[0022] In the predation rate inversion module, behavior classification refers to the behavior stage category (label) to which each set of monitoring data output by the behavior stage discrimination module belongs; pest density change refers to the range of change in the number of pests recorded by the pest monitoring node within a specific period and a specific spatial range, used to measure the population growth and decline trend of pests; number of predatory actions by natural enemies refers to the total number of predatory actions counted by the natural enemy monitoring device within the same period, used to quantitatively describe the actual predatory activity level of natural enemies; linkage trend refers to comparing the correlation, synergistic increase or decrease, or disconnection of the two over time by pairing pest density change with the number of predatory actions by one-to-one; behavior at each stage refers to the set of data on the behavior of natural enemies and pest changes at different stages of the life cycle (such as development, activity, and decline).

[0023] In the phase indicator mapping module, phased predation action data refers to the statistical data related to predation actions within each behavioral phase after classification by behavioral phase (such as the total number of predators and average frequency during the active period); ratio conversion refers to the quantitative pairing of predation actions of natural enemies with changes in the number of pests during the same period through mathematical ratios (such as predation rate and inhibition index) to form comparable indicators; corresponding behavior labels mean that each pair of data must be assigned to the corresponding life cycle phase to ensure that the data can be traced back to the behavior type; natural enemy control performance refers to the ability of natural enemies to regulate and inhibit the number of pests in the form of data, which is reflected in indicators such as the extent of pest reduction after predation.

[0024] In the parameter feedback correction module, the difference between the simulation prediction results (such as predation frequency and pest changes) refers to the numerical difference between the simulation prediction results and the subsequent actual monitoring data; the strategy parameter setting refers to the real-time adjustment and setting of core control parameters (such as stage weights and discrimination criteria) for behavior discrimination and predation rate inversion based on the difference analysis; the linkage feedback refers to the system automatically adjusting the parameters according to the feedback difference, and using the adjusted parameters for the next cycle of data processing to achieve model adaptation and accuracy optimization.

[0025] Please see Figure 2 The ecological monitoring integration module includes: The data stream receiving submodule is based on forest pest monitoring nodes. It analyzes the pest quantity and insect density monitoring data uploaded by the nodes, compares the pest quantity change trends of each node, judges the change characteristics of sensor status and natural enemy activity signals, identifies the monitoring data that changes in continuous period, and obtains signal fluctuation spatial positioning information. For each monitoring terminal, the records of pest count and density per unit area uploaded within a fixed sampling period are read one by one. The data are arranged in order of monitoring node number and collection time. For the same node, the pest count in two adjacent periods is numerically differentiated, and the change value is obtained by subtracting the number of the previous period from the number of the later period. This change value is then compared with a manually set change range. When the absolute value of the change is between 0 and 2 pests, it is marked as a stable state; when the absolute value of the change is greater than 2 and less than or equal to 10 pests, it is marked as a moderate fluctuation state; when the absolute value of the change exceeds 10 pests, it is marked as a significant fluctuation state. The node number corresponding to the significant fluctuation state is recorded. Then, the sensor operation status data of that node in the same period is retrieved, including the number of infrared triggers and image acquisition. The number of times the vibration signal is collected and the recorded values ​​are compared with the corresponding data of the previous period. By directly comparing the values, it is determined whether there is an abnormal increase or a sudden decrease. For example, if the number of infrared triggers increases by more than five times compared with the previous period, it is marked as an enhanced activity state. Then, the activity records uploaded by the natural enemy activity monitoring device are read synchronously. The number of natural enemy appearances, the duration of a single activity, and the number of predation events are summarized periodically and compared with the data of the previous period. When the number of predation events increases by more than three times or the duration of a single event increases by more than ten seconds, it is determined as a natural enemy activity change node. Finally, the nodes with obvious fluctuations in the number of pests are matched with the natural enemy activity change nodes according to their geographic numbers. Nodes with the same number or adjacent spatial locations are merged and their corresponding spatial numbers and location identifiers are output to form the spatial positioning information of signal fluctuations for subsequent processing.

[0026] The predation action analysis submodule analyzes predation action event data based on signal fluctuation spatial positioning information, calculates the start and end time distribution and frequency of predation actions at different sampling points in each sampling period, compares the duration of actions with the interval of actions, judges the distribution characteristics of predation behavior among sampling points, and obtains the predation behavior distribution feature group. Data on predator predation events recorded at each sampling point within the monitoring period were retrieved one by one. The start time, end time, and duration of each predation event were extracted and arranged chronologically. Within a single sampling period, all predation events at the same sampling point were statistically analyzed, and the total activity duration was obtained by directly summing the durations of all events within that period. Simultaneously, the total number of events was counted, and the time interval between two adjacent predation events was calculated using a manually set method. For each sampling point, three indicators were compiled: the number of predation events, the average duration, and the average interval. These indicators were then compared horizontally across different sampling points. When the number of predation events at a sampling point was significantly higher than that of surrounding sampling points, and the average interval was significantly shorter, that sampling point was marked as a concentrated predation distribution point. When the number of predation events was low and the interval was long, it was marked as a dispersed distribution point. Based on this, the distribution types of all sampling points within the same period were summarized to form distribution description data describing the differences in predation behavior at different spatial locations. The distribution type, event statistics, and time characteristics corresponding to each sampling point were combined and organized, and output as a predation behavior distribution feature group.

[0027] The time-label association submodule analyzes the timestamps of various monitoring data and periodic meteorological data based on the distribution feature group of predatory behavior, optimizes the temporal distribution relationship between the pest quantity change sequence and the predatory action record of natural enemies, and filters the corresponding positions of multi-source data under a unified time scale to obtain the ecological status sequence of the forest area. The predation event time records at each sampling point were read one by one and compared with the collection time of pest population monitoring data. Predation events occurring in similar time periods were manually time-matched with pest population change records. Data pairs with time intervals within a set range were identified as valid correlation data. Then, meteorological monitoring data within the same time period were read, and temperature, humidity, and wind speed records were compared item by item. Only data records with small changes in meteorological conditions between the occurrence time of predation events were retained, while data from time periods with large fluctuations in meteorological conditions were directly removed. Subsequently, the filtered predation behavior data, corresponding pest population change records, and synchronous meteorological data were rearranged in a unified time order. Data from different sources were aligned at the same time scale so that each time point corresponds to a clear predation behavior state, pest population state, and environmental state. The completed multi-source data sequence was output as the forest area ecological state sequence.

[0028] Please see Figure 3 The behavior stage discrimination module includes: The frequency change extraction submodule analyzes the predation behavior data of natural enemies in each monitoring period based on the ecological state sequence of the forest area, calculates the rate of change of the number of predation behaviors between each node, judges the activity continuity characteristics of each node within the period, filters data that meet the continuous activity state, and obtains the predation frequency continuity parameter. Data on predator predation behavior recorded by each monitoring node at different time periods were read one by one. The number of predations by each node in each sampling period was linked to its corresponding time period, forming a set corresponding to the number of predations and the period length. The rate of change of the number of predations was obtained by calculating the difference in the number of predations between two adjacent periods for each node and dividing it by the period interval. In actual calculations, when the period length was 60 minutes, the number of predations in period one was 18, and the number of predations in period two was 25, the rate of change was 0.117 times per minute. The rate was calculated for each continuous period of all nodes in this way, and the rate of change was recorded and compared with a preset rate of change threshold. When the absolute value of the rate of change was not greater than 0.1, the predation activity level was considered stable. When the rate of change is positive and greater than 0.1 for at least two consecutive adjacent periods, it is considered that predation activity is enhanced; when the rate of change is negative and less than -0.1 for at least two consecutive adjacent periods, it is considered that predation activity is weakened. Next, the predation duration of each node in a single period is summed to determine whether it has continuous activity characteristics. The criteria for continuous activity are set as follows: the cumulative duration of predation behavior in a single period exceeds 180 seconds and the number of event pairs with an action interval of no more than 30 seconds is no less than 3. When the number of data records that meet this condition exceeds two consecutive periods, it is considered to meet the continuous activity state. The data that meet the above conditions are filtered and the relevant node numbers and corresponding period indices are extracted and summarized to form the predation frequency duration parameter.

[0029] The action trend calculation submodule analyzes the combined performance of periodic action frequency and activity duration characteristics based on the predation frequency duration parameter, calculates the direction of change of action frequency in the periodic sequence, compares the continuity of action frequency changes in multiple periods, and statistically analyzes the behavior change trend of each node to obtain a predation behavior trend vector set. The action frequency and activity duration values ​​of each node are merged according to the cycle. The action frequency is defined as the number of predations within a cycle divided by the cycle duration. The activity duration characteristic is defined as the combination of the average duration of each predation behavior and the average interval between events. Feature combination data describing the strength of the node's behavior are constructed. The direction of the frequency change of each node in three or more consecutive cycles is determined. If the frequency value shows a periodic increasing trend, it is marked as an upward direction. If the frequency value decreases, it is marked as a downward direction. If the frequency fluctuates within ±0.02, it is marked as a stable direction. According to the action frequency change trend of the same node in multiple cycles, the cycle segments with the same direction are continuously statistically analyzed. When the upward or downward direction appears for three or more consecutive cycles, the sequence is defined as a stable segment of behavior change. After performing the above statistical process on all nodes, the stable segment frequency direction, average frequency change amplitude, and average activity duration of each node are combined to generate the behavior trend data unit of the node. Then, the data units of all nodes are summarized and organized into a predation behavior trend vector set containing fields such as node number, trend direction, number of consecutive trend cycles, and frequency change amplitude.

[0030] The stage attribution labeling submodule is based on the predatory behavior trend vector set. It compares the trend change performance with the behavior intensity classification benchmark, determines the behavior activity category corresponding to each period, adjusts the mapping relationship between monitoring data and stage labels, updates the label binding method, and obtains the life cycle stage label set. Each node's behavior trend data unit is called sequentially, and the trend direction field is judged. Records with an upward trend and a continuous period of not less than 3 are matched with the "active growth segment" type in the behavior intensity classification benchmark. Records with a downward trend and an amplitude value exceeding the set standard value of more than 0.1 are corresponding to the "active decay segment". Records with a stable trend and an amplitude value fluctuating within 0.02 are corresponding to the "stable behavior segment". Each trend data unit is categorized according to its trend direction and amplitude value, and is assigned label numbers 1, 2, and 3 to represent the corresponding behavior category. Next, the sampling period index corresponding to the trend data in the node's original monitoring data is called, and the label number is re-bound and replaced with the data of that period. During the replacement process, the original timestamp and geographic number information are retained, and only the behavior label field is adjusted. The label update process is performed on the monitoring data of all nodes for all periods to form a life cycle stage label set.

[0031] Please see Figure 4 The predation rate inversion module includes: The time period data alignment submodule calculates the behavior classification under the life cycle stage label set, compares the ecological monitoring data segments of each behavior stage, analyzes the changes in the number of pests monitored by the pest monitoring nodes, calculates the number of predation actions recorded by the natural enemy activity monitoring device, and filters the matching data of pest change rate and predation action frequency in the same period to obtain the behavior time period array. The system retrieves the behavior category information marked by each node in the tag set for each cycle and establishes an index correspondence table for each behavior stage (such as "active period", "decline period", "development period"). It then extracts the monitoring cycle index covered by each behavior tag, binding the behavior type one-to-one with the ecological monitoring data within that cycle. Subsequently, the data under each tag is grouped, and the pest quantity records uploaded by the pest monitoring nodes are extracted according to the behavior stage. A cycle difference operation is performed on the pest quantity change at the same monitoring point within each cycle, calculating the pest quantity change value as the current cycle's pest count minus the previous cycle's pest count. The direction and magnitude of the change are recorded and compared with a preset pest change rate judgment benchmark. When the increase in pest quantity per unit area exceeds 10, it is judged as a density increase. In the long term, when the decrease value exceeds 10, it is determined to be the density decay stage; otherwise, it is determined to be basically stable. Then, the predation action data uploaded by the predator activity monitoring device within the corresponding period is called up. The number of predator predation events recorded at the same node in each period is directly summarized to obtain the total number of predation actions in the period. Further, the predation frequency per unit time is calculated based on the period length. When the predation frequency exceeds 2 times / minute and the pest decrease value is greater than 10, it is considered as a data point where predation and pest change are synchronous. This data pair is recorded to a temporary data pool. The above operation is repeated for all nodes and data within the period. Records in which the predation frequency and pest change rate change in the same direction within the same period are selected. The matched data are constructed into a structured array according to the period and node number to form a behavior time period array.

[0032] The linkage trend determination submodule is based on the behavior time period array to determine the changes in pest density and the number of predatory actions of natural enemies in each monitoring period, analyze the periodic trends of pest density changes and predatory action frequency, compare the synchronous changes of each type of data in multiple periods, and obtain the behavior linkage comparison set. The pest density change values ​​and predation frequency data of natural enemies recorded in the array for each monitoring period are read one by one and organized into a node period sequence in chronological order. Then, the direction and magnitude of the increase or decrease in pest density change within three consecutive periods are calculated. For example, the pest density in periods 1 to 3 is 35, 30, and 24 respectively, with a continuous decreasing direction and magnitudes of -5 and -6 respectively, showing an overall downward trend. Simultaneously, the predation frequency of natural enemies within this time period is read. If the predation frequency is 15, 18, and 21 respectively, it also shows an increasing trend. After classifying the above two sequences, trend pairing judgment is performed. When the pest change trend is decreasing and the predation trend is increasing, it is determined to be a complementary trend linkage. When both are in an increasing or decreasing direction, ... The interaction is considered to be synchronous. When the directions are opposite but the fluctuation amplitude does not exceed the set difference threshold of 5, it is considered weak interaction. When the directions are opposite and the amplitude difference exceeds 10, it is considered non-interaction. According to the above rules, the pest change trend and predation frequency change trend in all periodic behavior periods are classified. The records are archived by node number, and the frequency of each interaction type is counted. When a node is classified as synchronous in three or more consecutive periods, it is recorded as a strong interaction node. Otherwise, if the classification type is mostly non-interaction or weak interaction, it is marked as a low correlation node. The interaction type, trend direction, frequency change amplitude, pest change amplitude and other information of each node in each period are summarized to construct a structured dataset and output as a behavior interaction comparison set.

[0033] The predation response calculation submodule is based on a behavioral linkage comparison set and uses the following formula: ; The periodic response matching strength between predation activity and pest changes at each behavioral stage was obtained to acquire stage-specific predation response data. Indicates the first The intensity of the periodic response matching for each lifecycle behavior stage Indicates the first The rate of change in pest density during each stage of the life cycle behavior. Indicates the first The length of the time window corresponding to each lifecycle behavior stage. Indicates the first Frequency of predation actions within each life cycle behavioral phase Indicates the first Enemy behavior response parameters within each lifecycle behavior phase Indicates the first Pest disturbance parameters for each stage of the life cycle behavior. Indicates the first Fluctuation parameters of monitoring data for each life cycle behavior stage; Periodic response matching strength refers to the degree of periodic linkage between predator predation and pest population change in each life cycle behavior stage after normalizing key parameters such as pest density change rate and predation frequency. The larger the value, the higher the difference or mismatch between predation behavior and pest change in that stage; the smaller the value, the more consistent the response or the stronger the linkage between the two in that cycle. Based on the behavior linkage comparison set, computational processing is carried out for the first... For each life cycle behavior stage, the pest monitoring records and natural enemy activity records corresponding to that stage are first collected to form the required set of raw parameters. The raw value of the pest density change rate is calculated from the change in pest numbers within consecutive monitoring periods. The pest number changes in the three consecutive periods were a decrease of 6, a decrease of 9, and a decrease of 3, respectively. The raw value of the average pest density change rate within the corresponding stage is... The original value of the time window length The frequency of predatory actions is determined by the number of consecutive monitoring periods covered by this behavioral phase, which covers three periods. The total number of predatory actions and the number of cycles during this phase were calculated to be 14, corresponding to a predatory action frequency of [missing value]. Enemy behavior response parameters The original values ​​were obtained from experimental statistics on the change in the number of pests corresponding to a single predation action, and the average reduction in pests corresponding to a single predation action was recorded as follows: Pest disturbance parameters The raw values ​​were obtained from statistical analysis of the fluctuations in pest populations caused by non-predatory factors during the same period, and were recorded as follows: Monitoring data fluctuation parameters The original value was obtained by calculating the dispersion of the insect pest density change during this stage. After obtaining all the original parameters, maximum value normalization was performed on parameters with different dimensions, where... Normalization was performed using the maximum insect pest variation range of 7.5 within the period as the baseline, resulting in the normalized value. , Normalization based on the maximum allowed number of cycles (3) for this lifecycle stage yields the following result: , Normalized to the maximum predatory action frequency of 6.2 within the phase, the following results were obtained: , Normalization was performed using the experimentally observed maximum response of 0.2 as a baseline, resulting in... , Normalization was performed using the maximum pest disturbance level of 3 as the baseline, resulting in... , Normalization based on the maximum fluctuation of 3 within the period yields: After normalization, substitute each parameter into the formula to calculate the first term of the numerator: ; Calculate the second term in the numerator: ; Then the molecular difference is calculated: ; Next, calculate the denominator: ; Further, the fractional result is obtained: ; Taking the absolute value of the result and then performing a square root operation yields: ; This value is compared with a preset matching strength benchmark interval, which is defined as follows: when This indicates a high degree of correlation between predatory behavior and changes in pest populations, meaning that changes in pest density can be effectively responded to by predatory actions of natural enemies. when At this time, it indicates that there is a moderate correlation between predation behavior and changes in pests, and the two partially coincide in terms of trend but there is a response lag or partial disconnect. when This indicates a low correlation between predation behavior and pest changes, meaning that predation activities have failed to effectively synchronize and regulate changes in pest numbers. Since the calculation result is approximately Its satisfaction The conditions are met, therefore it should be classified as a medium-level linkage stage, indicating that the predatory behavior of natural enemies has a response trend to the regulation of pest density, but has not yet reached a high degree of consistency. This value is the predatory response data corresponding to the current behavioral stage. This response data will be used in subsequent modules to perform operations such as behavior classification matching, control strategy switching and parameter feedback adjustment, and will serve as the input basis for mapping behavior labels and control parameters.

[0034] Please see Figure 5 The phase indicator mapping module includes: The behavior data filtering submodule compares the frequency and duration of each predation action based on the predation response data of each stage, filters events whose frequency fluctuations and durations both conform to the characteristics of the behavior cycle, determines whether abnormal predation actions are excluded, calculates the frequency and average duration of predation actions within the monitoring period, and obtains the predation behavior statistics to be filtered. The system reads the recorded sequence of predator predation events in each behavioral phase. Within each monitoring cycle, the number of predation events recorded by each monitoring node is counted, defined as the predation frequency of that node in that cycle. Simultaneously, the start and end times of each event are extracted, and the corresponding duration is calculated, forming a set of all action durations within the cycle. Then, the mean and standard deviation of the action duration are calculated for each node. The frequency and duration of all nodes are jointly evaluated, and a manually defined behavioral cycle characteristic judgment criterion is used for screening. Frequency volatility is defined as the ratio of the standard deviation to the mean; if this ratio is between 0.2 and 0.5, the frequency volatility is considered moderate. The mean duration must be no less than 10 seconds and no more than 60 seconds. Data falling within this range is initially considered to conform to the behavioral cycle characteristics. Subsequently, it is determined whether each event constitutes an abnormal predation behavior by setting an abnormality threshold. For example, if the frequency within a certain cycle is more than twice the average of the past five cycles of this node, or the duration is less than 3 seconds or more than 180 seconds, it is marked as an abnormal predation behavior. Whether to exclude abnormal events can be determined based on the exclusion ratio limit. If the proportion of abnormal data exceeds 20%, all data is excluded, otherwise it is retained. Based on the filtered data, the predation frequency and average duration of each node in each cycle are recalculated. The results are calculated as structured fields based on the event value and event duration set per unit time, and the results are time-aligned and bound to spatial numbers to form a filtered predation behavior statistic.

[0035] The predation ratio calculation submodule, based on the statistics of predation behavior, determines the change in pest population within the monitoring period, calculates the ratio of pest population to predation frequency at each behavioral stage, and compares the correlation between frequency deviation and pest changes using the following formula: ; The predation rate at each stage was obtained, among which, Representing the Class behavior during the monitoring period The behavioral response difference factor below Representing the Class behavior during the monitoring period The Statistical count of predation actions Representing the Class behavior during the monitoring period The mean of the statistics of endopredatory actions. Represents the monitoring period Next Changes in the number of pests corresponding to a given behavior. Represents the monitoring period Next The frequency fluctuation range of predation actions in this type of behavior. Represents the monitoring period Next The average difference between the number of predatory actions of a certain type of behavior and the change in the number of pests. This represents the total number of predatory actions. The behavioral response difference factor is a numerical indicator reflecting the degree of response difference of a certain behavioral category within a specified monitoring period, under the combined influence of multiple factors such as the fluctuation range of predation action statistics and changes in pest population and predation action frequency. This factor analyzes the deviation of the statistical data of all predation actions of the same behavioral category within the current monitoring period from the mean value of that behavior within the period (numerator, representing the total absolute deviation). Simultaneously, it incorporates related factors such as changes in pest population, fluctuations in behavioral frequency, and differences between behavior and pests (denominator, comprehensively characterizing the overall level of environmental fluctuations and behavioral responses), quantifying them into a measurable value to describe the volatility, coordination, or abnormality of behavioral responses as they change with environmental changes. The behavioral response difference factor reflects whether the performance of a certain type of behavior is stable and synchronized with environmental changes within a specific period. A larger value indicates stronger behavioral response volatility or greater adaptability to the environment; a smaller value indicates a more stable behavioral response or greater coordination with environmental changes. Analyze the correlation between different behavioral stages and changes in pest populations within the same monitoring period to obtain the first... Class behavior during the monitoring period The predation action statistics within the area, with the original sample value being... , , The maximum value is calculated to be 7 and the minimum value to be 4. Then, using interval normalization, it is converted to... Further calculate the normalized mean. The change in the number of pests was obtained by measuring the decrease from 180 to 150 during the monitoring period. Let the maximum value of the pest variation in the whole sample be 50 and the minimum value be 10. Then, after normalization... Based on the time interval sequence of predation actions, the maximum frequency interval was 5 seconds and the minimum was 2 seconds, thus obtaining the original value of the frequency fluctuation range. After normalization, it becomes The total number of predatory actions during the statistical period was 15. Combined with the reduction of 30 pests, the average difference was calculated to be... Let the maximum reference difference be 30 and the minimum be 0. After normalization Substitute all the above normalized values ​​into the behavioral response difference factor formula: ; The molecular part is: ; The denominator is: ; Therefore, we can calculate: ; Based on the established baseline interval for the response difference factor: when This indicates that the behavior phase is highly stable, and the predation behavior is clearly consistent with the changes in the number of pests; when This indicates that the behavior at this stage has moderate responsiveness and coordination, with some deviations but still acceptable. when This indicates that the behavior is in a marginal deviation state, and the relationship between predation frequency fluctuations and pest changes is unstable; when When this occurs, it is determined to be a high response difference stage, in which the behavior exhibits high-frequency irregular fluctuations or a trend of being out of sync with pest changes within the cycle.

[0036] The current calculated value is approximately If the value falls into the high response difference stage, it means that the predation behavior in this stage cannot effectively correspond to the change in pest density. This stage belongs to the abnormal behavior response stage. This value is directly used as the numerical expression result of the predation effect ratio in this stage. In the subsequent labeling and control strategy, it will be classified as the abnormal stage category of the behavior response index set as "low consistency behavior stage".

[0037] The behavior tag aggregation submodule determines the distribution of predatory behavior across all monitoring periods under the same tag based on the predatory action ratio at each stage, calculates the average ratio, frequency fluctuation range, and synchronization characteristics of pest numbers, and obtains a set of behavior response indicators. Extract all monitoring cycle data corresponding to each life cycle stage label, and group the cycle data of the same label together. Then, read the predation frequency data and pest population change data within each group. Use the predation ratio as the ratio between the predation frequency and pest population change within that cycle. Calculate the ratio value for each cycle, and then sum the average ratio value for all cycles under that label. Simultaneously, extract the predation frequency for each cycle and calculate the minimum and maximum frequency values. The difference between these two values ​​is used as the frequency fluctuation range for that label. A fluctuation range of less than 1 time / minute is defined as a low fluctuation label, a range of 1 to 3 times / minute is defined as a medium fluctuation label, and a range exceeding 3 times / minute is defined as a low fluctuation label. The data per minute is labeled as high fluctuation. The synchronization characteristics of pest population are then evaluated. In each cycle, it is determined whether the pest change trend and the predation frequency change trend are consistent. If the pest density decreases while the predation frequency increases during the cycle, it is recorded as a synchronization state. The proportion of synchronization cycles is used as a quantitative parameter of synchronization characteristics. If the proportion of synchronization cycles to the total number of cycles exceeds 60%, it is marked as a high synchronization label. If it is between 40% and 60%, it is medium synchronization. If it is below 40%, it is low synchronization. The average predation ratio, frequency fluctuation range, and pest synchronization characteristic ratio fields under each label are sorted and output, classified by label, and summarized to construct a behavioral response index set.

[0038] Please see Figure 6 The parameter feedback correction module includes: The behavioral data comparison submodule adjusts the data of each stage of the behavioral response index set, analyzes the inhibition effect parameters and behavioral action indicators of each stage, compares the predation frequency data with the stage performance of each indicator in the subsequent monitoring period, judges the difference distribution during each week, identifies the data of stage difference fluctuations, and obtains the stage difference trend sequence. The inhibition effect parameters and behavioral indicators recorded for each stage of the life cycle are retrieved. The inhibition effect parameters are calculated by the ratio of the decrease in pest population to the predation frequency during the same period. The behavioral indicators are expressed by the degree of matching between the predation frequency of natural enemies and the synchronous decrease in pest population during the period. The periodic values ​​of the parameters under the labels of "development stage", "active stage", and "decline stage" are read respectively, and a stage indicator comparison table is constructed. The deviation of the current period data from the historical average data is compared. When the deviation of the inhibition parameter exceeds ±20% of the original stage average, or the behavioral indicator is lower than the stage benchmark value set below the lower limit (such as 80% of the historical average of the behavioral indicator), it is determined that there is an abnormal behavior in that period. Then, the data from the subsequent monitoring period are extracted. The predation frequency data recorded in the actual data is directly compared with the frequency range of the same stage in the response index set. For example, if the frequency index range of a certain node in the "active period" is 2.5 to 4.0 times / minute, while the actual predation frequency in the current period is only 1.7 times / minute, it is determined that it is not within the standard performance range and is recorded as a stage abnormality record. Then, such abnormal frequencies in three consecutive periods are recorded and accumulated. When the abnormality rate exceeds 50%, it is determined that the behavioral response data of that stage has fluctuated. The pest change amplitude, predation frequency, synchronization ratio, etc. corresponding to the abnormal period are uniformly numbered, marked and recorded. Finally, all the identified stage deviation records are sorted in order and a complete trend sequence is constructed according to the period time label, and the output is a stage difference trend sequence.

[0039] The difference change analysis submodule analyzes the cumulative increase or decrease of difference changes between stages over multiple periods based on the stage difference trend sequence, calculates the offset range between behavioral response changes and stage difference changes, determines whether there are periodic concentrated changes, and obtains the offset relationship parameter set. The system sequentially reads the stage name, indicator offset value, behavior frequency, and pest synchronization characteristics recorded at each time point. It statistically analyzes the positive and negative changes and absolute increments of the indicator offset value for the same behavior stage in adjacent periods, and calculates the total cumulative offset value for every three consecutive periods. When the cumulative offset exceeds 30% of the historical average offset for that stage, the stage is considered to have entered a significant fluctuation period. The system then normalizes and subtracts the change in behavior frequency and pest synchronization ratio within each fluctuation period to obtain the response offset. The offset values ​​for all behavior stages are divided into intervals. A value between 0 and 0.2 indicates a slight fluctuation, 0.2 to 0.5 indicates a moderate shift, and values ​​above 0.5 indicate a sharp shift. Subsequently, the number of cycles in which sharp shifts occur in each stage is counted according to the cycle time axis. When sharp shift cycles occur in four or more consecutive monitoring cycles, they are classified as periodic concentrated change events. They are then categorized by behavioral stage to identify which stages these events occur more frequently. Information such as the concentrated change period, maximum shift amplitude, and number of fluctuation cycles for each stage is output, constructing a set of shift relationship parameters that includes fields for shift type, shift amplitude, and concentrated trend identifier.

[0040] The strategy parameter correction submodule, based on the offset relationship parameter group, optimizes the setting of the discrimination conditions and response ratio of the behavior stage according to the offset characteristics of the predation frequency performance, adjusts the configuration method of the strategy control item, updates the parameter content of each behavior stage, and obtains the stage parameter update results. Extract the offset type field and corresponding predation frequency performance values ​​recorded in each stage. Perform behavior discrimination condition correction operations on records with offset amplitudes of medium or higher. Specifically, reset the behavior frequency threshold and duration judgment boundary for the current stage. For example, if the original frequency judgment threshold for the "active period" stage was 2.5 to 4.0 times / minute, and the frequency performance in the offset records is concentrated between 2.0 and 2.8, then adjust the lower limit to 2.0 times / minute, simultaneously increase the duration reference value by 10%, and synchronously adjust the predation effect ratio setting value. If the original value was for pest reduction and predation... The frequency ratio is no less than 2.0, and can now be set to no less than 1.5. The compensation correction value calculated by the offset correction coefficient is added. Then, the configuration method of the strategy control item is uniformly adjusted. The original stage classification label binding rule is updated to support the range expansion and change tolerance mechanism. The core parameter content records in each behavior stage are updated and replaced. Fields such as "correction source cycle", "correction magnitude" and "offset type" are added to the parameter table to indicate the logical basis and data source of this adjustment. The corrected parameter entries are integrated back into the life cycle stage definition table, and the output is the stage parameter update result.

[0041] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. An intelligent simulation system for the control effect of natural enemies in forestry pest and disease control, characterized in that, The system includes: The ecological monitoring integration module analyzes the number of pests uploaded based on forest pest monitoring nodes, determines the sampling points corresponding to signal changes, compares the differences in signal duration by associating with predation sensing records, and integrates meteorological and environmental information to obtain a forest ecological status sequence. The behavior stage discrimination module judges the changes in the frequency of predation actions and the duration of activities based on the ecological state sequence of the forest area, calculates the predation trend within the monitoring period, compares the predation trend with the intensity classification standard, and obtains the life cycle stage label set. The predation rate inversion module calculates the behavioral classification under the life cycle stage label set, analyzes the ecological monitoring data of the corresponding time period, compares the changes in pest density with the number of predatory actions of natural enemies, and statistically analyzes the behavioral differences of each stage to obtain stage predation response data. Based on the predation response data of the stage, the stage indicator mapping module filters the predation actions of the stage, associates them with the changes in the number of pests within the monitoring period, converts the ratio of predation to pest data, and summarizes the results by stage to obtain a set of behavioral response indicators. The parameter feedback correction module adjusts the data of each stage of the behavior response index set, acquires periodic predation frequency monitoring, analyzes data differences, corrects strategy parameters based on the changing trends, and obtains stage parameter update results.

2. The intelligent simulation system for natural enemy control effects in forestry pest and disease control according to claim 1, characterized in that, The forest ecological state sequence includes the spatial distribution of pests, data on natural enemy activity, and environmental monitoring information. The life cycle stage label set includes stage type, behavior cycle, and label index number. The stage predation response data includes predation response rate, corresponding changes in behavior stages, and interactive response factors. The behavior response index set includes inhibition effect parameters, stage response ratios, and behavior effect indicators. The stage parameter update results include parameter adjustment items, feedback correction amounts, and adaptation factors.

3. The intelligent simulation system for natural enemy control effects in forestry pest and disease control according to claim 1, characterized in that, The ecological monitoring integration module includes: The data stream receiving submodule is based on forest pest monitoring nodes. It analyzes the pest quantity and insect density monitoring data uploaded by the nodes, compares the pest quantity change trends of each node, judges the change characteristics of sensor status and natural enemy activity signals, identifies the monitoring data that changes in continuous period, and obtains signal fluctuation spatial positioning information. The predation action analysis submodule analyzes predation action event data based on the spatial positioning information of the signal fluctuation, calculates the start and end time distribution and frequency of predation actions at different sampling points in each sampling period, compares the duration of actions with the interval of actions, judges the distribution characteristics of predation behavior among sampling points, and obtains a predation behavior distribution feature group. The time-label association submodule analyzes the timestamps of various monitoring data and periodic meteorological data based on the predation behavior distribution feature group, optimizes the time distribution relationship between the pest quantity change sequence and the predation action record of natural enemies, and filters the corresponding positions of multi-source data under a unified time scale to obtain the forest area ecological status sequence.

4. The intelligent simulation system for natural enemy control effects in forestry pest and disease control according to claim 1, characterized in that, The behavior phase discrimination module includes: The frequency change extraction submodule analyzes the predation action data of natural enemies in each monitoring period based on the ecological state sequence of the forest area, calculates the rate of change of the number of predation actions between each node, judges the activity continuity characteristics of each node within the period, filters data that meet the continuous activity state, and obtains the predation frequency continuity parameter. The action trend calculation submodule analyzes the combined performance of periodic action frequency and activity duration characteristics based on the predation frequency duration parameter, calculates the direction of change of action frequency in the periodic sequence, compares the continuity of action frequency changes in multiple periods, and statistically analyzes the behavior change trend of each node to obtain a predation behavior trend vector set. The stage attribution labeling submodule compares the trend change performance with the behavior intensity classification benchmark based on the predation behavior trend vector set, determines the behavior activity category corresponding to each cycle, adjusts the mapping relationship between monitoring data and stage labels, updates the label binding method, and obtains the life cycle stage label set.

5. The intelligent simulation system for natural enemy control effects in forestry pest and disease control according to claim 1, characterized in that, The predation rate inversion module includes: The time period data alignment submodule calculates the behavior classification under the life cycle stage label set, compares the ecological monitoring data segments of each behavior stage, analyzes the changes in the number of pests monitored by the pest monitoring nodes, calculates the number of predation actions recorded by the natural enemy activity monitoring device, and filters matching data of pest change rate and predation action frequency within the same period to obtain the behavior time period array. The linkage trend determination submodule, based on the behavior time period array, determines the changes in pest density and the number of predatory actions of natural enemies in each monitoring period, analyzes the periodic trends of pest density changes and predatory action frequency, compares the synchronous changes of each type of data in multiple periods, and obtains a behavior linkage comparison set. The predation response calculation submodule uses the following formula based on the aforementioned behavior linkage comparison set: ； The periodic response matching strength between predation activity and pest changes at each behavioral stage was obtained to acquire stage-specific predation response data. Indicates the first The intensity of the periodic response matching for each lifecycle behavior stage Indicates the first The rate of change in pest density during each stage of the life cycle behavior. Indicates the first The length of the time window corresponding to each lifecycle behavior stage. Indicates the first Frequency of predation actions within each life cycle behavioral phase Indicates the first Enemy behavior response parameters within each lifecycle behavior phase Indicates the first Pest disturbance parameters for each stage of the life cycle behavior. Indicates the first Fluctuation parameters of monitoring data for each stage of the life cycle behavior.

6. The intelligent simulation system for natural enemy control effects in forestry pest and disease control according to claim 1, characterized in that, The stage indicator mapping module includes: The behavior data filtering submodule compares the frequency and duration of each predation action based on the predation response data of the aforementioned stage, filters events whose frequency fluctuations and durations both conform to the characteristics of the behavior cycle, determines whether abnormal predation actions are excluded, calculates the frequency and average duration of predation actions within the monitoring period, and obtains the predation behavior statistics to be filtered. The predation ratio calculation submodule, based on the predation behavior statistics, judges the change in the number of pests within the monitoring period, calculates the ratio of the number of pests to the frequency of predation actions in each behavioral stage, compares the correspondence between the frequency deviation and the change in pests, and obtains the predation action ratio of each stage. The behavior tag aggregation submodule determines the distribution of predatory behavior across all monitoring periods under the same tag based on the predatory action ratio of the aforementioned stage, calculates the average ratio, frequency fluctuation range, and synchronization characteristics of pest numbers, and obtains a set of behavior response indicators.

7. The intelligent simulation system for natural enemy control effects in forestry pest and disease control according to claim 1, characterized in that, The parameter feedback correction module includes: The behavior data comparison submodule adjusts the data of each stage of the behavior response index set, analyzes the inhibition effect parameters and behavior action indicators of each stage, compares the predation frequency data with the stage performance of each index within the monitoring period, judges the difference distribution during each week, identifies the data of stage difference fluctuations, and obtains the stage difference trend sequence. The difference change analysis submodule analyzes the cumulative increase or decrease of difference changes between stages over multiple periods based on the stage difference trend sequence, calculates the offset range between behavioral response changes and stage difference changes, determines whether there are periodic concentrated changes, and obtains the offset relationship parameter set. Based on the offset relationship parameter group, the strategy parameter correction submodule corrects the behavior stage discrimination conditions and response ratio parameter settings according to the offset characteristics of predation frequency performance, adjusts the strategy control item configuration method, updates the parameter content of each behavior stage, and obtains the stage parameter update results.

8. The intelligent simulation system for natural enemy control effects in forestry pest and disease control according to claim 1, characterized in that, The frequency of predation refers to the number of times predation occurs by the predator in each monitoring period and its increasing or decreasing trend. The duration of activity refers to the length of time that a single or continuous predation behavior by the predator lasts. The behavior classification refers to the behavior stage category to which each set of monitoring data belongs.

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