Bird flight path prediction method, system and device and storage medium

Abstract

The invention provides a bird flight trajectory prediction method, system and device and a storage medium, and belongs to the technical field of flight trajectory prediction.The method comprises the steps that feature data in a target time period is obtained, and the feature data represents related information influencing the bird flight trajectory; preprocessing the obtained feature data to obtain processed feature data; inputting the processed feature data into a pre-constructed LSTM (Long Short Term Memory) model for model training; and predicting a bird flight path based on the trained LSTM model, and outputting a prediction result. According to the method, more accurate and real-time bird flight path prediction can be provided, power interruption and equipment damage caused by bird activities are reduced, the safety and reliability of a power system are improved, and meanwhile, the labor cost and the maintenance cost are also reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of bird behavior activity trajectory prediction, and in particular relates to a bird flight trajectory prediction method, system, device and storage medium. Background Art

[0002] The relationship between power systems and bird activity has received increasing attention, especially in the safe operation of high-voltage transmission lines. Accurate prediction of bird flight trajectories is of great significance for preventing bird-damaged accidents, ensuring the continuity of power supply, and reducing harm to birds. The safe operation of high-voltage transmission lines is essential to ensure uninterrupted power supply to users.

[0003] Bird damage accidents have become a major hidden danger affecting the safe operation of high-voltage transmission lines. By predicting the flight paths of birds, bird damage accidents can be effectively prevented and power outages and equipment damage caused by bird activities can be reduced.

[0004] To this end, the present invention provides a bird flight trajectory prediction method, system, device and storage medium. Summary of the invention

[0005] The present invention provides a bird flight trajectory prediction method, system, device and storage medium to solve the above-mentioned problems.

[0006] In a first aspect, an embodiment of the present application provides a method for predicting a bird flight trajectory, the method comprising: Acquiring characteristic data within a target time period, wherein the characteristic data represents relevant information affecting the flight trajectory of the bird; Preprocessing the acquired feature data to obtain processed feature data; Input the processed feature data into the pre-built LSTM model for model training; Predict the bird flight trajectory based on the trained LSTM model and output the prediction results.

[0007] Furthermore, the characteristic data includes: Flight speed, flight direction and flight altitude.

[0008] Furthermore, the acquired feature data is preprocessed, including: The acquired feature data is processed for missing data filling, abnormal data elimination and / or data normalization. The most recently used method is adopted, that is, the most recently used data is used as the current filling to fill the missing data.

[0009] Further, the pre-built LSTM model includes a cell state unit and a multiplication unit, wherein the multiplication unit includes a forget gate, an input gate, and an output gate; Determine the information that needs to be discarded from the cell state unit through the forget gate; The input gate determines the information to be saved in the cell state unit; The output information from the cell state unit is determined by the output gate. Furthermore, the expression of the output value of the forget gate is:

[0010] In the formula, Indicates that the LSTM model is The output value of the forget gate at all times, represents the Sigmoid activation function, Represents the weight matrix in the forget gate Sigmoid activation function, Represents the bias vector in the forget gate Sigmoid activation function, Indicates that the LSTM model is The output value at time, Indicates that the LSTM model is The input value at the moment; The expression of the output value of the input gate is:

[0011] In the formula, Indicates that the LSTM model is The output value of the input gate at any moment, represents the weight matrix in the input gate Sigmoid activation function, Represents the bias vector in the input gate Sigmoid activation function; The expression of the cell state unit is:

[0012] In the formula, Indicates that the LSTM model is The cell state at a moment in time, Indicates that the LSTM model is The cell state at a moment in time, represents the candidate cell state; The expression of the candidate cell state is:

[0013] In the formula, express Activation function, represents the weight matrix of candidate values, A bias vector representing the candidate values.

[0014] Furthermore, the expression of the output value of the output gate is:

[0015] In the formula, represents the output value of the output gate, represents the weight matrix of the output gate, represents the bias vector of the output gate; The expression of the hidden state is:

[0016] In the formula, Indicates the current hidden state.

[0017] Furthermore, the processed feature data is input into the pre-built LSTM model for model training, including: Configure the functions required for model training, select Adam as the optimizer, and select mean square error MSE as the loss function; The processed feature data is input into the pre-built LSTM model, and the number of iterations Epoch is adjusted until the minimum MSE value is obtained, and the model training is completed.

[0018] In a second aspect, the embodiments of the present application further provide a system for the bird flight trajectory prediction method as described in the above aspects, the system comprising: An acquisition module, used to acquire characteristic data within a target time period, wherein the characteristic data represents relevant information affecting the flight trajectory of the bird; A data processing module is used to pre-process the acquired feature data to obtain processed feature data; The training module is used to input the processed feature data into the pre-built LSTM model for model training; The output module is used to predict the flight trajectory of birds based on the trained LSTM model and output the prediction results.

[0019] In a third aspect, an electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of the bird flight trajectory prediction method as described in the above aspects are implemented.

[0020] In a fourth aspect, a storage medium stores a computer program, which, when executed by a processor, implements the steps of the bird flight trajectory prediction method as described in the above aspects.

[0021] It can be seen from the above technical solutions that the present invention has the following advantages: In the bird flight trajectory prediction method provided in the present application, the flight trajectory of birds is predicted using a trained LSTM model, which can provide more accurate and real-time bird flight trajectory predictions, reduce power outages and equipment damage caused by bird activities, improve the safety and reliability of the power system, and also reduce labor costs and maintenance costs.

[0022] By continuously fine-tuning the model parameters, the prediction results are optimized to adapt to the changes in flight trajectories of different birds and different environmental conditions.

[0023] The output prediction results are applied to the real-time monitoring system. When it is detected that the flight trajectory of birds may intersect with high-voltage transmission lines, an early warning is issued in time and corresponding preventive measures are taken. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solution of the present invention, the accompanying drawings required for use in the description will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0025] Figure 1 The present invention is a method flow chart of the bird flight trajectory prediction method. DETAILED DESCRIPTION

[0026] In the substation abnormal signal reset device which will be described in detail below, various embodiments of the present disclosure will be described more fully. The present disclosure may have various embodiments, and adjustments and changes may be made therein. However, it should be understood that there is no intention to limit the various embodiments of the present disclosure to the specific embodiments disclosed herein, but the present disclosure should be understood to cover all adjustments, equivalents and / or alternatives that fall within the spirit and scope of the various embodiments of the present disclosure.

[0027] Hereinafter, the terms "include" or "may include" used in various embodiments of the present disclosure indicate the presence of disclosed functions, operations, or elements, and do not limit the addition of one or more functions, operations, or elements. In addition, as used in various embodiments of the present disclosure, the terms "include", "have", and their cognates are intended only to indicate specific features, numbers, steps, operations, elements, components, or a combination of the foregoing, and should not be understood as first excluding the presence of one or more other features, numbers, steps, operations, elements, components, or a combination of the foregoing or the possibility of adding one or more features, numbers, steps, operations, elements, components, or a combination of the foregoing.

[0028] In various embodiments of the present disclosure, the expression "or" or "at least one of A or / and B" includes any combination or all combinations of the words listed at the same time. For example, the expression "A or B" or "at least one of A or / and B" may include A, may include B, or may include both A and B.

[0029] The expressions (such as "first", "second", etc.) used in the various embodiments of the present disclosure may modify the various constituent elements in the various embodiments, but may not limit the corresponding constituent elements. For example, the above expressions do not limit the order and / or importance of the elements. The above expressions are only used for the purpose of distinguishing one element from other elements. For example, the first user device and the second user device indicate different user devices, although both are user devices. For example, without departing from the scope of the various embodiments of the present disclosure, the first element may be referred to as the second element, and similarly, the second element may also be referred to as the first element.

[0030] It should be noted that if it is described that one component element is “connected” to another component element, the first component element may be directly connected to the second component element, and a third component element may be “connected” between the first component element and the second component element. Conversely, when one component element is “directly connected” to another component element, it can be understood that there is no third component element between the first component element and the second component element.

[0031] The term “user” used in various embodiments of the present disclosure may indicate a person who uses an electronic device, and may be a monitoring person, a test person, or an operator.

[0032] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0033] The embodiment of the present application provides a method for predicting bird activity trajectories, which solves the currently urgently needed technical problem of effectively preventing bird damage accidents and reducing power outages and equipment damage caused by bird activities by predicting the flight trajectories of birds.

[0034] The technical solution proposed in the embodiments of the present application is described in detail below with reference to the accompanying drawings.

[0035] Figure 1 This is a flow chart of a method for predicting bird activity trajectories provided in an embodiment of the present application. Figure 1 As shown, a bird activity trajectory prediction method provided in an embodiment of the present application specifically includes the following steps: A method for predicting a bird's flight trajectory comprises: acquiring feature data within a target time period, wherein the feature data represents relevant information affecting the bird's flight trajectory; preprocessing the acquired feature data to obtain processed feature data; inputting the processed feature data into a pre-built LSTM model for model training; predicting the bird's flight trajectory based on the trained LSTM model, and outputting a prediction result.

[0036] Aiming at the limitations of existing bird identification and detection technology, a bird activity trajectory prediction method based on LSTM time series network is proposed. This method can achieve accurate bird flight trajectory prediction through data collection and preprocessing, model training, and trajectory prediction.

[0037] Compared with traditional technologies, the present invention utilizes the advantages of the LSTM model in processing time series data, captures the long-term dependencies of bird flight, improves prediction accuracy, and can reduce power outages and equipment damage. At the same time, it provides a scientific basis for bird protection, improves the safety and reliability of the power system, and reduces labor and maintenance costs.

[0038] The output prediction results are applied to the real-time monitoring system. When it is detected that the flight trajectory of birds may intersect with high-voltage transmission lines, an early warning is issued in time and corresponding preventive measures are taken.

[0039] It should be noted that the characteristic data include: flight speed, flight direction and flight altitude.

[0040] In an exemplary embodiment, preprocessing the acquired feature data includes: The acquired feature data is processed by missing data filling, abnormal data elimination and / or data normalization. Since the acquired feature data may have certain data missing, resulting in uneven time distribution, the last recently used method (LRU) adopted in this embodiment is to use the last recently used data as the current filling to fill the missing data. In this embodiment, when there is no data for a certain parameter interval of more than 1 minute, the data of other parameters in this period of time are correspondingly eliminated to ensure the data consistency of each parameter in each time period. In this embodiment, the data normalization process includes: generating respective multidimensional time series data from the time series data of flight speed, flight direction and flight altitude. Then, the maximum and minimum value normalization is used to weaken the influence of excessive numerical differences on the accuracy of the LSTM model, and the data distribution interval is mapped to [0, 1] to improve the accuracy and calculation speed of the LSTM model. In at least one embodiment, the normalization formula using the maximum and minimum value normalization process is: x′=(x-xmin) / (xmax-xmin) Among them, x′ represents the value of a single data, xmin is the minimum value of the column where the data is located, xmax is the maximum value of the column where the data is located, and the normalization range used is [-1, 1].

[0041] In one embodiment, the pre-built LSTM model includes a cell state unit and a multiplication unit, wherein the multiplication unit includes a forget gate, an input gate, and an output gate; Determine the information that needs to be discarded from the cell state unit through the forget gate; The input gate determines the information to be saved in the cell state unit; The output information from the cell state unit is determined by the output gate. As an example, the output value of the forget gate is expressed as:

[0042] In the formula, Indicates that the LSTM model is The output value of the forget gate at all times, Represents the Sigmoid activation function. Specifically, It represents the Sigmoid activation function that compresses the value to between 0 and 1. Represents the weight matrix in the forget gate Sigmoid activation function, Represents the bias vector in the forget gate Sigmoid activation function, Indicates that the LSTM model is The output value at time, Indicates that the LSTM model is The input value at the moment; let the output value of the LSTM model at the previous moment be , let the input value of the LSTM model at the current moment be , the output value of the LSTM model at the previous moment and the input value of the LSTM model at the current moment Input into the Sigmoid activation function and get the value between 0 and 1 output to the cell state unit, 0 means all is forgotten, 1 means all information is retained.

[0043] The expression of the output value of the input gate is:

[0044] In the formula, Indicates that the LSTM model is The output value of the input gate at any moment, represents the weight matrix in the input gate Sigmoid activation function, Represents the bias vector in the input gate Sigmoid activation function; the output value of the LSTM model at the previous moment and the input value of the LSTM model at the current moment Input into the Sigmoid activation function to get the LSTM model The output value of the input gate at any time , the output value of the input gate Determines the information stored in the cell state unit.

[0045] The expression of the cell state unit is:

[0046] In the formula, Indicates that the LSTM model is The cell state at a moment in time, Indicates that the LSTM model is The cell state at a moment in time, represents the candidate cell state; The expression of the candidate cell state is:

[0047] In the formula, express Activation function, represents the weight matrix of candidate values, represents the bias vector of the candidate value. In this embodiment, It is used to adjust the value flowing through the network. The value is always limited between -1 and 1, thereby adjusting the activation function flowing through the network output.

[0048] It should be further explained that the expression of the output value of the output gate is:

[0049] In the formula, Represents the output value of the output gate. It can be understood that the output gate determines which information is output from the state cell and is part of the hidden state. represents the weight matrix of the output gate, represents the bias vector of the output gate; The expression of the hidden state is:

[0050] In the formula, Indicates the current hidden state.

[0051] Exemplarily, the processed feature data is input into a pre-built LSTM model for model training, including: Configure the functions required for model training, select Adam as the optimizer, and select mean square error MSE as the loss function. The processed feature data is input into the pre-built LSTM model, and the number of iterations Epoch is adjusted until the minimum MSE value is obtained, and the model training is completed.

[0052] By continuously fine-tuning the parameters of the LSTM model, the prediction results are optimized to adapt to the changes in flight trajectories of different birds and different environmental conditions.

[0053] The present invention also provides a system for the bird flight trajectory prediction method according to the above aspects, the system comprising: The acquisition module is used to acquire characteristic data within a target time period, wherein the characteristic data represents relevant information affecting the flight trajectory of the bird.

[0054] The data processing module is used to pre-process the acquired feature data to obtain processed feature data.

[0055] The training module is used to input the processed feature data into the pre-built LSTM model for model training.

[0056] The output module is used to predict the flight trajectory of birds based on the trained LSTM model and output the prediction results.

[0057] According to an embodiment of the present application, an electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps of the bird flight trajectory prediction method described in the above embodiments are implemented.

[0058] According to another embodiment of the present invention, a storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the bird flight trajectory prediction method described in the above embodiments are implemented.

[0059] A bird activity trajectory prediction method provided in an embodiment of the present application can be applied to electronic devices. Those skilled in the art will appreciate that the electronic device structure involved in the embodiment of the present invention does not constitute a limitation on the electronic device, and the electronic device may include more or fewer components than shown, or combine certain components, or arrange different components. In an embodiment of the present invention, the electronic device includes but is not limited to a laptop computer, a desktop computer, a workbench, a personal digital assistant, a server, a blade server, a mainframe computer, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the embodiments of the present application described and / or required herein.

[0060] The electronic device may include a processor, an external memory interface, an internal memory, a universal serial bus (USB) interface, a charging management module, a power management module, a battery, a wireless communication module, an audio module, a speaker, a microphone, a sensor module, buttons, a camera, a display, and a SIM card interface, etc.

[0061] It is to be understood that the structure illustrated in the embodiments of the present application does not constitute a specific limitation on the electronic device. In other embodiments of the present application, the electronic device may include more or fewer components than shown in the figure, or combine certain components, or split certain components, or arrange the components differently. The components shown in the figure may be implemented in hardware, software, or a combination of software and hardware.

[0062] The processor may include one or more processing units, for example, the processor may include a central processing unit (CPU), an application processor (AP), a modem processor, a graphics processing unit (GPU), an image signal processor (ISP), a controller, a memory, a video codec, a digital signal processor (DSP), a baseband processor, and / or a neural-network processing unit (NPU), etc. Among them, different processing units may be independent devices or integrated into one or more processors.

[0063] The processor can be the nerve center and command center of the electronic device. The controller can generate an operation control signal according to the instruction operation code and timing signal to complete the control of fetching and executing instructions.

[0064] A memory may also be provided in the processor for storing instructions and data. In some embodiments, the memory in the processor is a cache memory. The memory may store instructions or data that the processor has just used or is cyclically used. If the processor needs to use the instruction or data again, it may be directly called from the memory. This avoids repeated access, reduces the waiting time of the processor, and thus improves system efficiency.

[0065] The external memory interface can be used to connect an external memory card, such as a MicroSD card, to expand the storage capacity of the electronic device. The external memory card communicates with the processor through the external memory interface to implement data storage functions. For example, files such as music and videos can be saved in the external memory card.

[0066] The internal memory can be used to store computer executable program codes, which include instructions. The processor executes various functional applications and data processing of the electronic device by running the instructions stored in the internal memory. The internal memory may include a program storage area and a data storage area. The internal memory may include a high-speed random access memory and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, a universal flash storage (UFS), etc.

[0067] The wireless communication function of an electronic device can be realized through an antenna, a wireless communication module, a modem processor, and a baseband processor.

[0068] The wireless communication module can provide wireless communication solutions for electronic devices, including wireless local area networks (WLAN) (such as wireless fidelity (Wi-Fi) networks), Bluetooth (BT), global navigation satellite system (GNSS), frequency modulation (FM), near field communication (NFC), infrared technology (IR), etc.

[0069] Electronic devices can implement audio functions, etc. through audio modules, speakers, receivers, microphones, headphone jacks, and application processors.

[0070] Electronic devices can achieve shooting functions through ISP, camera, video codec, GPU, display and application processor.

[0071] Electronic devices can achieve display functions through GPU, display screen and application processor.

[0072] The GPU is a microprocessor for image processing that connects the display screen and the application processor. The GPU is used to perform mathematical and geometric calculations for graphics rendering. The processor may include one or more GPUs that execute program instructions to generate or change display information.

[0073] The display screen is used to display images, videos, etc. The display screen includes a display panel.

[0074] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0075] The flowchart and block diagram in the accompanying drawings illustrate the possible architecture, functions and operations of the equipment, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. Exemplarily, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or the flowchart, and the combination of the boxes in the block diagram and / or the flowchart can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0076] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, or it can be an electrical, mechanical or other form of connection.

[0077] In addition, the described features, structures or characteristics may be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present invention. However, those skilled in the art will appreciate that the technical solution of the present invention can be practiced without one or more of the specific details, or other methods, components, devices, steps, etc. may be adopted. In other cases, known methods, devices, implementations or operations are not shown or described in detail to avoid blurring various aspects of the present invention.

[0078] The electronic device implements the method of predicting the trajectory of bird activity in the present application, which includes obtaining characteristic data within a target time period, wherein the characteristic data represents relevant information affecting the flight trajectory of birds; preprocessing the acquired characteristic data to obtain processed characteristic data; inputting the processed characteristic data into a pre-built LSTM model for model training; predicting the flight trajectory of birds based on the trained LSTM model, and outputting the prediction result. Compared with traditional technologies, the present invention utilizes the advantages of the LSTM model in processing time series data, captures the long-term dependency of bird flight, improves prediction accuracy, can reduce power outages and equipment damage, and provides a scientific basis for bird protection, improves the safety and reliability of the power system, and reduces labor costs and maintenance costs.

[0079] The storage medium provided in the present application stores a program product capable of implementing a method for predicting bird activity trajectories.

[0080] A method for predicting bird activity trajectories includes: acquiring feature data within a target time period, wherein the feature data represents relevant information affecting the bird flight trajectory; preprocessing the acquired feature data to obtain processed feature data; inputting the processed feature data into a pre-built LSTM model for model training; predicting the bird flight trajectory based on the trained LSTM model, and outputting a prediction result.

[0081] Aiming at the limitations of existing bird identification and detection technology, a bird activity trajectory prediction method based on LSTM time series network is proposed. This method can achieve accurate bird flight trajectory prediction through data collection and preprocessing, model training, and trajectory prediction.

[0082] In some possible implementations, a bird activity trajectory prediction method disclosed herein can be implemented in the form of a program product, which includes a program code. When the program product is run on a terminal device, the program code is used to enable the terminal device to execute the steps described in the above “Exemplary Method” section of this specification according to various exemplary implementations of the present disclosure.

[0083] The storage medium of the present disclosure can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0084] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

[0085] For those skilled in the art, designing different forms of control circuits according to the teachings of the present invention does not require creative work. These changes, modifications, substitutions and variations of the embodiments without departing from the principles and spirit of the present invention still fall within the scope of protection of the present invention.

Claims

1. A method for predicting bird flight trajectories, characterized in that: The method comprises: Acquiring characteristic data within a target time period, wherein the characteristic data represents relevant information affecting the flight trajectory of the bird; Preprocessing the acquired feature data to obtain processed feature data; Input the processed feature data into the pre-built LSTM model for model training; Predict the bird flight trajectory based on the trained LSTM model and output the prediction results.

2. The bird flight trajectory prediction method according to claim 1, characterized in that: The characteristic data includes: Flight speed, flight direction and flight altitude.

3. The bird flight trajectory prediction method according to claim 2, characterized in that: Preprocess the acquired feature data, including: The acquired feature data is processed to fill missing data, remove abnormal data and / or normalize data.

4. The bird flight trajectory prediction method according to claim 1, characterized in that: The pre-built LSTM model includes a cell state unit and a multiplication unit, wherein the multiplication unit includes a forget gate, an input gate, and an output gate; Determine the information that needs to be discarded from the cell state unit through the forget gate; The input gate determines the information to be saved in the cell state unit; The output information from the cell state unit is determined by the output gate.

5. The bird flight trajectory prediction method according to claim 4, characterized in that: The expression of the output value of the forget gate is: In the formula, Indicates that the LSTM model is The output value of the forget gate at all times, represents the Sigmoid activation function, Represents the weight matrix in the forget gate Sigmoid activation function, Represents the bias vector in the forget gate Sigmoid activation function, Indicates that the LSTM model is The output value at time, Indicates that the LSTM model is The input value at the moment; The expression of the output value of the input gate is: In the formula, Indicates that the LSTM model is The output value of the input gate at any moment, represents the weight matrix in the input gate Sigmoid activation function, Represents the bias vector in the input gate Sigmoid activation function; The expression of the cell state unit is: In the formula, Indicates that the LSTM model is The cell state at a moment in time, Indicates that the LSTM model is The cell state at a moment in time, represents the candidate cell state; The expression of candidate cell states is: In the formula, express Activation function, represents the weight matrix of candidate values, A bias vector representing the candidate values.

6. The bird flight trajectory prediction method according to claim 5, characterized in that: The expression of the output value of the output gate is: In the formula, represents the output value of the output gate, represents the weight matrix of the output gate, represents the bias vector of the output gate; The expression of the hidden state is: In the formula, Indicates the current hidden state.

7. The bird flight trajectory prediction method according to claim 1, characterized in that: The processed feature data is input into the pre-built LSTM model for model training, including: Configure the functions required for model training, select Adam as the optimizer, and select mean square error MSE as the loss function; The processed feature data is input into the pre-built LSTM model, and the number of iterations Epoch is adjusted until the minimum MSE value is obtained, and the model training is completed.

8. A system applied to the bird flight trajectory prediction method according to any one of claims 1 to 7, characterized in that: The system comprises: An acquisition module, used to acquire characteristic data within a target time period, wherein the characteristic data represents relevant information affecting the flight trajectory of the bird; A data processing module is used to pre-process the acquired feature data to obtain processed feature data; The training module is used to input the processed feature data into the pre-built LSTM model for model training; The output module is used to predict the flight trajectory of birds based on the trained LSTM model and output the prediction results.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the bird flight trajectory prediction method according to any one of claims 1 to 7 are implemented.

10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the bird flight trajectory prediction method according to any one of claims 1 to 7 are implemented.

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