Ecological management system of forestry areas based on risk prediction

Abstract

The present invention relates to the technical field of forestry ecological management, and discloses an ecological management system for forestry areas based on risk prediction. The system includes an ecological environment monitoring module, a risk prediction and analysis module, an emergency control module, a risk warning output module, and a background management terminal. The ecological environment monitoring module collects ecological environment parameters in real time through multiple types of sensors, and sends them to the risk prediction and analysis module and the background management terminal, and the background dynamically displays the parameters. The risk prediction and analysis module combines real-time parameters with the historical ecological risk database to determine the ecological risk factors, calculates the control instructions through the risk prediction model, and sends them to the emergency control module. The emergency control module performs intervention operations to adjust the ecological status. The risk warning output module monitors the intervention process, evaluates the operating status of the emergency control module, generates a normal intervention signal or a risk warning signal and sends it to the background, which triggers a prompt when receiving the warning signal. The system realizes real-time, scientific and closed-loop forestry ecological management.

Description

Technical Field

[0001] The present invention relates to the technical field of forestry ecological management, and in particular to a forestry area ecological management system based on risk prediction. Background Art

[0002] The forest ecosystem is a complex and dynamically changing entity, influenced by a variety of factors, including climate, soil, and organisms. Even the slightest change in any link can trigger a chain reaction. Currently, ecological management within forestry areas still largely relies on traditional models, which appear inadequate to cope with complex ecological changes.

[0003] Traditionally, obtaining ecological and environmental information relies primarily on regular manual sampling and recording. This not only consumes significant manpower and resources, but also fails to capture environmental changes between sampling intervals, leading to temporal gaps in our understanding of ecosystems. Manual methods struggle to achieve comprehensive coverage in vast forest areas with diverse terrain, leaving some remote or rugged areas without monitoring coverage for extended periods, making it difficult to detect potential ecological problems in a timely manner.

[0004] Traditional risk assessment methods rely heavily on past cases and managers' personal experience, lacking effective integration of real-time environmental data. When environmental factors fluctuate unexpectedly, the lack of scientific analytical models makes it difficult to accurately determine the type, extent, and development trend of risk, often leading to misjudgments or omissions.

[0005] The formulation and implementation of ecological intervention measures also suffer from blindness, often taking action only after problems surface. The intensity and approach of these interventions lack precise basis. During the intervention process, there's no way to track the effectiveness of measures in real time, making it difficult to adjust strategies based on ecosystem feedback. This disconnects interventions from the ecosystem's actual needs and hinders the formation of an effective closed-loop management system. This management model struggles to adapt to the dynamic nature of forest ecosystems and meets the practical needs of modern forestry conservation. Summary of the Invention

[0006] The purpose of the present invention is to provide a forestry area ecological management system based on risk prediction to solve the problems raised in the above background technology.

[0007] To achieve the above objectives, the present invention provides a forestry area ecological management system based on risk prediction, the system comprising:

[0008] Ecological environment monitoring module, risk prediction and analysis module, emergency control module, risk warning output module and backend management terminal;

[0009] The ecological environment monitoring module collects ecological environment parameters of the forestry area in real time through multiple types of sensors, and sends the collected real-time ecological environment parameters to the risk prediction and analysis module and the backend management terminal, which dynamically displays the real-time ecological environment parameters;

[0010] The risk prediction and analysis module determines the ecological risk factors based on real-time ecological environment parameters and the historical ecological risk database, calculates the control instructions of the emergency control module based on the ecological risk factors and the risk prediction model, and sends the control instructions to the emergency control module;

[0011] The emergency control module performs ecological intervention operations according to control instructions and adjusts the ecological environment status of the forestry area;

[0012] The risk warning output module monitors the intervention process of the emergency control module in real time, evaluates the operating status of the emergency control module, and generates a normal intervention signal or a risk warning signal based on this, and sends the normal intervention signal or the risk warning signal to the background management end. When the background management end receives the risk warning signal, it triggers a warning prompt.

[0013] Preferably, the specific analysis process of the risk warning output module includes: marking the corresponding ecological intervention process as a low-risk process or a high-risk process through a risk level assessment mechanism, setting an assessment period, and calculating the ratio of the number of high-risk processes in the assessment period to the total number of ecological intervention processes to obtain a risk ratio value. If the risk ratio value exceeds the preset risk ratio threshold, a risk warning signal is generated; if the risk ratio value does not exceed the preset risk ratio threshold, the response time value of the corresponding ecological intervention process and the benchmark value of the preset response time range are calculated by difference and the absolute value is taken to obtain a time deviation value, and the ratio of the control stability value of the corresponding intervention process to the preset control stability threshold is marked as a stability coefficient; the average time deviation is obtained by averaging the time deviation values ​​of all ecological intervention processes in the assessment period, and the average stability coefficient is obtained by averaging the stability coefficients of all ecological intervention processes in the assessment period; the risk warning index is obtained by comprehensively calculating the risk ratio value, the average time deviation and the average stability coefficient. If the risk warning index exceeds the preset warning index threshold, a risk warning signal is generated. If the risk warning index does not exceed the preset warning index threshold, a normal intervention signal is generated.

[0014] Preferably, the specific analysis process of the risk level assessment mechanism is as follows: the moment when the emergency control module receives the control instruction is collected and marked as the start-up moment, and the moment when the emergency control module completes the corresponding ecological intervention operation is collected and marked as the completion moment, and the interval between the start-up moment and the completion moment is marked as the intervention duration; the ratio of the intensity value of the control instruction to the intervention duration is marked as the response efficiency value, and the control stability value is obtained through control stability analysis. If the response efficiency value is not within the preset efficiency value range or the control stability value exceeds the preset stability threshold, the corresponding ecological intervention process is marked as a high-risk process; if the response efficiency value is within the preset efficiency value range and the control stability value does not exceed the preset stability threshold, the corresponding ecological intervention process is marked as a low-risk process.

[0015] Preferably, the specific analysis process of the regulation stability analysis is as follows: a plane rectangular coordinate system is established with time as the horizontal axis and the ecological environment parameters as the vertical axis, the ecological environment parameter change curve of the forestry area during the corresponding ecological intervention process is obtained, the ecological environment parameter change curve is placed in the rectangular coordinate system, and the starting point of the change curve corresponds to the intervention start time; a number of monitoring nodes are selected on the change curve, and the vertical axis difference between two adjacent groups of monitoring nodes is marked as a fluctuation value, and all fluctuation values ​​are calculated for variance to obtain a fluctuation index, and the proportion of fluctuation values ​​that are not within the preset fluctuation value range is marked as an abnormal fluctuation coefficient; the regulation stability value of the corresponding ecological intervention process is obtained by comprehensively calculating the fluctuation index and the abnormal fluctuation coefficient.

[0016] Preferably, the risk warning output module is communicatively connected to the abnormal intervention analysis module, and the risk warning output module sends the normal intervention signal to the abnormal intervention analysis module. When the abnormal intervention analysis module receives the normal intervention signal, it performs abnormal intervention analysis on the emergency control module, generates an abnormal intervention signal or a normal intervention signal through analysis, and sends the abnormal intervention signal or the normal intervention signal to the background management end. When the background management end receives the abnormal intervention signal, it triggers a warning prompt.

[0017] Preferably, the specific analysis process of abnormal intervention analysis is as follows:

[0018] During the operation of the emergency control module, its equipment temperature and energy consumption values ​​are collected. If the equipment temperature or energy consumption value exceeds the corresponding preset safety threshold, the emergency control module is judged to be in an abnormal operation state; the duration of the emergency control module in the abnormal operation state during the evaluation period is obtained and the ratio is calculated with the total operation time of the emergency control module during the evaluation period to obtain the abnormal operation ratio, and the number of occurrences in which the emergency control module is in an abnormal operation state for a single time during the evaluation period exceeds the corresponding preset single time threshold is marked as the number of excessive abnormalities, and the maximum value of the single time duration of the emergency control module in the abnormal operation state during the evaluation period is marked as the longest abnormal time;

[0019] The abnormal intervention index is obtained by comprehensively calculating the abnormal operation ratio, the number of excessively long abnormalities and the longest abnormal duration. If the abnormal intervention index exceeds the preset abnormal index threshold, an abnormal intervention signal is generated; if the abnormal intervention index does not exceed the preset abnormal index threshold, a normal intervention signal is generated.

[0020] Preferably, the background management end is communicated with the equipment health diagnosis module. When a risk warning signal or an abnormal intervention signal is generated, the equipment health diagnosis module diagnoses the health status of the emergency control module, determines through analysis whether to generate an equipment replacement signal, and sends the equipment replacement signal to the background management end. When the background management end receives the equipment replacement signal, it triggers a warning prompt.

[0021] Preferably, the specific analysis process of the equipment health diagnosis module is as follows: the production date of the emergency control module is collected, the time difference between the current date and the production date of the emergency control module is calculated to obtain the equipment usage time, and the cumulative operation time of the emergency control module in the historical operation stage is marked as the total operation time; and the environmental loss time of the emergency control module is obtained through analysis, and the frequency of the maintenance interval time of the emergency control module in the historical operation stage exceeding the preset maintenance interval threshold is marked as the maintenance abnormality coefficient; the equipment health index is obtained by comprehensively calculating the equipment usage time, total operation time, environmental loss time and maintenance abnormality coefficient. If the equipment health index exceeds the preset health threshold, an equipment replacement signal is generated.

[0022] Preferably, the analysis and acquisition method of the environmental loss duration is as follows: the ambient temperature and ambient humidity of the installation environment where the emergency control module is located are collected, and the deviation value of the ambient temperature compared to the set suitable temperature range is marked as the temperature deviation value, and the deviation value of the ambient humidity compared to the set suitable humidity range is marked as the humidity deviation value; and the dust concentration of the installation environment where the emergency control module is located is collected and marked as the dust concentration value, and the environmental loss index is obtained by comprehensively calculating the temperature deviation value, the humidity deviation value and the dust concentration value. If the environmental loss index exceeds the preset loss threshold, it is judged that the emergency control module is in a high loss state, and the total time when the emergency control module is in a high loss state during the historical operation stage is obtained and marked as the environmental loss duration.

[0023] Preferably, the risk prediction and analysis module is communicatively connected to the prediction model optimization module. During the operation of the system, the prediction model optimization module regularly collects historical ecological risk factors, the intervention results of the emergency control module and the corresponding ecological environment parameter change data, performs feature extraction and pattern recognition on the historical data through the model iteration algorithm, and generates a parameter adjustment plan for the risk prediction model; the prediction model optimization module sends the parameter adjustment plan to the risk prediction and analysis module, and the risk prediction and analysis module updates the weight coefficient and threshold parameter of the risk prediction model according to the parameter adjustment plan.

[0024] Compared with the prior art, the present invention has the following beneficial effects:

[0025] By collecting ecological and environmental parameters in real time through multiple sensors, we can comprehensively and continuously monitor the environmental status of forestry areas, breaking the time and space limitations of traditional manual inspections and making ecological monitoring more detailed and timely. Real-time parameters are simultaneously transmitted to the risk prediction and analysis module and the backend management terminal. The backend's dynamic display function allows managers to intuitively understand the current status of the forest area, providing immediate information reference for management decisions.

[0026] The risk prediction and analysis module combines real-time parameters with a historical ecological risk database to determine ecological risk factors. It then calculates regulatory directives using the risk prediction model, making risk identification and the generation of regulatory directives more scientific and targeted. This approach avoids the potential bias associated with relying on empirical judgment, provides a clear direction for emergency response and better aligns with the forest region's actual ecological conditions.

[0027] The emergency control module executes ecological intervention operations according to the control instructions, so that ecological intervention is no longer blind, but carried out according to precise instructions, which helps to take appropriate measures at the right time to adjust the ecological environment status and maintain the ecological balance of the forest area.

[0028] The risk warning output module monitors the emergency control process in real time and evaluates its operational status. It then sends normal intervention signals or risk warning signals to the backend. Receiving a risk warning signal triggers a prompt, allowing managers to promptly identify the effectiveness of the intervention. If an intervention anomaly occurs, a swift response can be made and the intervention strategy adjusted, forming a closed-loop management process that makes the entire ecosystem management process more coherent and efficient.

[0029] The various modules of the entire system work together, from environmental monitoring, risk prediction, instruction generation, emergency regulation to process monitoring, to form a complete ecological management chain, which adapts to the complex and changeable characteristics of forestry ecosystems, can better respond to various potential ecological risks, and ensure the stable development of the ecosystem in the forestry area. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1This is a working principle diagram of the forestry area ecological management system based on risk prediction according to the present invention;

[0031] Figure 2 Flowchart for risk warning output module analysis;

[0032] Figure 3 Flowchart for regulatory stability analysis;

[0033] Figure 4 Flowchart for abnormal intervention analysis;

[0034] Figure 5 Flowchart for equipment health diagnostic analysis. DETAILED DESCRIPTION

[0035] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0036] See also Figure 1 The present invention provides a forestry area ecological management system based on risk prediction, the system comprising:

[0037] The ecological and environmental monitoring module collects real-time ecological and environmental parameters within the forestry area through multiple sensors, including temperature, humidity, soil nutrient, and wind speed sensors. These sensors cover air temperature, humidity, soil moisture, soil pH, wind speed, and vegetation coverage. These real-time ecological and environmental parameters are synchronously transmitted to the risk prediction and analysis module and the backend management terminal, which dynamically displays them via display screens and other devices. These dynamic displays can include numerical jump displays, curve change graphs, and bar charts.

[0038] After receiving real-time ecological and environmental parameters, the risk prediction and analysis module analyzes them against a historical ecological risk database to determine ecological risk factors. This database stores data on past ecological risk events within forestry jurisdictions, along with their corresponding environmental parameter characteristics and risk factors. Based on these identified ecological risk factors, the risk prediction and analysis module uses a pre-trained risk prediction model to generate control instructions for the emergency control module. These instructions may include irrigation and ventilation operations, pest and disease control instructions, and other instructions. These instructions are then sent to the emergency control module.

[0039] After receiving the control instructions, the emergency control module performs corresponding ecological intervention operations, such as starting irrigation equipment to replenish water, starting ventilation equipment to adjust air circulation, starting spray equipment for pest control, etc., to adjust the ecological environment status of the forestry area.

[0040] The risk warning output module monitors the emergency control module's intervention operations in real time, continuously assessing its operational status. Based on the assessment results, it generates either a normal intervention signal or a risk warning signal, which it then sends to the backend management terminal. Upon receiving a risk warning signal, the backend management terminal triggers a warning alert through sound alarms, flashing lights, and pop-up notifications.

[0041] Example 1: See Figure 2 The specific analysis process of the risk warning output module is as follows: The corresponding ecological intervention process is marked through a risk level assessment mechanism, classifying it as low-risk or high-risk. The assessment period can be flexibly set based on the actual management needs of the forestry jurisdiction. For example, a longer assessment period can be set during seasons with relatively stable ecological environments, while a shorter assessment period can be set during periods of higher ecological risk.

[0042] After determining the assessment period, the total number of ecological intervention processes occurring during that period is counted, along with the number of processes marked as high-risk. The ratio of the high-risk processes to the total number is calculated to determine the risk ratio. If the risk ratio exceeds the preset threshold, a risk warning signal is generated. If the risk ratio does not exceed the threshold, further analysis is performed.

[0043] For situations where the risk ratio does not exceed the threshold, the difference between the response time value of the corresponding ecological intervention process and the baseline value of the preset response time range is calculated, and the absolute value of this difference is taken to obtain the time deviation value. At the same time, the control stability value of the corresponding intervention process is calculated by comparing it with the preset control stability threshold, and the resulting value is marked as the stability coefficient.

[0044] The time-efficiency deviation values ​​of all ecological intervention processes during the assessment period are averaged to obtain the average time-efficiency deviation. The stability coefficients of all ecological intervention processes during the assessment period are averaged to obtain the average stability coefficient. The risk proportion, average time-efficiency deviation, and average stability coefficient are combined to obtain the risk warning index. If the risk warning index exceeds the preset warning index threshold, a risk warning signal is generated; if it does not exceed the preset warning index threshold, an intervention normal signal is generated.

[0045] The specific analysis process of the risk level assessment mechanism is as follows: the moment when the emergency control module receives the control instruction is collected and marked as the start time; the moment when the emergency control module completes the corresponding ecological intervention operation is collected and marked as the completion time; the interval between the start time and the completion time is calculated and marked as the intervention time.

[0046] The response efficiency value is calculated by comparing the intensity of the control instruction with the intervention duration. The control stability value is obtained through control stability analysis. If the response efficiency value is not within the preset efficiency value range or the control stability value exceeds the preset stability threshold, the corresponding ecological intervention process is marked as a high-risk process. If the response efficiency value is within the preset efficiency value range and the control stability value does not exceed the preset stability threshold, the corresponding ecological intervention process is marked as a low-risk process.

[0047] The intensity of control instructions is determined by the type and scale of the intervention. For example, the intensity of irrigation operations can be divided according to preset irrigation volume levels, while the intensity of pest control operations can be determined by the concentration and range of pesticide spraying. Preset efficiency ranges are set for different types of ecological intervention operations. For example, for emergency firefighting interventions, the preset efficiency range is set higher, while for routine vegetation watering interventions, the preset efficiency range is relatively lower.

[0048] The preset stability threshold is also set according to the characteristics of the intervention operation. For intervention operations that require precise control, such as soil pH adjustment, the preset stability threshold will be set lower to strictly control the stability of the regulation process; for intervention operations that do not require high stability, such as simple ventilation adjustment, the preset stability threshold can be appropriately increased.

[0049] When calculating the average timeliness deviation, all ecological intervention processes within the assessment period must be included, including interventions of varying types and intensities, to ensure that the calculated results fully reflect the overall performance of the emergency control module in terms of response timeliness during that period. The same principle applies to the calculation of the average stability coefficient. By combining the stability coefficients of all intervention processes, the overall level of control stability during that period is determined.

[0050] The comprehensive calculation of the risk warning index requires comprehensive consideration of three indicators: risk ratio, average timeliness deviation, and average stability coefficient. The weighting of each indicator in the calculation can be adjusted based on the ecological characteristics and management priorities of the forestry area. Specifically, for fire risk types, the weighting of risk ratio, average timeliness deviation, and average stability coefficient is 30%, 50%, and 20%. Response efficiency (average timeliness deviation) during fire emergencies directly determines fire control effectiveness, so it carries the highest weight. For pest and disease risk types, the weightings are adjusted to 40%, 20%, and 40%. The spread of pests and diseases relies on long-term, stable regulation, so the average stability coefficient carries a higher weight. The comprehensive calculation formula is: Risk Warning Index = (Risk Ratio × Corresponding Weight) + (Average Timeliness Deviation × Corresponding Weight) + (Average Stability Coefficient × Corresponding Weight). When conflicting indicators exist (e.g., high efficiency but high volatility), the average timeliness deviation is prioritized for fire scenarios (a deviation within a preset range is considered acceptable), while the average stability coefficient is prioritized for pest and disease scenarios (a coefficient exceeding a threshold is considered unacceptable). For example, in forestry areas with high incidence of fires, the proportion of risk ratio will be appropriately increased; in nature reserves with higher requirements for ecological environment stability, the proportion of average stability coefficient will increase accordingly.

[0051] The preset early warning index threshold is set based on historical data and management experience. This threshold is regularly adjusted based on ecological changes and management effectiveness within the forestry jurisdiction to adapt to ecological management needs at different times. When the risk warning index exceeds this threshold, it indicates that there are major operational issues with the emergency control module, requiring timely intervention. When the risk warning index does not exceed this threshold, it indicates that the emergency control module is operating normally and ecological management can continue according to the current model.

[0052] Through the above analysis process, the risk warning output module can comprehensively and accurately evaluate the operating status of the emergency control module, generate corresponding signals in a timely manner and send them to the background management end, so that managers can take appropriate measures according to the signals to ensure the stability of the ecological environment in the forestry area.

[0053] Example 2: See Figure 3 The specific process of regulation stability analysis is as follows: a rectangular coordinate system is established with time as the horizontal axis and ecological and environmental parameters as the vertical axis. The horizontal axis units can be set according to the duration of the ecological intervention process, such as minutes or hours, while the vertical axis units are determined by the specific ecological and environmental parameters, such as temperature in degrees Celsius and humidity in percentage. A curve of ecological and environmental parameter changes in the forestry area during the corresponding ecological intervention process is obtained. This curve is drawn in real time by the ecological and environmental monitoring module, which continuously collects data during the intervention process. The starting point of the curve corresponds to the intervention start time, that is, the moment when the emergency control module begins to execute the control instructions.

[0054] Several monitoring nodes are selected on the change curve. The selection interval of the monitoring nodes can be determined based on the duration of the intervention process and the rate of change of the ecological and environmental parameters. If several pre-processes are short and the parameters change rapidly, the selection interval can be set to a smaller value; if several pre-processes are long and the parameters change slowly, the selection interval can be appropriately increased. The vertical axis difference between two adjacent groups of monitoring nodes is calculated and marked as the fluctuation value. The magnitude of the fluctuation value reflects the magnitude of the change in the ecological and environmental parameters at two adjacent moments. The variance of all fluctuation values ​​is calculated to obtain the fluctuation index, which can reflect the degree of dispersion of the changes in the ecological and environmental parameters during the entire intervention process. At the same time, the number of fluctuation values ​​that are not within the preset fluctuation value range is counted, and the ratio of this number to the total number of fluctuation values ​​is calculated to obtain the abnormal fluctuation coefficient. The preset fluctuation value range is set according to different ecological and environmental parameters and intervention goals. For example, in the soil moisture adjustment process, the preset fluctuation value range will be determined based on the humidity fluctuation range suitable for plant growth.

[0055] By comprehensively calculating the fluctuation index and the abnormal fluctuation coefficient, the regulatory stability value of the corresponding ecological intervention process is obtained. The comprehensive calculation will take into account the degree of influence of the two on the regulatory stability.

[0056] The risk warning output module is communicatively connected to the abnormal intervention analysis module. When the risk warning output module generates a normal intervention signal, the signal will be immediately sent to the abnormal intervention analysis module. After receiving the normal intervention signal, the abnormal intervention analysis module immediately starts the abnormal intervention analysis process for the emergency control module. During the analysis process, the temperature of the equipment is collected in real time by temperature sensors deployed on the key components of the emergency control module. The installation position of the temperature sensor is determined according to the heating characteristics of the equipment. For example, corresponding sensors will be installed on components prone to heating, such as motors and controllers. At the same time, the energy consumption value of the emergency control module is collected through the electric energy metering device. The energy consumption value collection frequency is consistent with the temperature collection frequency to ensure data synchronization.

[0057] The collected device temperature is compared with the preset temperature safety threshold. This threshold is determined by the device type and operating standards of the emergency control module, and different thresholds may be set for different device models. Similarly, the energy consumption value is compared with the preset energy consumption safety threshold. This threshold is determined by the device's rated power and operating conditions, and may have different thresholds for full load operation and standby mode, for example. If the device temperature exceeds the preset temperature safety threshold, or the energy consumption value exceeds the preset energy consumption safety threshold, the emergency control module will be judged to be in an abnormal operating state.

[0058] During the abnormal intervention analysis process, the emergency control module's operating status is continuously tracked, and periods of abnormal operation are recorded, including the start and end times of the abnormality. Furthermore, for the entire evaluation period covered by the intervention normal signal, the number of occurrences and duration of abnormal operation are counted, providing basic data for subsequent analysis. The abnormal intervention analysis module also stores collected temperature and energy consumption data in real time. This data includes information such as acquisition time, temperature value, energy consumption value, and status determination results. This data is used for subsequent trend analysis and historical comparison.

[0059] The operation of the Abnormal Intervention Analysis Module will not affect the normal operation of the Emergency Control Module. The two maintain independence in data transmission and processing to ensure that the Emergency Control Module can continue to perform ecological intervention operations. When the Abnormal Intervention Analysis Module completes its analysis, it generates a corresponding abnormal intervention signal or normal intervention signal based on the analysis results and sends the signal to the backend management terminal, which processes and displays the received signal accordingly.

[0060] Through the above process, the abnormal intervention analysis module can further detect the operating status of the emergency control module and timely discover potential abnormal situations when the risk warning output module determines that the intervention is normal, thereby providing more comprehensive protection for the ecological management of the forestry area.

[0061] Example 3: See Figure 4 The specific process for regulatory stability analysis is as follows: A rectangular coordinate system is established with time as the horizontal axis and the ecological and environmental parameters as the vertical axis. The horizontal axis scale unit can be selected based on the duration of the ecological intervention process, such as minutes or hours. The vertical axis scale unit is determined by the specific ecological and environmental parameter type, such as degrees Celsius for temperature and percentages for humidity. A curve of ecological and environmental parameter changes in the forestry area during the corresponding ecological intervention is obtained. This curve is generated in real time by the ecological and environmental monitoring module, which continuously collects data during the intervention process. The starting point of the curve corresponds to the time the intervention starts. Several monitoring nodes are selected on the change curve. The interval selected can be adjusted based on the rate of change of the ecological and environmental parameters during the intervention process: the interval is shortened when the parameter changes rapidly and increased when the change is slow. The vertical axis difference between two adjacent groups of monitoring nodes is calculated and marked as the fluctuation value. The variance of all fluctuation values ​​is calculated to obtain the fluctuation index. The number of fluctuation values ​​that are not within the preset fluctuation range is counted, and the ratio of this value to the total number of fluctuation values ​​is marked as the abnormal fluctuation coefficient. The regulatory stability value of the corresponding ecological intervention process is calculated by combining the fluctuation index and the abnormal fluctuation coefficient.

[0062] The risk warning output module communicates with the abnormal intervention analysis module. When the risk warning output module generates a normal intervention signal, it sends this signal to the abnormal intervention analysis module. After receiving the normal intervention signal, the abnormal intervention analysis module conducts abnormal intervention analysis on the emergency control module. During the analysis, the device temperature is collected using temperature sensors installed on key components of the emergency control module. The temperature sensors are located based on the heat-generating areas of the device, such as the motor and control chip. Simultaneously, energy consumption values ​​from the emergency control module are collected using an energy meter. The collection frequency is consistent with the temperature collection frequency to ensure time synchronization of the data. The collected device temperature is compared with a preset temperature safety threshold. The preset temperature safety threshold is determined by the device material and operating standards, and may vary for different components. The energy consumption value is also compared with a preset energy safety threshold. The preset energy safety threshold is set based on the device's rated power and operating mode, for example, different thresholds for standby and operating modes. If the device temperature exceeds the corresponding preset temperature safety threshold or the energy consumption exceeds the corresponding preset energy safety threshold, the emergency control module is determined to be in an abnormal operating state.

[0063] In the abnormal intervention analysis, the start time and end time of each time the emergency control module enters the abnormal operation state will be recorded to calculate the duration of each abnormal operation. For the evaluation period corresponding to the normal intervention signal, the total duration of the emergency control module in the abnormal operation state during the period is counted, and it is divided by the total operation time of the emergency control module during the period to obtain the abnormal operation ratio. At the same time, a preset single duration threshold is set, and the number of times the single duration of abnormal operation exceeds the threshold during the evaluation period is counted, and marked as the number of excessive abnormalities. The maximum value of the single duration of all abnormal operations during the evaluation period is recorded and marked as the longest abnormal duration. The abnormal intervention index is obtained by comprehensively calculating the abnormal operation ratio, the number of excessive abnormalities and the longest abnormal duration. If the abnormal intervention index exceeds the preset abnormal index threshold, an abnormal intervention signal is generated; if it does not exceed, a normal intervention signal is generated, and the generated signal is sent to the background management end.

[0064] The calculation formula of abnormal intervention index is:

[0065]

[0066] in, represents the abnormal intervention index, Represents the abnormal operation ratio, The weight representing the proportion of abnormal operation, Represents the number of extremely long exceptions. Represents the weight of the number of extremely long exceptions, Represents the longest abnormal duration, Represents the weight of the longest abnormal duration. The formula logic needs to be dynamically adjusted based on the correlation between indicators: When the ratio of abnormal operation percentage (s) to the longest abnormal duration (w) is greater than 3 (indicating that abnormalities occur primarily frequently and for short periods of time), the weight of the abnormal operation percentage (t) is increased by 20% and the weight of the longest abnormal duration (x) is reduced by 20%. When the ratio is less than 0.3 (indicating that abnormalities occur primarily in single, long periods of time), the weight of the longest abnormal duration (x) is increased by 20% and the weight of the abnormal operation percentage (t) is reduced by 20%.

[0067] At the same time, the weight setting needs to be differentiated according to the type of equipment: for precision control equipment such as pest sprayers, t=40%, v=40%, and x=20% are used (because frequent failures have a greater impact on precise control); for large-scale irrigation equipment, t=20%, v=20%, and x=60% are used (because a single long-term abnormality may cause large-scale ecological imbalance).

[0068] The backend management terminal communicates with the equipment health diagnosis module. When a risk warning signal or abnormal intervention signal is generated, the equipment health diagnosis module is activated to diagnose the health status of the emergency control module. During the diagnosis process, the emergency control module's operating data, maintenance records, and other information are collected. This information is analyzed to determine whether an equipment replacement signal is necessary. If so, the equipment replacement signal is sent to the backend management terminal. Upon receiving the signal, the backend management terminal triggers a warning prompt through a preset prompt method, such as an audible alarm, a pop-up screen, or a flashing indicator light, so that management personnel are promptly informed and can take appropriate measures.

[0069] Example 4: See Figure 5 The specific analysis process of the equipment health diagnosis module is as follows: First, the factory date of the emergency control module is collected. This date can be obtained from the equipment's factory certificate, the fuselage nameplate, or the equipment file stored in the system. The time difference between the current date and the factory date is calculated to obtain the equipment usage time. The time difference is calculated accurately to the day to fully reflect the equipment's life cycle from production to the current time. At the same time, the historical operation records stored in the system are retrieved, and the cumulative operation time of the emergency control module during the historical operation phase is counted and marked as the total operation time. The cumulative operation time statistics cover the time of each startup of the equipment, including all periods of normal and abnormal operation.

[0070] To analyze and obtain the environmental wear and tear duration of the emergency control module, the ambient temperature and humidity of the installation environment must be collected using environmental sensors installed around the device. The sensors should be installed in a location that accurately reflects the device's actual environment. They are typically located near the device casing, out of direct sunlight or airflow. The collected ambient temperature is compared with the set optimal temperature range, and the deviation of the ambient temperature from this range, known as the temperature deviation, is calculated. If the ambient temperature is above the upper limit of the set optimal temperature range, the deviation is positive; if it is below the lower limit, it is negative. Similarly, the ambient humidity is compared with the set optimal humidity range, and the humidity deviation is calculated using the same method as the temperature deviation. Furthermore, the dust concentration of the device's installation environment is collected using a dust sensor and directly marked as the dust concentration value. The dust sensor should be installed at a height consistent with the device's main heat dissipation components to ensure that the collected data reflects the actual impact of dust on the device.

[0071] The environmental loss index is calculated by comprehensively processing temperature deviation, humidity deviation, and dust concentration values. A preset loss threshold is set. If the environmental loss index exceeds this threshold, the emergency control module is deemed to be in a high-loss state. All periods of high-loss equipment in historical operation records are retrieved, and the total duration of these periods is calculated and marked as environmental loss duration. The high-loss period when the equipment was not operating is excluded from the statistical process, and only the high-loss period when the equipment was operating is calculated.

[0072] Retrieve the emergency control module's historical maintenance records, which include the time of each maintenance session. Calculate the interval between two consecutive maintenance sessions and compare it to the preset maintenance interval threshold. Count the number of times the maintenance interval exceeded this threshold during the historical operation phase and mark it as the maintenance anomaly coefficient. The maintenance interval threshold is set based on the recommendations in the equipment's instruction manual and maintenance manual. Different types of maintenance items may have different thresholds. This count is the total number of times the corresponding threshold was exceeded for all types of maintenance items.

[0073] The device health index is derived by comprehensively processing the device's usage time, total operating time, environmental wear and tear, and maintenance anomaly coefficient. A preset health threshold is set, and if the device health index exceeds this threshold, a device replacement signal is generated. The comprehensive processing of the device health index must consider the actual impact of each parameter. For example, long device usage time may indicate a high degree of device aging, excessive total operating time may lead to increased component wear, environmental wear and tear time reflects the cumulative damage to the device caused by harsh environments, and the maintenance anomaly coefficient reflects the impact of improper maintenance on device health.

[0074] Preset health thresholds are set based on the device's designed lifespan, failure rate data, and other factors. Different emergency control modules may have different thresholds. When the device health index exceeds this threshold, it indicates that the device's health status can no longer guarantee normal operation. Continued use may cause ecological intervention operations to fail or lead to greater ecological risks. At this time, a device replacement signal is generated and sent to the backend management terminal. Upon receiving this signal, the backend management terminal notifies the management staff to promptly replace the device through a preset prompt method. This prompt method can be distinguished from other early warning prompts, allowing management staff to quickly identify and handle it.

[0075] Example 5: The risk prediction and analysis module establishes a communication connection with the prediction model optimization module, and the two realize information exchange through an encrypted data transmission protocol to ensure the integrity and security of the data during the transmission process. During the operation of the system, the prediction model optimization module starts the data collection program according to a fixed time period. The collection period can be adjusted according to the rate of change of the ecological environment in the forestry area. If the ecological environment parameters fluctuate frequently, the collection period can be shortened appropriately; if the ecological environment is relatively stable, the collection period can be extended. The collected data covers historical ecological risk factors, the intervention results of the emergency control module and the corresponding ecological environment parameter change data. The historical ecological risk factors include the types and spread rates of pests and diseases that occurred in the past, the starting points and spread ranges of forest fires, the degree of soil desertification and the direction of expansion, etc.; the intervention results include the improvement of the ecological environment after the implementation of emergency control measures, the degree of mitigation of risk factors, etc.; the ecological environment parameter change data include the specific values ​​and change trends of parameters such as air temperature, humidity, soil moisture content, and vegetation growth conditions before and after the intervention.

[0076] After data collection is complete, the predictive model optimization module preprocesses the acquired historical data, including data cleaning and format standardization. Data cleaning primarily removes outliers and missing values. Abnormal data due to sensor failure is identified and deleted by comparing it with normal data from adjacent time periods. Missing data due to transmission interruptions is supplemented using linear interpolation. Format standardization converts data from different sources and formats into a unified format. For example, dates are standardized to year-month-day hour:minute:second format, and parameter values ​​are rounded to two decimal places to facilitate smooth reading and analysis by subsequent processing programs.

[0077] After preprocessing, historical data is fed into the model's iterative algorithm, which first performs feature extraction, filtering out key features relevant to risk prediction from the massive data. For example, in pest and disease risk prediction, features such as temperature change rate, humidity duration, and vegetation density are extracted; in fire risk prediction, features such as wind speed, combustible moisture content, and peak temperature are extracted. During the feature extraction process, the correlation between different features and the probability of risk occurrence is analyzed, retaining those with strong correlations and removing redundant information with weaker correlations.

[0078] After feature extraction, the model's iterative algorithm performs pattern recognition. By deeply mining historical data, it can identify patterns in environmental parameter changes prior to the occurrence of different ecological risks, as well as patterns in the correspondence between intervention measures and risk mitigation effects. For example, it can identify patterns in which the likelihood of forest fires increases significantly when humidity remains below a certain value for three consecutive days and wind speeds exceed a specific level; or it can identify patterns in which the spread of a particular pest or disease decreases within a week after biological control measures are implemented.

[0079] Based on the extracted key features and identified patterns, the prediction model optimization module generates a parameter adjustment plan for the risk prediction model. This parameter adjustment plan includes assigning weights to each input feature in the model, adjusting the thresholds for risk classification, and modifying the coefficients used to calculate the intensity of intervention measures. For example, if historical data shows that the impact of a certain ecological risk factor on the forestry area has increased significantly in recent years, the parameter adjustment plan will increase the weight of that factor in the model calculation accordingly. If the effectiveness of a certain intervention measure changes over time, the plan will adjust the corresponding coefficients to reflect this change.

[0080] The prediction model optimization module packages the generated parameter adjustment plan into a specially formatted data packet containing information such as the version number, adjustment time, and detailed parameter modification information. This data packet is then sent to the risk prediction analysis module via a communication connection. Upon receiving the data packet, the risk prediction analysis module first verifies the data for integrity and transmission errors. If the verification passes, the model update process is initiated. If the verification fails, a retransmission request is sent to the prediction model optimization module until a complete and correct data packet is received.

[0081] The model update program updates the risk prediction model's weight coefficients and threshold parameters according to the parameter adjustment plan. During the update process, the risk prediction and analysis module temporarily stops accepting new ecological and environmental parameters and resumes normal operation after the update is complete to avoid data processing errors. After the update is complete, the risk prediction and analysis module automatically generates an update log, recording parameter values ​​before and after the update, update time, and operator information. This log is stored in the system database for subsequent query and audit. The log should also include traceability information on the calculation process of the warning results: clearly marking the values ​​of each component of the risk warning index / abnormal intervention index (such as risk contribution value and average timeliness deviation), the corresponding weights, and the calculation steps for substitution into the formula. For example, for a fire warning, the following should be noted: "Risk contribution value 0.2 (weight 30%) + average timeliness deviation 5 (weight 50%) + average stability coefficient 1.2 (weight 20%) = warning index 3.26 (exceeding the threshold of 3.0)." During model optimization, traceability information can be used to identify indicator conflicts or improper weighting issues (e.g., insufficient weighting of the stability coefficient in pest and disease warnings leading to false alarms) and adjust parameters accordingly.

[0082] In addition, the prediction model optimization module will back up each parameter adjustment plan generated. The backup file is stored in an independent storage medium and is regularly backed up off-site to prevent the loss of the plan due to hardware failure or unexpected events. At the same time, the system will record the operating status of each model after the update, including the accuracy of the risk prediction, the applicability of the intervention measures, and other information. This information will serve as a reference for the next data collection and model optimization, forming a continuous iterative optimization closed loop. Through the above process, the risk prediction model can continuously adapt to changes in the ecological environment of the forestry area, so that the control instructions it outputs are more in line with actual needs, thereby better supporting the ecological management work in the forestry area.

[0083] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0084] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. The forestry area ecological management system based on risk prediction is characterized by: It includes ecological environment monitoring module, risk prediction and analysis module, emergency control module, risk warning output module and background management terminal; The ecological environment monitoring module collects ecological environment parameters of the forestry area in real time through multiple types of sensors, and sends the collected real-time ecological environment parameters to the risk prediction and analysis module and the backend management terminal, which dynamically displays the real-time ecological environment parameters; The risk prediction and analysis module determines the ecological risk factors based on real-time ecological environment parameters and the historical ecological risk database, calculates the control instructions of the emergency control module based on the ecological risk factors and the risk prediction model, and sends the control instructions to the emergency control module; The emergency control module performs ecological intervention operations according to control instructions and adjusts the ecological environment status of the forestry area; The risk warning output module monitors the intervention process of the emergency control module in real time, evaluates the operating status of the emergency control module, and generates a normal intervention signal or a risk warning signal based on the information. The normal intervention signal or the risk warning signal is sent to the background management end, and the background management end triggers a warning prompt when receiving the risk warning signal; The specific analysis process of the risk warning output module includes: marking the corresponding ecological intervention process as a low-risk process or a high-risk process through the risk level assessment mechanism, setting an assessment period, and calculating the ratio of the number of high-risk processes in the assessment period to the total number of ecological intervention processes to obtain a risk ratio value. If the risk ratio value exceeds the preset risk ratio threshold, a risk warning signal is generated; if the risk ratio value does not exceed the preset risk ratio threshold, the response time value of the corresponding ecological intervention process and the benchmark value of the preset response time range are calculated by difference and the absolute value is taken to obtain a time deviation value, and the ratio of the control stability value of the corresponding intervention process to the preset control stability threshold is marked as a stability coefficient; the average time deviation is obtained by averaging the time deviation values ​​of all ecological intervention processes in the assessment period, and the average stability coefficient is obtained by averaging the stability coefficients of all ecological intervention processes in the assessment period; the risk warning index is obtained by comprehensively calculating the risk ratio value, the average time deviation and the average stability coefficient. If the risk warning index exceeds the preset warning index threshold, a risk warning signal is generated. If the risk warning index does not exceed the preset warning index threshold, a normal intervention signal is generated.

2. The forestry area ecological management system based on risk prediction according to claim 1 is characterized in that: The specific analysis process of the risk level assessment mechanism is as follows: the moment when the emergency control module receives the control instruction is collected and marked as the start time, and the moment when the emergency control module completes the corresponding ecological intervention operation is collected and marked as the completion time, and the interval between the start time and the completion time is marked as the intervention duration; the ratio of the intensity value of the control instruction to the intervention duration is marked as the response efficiency value, and the control stability value is obtained through control stability analysis. If the response efficiency value is not within the preset efficiency value range or the control stability value exceeds the preset stability threshold, the corresponding ecological intervention process is marked as a high-risk process; If the response efficiency value is within the preset efficiency value range and the regulation stability value does not exceed the preset stability threshold, the corresponding ecological intervention process will be marked as a low-risk process.

3. The forestry area ecological management system based on risk prediction according to claim 2 is characterized in that: The specific analysis process of the regulation stability analysis is as follows: a plane rectangular coordinate system is established with time as the horizontal axis and the ecological environment parameters as the vertical axis, and the ecological environment parameter change curve of the forestry area during the corresponding ecological intervention process is obtained. The ecological environment parameter change curve is placed in the rectangular coordinate system, and the starting point of the change curve corresponds to the intervention start time; a number of monitoring nodes are selected on the change curve, and the vertical axis difference between two adjacent groups of monitoring nodes is marked as the fluctuation value, and the variance of all fluctuation values ​​is calculated to obtain the fluctuation index, and the proportion of the number of fluctuation values ​​that are not within the preset fluctuation value range is marked as the abnormal fluctuation coefficient; The regulation stability value of the corresponding ecological intervention process is obtained by comprehensively calculating the fluctuation index and the abnormal fluctuation coefficient.

4. The forestry area ecological management system based on risk prediction according to claim 1 is characterized in that: The risk warning output module is communicated with the abnormal intervention analysis module. The risk warning output module sends the normal intervention signal to the abnormal intervention analysis module. When the abnormal intervention analysis module receives the normal intervention signal, it performs abnormal intervention analysis on the emergency control module, generates an abnormal intervention signal or a normal intervention signal through analysis, and sends the abnormal intervention signal or the normal intervention signal to the background management end. When the background management end receives the abnormal intervention signal, it triggers a warning prompt.

5. The forestry area ecological management system based on risk prediction according to claim 4 is characterized in that: The specific analysis process of abnormal intervention analysis is as follows: During the operation of the emergency control module, the device temperature and energy consumption values ​​are collected. If the device temperature or energy consumption value exceeds the corresponding preset safety threshold, the emergency control module is judged to be in an abnormal operating state; The duration of the emergency control module being in an abnormal operation state during the evaluation period is obtained and its ratio with the total operation time of the emergency control module during the evaluation period is calculated to obtain the abnormal operation ratio, and the number of occurrences in which the emergency control module is in an abnormal operation state for a single time during the evaluation period exceeds the corresponding preset single time threshold is marked as the number of excessive abnormalities, and the maximum value of the single time duration of the emergency control module being in an abnormal operation state during the evaluation period is marked as the longest abnormal time; The abnormal intervention index is obtained by comprehensively calculating the abnormal operation ratio, the number of excessively long abnormalities and the longest abnormal duration. If the abnormal intervention index exceeds the preset abnormal index threshold, an abnormal intervention signal is generated; if the abnormal intervention index does not exceed the preset abnormal index threshold, a normal intervention signal is generated.

6. The forestry area ecological management system based on risk prediction according to claim 1 is characterized in that: The backend management terminal is connected to the equipment health diagnosis module. When a risk warning signal or an abnormal intervention signal is generated, the equipment health diagnosis module diagnoses the health status of the emergency control module, determines through analysis whether to generate an equipment replacement signal, and sends the equipment replacement signal to the backend management terminal. When the backend management terminal receives the equipment replacement signal, it triggers a warning prompt.

7. The forestry area ecological management system based on risk prediction according to claim 6 is characterized in that: The specific analysis process of the equipment health diagnosis module is as follows: the factory date of the emergency control module is collected, the time difference between the current date and the factory date of the emergency control module is calculated to obtain the equipment usage time, and the cumulative operation time of the emergency control module in the historical operation stage is marked as the total operation time; And through analysis, the environmental loss time of the emergency control module is obtained, and the frequency of the maintenance interval time of the emergency control module in the historical operation stage exceeding the preset maintenance interval threshold is marked as the maintenance abnormality coefficient; the equipment health index is obtained by comprehensively calculating the equipment usage time, total operating time, environmental loss time and maintenance abnormality coefficient. If the equipment health index exceeds the preset health threshold, an equipment replacement signal is generated.

8. The forestry area ecological management system based on risk prediction according to claim 7 is characterized in that: The specific method for analyzing and obtaining the environmental loss duration is as follows: the ambient temperature and ambient humidity of the installation environment where the emergency control module is located are collected, and the deviation value of the ambient temperature compared to the set suitable temperature range is marked as the temperature deviation value, and the deviation value of the ambient humidity compared to the set suitable humidity range is marked as the humidity deviation value; and the dust concentration of the installation environment where the emergency control module is located is collected and marked as the dust concentration value. The environmental loss index is obtained by comprehensively calculating the temperature deviation value, the humidity deviation value and the dust concentration value. If the environmental loss index exceeds the preset loss threshold, it is judged that the emergency control module is in a high loss state, and the total time that the emergency control module is in a high loss state during the historical operation stage is obtained and marked as the environmental loss duration.

9. The forestry area ecological management system based on risk prediction according to claim 1 is characterized in that: The risk prediction and analysis module is communicated with the prediction model optimization module. During the operation of the system, the prediction model optimization module regularly collects historical ecological risk factors, the intervention results of the emergency control module and the corresponding ecological environment parameter change data, and performs feature extraction and pattern recognition on the historical data through the model iteration algorithm to generate a parameter adjustment plan for the risk prediction model; the prediction model optimization module sends the parameter adjustment plan to the risk prediction and analysis module, and the risk prediction and analysis module updates the weight coefficient and threshold parameter of the risk prediction model according to the parameter adjustment plan.

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