Data processing method and device, equipment and storage medium

Abstract

The invention provides a data processing method, apparatus and device, and a storage medium. The method comprises the steps of receiving collection data sent by a sensing device; performing preprocessing operation on the collected data to obtain to-be-processed data; performing identification processing on the to-be-processed data based on a target identification class algorithm to obtain track data of a target object; and performing compression processing on the trajectory data based on an attention feature mechanism, and sending the compressed trajectory data to a cloud server. According to the invention, by constructing a storage and calculation integrated data processing mechanism at the satellite terminal, the amount of original data needing to be transmitted to the cloud server is reduced, so that the data processing efficiency is improved.

Description

Technical Field

[0001] The present application relates to the field of satellite communication technology, and in particular to a data processing method, device, equipment and storage medium. Background Art

[0002] With the deepening of national park construction, the demand for intelligent management and assessment of biodiversity, dynamic monitoring and protection technology of endangered species is growing. Traditional means and manpower investment can no longer meet management needs. Therefore, integrated sky-ground monitoring technology is being fully integrated into the protection, construction and management of national parks. At the same time, the evolution of network services, such as cloud-edge-end collaboration and cloud-network integration, provides support for network and computing power applications in different scenarios, and has been increasingly widely used in current resource management, biodiversity background surveys, forest and grassland fire prevention, pest and disease monitoring, and flagship species protection.

[0003] Under the technical development trend of cloud, edge and end collaboration, the sensing end, edge devices and cloud systems work together to form an efficient data processing, resource utilization and business response mechanism. However, in the field of satellite Internet applications, the current development stage is mainly focused on end-to-cloud collaboration or cloud-based data post-processing. In this process, satellite links mostly exist as pipelines for data transmission, while satellite terminals serve as network access points and have not yet been fully integrated into the real-time processing and analysis of application data. Summary of the invention

[0004] In view of this, the purpose of the present application is to provide a data processing method, device, equipment and storage medium.

[0005] As one aspect of the present application, a data processing method is provided, comprising:

[0006] Receive the collected data sent by the sensing device;

[0007] Performing a preprocessing operation on the collected data to obtain data to be processed;

[0008] Based on the target recognition algorithm, the data to be processed is identified and processed to obtain the trajectory data of the target object;

[0009] The trajectory data is compressed based on the attention feature mechanism, and the compressed trajectory data is sent to the cloud server.

[0010] Optionally, the satellite terminal includes a plurality of data service modules for identifying and processing the data to be processed;

[0011] The method of performing identification processing on the data to be processed based on a target identification algorithm to obtain trajectory data of the target object includes:

[0012] Determine resource allocation of each data service module based on the acquired data priority, resource requirements and idleness of system resources in the satellite terminal;

[0013] For each of the multiple data service modules, resources are allocated to the data service module based on the resource allocation situation, so that the data service module identifies and processes the data to be processed based on the acquired system resources and the target recognition algorithm to obtain initial trajectory data of the target object;

[0014] The initial trajectory data corresponding to the multiple data service modules are integrated to obtain the trajectory data of the target object.

[0015] Optionally, the data to be processed includes relevant data of multiple objects, and the multiple objects include: target objects and non-target objects;

[0016] The data service module identifies and processes the data to be processed based on the acquired system resources and the target recognition algorithm to obtain the initial trajectory data of the target object, including:

[0017] The data service module performs identification analysis on the data to be processed based on the target identification algorithm, and deletes the relevant data of non-target objects in the data to be processed to obtain the relevant data of the target object; the identification analysis includes: population identification analysis, species identification analysis, and individual identification analysis;

[0018] The data service module generates initial trajectory data of the target object based on the behavior trajectory and the motion trajectory of the target object in the relevant data of the target object.

[0019] Optionally, the integrating and processing the initial trajectory data corresponding to the multiple data service modules to obtain the trajectory data of the target object includes:

[0020] Acquire data quality of the initial trajectory data corresponding to the multiple data service modules; the data quality includes data clarity and data integrity;

[0021] The initial trajectory data whose data quality is higher than a preset quality threshold is integrated to obtain the trajectory data.

[0022] Optionally, the compressing the trajectory data based on the attention feature mechanism includes:

[0023] Acquire and align features of the trajectory data based on the attention feature mechanism;

[0024] The trajectory data is reconstructed based on the features of the trajectory data and known frame information in the trajectory data; the amount of trajectory data before reconstruction is greater than the amount of trajectory data after reconstruction.

[0025] Optionally, the receiving the collected data sent by the sensing device includes:

[0026] Receiving the collected data sent by the sensing device;

[0027] Acquire the communication protocol used by the sensing device to send the collected data;

[0028] Determining a data format of the collected data based on the communication protocol;

[0029] The data format of the collected data is standardized to convert the data format of the collected data into a standard format.

[0030] Optionally, sending the compressed trajectory data to a cloud server includes:

[0031] Encrypting the compressed trajectory data based on a preset encryption algorithm;

[0032] Send the encrypted trajectory data to the cloud server.

[0033] As a second aspect of the present application, a data processing device is provided, comprising: a receiving module, a processing module and an identification module;

[0034] The receiving module is used to receive the collected data sent by the sensing device;

[0035] The processing module is used to perform preprocessing operations on the collected data to obtain data to be processed;

[0036] The recognition module is used to perform recognition processing on the data to be processed based on a target recognition algorithm to obtain trajectory data of the target object;

[0037] The processing module is also used to compress the trajectory data based on the attention feature mechanism, and send the compressed trajectory data to the cloud server.

[0038] Optionally, the satellite terminal includes a plurality of data service modules for identifying and processing the data to be processed;

[0039] The identification module is specifically used to determine the resource allocation status of each data service module based on the acquired data priority, resource requirements and idle status of system resources in the satellite terminal of each data service module in the multiple data service modules;

[0040] For each of the multiple data service modules, resources are allocated to the data service module based on the resource allocation situation, so that the data service module identifies and processes the data to be processed based on the acquired system resources and the target recognition algorithm to obtain initial trajectory data of the target object;

[0041] The initial trajectory data corresponding to the multiple data service modules are integrated to obtain the trajectory data of the target object.

[0042] Optionally, the data to be processed includes relevant data of multiple objects, and the multiple objects include: target objects and non-target objects;

[0043] The identification module is further specifically used for the data service module to perform identification analysis on the data to be processed based on the target identification algorithm, and delete relevant data of non-target objects in the data to be processed to obtain relevant data of the target object; the identification analysis includes: population identification analysis, species identification analysis, and individual identification analysis;

[0044] The data service module generates initial trajectory data of the target object based on the behavior trajectory and the motion trajectory of the target object in the relevant data of the target object.

[0045] Optionally, the identification module is further used to obtain data quality of the initial trajectory data corresponding to the multiple data service modules; the data quality includes data clarity and data integrity;

[0046] The initial trajectory data whose data quality is higher than a preset quality threshold is integrated to obtain the trajectory data.

[0047] Optionally, the processing module is specifically used to obtain and align features of the trajectory data based on the attention feature mechanism;

[0048] The trajectory data is reconstructed based on the features of the trajectory data and known frame information in the trajectory data; the amount of trajectory data before reconstruction is greater than the amount of trajectory data after reconstruction.

[0049] Optionally, the receiving module is specifically used to receive the collected data sent by the sensing device;

[0050] Acquire the communication protocol used by the sensing device to send the collected data;

[0051] Determining a data format of the collected data based on the communication protocol;

[0052] The data format of the collected data is standardized to convert the data format of the collected data into a standard format.

[0053] Optionally, the processing module is further configured to encrypt the compressed trajectory data based on a preset encryption algorithm;

[0054] Send the encrypted trajectory data to the cloud server.

[0055] As a third aspect 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, wherein the processor implements the above-mentioned data processing method when executing the program.

[0056] As a fourth aspect of the present application, a non-transitory computer-readable storage medium is provided, wherein the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the above-mentioned data processing method provided in the present application.

[0057] As can be seen from the above, the data processing method, device, equipment and storage medium provided by the present application first receive the collected data from the sensing device, integrate the data from multiple sources through the satellite terminal, and then perform preprocessing operations on the collected data, which can filter out noise, correct errors or perform formatting processing, thereby improving the data quality and providing accurate and reliable input for subsequent processing steps. Further, by applying the target recognition algorithm, the target object in the data to be processed is automatically identified, and its trajectory data is generated, thereby improving the processing efficiency and accuracy. Then, the attention feature mechanism is introduced to compress the trajectory data. The attention feature mechanism can identify the important part of the data and give priority to compressing and transmitting it, thereby reducing the amount of data while ensuring the data quality. The compressed data reduces the time and bandwidth required for transmission, improves the real-time and efficiency of data processing, and reduces the communication cost. Finally, the compressed trajectory data is sent to the cloud server, so that the satellite terminal can work in collaboration with cloud resources. Directly performing data preprocessing and target recognition at the satellite terminal reduces the amount of original data that needs to be transmitted to the cloud server, thereby improving the efficiency of data processing, and at the same time, reduces the computing burden of the cloud server and optimizes the utilization of computing resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the technical solutions in the present application or related technologies, the drawings required for use in the embodiments or related technical descriptions are briefly introduced below. Obviously, the drawings described below are only embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0059] Figure 1A communication system schematic diagram of a data processing method provided in an embodiment of the present application;

[0060] Figure 2 A schematic diagram of the software and hardware composition of a satellite terminal provided in an embodiment of the present application;

[0061] Figure 3 A flowchart of a data processing method provided in an embodiment of the present application;

[0062] Figure 4 A flowchart of another data processing method provided in an embodiment of the present application;

[0063] Figure 5 A flowchart of another data processing method provided in an embodiment of the present application;

[0064] Figure 6 A schematic diagram of a data service deployment process provided in an embodiment of the present application;

[0065] Figure 7 A flowchart of another data processing method provided in an embodiment of the present application;

[0066] Figure 8 A flowchart of another data processing method provided in an embodiment of the present application;

[0067] Fig. 9 A flowchart of another data processing method provided in an embodiment of the present application;

[0068] Fig.10 A flowchart of another data processing method provided in an embodiment of the present application;

[0069] Fig.11 A schematic diagram of a data processing architecture and scheduling mechanism provided in an embodiment of the present application;

[0070] Fig.12 A schematic diagram of the composition of a data processing device provided in an embodiment of the present application;

[0071] Fig.13 A schematic diagram of the composition of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0072] In order to make the purpose, technical solutions and advantages of the present application more clearly understood, the present application is further described in detail below in combination with specific embodiments and with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0073] It should be noted that, in the embodiments of the present application, words such as "exemplarily" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplarily" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplarily" or "for example" is intended to present related concepts in a concrete way. Unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present application should have the usual meaning understood by people with ordinary skills in the field to which the present application belongs. SUMMARY OF THE INVENTION

[0075] In related technologies, the continuous expansion of network coverage has promoted the transformation and upgrading of network services to advanced application modes such as cloud-edge-end collaboration, cloud-network integration, and integrated computing, aiming to meet the network and computing power requirements in various complex scenarios. From aerospace equipment such as satellites, airships, and drones that provide communication, remote sensing, and navigation services, to ground-based Internet of Things systems composed of ground communications, radars, and sensors, to achieve real-time perception and data transmission, to the integration of innovative algorithms and technical products such as digital twins, AI algorithms, and intelligent visualization, the integrated sky-ground monitoring field of national parks has initially built a new technical development pattern of cloud, edge, and end collaboration. The cloud, edge, and end are closely connected to form an efficient and collaborative organic system. The sensing end device is responsible for data collection and preliminary processing, while the edge device is responsible for managing and controlling the sensing device, while providing it with necessary computing, storage, and network resource support. The cloud system focuses on the two core elements of application data and equipment facilities to achieve overall overall management, scientific decision-making, and optimal resource allocation. Through this collaborative working mode, not only the efficient processing of data and the rational use of resources are achieved, but also the response speed of the business is greatly improved, which injects new vitality and impetus into the protection and management of national parks. However, in the field of satellite Internet applications, the current main application mode is still limited to end-cloud collaboration or post-processing of cloud data. In this mode, the satellite link mainly plays the role of a data transmission pipeline, and the satellite terminal mainly serves as a network connection entrance, and does not perform substantial processing and processing on the application data. Although computing facilities are deployed on the terminal side in some scenarios, there is still no complete and effective mechanism for the processing of application data, resulting in the failure to fully realize the potential of satellite Internet in data processing and real-time response.

[0076] The inventor of this application found that, on the one hand, by deploying multiple sets of wildlife identification terminals in the monitoring area, using the terminals to shoot wildlife images and transmitting them to the wildlife detection platform through the Xingyun low-orbit narrowband satellite Internet of Things, the image parsing, display and analysis can be achieved, so as to monitor wildlife information around the clock and at all times, and provide technical support for wildlife protection, research and intelligent management. On the other hand, the video image data of the wildlife protection area monitoring device is obtained through the Beidou satellite to determine whether there are protected species, and at the same time, the satellite remote sensing image is combined to monitor environmental anomalies, and the early warning monitoring device is used to monitor suspicious persons, so as to achieve comprehensive remote monitoring of wildlife and its protection areas. These systems and methods have obvious limitations: they are highly dependent on the intelligence level of the sensing end equipment. When the sensor or camera lacks intelligent processing capabilities, the system cannot effectively perform intelligent data analysis and processing; in addition, the space-based network is only used as a pipeline for data transmission, lacks the ability to adaptively adjust the transmission rate and the network's anti-destruction capability, and the software-defined nature of the system is also limited by the capabilities of the sensing end equipment, resulting in the degree and flexibility of software definition to be improved.

[0077] In order to solve the above problems, the present application provides a data processing method, first receiving the collected data from the sensing device, integrating the data from multiple sources through the satellite terminal, and then performing preprocessing operations on the collected data, which can filter out noise, correct errors or perform formatting processing, thereby improving the data quality and providing accurate and reliable input for subsequent processing steps. Further, by applying the target recognition algorithm, the target object in the data to be processed is automatically identified, and its trajectory data is generated, thereby improving the processing efficiency and accuracy. Then, an attention feature mechanism is introduced to compress the trajectory data. The attention feature mechanism can identify the important part of the data and give priority to compressing and transmitting it, thereby reducing the amount of data while ensuring the data quality. The compressed data reduces the time and bandwidth required for transmission, improves the real-time and efficiency of data processing, and reduces the communication cost. Finally, the compressed trajectory data is sent to the cloud server, so that the satellite terminal can work in collaboration with cloud resources. Directly performing data preprocessing and target recognition at the satellite terminal reduces the amount of original data that needs to be transmitted to the cloud server, thereby improving the efficiency of data processing, and at the same time, reducing the computing burden of the cloud server and optimizing the utilization of computing resources.

[0078] After introducing the basic principles of the present application, various non-limiting implementation methods of the present application are described in detail below.

[0079] Application Scenario Overview

[0080] Figure 1 A communication system diagram of a data processing method provided in an embodiment of the present application. Figure 1 As shown, the communication system includes a sensing device 101, a satellite terminal 102, a satellite 103 and a cloud server 104. The sensing device 101 and the satellite terminal 102 are connected through a wired or wireless communication network, including: Ethernet, static communication, Bluetooth, WiFi, LoRa, ZigBee, Star Flash, ad hoc network, etc. The satellite terminal 102 and the satellite 103, and the satellite 103 and the cloud server 104 can be connected through a wireless communication network.

[0081] Among them, the sensing device 101 includes but is not limited to high-definition cameras, infrared cameras, sound sensors, drones, airships, patrol robots, bird trackers or other electronic devices that can capture and record images, videos, sounds and activity data of animals.

[0082] In some embodiments, the satellite terminal 102 is responsible for receiving the data sent by the sensing device 101 and transmitting the data to the satellite 103 via a satellite link. Figure 2As shown, the satellite terminal 102 includes network hardware and computing hardware, wherein the computing hardware includes basic computing components such as the central processing unit (CPU), memory, power supply, and graphics processing unit / neural network processing unit (GPU / NPU) for accelerating specific types of computing tasks, such as video encoding and decoding; the network hardware provides network connection functions, including Ethernet interface (RJ45), high-definition multimedia interface (HDMI), universal serial bus (USB) and mobile industry processor interface (MIPI) and other expansion interfaces. In addition, the satellite terminal 102 provides a data service software system framework, which combines Ubuntu and K3S as operating systems to implement a data processing architecture and scheduling mechanism (Application Operation System, referred to as AOS). AOS includes a variety of data services, such as: protocol parsing, which is responsible for parsing different communication protocols to ensure that data can be correctly understood and processed; data access, which involves data storage and retrieval, including database management, file system operations, etc.; audio and video encoding and decoding, which handles the encoding and decoding of audio and video data for easy transmission and storage; interactive configuration pages, which provide user interfaces and allow users to configure and monitor the operation of satellite terminals; and other supporting environments, including information logs, deployment, verification and security, etc., to ensure the stable operation of the system and the security of data. Through the AOS satellite terminal 102, it has an end system service architecture for end-side device access, data processing and distribution, flexible resource scheduling, and customized loading and unloading of algorithm models, and the business logic is separated from data storage. In addition, based on a unified distributed service management (discovery, synchronization, registration, and call) mechanism, remote startup, remote call, binding / unbinding, editing, migration, and other operations of cross-device monitoring applications are realized, and distributed synaesthesia computing task processing is carried out according to the capabilities, locations, business operation status, and resource usage of the end-side sensing device 101 and the edge-side satellite terminal 102, combined with the characteristics of the monitoring task.

[0083] In some embodiments, the satellite 103 operates in an earth orbit, for example, a high-orbit satellite at 36,000 km above the earth. The satellite 103 can serve as an aerial base station to receive data from the satellite terminal 102 and forward it to the cloud server 104.

[0084] The satellite terminal 102 and the cloud server 104 mentioned here can be independent physical servers, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. Optionally, the server can also be implemented on a cloud platform, for example, the cloud platform can include private clouds, public clouds, hybrid clouds, community clouds, distributed clouds, inter-clouds, and multi-clouds, etc., or any combination thereof. The embodiments of the present application are not limited to this.

[0085] In some embodiments, the cloud server 104 can receive data from the satellite 103 and store, analyze and process it. The cloud server 104 is a comprehensive data processing and management center, which plays a vital role in the satellite communication system. It integrates multiple key components such as the network operation center, the cloud-side service operation platform and the user data center. The network operation center is responsible for monitoring the operating status of the entire satellite Internet system and making necessary adjustments and optimizations to ensure the normal operation and efficient performance of the system. The cloud-side service operation platform provides data collaborative processing and management services between the cloud, edge computing and terminal devices, supports the deployment and operation of IoT applications, ensures the real-time and accuracy of data, optimizes resource utilization, and improves the overall performance and response speed of the system. The user data center is responsible for storing and processing user data, including personal information, device information, application data, etc., supports various data analysis and decision support functions, provides users with personalized and intelligent service experience, and ensures the security and privacy of user data. Through the collaborative work of these components, the cloud server 104 realizes the monitoring and management of various types of information such as application data, data services, equipment facilities, network topology and system status.

[0086] In some embodiments, in the satellite terminal 102, by carrying a computing power board, an integrated computing service capability can be achieved. The satellite terminal 102 can not only receive and forward data, but also process and store data on the edge side. When all the sensing devices 101 on the end side converge the information to the satellite terminal 102 on the edge side, the satellite terminal 102 can use its software and hardware capabilities to store, process, transmit back and back up the data. At the same time, the satellite terminal 102 can also implement configuration management of various sensing devices 101 based on the cloud-side service operation platform, including monitoring, adjustment and optimization of the sensing devices 101, to achieve more efficient flagship animal monitoring. In this way, it can be ensured that the monitoring system can respond in real time, quickly analyze and react to animal behavior, thereby improving the accuracy and timeliness of monitoring.

[0087] It should be understood that Figure 1 is an exemplary structural diagram, Figure 1 The communication system shown includes an unlimited number of devices, for example, an unlimited number of sensing devices 101 and an unlimited number of satellite terminals 102. Figure 1 In addition to the equipment shown, Figure 1 The communication system shown may also include other devices, which is not limited thereto.

[0088] Combine the following Figure 1 The data processing method according to the exemplary embodiment of the present application is described by using a communication system. It should be noted that the above communication system is only shown to facilitate understanding of the spirit and principle of the present application, and the embodiments of the present application are not limited in this respect. On the contrary, the embodiments of the present application can be applied to any applicable scenario.

[0089] Figure 3 A flow chart of a data processing method provided in an embodiment of the present application. Figure 3 As shown, the data processing method provided by the present application can be implemented by the above-mentioned satellite terminal 102, and specifically includes the following steps:

[0090] S201. Receive collected data sent by a sensing device.

[0091] In some embodiments, the sensing devices may be various types of sensors, such as radars, cameras, infrared sensors, etc., which collect data and transmit them to the satellite terminal through a specific communication protocol. These data include images, videos, audio or other sensor information.

[0092] In some embodiments, Figure 4 As shown, S201 can be specifically implemented as S2011-S2014 as follows:

[0093] S2011. Receive collected data sent by the sensing device.

[0094] In some embodiments, the satellite terminal receives the collected data via a network socket, a message queue (such as Kafka, RabbitMQ), a file transfer (such as FTP, SFTP), or the like.

[0095] S2012. Obtain the communication protocol used by the sensing device to send collected data.

[0096] In some embodiments, the communication protocol is identified by pattern matching based on the pattern of the header or specific field of the collected data, or by parsing the header information of the data packet to obtain the relevant identification of the communication protocol.

[0097] S2013. Determine the data format of the collected data based on the communication protocol.

[0098] In some embodiments, according to the identified communication protocol, the corresponding data format is searched from the protocol specification. For example, Message Queuing Telemetry Transport (MQTT) uses JSON or binary format, while Hypertext Transfer Protocol (HTTP) uses XML or JSON.

[0099] S2014. Standardize the data format of the collected data to convert the data format of the collected data into a standard format.

[0100] In some embodiments, in order to unify the data format and facilitate subsequent data services to process, the collected data needs to be standardized, including converting unstructured data (such as binary data) into a structured format (such as JSON or XML); processing differences in timestamps, encoding methods, data units, etc.; removing or adding specific fields to meet standards, etc.

[0101] S202: Preprocess the collected data to obtain data to be processed.

[0102] In some embodiments, the satellite terminal can perform pre-processing operations on the collected data, including data cleaning, format conversion, noise filtering, etc. For example, the collected data can be processed to remove noise data, erroneous data, contrast enhancement, illumination enhancement, etc. to obtain data to be processed.

[0103] S203: Perform recognition processing on the data to be processed based on a target recognition algorithm to obtain trajectory data of the target object.

[0104] In some embodiments, target recognition algorithms are used to identify and classify specific objects from images or videos. In animal monitoring, satellite terminals can use target recognition algorithms to identify flagship animal species and track their movements.

[0105] In some embodiments, the satellite terminal includes a plurality of data service modules for identifying and processing the data to be processed; Figure 5 As shown, S203 can be specifically implemented as follows S2031-S2033:

[0106] S2031. Determine resource allocation status of each data service module based on the acquired data priority, resource requirements and idle status of system resources in the satellite terminal of each data service module among the multiple data service modules.

[0107] In some embodiments, when resources are limited, priorities are assigned based on the importance or urgency of the data, and data service modules with high priorities are given priority in allocating resources to ensure that critical data can be processed in a timely manner. Resource requirements include computing resources (CPU, GPU time slices), storage resources (read and write permissions and space allocation of local hard disks), and network resources (data transmission bandwidth) required by each data service module. The idle state of system resources refers to the resource status of the satellite terminal that is not currently occupied, including CPU cycles, memory space, and network bandwidth.

[0108] In some embodiments, the satellite terminal adjusts the resource allocation strategy according to the real-time resource usage and the needs of the data service module. The allocation principle includes giving priority to meeting the needs of high-priority modules while ensuring the effective use of resources and avoiding resource waste.

[0109] It should be noted that data services can be deployed locally or through over-the-air (OTA) technology, and can only be called after being registered to the satellite terminal by using a plug-in registration method, which reduces the data service's access to resources. At the same time, resources are scheduled on demand according to the characteristics of the business scenario to ensure the effective use of resources. During the system startup or operation process, the plug-in will send a registration request to the satellite terminal, including the plug-in name, function description, required resource interface information, etc. After receiving the registration request, the system core will verify it, including checking whether the plug-in complies with the interface standard and whether there is a conflict. After successful registration, the system core will include the plug-in in the list of schedulable services for subsequent calls. In addition, industry users can achieve comprehensive management of data services, application data and terminal devices through the cloud-side service operation platform. Through the data service subsystem, the full life cycle management of data services from demand release, retrieval and ordering, deployment and use, operation status monitoring to return data management and after-sales maintenance can be realized online. Through the application data subsystem, based on the data service edge-cloud collaborative configuration, the message management, reading and editing, correlation analysis, sharing and backup, space expansion and other management of return data can be realized online. At the same time, the operating status and historical logs of satellite terminals, sensing devices, edge terminals and satellite-to-ground network topology are managed online.

[0110] For example, Figure 6 As shown in the figure, on the user side, the user first accesses the service management interface of the satellite terminal (user access), and then filters the available services according to the needs (service filtering), for example, selecting the service options of "initial" or "upgrade". Next, the user confirms the service content, price and other information and places an order (service ordering). The ordered service is deployed to the satellite terminal (service deployment). After the deployment is completed, the user can start using the new service (service application) to ensure the normal operation of the service. During the service operation process, the user can monitor the service status and receive relevant information push (service operation). In addition, the user can also perform troubleshooting, service migration, service upgrade, order management and value-added services. At the same time, on the operation side, the operation personnel obtain service requirements from the user side (demand acquisition), classify and prioritize the requirements (demand management), and then publish the sorted requirements to the relevant teams or departments (demand release). The operation side is also responsible for formulating service standards and specifications (specification management) and publishing these specifications for reference and execution by relevant personnel (specification release). At the same time, the operation side manages the software and hardware components required for the service (component management) and publishes these components for service research and development (component release). Finally, the development team conducts service development and testing (service development) according to the requirements and specifications. Through the above process, users can effectively change the services and configurations on the satellite terminal, while the operation side is responsible for managing and supporting these changes to ensure the quality and stability of the service.

[0111] S2032. For each of the multiple data service modules, allocate resources to the data service module based on the resource allocation situation, so that the data service module identifies and processes the data to be processed based on the acquired system resources and the target recognition algorithm to obtain initial trajectory data of the target object.

[0112] In some embodiments, corresponding system resources are allocated to each data service module according to the resource allocation determined in S2031. These resources include but are not limited to CPU time slices, memory space, communication channels, etc. After the resources are allocated, the data service module will use these resources in combination with target recognition algorithms to identify the data to be processed. The target recognition algorithm includes a machine learning model, such as a deep learning network. These models can identify the target object from the raw data and finally extract the initial trajectory data of the target object.

[0113] It should be understood that the initial trajectory data may contain motion parameters such as the position, velocity, acceleration, etc. of the target object, which are crucial for the subsequent trajectory tracking, prediction, and behavioral analysis of the flagship animal.

[0114] In some embodiments, the data to be processed includes relevant data of multiple objects, and the multiple objects include: target objects, non-target objects; Figure 7 As shown, S2032 can be specifically implemented as S2032a-S2032b as follows:

[0115] S2032a, the data service module identifies and analyzes the data to be processed based on the target recognition algorithm, deletes the relevant data of non-target objects in the data to be processed, and obtains the relevant data of the target object.

[0116] Among them, identification analysis includes: population identification analysis, species identification analysis, and individual identification analysis.

[0117] In some embodiments, target recognition algorithms are applied to identify individuals belonging to a specific animal population in the data to be processed. Target recognition algorithms include machine learning algorithms, deep learning algorithms, or traditional image processing and computer vision techniques. These algorithms identify the target object by extracting features (such as shape, color, texture, motion pattern, etc.) from the data and matching them with known target object features. The data service module then traverses the entire data set and deletes data that is not related to the target object, which helps reduce the computational burden of subsequent processing and improves the accuracy and relevance of the data. After the above processing, the data service module will retain all data related to the target object.

[0118] S2032b: The data service module generates initial trajectory data of the target object based on the behavior trajectory and motion trajectory of the target object in the relevant data of the target object.

[0119] In some embodiments, the data service module analyzes the target object's behavior trajectory at different time points, such as movement direction, speed change, dwell time, etc. This information helps to understand the target object's behavioral characteristics and habits. In addition to the behavior trajectory, the data service module also analyzes the target object's movement path in space. This includes recording the target object's position information at different time points, and calculating its movement speed, acceleration and other motion parameters. Finally, an initial trajectory data containing the target object's behavior trajectory and motion trajectory is generated. These data can be presented in the form of a time series, showing the target object's movement and behavior changes over a period of time.

[0120] S2033: Integrate and process the initial trajectory data corresponding to the multiple data service modules to obtain the trajectory data of the target object.

[0121] In some embodiments, since different data service modules can process data based on different data sources, algorithms or time intervals, the initial trajectory data generated by them will be different, such as timestamp mismatch, inconsistent coordinate system, missing or redundant data points. First, the time difference can be eliminated by methods such as timestamp alignment, interpolation or smoothing. Then, the initial trajectory data of multiple data sources are converted into a unified coordinate system for subsequent integration and analysis. On the basis of time synchronization and coordinate system unification, the initial trajectory data from different data service modules are fused, which includes processing overlapping, conflicting and missing data points. For example, for overlapping data points, weighted averaging, Kalman filtering or particle filtering are used for fusion; for missing data points, interpolation or prediction is used to fill them. At the same time, the initial trajectory data from different data service modules are associated with the same specific target object to obtain the trajectory data of the target object.

[0122] In some embodiments, Figure 8 As shown, S2033 can be specifically implemented as S2033a-S2033b as follows:

[0123] S2033a, obtaining data quality of initial trajectory data corresponding to multiple data service modules.

[0124] Among them, data quality includes data clarity and data integrity.

[0125] In some embodiments, data clarity includes the readability and accuracy of the data, such as the resolution of image data, the continuity of the trajectory, the density of points, and whether there is obvious noise or outliers; data integrity refers to whether the data is complete and has no missing or damaged parts. For trajectory data, integrity involves whether the trajectory covers the entire time period or spatial range, and whether there are missing trajectory points or data segments. The satellite terminal quantifies the clarity and integrity of the initial trajectory data through statistical analysis, visual inspection, or machine learning algorithms.

[0126] S2033b, integrating and processing the initial trajectory data whose data quality is higher than a preset quality threshold to obtain trajectory data.

[0127] In some embodiments, according to a pre-set quality threshold, the initial trajectory data that meets the requirements of data clarity and completeness are screened out. The screened data is then integrated and processed, including time synchronization, spatial alignment, data deduplication, and outlier processing. Time synchronization ensures that the timestamps of different data sources are consistent; spatial alignment adjusts the spatial coordinate systems of different data sources so that they can be compared under the same reference system; data deduplication deletes duplicate data points or trajectory segments; outlier processing identifies and processes data points that do not conform to the expected pattern or range. After integration processing, the system will obtain a trajectory dataset containing multiple high-quality trajectory data. This dataset can be used for subsequent analysis, visualization, or decision support tasks.

[0128] S204: compress the trajectory data based on the attention feature mechanism, and send the compressed trajectory data to the cloud server.

[0129] In some embodiments, the attention feature mechanism is a technique in the field of deep learning that is used to extract key information from large amounts of data. On a satellite terminal, this mechanism can be applied to the compression processing of trajectory data, using an attention mechanism, such as self-attention in the Transformer model, to filter the trajectory data and only retain key points where animals move quickly or change direction, in order to extract the most important features and reduce the storage and transmission costs of the data. The compressed trajectory data is sent to a cloud server for further analysis and processing.

[0130] In some embodiments, in high-throughput satellite link transmission scenarios, taking into account the problem of reduced transmission quality caused by factors such as rainfall and obstruction, a transmission cache area is set at the edge (satellite terminal) and the cloud (cloud server), and the cache size can be configured through cloud-edge collaboration. Dynamically adjust the data transmission speed according to the network bandwidth and data traffic to avoid network congestion. When the network bandwidth is low, increase the amount of cached data at the edge, and wait for the network conditions to improve before accelerating the transmission. At the same time, a receiving cache area is set in the cloud to smooth data reception and schedule subsequent processing, reducing data loss. Through this transmission flow control and caching strategy, high-efficiency, high-reliability, and low-latency data transmission can be achieved, thereby meeting the data transmission requirements in satellite link transmission scenarios.

[0131] In some embodiments, Fig. 9 As shown, S204 can be specifically implemented as follows S2041-S2042:

[0132] S2041. Acquire and align features of trajectory data based on an attention feature mechanism.

[0133] In some embodiments, the data service module may use an encoder to utilize an attention feature mechanism to identify and extract key features in the trajectory data, and then use a decoder to perform feature decoding to help identify key points in the trajectory, such as turning points, speed change points, etc., and align these features for subsequent analysis and processing.

[0134] S2042: Reconstruct the trajectory data based on the features of the trajectory data and known frame information in the trajectory data.

[0135] The data volume of the trajectory data before reconstruction is greater than the data volume of the trajectory data after reconstruction.

[0136] In some embodiments, key features such as the position, velocity, acceleration, etc. of the trajectory points are extracted from the original trajectory data. At the same time, the trajectory points are sorted and filtered using known frame information in the trajectory data, such as frame number, timestamp, etc., which helps to ensure that the reconstructed trajectory data remains consistent and coherent in time and space, and reduces transmission bandwidth requirements.

[0137] In some embodiments, the extracted features are compressed and encoded to reduce the amount of data, and then the trajectory is reconstructed using the known frame information and the compressed feature data. In the reconstruction process, interpolation, fitting and other methods can be used to restore lost or redundant data points.

[0138] In some embodiments, Fig.10 As shown, S204 can be specifically implemented as follows S2043-S2044:

[0139] S2043: Encrypt the compressed trajectory data based on a preset encryption algorithm.

[0140] In some embodiments, the encryption algorithm includes a symmetric encryption algorithm (such as AES, DES, etc.) and an asymmetric encryption algorithm (such as RSA, ECC, etc.). After selecting the encryption algorithm, the compressed trajectory data needs to be encrypted to ensure that only the recipient with the correct key can decode and access the data, thereby protecting the security of the data during transmission and preventing unauthorized access and data leakage.

[0141] S2044: Send the encrypted trajectory data to the cloud server.

[0142] In some embodiments, after encryption is completed, the satellite terminal sends the encrypted trajectory data to the cloud server via a satellite link. After receiving the encrypted data, the cloud server will use the corresponding decryption key to decrypt the data for storage and further analysis and processing. This encryption and secure transmission method ensures the security and privacy of the data during transmission.

[0143] The data processing method provided by the present application first receives the collected data from the sensing device, integrates the data from multiple sources through the satellite terminal, and then performs preprocessing operations on the collected data, which can filter out noise, correct errors or perform formatting processing, thereby improving the data quality and providing accurate and reliable input for subsequent processing steps. Furthermore, by applying the target recognition algorithm, the target object in the data to be processed is automatically identified, and its trajectory data is generated, thereby improving the processing efficiency and accuracy. Then, the attention feature mechanism is introduced to compress the trajectory data. The attention feature mechanism can identify the important part of the data and give priority to compressing and transmitting it, thereby reducing the amount of data while ensuring the data quality. The compressed data reduces the time and bandwidth required for transmission, improves the real-time and efficiency of data processing, and reduces the communication cost. Finally, the compressed trajectory data is sent to the cloud server, so that the satellite terminal can work in collaboration with cloud resources. Directly performing data preprocessing and target recognition at the satellite terminal reduces the amount of original data that needs to be transmitted to the cloud server, thereby improving the efficiency of data processing, and at the same time, reduces the computing burden of the cloud server and optimizes the utilization of computing resources.

[0144] like Fig.11As shown, the satellite terminal provides a data processing architecture and scheduling mechanism for implementing the data processing method of the present application. First, data is collected through acquisition devices such as video cameras and sensors, and the data is transmitted to the satellite terminal through the acquisition device interactive interface. The data first enters the backup data queue and is then moved into the ready data queue for processing. Multiple data service modules (such as data fusion, target recognition, data compression, etc.) process the data at different levels, and these modules are managed and scheduled by AOS. AOS includes functional modules such as registration management, service scheduling, scheduling center, message / log, status inspection and capability support to ensure the efficiency and real-time performance of data processing. The basic capability support layer includes storage, computing and network resources. These support layers provide key functions such as protocol parsing, data encoding and decoding, container engine and interface services, providing a solid foundation for data processing. Among them, the role of the container engine is to provide an independent operating environment for data services, ensure that different data services are isolated from each other and can be flexibly deployed and migrated, avoid mutual interference and dependency conflicts, thereby ensuring the stability and scalability of the entire system. Through the satellite-to-ground link, the processed data is transmitted from the satellite terminal to the ground application cloud platform through configuration transmission and data transmission, and finally used by the industry user data center. At the same time, the message / log function and status inspection function are used to monitor the operating status of various information and components in the processing process, so as to promptly discover and deal with potential problems. Finally, the processed data will be sent to the output data queue, waiting for further distribution (on-board) or storage (local backup). In addition, according to the growth of the amount of data accessed by the satellite terminal and the dynamic changes in data processing requirements, AOS can further have intelligent elastic expansion capabilities. When the system detects a sudden increase in data traffic or a sharp increase in the processing pressure of a specific data service, AOS can automatically start the elastic computing resources at the edge and quickly create a new container instance to deploy data service replicas to share data processing tasks, ensuring that the response speed and processing efficiency of the entire system are not affected. And after the data traffic returns to normal, AOS can automatically recycle excess resources to reduce system energy consumption. Due to the large scale and small number of people in national parks and nature reserves, in the actual deployment process, multiple sensing devices and multiple satellite terminals will be networked and coordinated in a wide area. AOS can coordinate the data transmission and processing task allocation between different satellite terminals. For tasks that require multi-source data fusion processing, AOS can dispatch different satellite terminals to transmit locally cached data to designated processing nodes for centralized processing, achieving more comprehensive and accurate data processing results, and improving the data processing capabilities and adaptability of the entire system in complex environments. Through the coordinated work of the dispatch center and the basic capability support layer in AOS, the reasonable scheduling and stable operation of the data service module are realized, thereby providing satellite terminals with an efficient and reliable data processing method to meet the data processing needs in complex application scenarios.

[0145] It should be noted that the method of the embodiment of the present application can be performed by a single device, such as a computer or server. The method of this embodiment can also be applied to a distributed scenario and completed by multiple devices cooperating with each other. In the case of such a distributed scenario, one of the multiple devices can only perform one or more steps in the method of the embodiment of the present application, and the multiple devices will interact with each other to complete the described method.

[0146] It should be noted that the above describes some embodiments of the present application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the above embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0147] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, the present application also provides a data processing device.

[0148] refer to Fig.12 , the data processing device comprises: a receiving module 1201, a processing module 1202 and an identification module 1203;

[0149] The receiving module 1201 is used to receive the collected data sent by the sensing device;

[0150] The processing module 1202 is used to perform preprocessing operations on the collected data to obtain data to be processed;

[0151] The identification module 1203 is used to identify the data to be processed based on a target recognition algorithm to obtain trajectory data of the target object;

[0152] The processing module 1202 is also used to compress the trajectory data based on the attention feature mechanism, and send the compressed trajectory data to the cloud server.

[0153] In some embodiments, the satellite terminal includes a plurality of data service modules for identifying and processing the data to be processed;

[0154] The identification module 1203 is specifically used to determine the resource allocation status of each data service module based on the acquired data priority, resource requirements and idle status of system resources in the satellite terminal of each data service module in the multiple data service modules;

[0155] For each of the multiple data service modules, resources are allocated to the data service module based on the resource allocation situation, so that the data service module identifies and processes the data to be processed based on the acquired system resources and the target recognition algorithm to obtain initial trajectory data of the target object;

[0156] The initial trajectory data corresponding to the multiple data service modules are integrated to obtain the trajectory data of the target object.

[0157] In some embodiments, the data to be processed includes relevant data of multiple objects, and the multiple objects include: target objects and non-target objects;

[0158] The identification module 1203 is further specifically used for the data service module to perform identification analysis on the data to be processed based on the target identification algorithm, and delete the relevant data of non-target objects in the data to be processed to obtain the relevant data of the target object; the identification analysis includes: population identification analysis, species identification analysis, and individual identification analysis;

[0159] The data service module generates initial trajectory data of the target object based on the behavior trajectory and the motion trajectory of the target object in the relevant data of the target object.

[0160] In some embodiments, the identification module 1203 is further used to obtain data quality of the initial trajectory data corresponding to the multiple data service modules; the data quality includes data clarity and data integrity;

[0161] The initial trajectory data whose data quality is higher than a preset quality threshold is integrated to obtain the trajectory data.

[0162] In some embodiments, the processing module 1202 is specifically used to obtain and align features of the trajectory data based on the attention feature mechanism;

[0163] The trajectory data is reconstructed based on the features of the trajectory data and known frame information in the trajectory data; the amount of trajectory data before reconstruction is greater than the amount of trajectory data after reconstruction.

[0164] In some embodiments, the receiving module 1201 is specifically used to receive the collected data sent by the sensing device;

[0165] Acquire the communication protocol used by the sensing device to send the collected data;

[0166] Determining a data format of the collected data based on the communication protocol;

[0167] The data format of the collected data is standardized to convert the data format of the collected data into a standard format.

[0168] In some embodiments, the processing module 1202 is further configured to encrypt the compressed trajectory data based on a preset encryption algorithm;

[0169] Send the encrypted trajectory data to the cloud server.

[0170] For the convenience of description, the above device is described in terms of functions divided into various modules. Of course, when implementing the present application, the functions of each module can be implemented in the same or multiple software and / or hardware.

[0171] The device of the above embodiment is used to implement the corresponding data processing method in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be described in detail here.

[0172] Based on the same inventive concept, corresponding to any of the above-mentioned embodiments and methods, the present application also provides an electronic device, including 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 data processing method described in any of the above embodiments is implemented.

[0173] Fig.13 A more specific schematic diagram of the hardware structure of an electronic device provided in this embodiment is shown, and the device may include: a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1060. The processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040 are connected to each other through the bus 1060 in the device.

[0174] The processor 1010 can be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0175] The memory 1020 may be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 1020 may store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program codes are stored in the memory 1020 and are called and executed by the processor 1010.

[0176] The input / output interface 1030 is used to connect the input / output module to realize information input and output. The input / output module can be configured in the device as a component (not shown in the figure), or it can be externally connected to the device to provide corresponding functions. The input device may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output device may include a display, a speaker, a vibrator, an indicator light, etc.

[0177] The communication interface 1040 is used to connect a communication module (not shown) to realize communication interaction between the device and other devices. The communication module can realize communication through a wired mode (such as USB, network cable, etc.) or a wireless mode (such as mobile network, WIFI, Bluetooth, etc.).

[0178] The bus 1050 includes a path that transmits information between the various components of the device (eg, the processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040).

[0179] It should be noted that, although the above device only shows the processor 1010, the memory 1020, the input / output interface 1030, the communication interface 1040 and the bus 1050, in the specific implementation process, the device may also include other components necessary for normal operation. In addition, it can be understood by those skilled in the art that the above device may also only include the components necessary for implementing the embodiments of the present specification, and does not necessarily include all the components shown in the figure.

[0180] The electronic device of the above embodiment is used to implement the corresponding data processing method in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be described in detail here.

[0181] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, the present application also provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the data processing method described in any of the above embodiments.

[0182] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, read-only compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device.

[0183] The computer instructions stored in the storage medium of the above embodiments are used to enable the computer to execute the data processing method described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0184] Based on the same inventive concept, corresponding to the data processing method described in any of the above embodiments, the present disclosure also provides a computer program product, which includes a computer program. In some embodiments, the computer program is executable by one or more processors so that the processors execute the data processing method. Corresponding to the execution subject corresponding to each step in each embodiment of the data processing method, the processor that executes the corresponding step may belong to the corresponding execution subject.

[0185] The computer program product of the above embodiment is used to enable the processor to execute the data processing method described in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be repeated here.

[0186] Those skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the present application (including the claims) is limited to these examples. In line with the concept of the present application, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of the different aspects of the embodiments of the present application as described above, which are not provided in detail for the sake of simplicity.

[0187] In addition, to simplify the description and discussion, and in order not to make the embodiments of the present application difficult to understand, the known power supply / ground connection with the integrated circuit (IC) chip and other components may or may not be shown in the provided drawings. In addition, the device can be shown in the form of a block diagram to avoid making the embodiments of the present application difficult to understand, and this also takes into account the fact that the details of the implementation of these block diagram devices are highly dependent on the platform to be implemented in the embodiments of the present application (that is, these details should be fully within the scope of understanding of those skilled in the art). In the case of elaborating specific details (e.g., circuits) to describe exemplary embodiments of the present application, it is obvious to those skilled in the art that the embodiments of the present application can be implemented without these specific details or when these specific details are changed. Therefore, these descriptions should be considered to be illustrative rather than restrictive.

[0188] Although the present application has been described in conjunction with specific embodiments of the present application, many replacements, modifications and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may use the embodiments discussed.

[0189] The embodiments of the present application are intended to cover all such substitutions, modifications and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present application should be included in the scope of protection of the present application.

Claims

1. A data processing method, characterized in that: Applied to a satellite terminal, the method comprises: Receive the collected data sent by the sensing device; Performing a preprocessing operation on the collected data to obtain data to be processed; Based on the target recognition algorithm, the data to be processed is identified and processed to obtain the trajectory data of the target object; The trajectory data is compressed based on the attention feature mechanism, and the compressed trajectory data is sent to the cloud server.

2. The method according to claim 1, characterized in that The satellite terminal comprises a plurality of data service modules for identifying and processing the data to be processed; The method of performing identification processing on the data to be processed based on a target identification algorithm to obtain trajectory data of the target object includes: Determine resource allocation of each data service module based on the acquired data priority, resource requirements and idleness of system resources in the satellite terminal; For each of the multiple data service modules, resources are allocated to the data service module based on the resource allocation situation, so that the data service module identifies and processes the data to be processed based on the acquired system resources and the target recognition algorithm to obtain initial trajectory data of the target object; The initial trajectory data corresponding to the multiple data service modules are integrated to obtain the trajectory data of the target object.

3. The method according to claim 2, characterized in that The data to be processed includes relevant data of multiple objects, and the multiple objects include: target objects and non-target objects; The data service module identifies and processes the data to be processed based on the acquired system resources and the target recognition algorithm to obtain the initial trajectory data of the target object, including: The data service module performs identification analysis on the data to be processed based on the target identification algorithm, and deletes the relevant data of non-target objects in the data to be processed to obtain the relevant data of the target object; the identification analysis includes: population identification analysis, species identification analysis, and individual identification analysis; The data service module generates initial trajectory data of the target object based on the behavior trajectory and the motion trajectory of the target object in the relevant data of the target object.

4. The method according to claim 2, characterized in that: The integrating and processing the initial trajectory data corresponding to the plurality of data service modules to obtain the trajectory data of the target object includes: Acquire data quality of the initial trajectory data corresponding to the multiple data service modules; the data quality includes data clarity and data integrity; The initial trajectory data whose data quality is higher than a preset quality threshold is integrated to obtain the trajectory data.

5. The method according to claim 1, characterized in that The compressing the trajectory data based on the attention feature mechanism includes: Acquire and align features of the trajectory data based on the attention feature mechanism; The trajectory data is reconstructed based on the features of the trajectory data and known frame information in the trajectory data; the amount of trajectory data before reconstruction is greater than the amount of trajectory data after reconstruction.

6. The method according to claim 1, characterized in that The receiving the collected data sent by the sensing device includes: Receiving the collected data sent by the sensing device; Acquire the communication protocol used by the sensing device to send the collected data; Determining a data format of the collected data based on the communication protocol; The data format of the collected data is standardized to convert the data format of the collected data into a standard format.

7. The method according to claim 1, characterized in that The step of sending the compressed trajectory data to the cloud server includes: Encrypting the compressed trajectory data based on a preset encryption algorithm; Send the encrypted trajectory data to the cloud server.

8. A data processing device, characterized in that: The device comprises: a receiving module, a processing module and an identification module; The receiving module is used to receive the collected data sent by the sensing device; The processing module is used to perform preprocessing operations on the collected data to obtain data to be processed; The recognition module is used to perform recognition processing on the data to be processed based on a target recognition algorithm to obtain trajectory data of the target object; The processing module is also used to compress the trajectory data based on the attention feature mechanism, and send the compressed trajectory data to the cloud server.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method according to any one of claims 1 to 7 when executing the program. 10 . A non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause a computer to execute the method according to claim 1 .

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