Wild animal AI monitoring and early warning system and method thereof

Abstract

The invention discloses a wildlife AI monitoring and early warning system and method, and relates to the technical field of wildlife ecological protection, and the method comprises the steps: receiving and analyzing wildlife activity data; historical ecological data and real-time monitoring information are utilized; monitoring the predicted activity mode in real time; analyzing a result according to the early warning signal and the behavior; according to real-time monitoring and early warning results, monitoring strategies and protection measures are dynamically adjusted so as to adapt to behavior changes of wild animals and changes of environmental conditions. Through timely manual intervention and adjustment, the system can effectively balance a supply-demand relationship, ensure ecological balance of wild animals, reduce an unstable phenomenon caused by ecological curve load fluctuation, improve stability and reliability, monitor activities of the wild animals in real time, predict and early warn human activities which may interfere with animal habitats, and improve the survival rate of the wild animals. Therefore, a scientific basis and timely response are provided for wild animal protection work.

Description

Technical Field

[0001] The present invention relates to the technical field of wildlife ecological protection, and in particular to a wildlife AI monitoring and early warning system and method thereof. Background Art

[0002] As human activities have an increasing impact on the natural environment, existing monitoring systems face many challenges in the field of wildlife protection, including high costs and inefficiencies caused by reliance on manual patrols and limited remote sensing technology, as well as insufficient monitoring accuracy in complex environments and changing climate conditions. With the destruction of wildlife habitats by human activities such as urbanization and agricultural expansion, as well as the impact of illegal hunting and climate change on animal migration patterns and reproductive cycles, the living environment of wildlife is under serious threat. In addition, with the increasing attention paid to biodiversity conservation and the formulation of international environmental laws and regulations, there is an urgent need for a technology that can monitor and warn wildlife activities in real time and accurately, so as to guide the formulation and implementation of protection measures based on scientific evidence and effectively respond to the challenges of wildlife protection. Summary of the invention

[0003] In view of the problems existing in the existing wild animal AI monitoring and early warning system and method, the present invention is proposed. Therefore, the problem to be solved by the present invention is how to provide a wild animal AI monitoring and early warning system and method.

[0004] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0005] In a first aspect, the present invention provides a wildlife AI monitoring and early warning system and method thereof, which includes receiving and analyzing wildlife activity data and determining key ecological indicators within the monitoring area;

[0006] Using historical ecological data and real-time monitoring information, AI algorithms are used to predict wildlife activity patterns and potential risk areas;

[0007] Real-time monitoring of predicted activity patterns, identification of situations that may cause disturbance or threat to wildlife through behavioral analysis, and issuance of early warning signals;

[0008] Provide human intervention and adjustment recommendations based on early warning signals and behavioral analysis results to protect wildlife from human activities and other threats;

[0009] Based on real-time monitoring and early warning results, monitoring strategies and protection measures are dynamically adjusted to adapt to changes in wildlife behavior and changes in environmental conditions.

[0010] As a preferred solution of the wildlife AI monitoring and early warning system and method described in the present invention, wherein: receiving and analyzing key ecological indicator data of a specific wildlife protection area, and having professionals verify the data and record the values ​​and time ranges of the key indicators;

[0011] When there is uncertainty in ecological indicator data or further information is needed, communicate with relevant environmental protection agencies or scientific research teams to obtain more accurate data and explanations;

[0012] Based on wildlife activity patterns and ecological needs detected by the monitoring system, assess and determine whether restrictions on human activities are necessary during a specific period of time, as well as the specific conditions for the restrictions;

[0013] If it is determined that restrictions on human activities are necessary, the monitoring strategy and protective measures will be adjusted during the restriction period to adapt to the new restriction requirements;

[0014] If no restrictions are determined to be necessary, continue to monitor and maintain current surveillance and protection measures to ensure all protections can be adapted to new restrictions or remain as they are.

[0015] As a preferred solution of the wildlife AI monitoring and early warning system and method thereof of the present invention, wherein: the system operation status covers the operation of the monitoring equipment, ensuring that the equipment is in an appropriate working state to meet the data collection requirements;

[0016] This includes the status of the data analysis module, analyzing its accuracy, speed and algorithm performance in processing wildlife activity data to ensure that the module operates stably at optimal performance;

[0017] This includes the adjustment of the early warning mechanism to ensure that the system can adjust the early warning thresholds and response measures in real time based on monitoring data and forecast results to adapt to changes in wildlife activities.

[0018] As a preferred solution of the wildlife AI monitoring and early warning system and method described in the present invention, the prediction of the ecological risk curve includes the following steps:

[0019] Collect historical load data and real-time monitoring information, perform feature engineering on historical data, extract time features, and perform data preprocessing;

[0020] According to the data characteristics and forecasting requirements, the load forecasting model is used for forecasting. The historical data is divided into a training set and a validation set. The training set is trained to optimize the model parameters to minimize the forecast error. The relevant expressions of the load forecasting model are as follows:

[0021]

[0022] In the formula, is the predicted value at time point t, c is a constant term, is the autoregressive coefficient, θ i is the moving average coefficient, ∈ t is white noise; p, d and q represent the autoregressive order, the difference order and the moving average order respectively;

[0023] Use the validation set to evaluate the trained model. The evaluation indicators include root mean square error and mean absolute error. Adjust the model parameters according to the evaluation results.

[0024] Use the trained model to predict the load for a period of time in the future, dynamically adjust the model input parameters, analyze the prediction results, compare them with the actual observation data, and evaluate the prediction accuracy;

[0025] The forecast results are organized into a forecast report, including forecast curve graph, forecast error analysis, and influencing factor analysis.

[0026] As a preferred solution of the wildlife AI monitoring and early warning system and method described in the present invention, the real-time monitoring includes the following steps:

[0027] Use the load forecasting model to forecast the load in the future to obtain the forecast value, and calculate the warning threshold by combining the distribution characteristics of historical load data and the forecast error of the model;

[0028] Collect load forecast data {R1, R2, …, R m}, calculate the warning threshold, the relevant calculation formula is as follows:

[0029]

[0030] In the formula, R i is the load forecast data, is the standard deviation of historical load forecast data, n is the number of forecasts;

[0031] Calculate the median M and interquartile range IQR of the load forecast data. If n is an odd number, the median is the value in the middle of the data set, expressed as:

[0032]

[0033] If n is an even number, the median is the average of the two middle values ​​of the data set, expressed as:

[0034]

[0035] IQR=Q3-Q1

[0036] Where Q3 is the median of the first 75% of the values ​​in the data set, and Q1 is the median of the first 25% of the values ​​in the data set. The calculation formula for the warning threshold is:

[0037]

[0038] Among them, F i is the warning threshold, k i is the adjustment coefficient, i=1, 2;

[0039] Compare the predicted value with the warning threshold to determine whether it exceeds the threshold. If the predicted value exceeds the threshold, it is considered that the load may exceed the limit, and the next step is to proceed to the next step of predictive value judgment.

[0040] The system automatically sends out an early warning signal to inform relevant personnel that the load forecast exceeds the limit, and indicates the severity of the overload and the expected impact range;

[0041] Record the time of occurrence and predicted value of the over-limit situation, as well as possible influencing factors, and subsequently improve the prediction model or adjust the operation strategy.

[0042] As a preferred solution of the wildlife AI monitoring and early warning system and method described in the present invention, the prediction value judgment includes:

[0043] When the load forecast value R ≥ the second warning limit F2, a high-level warning signal is issued to notify relevant personnel to take timely measures. It is necessary to activate backup decisions and adjust plans to ensure the stable operation of the ecosystem. The system issues a warning signal to predict that the load may exceed the limit;

[0044] When the first warning limit F1 ≤ load forecast value R < second warning limit F2, a low-level warning signal is issued to remind relevant personnel to pay attention to the load forecast and adjust manual output in time to cope with possible load fluctuations. The system predicts that the load is in a warning state and load overload may occur;

[0045] When the load prediction value R≤the first warning limit value F1, no warning signal is issued. It is considered that the load is within the normal range and no special measures are needed. The system predicts that the load is normal and no load overlimit is found.

[0046] As a preferred solution of the wildlife AI monitoring and early warning system and method thereof of the present invention, the manual intervention and adjustment includes the following steps:

[0047] When the system issues an early warning signal, the monitoring personnel first receive the early warning notification and check the predicted wildlife activity and the warning level;

[0048] Based on the warning level and forecast of wildlife activity, monitoring personnel assess whether manual intervention and adjustments are needed;

[0049] If manual intervention and adjustment are required, monitoring personnel will take appropriate measures based on the actual situation, including adjusting monitoring strategies and protection measures, and changing ecological management plans;

[0050] When the system indicates a high-risk situation may occur, monitoring personnel take actions to mitigate the risk, such as limiting human activities or increasing protected areas;

[0051] If the predicted risk value is between the preset first and second limits, the monitoring personnel will adjust the protection measures in a timely manner according to the warning level and actual conditions to balance the ecological protection needs;

[0052] If the predicted risk value is lower than the first limit, the system considers that the ecological status is within the normal range and no special measures are needed;

[0053] Changing ecological management plans may include increasing the investment of conservation resources or adjusting the boundaries and measures of protected areas when high-risk situations are predicted;

[0054] After artificial intervention and adjustments, monitoring personnel continue to monitor wildlife activities and ecological environment status to ensure the safety and stability of the ecosystem.

[0055] In a second aspect, the present invention provides a wildlife AI monitoring and early warning system, which includes: an acquisition module for receiving and analyzing wildlife activity and environmental data, determining ecological protection targets, and checking the state of the ecosystem within the monitoring area;

[0056] The monitoring module is used to combine historical ecological data and real-time monitoring information to predict the ecological risk curve, conduct real-time monitoring of the predicted ecological risk curve, identify possible risk situations, and issue early warning signals and protective measures;

[0057] The prompt module is used to conduct manual intervention and adjustments based on the early warning signals and protection measures recommended by the system, and to remind people to take corresponding protection actions based on wild animal activities and ecological risks.

[0058] In a third aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the processor executes the computer program, it implements the steps of a wildlife AI monitoring and early warning system and its method.

[0059] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, the steps of the wildlife AI monitoring and early warning system and method thereof are implemented.

[0060] The beneficial effects of the present invention are that through timely human intervention and adjustment, the supply and demand relationship can be effectively balanced, the ecological balance of wild animals can be ensured, the instability caused by fluctuations in the ecological curve load can be reduced, stability and reliability can be improved, the activities of wild animals can be monitored in real time, and human activities that may interfere with animal habitats can be predicted and warned, thereby providing a scientific basis and timely response for wildlife protection work. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0062] Figure 1 Image of computer equipment for the wildlife AI monitoring and early warning system. DETAILED DESCRIPTION

[0063] In order to make the above-mentioned purposes, features and advantages of the present invention more understandable, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0064] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0065] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0066] Example 1

[0067] Reference Figure 1 , which is the first embodiment of the present invention, provides a wild animal AI monitoring and early warning system and method thereof, including:

[0068] S1: Receive wildlife activity and environmental data, determine ecological protection targets within the monitoring area, and check the real-time status of wildlife habitats; after receiving protection instructions or environmental change information in wildlife habitats, monitoring personnel confirm the information and record the content and time range; if there is any ambiguity in the information, communicate with relevant departments or research institutions to clarify specific requirements; if there are specific ecological risks or protection needs, determine whether to specify specific activity restrictions or protection measures. If so, adjust the monitoring and protection strategies as required; if not, adjust the strategies based on the risk assessment results.

[0069] S2: The monitoring system combines historical ecological data and real-time monitoring information to predict wildlife activity patterns, monitor the predicted activity patterns in real time, identify potential risks or interference situations, and issue early warning signals and protection measures. Data collection and preparation: Collect historical wildlife activity data, including activity pattern data from the past few months or years, and record them regularly. Collect real-time monitoring information, such as current climate conditions, human activity interference, etc. Perform feature engineering and preprocessing on historical data, use ecological models for prediction, and optimize model parameters to adapt to different wildlife behaviors and environmental changes.

[0070] S3: Carry out manual intervention and adjustments based on the warning signals and protection measures suggested by the system, and take corresponding protection actions according to the wildlife activities and ecological risk conditions; after receiving the warning signal, the operator will evaluate the warning level and wildlife activities to decide whether manual intervention measures are needed; depending on the warning situation, this may include adjusting the deployment of monitoring equipment, restricting the scope of human activities, increasing the protected area or taking other protection measures; changes in protection strategies may involve increasing the monitoring frequency, changing patrol routes or adopting new protection technologies.

[0071] Furthermore, this embodiment also provides a wildlife AI monitoring and early warning system, including: an acquisition module, used to receive wildlife activity and environmental data, determine ecological protection goals, and check the status of wildlife habitats; a monitoring module, used to combine historical and real-time data, predict wildlife activity patterns, monitor and identify risks in real time, and issue early warnings and protection recommendations; a prompt module, used to perform manual intervention and adjustments based on early warning signals and protection recommendations, and remind people to take protective actions.

[0072] In summary, the wildlife AI monitoring and early warning system of the present invention uses advanced artificial intelligence technology to achieve real-time monitoring and analysis of wildlife activities and habitats, and has the ability to timely predict potential ecological risks and issue early warnings. The advantage of the system is that it can greatly improve the efficiency and response speed of wildlife protection, ensuring that effective prevention and intervention measures are taken before human activities or other factors may interfere with wildlife. In addition, the system optimizes resource allocation and the formulation of protection strategies through scientific data-driven methods, which helps maintain ecological balance and promote the protection of biodiversity, while reducing potential economic losses and social impacts caused by insufficient wildlife protection, providing strong technical support for achieving sustainable development and building an ecological civilization.

[0073] This embodiment also provides a computer device, which is applicable to the wildlife AI monitoring and early warning system and its method, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement all or part of the steps of the method described in the embodiment of the present invention as proposed in the above embodiment.

[0074] This embodiment also provides a storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the method in any optional implementation of the above embodiment is executed. Wherein, the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0075] The storage medium proposed in this embodiment and the data storage method proposed in the above embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be found in the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0076] It can be seen from the above that this method can effectively balance the supply and demand relationship, ensure the stable operation of the power grid, reduce the instability of the power system caused by load fluctuations, and improve the stability and reliability of the power grid through timely manual intervention and adjustment. The use of prediction models combined with manual intervention can be used to issue early warnings before the load exceeds the limit, so that operators have enough time to take measures to deal with it, thereby reducing the potential risks brought by the load exceeding the limit. By manually adjusting the unit output and changing the operation mode, the power generation capacity can be adjusted more flexibly, water resources and other power generation resources can be used reasonably, the load demand of the power grid can be met to the greatest extent, and the energy utilization efficiency can be improved. Timely manual intervention and adjustment can help avoid emergency shutdowns or overload operations caused by overload, reduce the risk of repairs and damage, and reduce operating and maintenance costs. Maintaining the stable operation of the power grid can ensure the normal power consumption of users, avoid power outages and voltage fluctuations caused by the instability of the power system, and improve the user's power consumption experience and satisfaction.

[0077] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A wild animal AI monitoring and early warning method, characterized by: include, Receive and analyze wildlife activity data to determine key ecological indicators within the monitoring area; Using historical ecological data and real-time monitoring information, AI algorithms are used to predict wildlife activity patterns and potential risk areas; Real-time monitoring of predicted activity patterns, identification of situations that may cause disturbance or threat to wildlife through behavioral analysis, and issuance of early warning signals; Provide human intervention and adjustment recommendations based on early warning signals and behavioral analysis results to protect wildlife from human activities and other threats; Based on real-time monitoring and early warning results, monitoring strategies and protection measures are dynamically adjusted to adapt to changes in wildlife behavior and changes in environmental conditions.

2. The wild animal AI monitoring and early warning method according to claim 1, characterized in that: Receive and analyze key ecological indicator data for specific wildlife conservation areas, and have professionals verify the data and record the values ​​and time ranges of key indicators; When there is uncertainty in ecological indicator data or further information is needed, communicate with relevant environmental protection agencies or scientific research teams to obtain more accurate data and explanations; Based on wildlife activity patterns and ecological needs detected by the monitoring system, assess and determine whether restrictions on human activities are necessary during a specific period of time, as well as the specific conditions for the restrictions; If it is determined that restrictions on human activities are necessary, the monitoring strategy and protective measures will be adjusted during the restriction period to adapt to the new restriction requirements; If no restrictions are determined to be necessary, continue to monitor and maintain current surveillance and protection measures to ensure all protections can be adapted to new restrictions or remain as they are.

3. The wild animal AI monitoring and early warning method according to claim 2, characterized in that: The system operation status covers the operation of the monitoring equipment to ensure that the equipment is in proper working condition to meet the data collection requirements; This includes the status of the data analysis module, analyzing its accuracy, speed and algorithm performance in processing wildlife activity data to ensure that the module operates stably at optimal performance; This includes the adjustment of the early warning mechanism to ensure that the system can adjust the early warning thresholds and response measures in real time based on monitoring data and forecast results to adapt to changes in wildlife activities.

4. The wild animal AI monitoring and early warning method according to claim 3, characterized in that: The predicting ecological risk curve comprises the following steps: Collect historical load data and real-time monitoring information, perform feature engineering on historical data, extract time features, and perform data preprocessing; According to the data characteristics and forecasting requirements, the load forecasting model is used for forecasting. The historical data is divided into a training set and a validation set. The training set is trained to optimize the model parameters to minimize the forecast error. The relevant expressions of the load forecasting model are as follows: In the formula, is the predicted value at time point t, c is a constant term, is the autoregressive coefficient, θ i is the moving average coefficient, ∈ t is white noise; p, d and q represent the autoregressive order, the difference order and the moving average order respectively; Use the validation set to evaluate the trained model. The evaluation indicators include root mean square error and mean absolute error. Adjust the model parameters according to the evaluation results. Use the trained model to predict the load for a period of time in the future, dynamically adjust the model input parameters, analyze the prediction results, compare them with the actual observation data, and evaluate the prediction accuracy; The forecast results are organized into a forecast report, including forecast curve graph, forecast error analysis, and influencing factor analysis.

5. The wild animal AI monitoring and early warning method according to claim 4, characterized in that: The real-time monitoring comprises the following steps: Use the load forecasting model to forecast the load in the future to obtain the forecast value, and calculate the warning threshold by combining the distribution characteristics of historical load data and the forecast error of the model; Collect load forecast data {R1, R2, …, R m }, calculate the warning threshold, the relevant calculation formula is as follows: In the formula, R i is the load forecast data, is the standard deviation of historical load forecast data, n is the number of forecasts; Calculate the median M and interquartile range IQR of the load forecast data. If n is an odd number, the median is the value in the middle of the data set, expressed as: If n is an even number, the median is the average of the two middle values ​​of the data set, expressed as: IQR=Q3-Q1 Where Q3 is the median of the first 75% of the values ​​in the data set, and Q1 is the median of the first 25% of the values ​​in the data set. The calculation formula for the warning threshold is: Among them, F i is the warning threshold, k i is the adjustment coefficient, i=1, 2; Compare the predicted value with the warning threshold to determine whether it exceeds the threshold. If the predicted value exceeds the threshold, it is considered that the load may exceed the limit, and the next step is to proceed to the next step of predictive value judgment. The system automatically sends out an early warning signal to inform relevant personnel that the load forecast exceeds the limit, and indicates the severity of the exceedance and the expected impact range; Record the time of occurrence and predicted value of the over-limit situation, as well as possible influencing factors, and subsequently improve the prediction model or adjust the operation strategy.

6. The wild animal AI monitoring and early warning method according to claim 5, characterized in that: The predicted value determination comprises: When the load forecast value R ≥ the second warning limit F2, a high-level warning signal is issued to notify relevant personnel to take timely measures. It is necessary to activate backup decisions and adjust plans to ensure the stable operation of the ecosystem. The system issues a warning signal to predict that the load may exceed the limit; When the first warning limit F1 ≤ load forecast value R < second warning limit F2, a low-level warning signal is issued to remind relevant personnel to pay attention to the load forecast and adjust manual output in time to cope with possible load fluctuations. The system predicts that the load is in a warning state and load overload may occur; When the load prediction value R≤the first warning limit value F1, no warning signal is issued. It is considered that the load is within the normal range and no special measures are needed. The system predicts that the load is normal and no load overlimit is found.

7. The wild animal AI monitoring and early warning method according to claim 6, characterized in that: The manual intervention and adjustment comprises the following steps: When the system issues an early warning signal, the monitoring personnel first receive the early warning notification and check the predicted wildlife activity and the warning level; Based on the warning level and forecast of wildlife activity, monitoring personnel assess whether manual intervention and adjustments are needed; If manual intervention and adjustment are required, monitoring personnel will take appropriate measures based on the actual situation, including adjusting monitoring strategies and protection measures, and changing ecological management plans; When the system indicates a high-risk situation may occur, monitoring personnel take actions to mitigate the risk, such as limiting human activities or increasing protected areas; If the predicted risk value is between the preset first and second limits, the monitoring personnel will adjust the protection measures in a timely manner according to the warning level and actual conditions to balance the ecological protection needs; If the predicted risk value is lower than the first limit, the system considers that the ecological status is within the normal range and no special measures are needed; Changing ecological management plans may include increasing the investment of conservation resources or adjusting the boundaries and measures of protected areas when high-risk situations are predicted; After artificial intervention and adjustments, monitoring personnel continue to monitor wildlife activities and ecological environment status to ensure the safety and stability of the ecosystem.

8. A wild animal AI monitoring and early warning system, based on the wild animal AI monitoring and early warning method according to any one of claims 1 to 7, characterized in that: include, The acquisition module is used to receive and analyze wildlife activity and environmental data, determine ecological protection targets, and check the status of the ecosystem within the monitoring area; The monitoring module is used to combine historical ecological data and real-time monitoring information to predict the ecological risk curve, conduct real-time monitoring of the predicted ecological risk curve, identify possible risk situations, and issue early warning signals and protective measures; The prompt module is used to conduct manual intervention and adjustments based on the early warning signals and protection measures recommended by the system, and to remind people to take corresponding protection actions based on wild animal activities and ecological risks.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the wild animal AI monitoring and early warning method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the wild animal AI monitoring and early warning system and method thereof are implemented in any one of claims 1 to 7.

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