A cloud track data processing method, device, equipment and medium

Abstract

The application discloses a cloud track data processing method, comprising the following steps: acquiring first track data of a target object T moment, acquiring a first geographical area where the target object is located and a first node track pool corresponding to the first geographical area according to the first track data, the node track pool comprising at least one data processing device, each data processing device processing track data of at least one geographical area, and the track data comprising position data; acquiring second track data of the target object T+1 moment, acquiring a second geographical area where the target object is located and a second node track pool corresponding to the second geographical area according to the second track data; judging whether the first node track pool and the second node track pool are the same, and if not, transferring the first track data in the first node track pool to the second node track pool.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a cloud-based method, apparatus, device, and medium for processing flight track data. Background Technology

[0002] Applications in fields such as intelligent transportation and vehicle-road cooperation are becoming increasingly widespread. Various types of computations based on perception data and perception targets are increasing, and the high frequency of data volume also leads to heavy computational tasks, making it impossible to guarantee the timeliness of computational results.

[0003] Therefore, there is an urgent need for a cloud-based method for processing flight track data to solve the existing problems. Summary of the Invention

[0004] This specification provides a cloud-based method, apparatus, device, and medium for processing flight track data, which addresses the problem of unreliable timeliness of calculation results due to increased data volume.

[0005] The embodiments in this specification adopt the following technical solutions:

[0006] In a first aspect, this illustrative embodiment provides a cloud-based method for processing flight track data, comprising the following steps:

[0007] The first trajectory data of the target object at time T is obtained. Based on the first trajectory data, the first geographical region where the target object is located and the first node trajectory pool corresponding to the first geographical region are obtained. The node trajectory pool includes at least one data processing device. Each data processing device processes trajectory data of at least one geographical region. The trajectory data includes location data.

[0008] Obtain the second track data of the target object at time T+1, and based on the second track data, obtain the second geographical region where the target object is located and the second node track pool corresponding to the second geographical region;

[0009] Determine whether the first node track pool and the second node track pool are the same. If they are different, transfer the first track data in the first node track pool to the second node track pool.

[0010] Secondly, this embodiment also provides a cloud-based trajectory data processing device, including:

[0011] The first data acquisition module is used to acquire the first trajectory data of the target object at time T, and based on the first trajectory data, acquire the first geographical region where the target object is located and the first node trajectory pool corresponding to the first geographical region. The node trajectory pool includes at least one data processing device, and each data processing device processes the trajectory data of at least one geographical region. The trajectory data includes location data.

[0012] The second data acquisition module is used to acquire the second track data of the target object at time T+1, and based on the second track data, acquire the second geographical region where the target object is located and the second node track pool corresponding to the second geographical region.

[0013] The judgment and execution module is used to determine whether the first node track pool and the second node track pool are the same. If they are different, the first track data in the first node track pool is transferred to the second node track pool.

[0014] Thirdly, this specification also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method described in any of the embodiments.

[0015] Fourthly, this specification also provides a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the methods described in any of the embodiments.

[0016] The above-mentioned at least one technical solution adopted in the embodiments of this specification can achieve the following beneficial effects: by planning a distributed architecture, the problem of insufficient computing resources caused by high data growth in centralized architecture is solved; the data management function is separated from the algorithm, so that the algorithm can focus on computing, and through unified data management, different algorithms avoid maintaining the same data, reduce memory consumption in the computing process, and improve data processing efficiency. Attached Figure Description

[0017] The accompanying drawings, which are included to provide a further understanding of the embodiments of this specification and form part of the embodiments of this specification, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0018] Figure 1 A flowchart illustrating a cloud-based trajectory data processing method provided in an embodiment of this specification;

[0019] Figure 2 This is a schematic diagram of the node track pool data processing flow in a cloud-based track data processing method provided in an embodiment of this specification.

[0020] Figure 3 This is a schematic diagram of a cloud-based trajectory data processing device provided in an embodiment of this specification. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments in this specification, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments in this specification without creative effort are within the scope of protection of this application.

[0022] In traditional technologies, a single server aggregates all data, and all sensing data is processed by a single server. Data is grouped by sensing device, and data generated by the same device is distributed to a single server. Services are divided by region, and data is first sent to a scheduling node, which then forwards the data to the corresponding computing node according to the data's location information. Computation is performed according to a specific algorithm, and each algorithm orchestrates and maintains its own data. This leads to situations where applications in fields like intelligent transportation and vehicle-road collaboration are typically high-concurrency, high-throughput business scenarios. Centralized systems can no longer meet the needs of high concurrency and low latency, and the data capacity and computing performance of a single server cannot support the ever-increasing data growth. When data is grouped by sensing devices, data cannot be exchanged between the sensing devices. After a sensed object crosses the sensing areas of different devices, the target cannot be continuously tracked. At the same time, some computing scenarios rely on specific area divisions, requiring secondary data scheduling to achieve target tracking and subsequent scenario calculations. The added processing steps require additional server resources, which also increases data latency. When data is distributed by scheduling nodes, a dedicated server is needed for scheduling calculations. At the same time, data transmission between servers becomes the main cause of high computing latency when the data volume is high and is difficult to optimize. When different algorithms for computing based on sensed data maintain their own data, it leads to each function maintaining a copy of the same data, resulting in a large amount of data redundancy and duplicate data arrangement problems. For example, some calculations that require window data require each algorithm to maintain historical data, consuming a lot of resources.

[0023] To address this, embodiments of this specification provide a cloud-based method, apparatus, device, and medium for processing flight track data. By planning a distributed architecture, it solves the problem of insufficient computing resources caused by high data growth inherent in centralized architectures. It separates data management functions from algorithms, allowing algorithms to focus on computation. Furthermore, unified data management avoids different algorithms maintaining the same data, reducing memory consumption during computation and improving data processing efficiency. Through a geographical partitioning strategy, it plans data transmission strategies for data links, ensuring data directly reaches the servers, processes, and threads where the computing units reside, eliminating the need for scheduling nodes and allowing more servers to be involved in data computation, thus increasing the cluster's computing power. Finally, the design of the computing units and the design of adjacent partitions residing on the same server and process reduce network resource consumption during data transfer, thereby improving the timeliness of real-time computing scenarios.

[0024] The technical solutions provided in the various embodiments of this specification are described in detail below with reference to the accompanying drawings.

[0025] Please see Figure 1 As shown, Figure 1 This is a flowchart illustrating a cloud-based trajectory data processing method provided in an embodiment of this specification. The embodiment of this specification provides a cloud-based trajectory data processing method, which includes the following steps:

[0026] S101. Obtain the first trajectory data of the target object at time T. Based on the first trajectory data, obtain the first geographical region where the target object is located and the first node trajectory pool corresponding to the first geographical region. The node trajectory pool includes at least one data processing device. Each data processing device processes the trajectory data of at least one geographical region. The trajectory data includes location data.

[0027] In practice, data processing equipment includes, but is not limited to, servers. Each independent server is a node track pool, and a cluster of such independent servers forms a globally distributed track pool cluster. The geographical area that generates track data is divided into several smaller regions, and the node track pool is responsible for track maintenance, scheduling, and calculation in multiple smaller regions.

[0028] Methods for acquiring trajectory data include, but are not limited to, using roadside sensing devices and vehicle-mounted sensing devices. Roadside sensing devices refer to sensor devices installed on roads, while vehicle-mounted sensing devices refer to sensor devices installed on vehicles. These sensor devices include, but are not limited to, cameras, speed sensors, position sensors, and acceleration sensors. For example, the first trajectory data can be obtained through roadside sensing devices and vehicle-mounted sensing devices. The trajectory data includes, but is not limited to, the target's lateral and longitudinal distances, speed, acceleration, type, and length, width, and height.

[0029] After the roadside sensing devices and vehicle-side sensing devices detect the first trajectory data, they select nearby geographic area data links to upload the data according to certain strategies or rules. These strategies and rules include, but are not limited to, geohash. For example, the data links are numbered based on the geohash value of the geographic area's GPS. When roadside devices report data, they send the data to the data link numbered with the corresponding geohash value based on the geohash value of the roadside device's GPS. When vehicle-side devices report data, they send the data to the data link numbered with the corresponding geohash value based on the geohash value of the current location's GPS.

[0030] It should be understood that the specific related content listed above is for illustrative purposes only and should not be construed as limiting the scope of this application.

[0031] S103. Obtain the second trajectory data of the target object at time T+1. Based on the second trajectory data, obtain the second geographical region where the target object is located and the second node trajectory pool corresponding to the second geographical region.

[0032] In practice, methods for acquiring trajectory data include, but are not limited to, using roadside sensing devices and vehicle-mounted sensing devices. Roadside sensing devices refer to sensor devices installed on roads, while vehicle-mounted sensing devices refer to sensor devices installed on vehicles. These sensor devices include, but are not limited to, cameras, speed sensors, position sensors, and acceleration sensors. For example, the first trajectory data can be obtained through roadside sensing devices and vehicle-mounted sensing devices. The trajectory data includes, but is not limited to, the target's lateral and longitudinal distances, speed, acceleration, type, and length, width, and height.

[0033] After the roadside sensing devices and vehicle-side sensing devices detect the second trajectory data, they select nearby geographic area data links to upload the data according to certain strategies or rules. These strategies and rules include, but are not limited to, geohash. For example, the data links are numbered based on the geohash value of the geographic area's GPS. When roadside devices report data, they send the data to the data link numbered with the corresponding geohash value based on the geohash value of the roadside device's GPS. When vehicle-side devices report data, they send the data to the data link numbered with the corresponding geohash value based on the geohash value of the current location's GPS.

[0034] It should be understood that the specific related content listed above is for illustrative purposes only and should not be construed as limiting the scope of this application.

[0035] S105. Determine whether the first node track pool and the second node track pool are the same. If they are different, transfer the first track data in the first node track pool to the second node track pool.

[0036] In practice, the targets sensed by roadside sensing devices, including but not limited to vehicles and pedestrians, are often in motion. When the first track data of a target is distributed to the track pool of node A for processing at time T, its sensing data is also stored on A. When the second track data of the target is distributed to B for processing at time T+1, and the track data needs to be stored on B, the first track data is transferred to B.

[0037] This embodiment solves the problem of insufficient computing resources caused by high data growth in centralized architecture by planning a distributed architecture; it separates the data management function from the algorithm, allowing the algorithm to focus on computing, and through unified data management, it avoids different algorithms maintaining the same data, reduces memory consumption during the computing process, and improves data processing efficiency.

[0038] For further details, please refer to Figure 2 As shown, Figure 2 This is a schematic diagram of the node track pool data processing flow in a cloud-based track data processing method provided in this embodiment. To better implement this embodiment, the first track data in the first node track pool is transferred to the second node track pool, followed by:

[0039] S201. Based on the location data, obtain the current geographic partition of the target object, determine whether the current geographic partition of the target object is using the current process, if so, allocate the second track data to the process corresponding to the geographic partition, if not, transfer the second track data to the corresponding process, and complete the selection of the running process; the geographic partition is one of several small areas divided by a geographic region, and each small area corresponds to at least one process.

[0040] S203. Convert the second trajectory data into system trajectory data;

[0041] In specific implementation, the methods for converting the second track data into system track data include, but are not limited to:

[0042] Obtain the second track data; based on the track attribute list, obtain the attribute values ​​in the track attribute list that correspond to the second track data; create a temporary track object, assign the attribute values ​​to the temporary track object, and generate standard track data in the track pool; convert the standard track data into system track data.

[0043] The methods for converting the standard track data into system track data include, but are not limited to:

[0044] Determine whether the standard track data is a new target. If so, mark the standard track data as system track data. If not, update the track data in the original system track data to form new system track data, so as to use the new system track data to complete the custom track algorithm.

[0045] S205. The system track data is used to complete the custom track algorithm during the running process.

[0046] This embodiment utilizes a geographical partitioning strategy to plan data transmission strategies for data links, enabling data to directly reach the servers, processes, and threads where the computing units reside. This eliminates the need for scheduling nodes, allowing more servers to be involved in data computation and increasing the cluster's computing power. Furthermore, the design of the computing units and adjacent partitions on the same server and process reduces network resource consumption during data transfer, thereby improving the timeliness of real-time computing scenarios.

[0047] Furthermore, the step of using the system track data to complete the custom track algorithm includes:

[0048] Associate system track data with geographic regions and add system track data to the track set of the current geographic region.

[0049] In practice, once the custom track algorithm and the custom geographic region algorithm are loaded, the dynamic attribute fields required by the track object are organized based on the input and output data items of the two types of algorithms. If multiple algorithms use the same data field, only one data field is retained as the dynamic attribute of the track object.

[0050] Furthermore, the step of associating system track data with geographical regions and adding system track data to the track set of the current geographical region further includes:

[0051] If the target object leaves the current geographical area, the system track data is transferred to the geographical area where the target object is located, and the system track data of the target object is removed from the track set of the original geographical area.

[0052] This embodiment can plan dynamic track attributes and manage track data in a unified manner, avoiding the need for different algorithms to maintain multiple sets of data.

[0053] For further details, please refer to Figure 3 As shown, Figure 3 This is a schematic diagram of a cloud-based trajectory data processing device provided in an embodiment of this specification. This embodiment provides a cloud-based trajectory data processing device, which includes:

[0054] The first data acquisition module 301 is used to acquire the first trajectory data of the target object at time T, and based on the first trajectory data, acquire the first geographical region where the target object is located and the first node trajectory pool corresponding to the first geographical region. The node trajectory pool includes at least one data processing device, and each data processing device processes the trajectory data of at least one geographical region. The trajectory data includes location data.

[0055] The second data acquisition module 302 is used to acquire the second track data of the target object at time T+1, and based on the second track data, acquire the second geographical region where the target object is located and the second node track pool corresponding to the second geographical region.

[0056] The judgment and execution module 303 is used to determine whether the first node track pool and the second node track pool are the same. If they are different, the first track data in the first node track pool is transferred to the second node track pool.

[0057] The data conversion module is used to convert the second track data into system track data.

[0058] Also includes:

[0059] The geographic partition scheduling module is used to obtain the current geographic partition of the target object based on the location data, determine whether the current geographic partition of the target object is using the current process, and if so, allocate the second track data to the process corresponding to the geographic partition; otherwise, pass the second track data to the corresponding process, thus completing the selection of the running process. The geographic partition is one of several small regions divided from a geographic region, and each small region corresponds to at least one process. It is also used to distribute the sensing data reported by each roadside (roadside sensing device) to different partition computing units (corresponding to different geographic regions) for calculation based on the geographical location of the sensing data. This enables multiple partition computing units to share the processing of massive amounts of roadside sensing data, achieving parallel computing under load balancing.

[0060] The geographic region custom algorithm management module is used to complete the custom algorithm of the trajectory using the system trajectory data; it is also used to divide the global map into several geographic regions, which is predefined and configured by the system management, so that the geographic partition scheduling module can distribute the perception data of different roadsides to different partition computing units.

[0061] The track lifecycle management module is used to maintain the track data of each target object sensed by the roadside sensing device, including but not limited to generating new tracks for targets appearing in the sensing area; updating track data periodically; and deleting track data when a target object leaves the sensing area.

[0062] The track caching module is used to associate system track data with geographical regions, add the system track data to the track set of the current geographical region, and, if the target object leaves the current geographical region, transfer the system track data to the geographical region where the target object is located and remove the system track data of the target object from the original track set of the geographical region. It is also used to cache track data calculated for track lifecycle management in memory for fast access by other modules. For example, a roadside sensing device senses several targets (such as vehicles, pedestrians, etc.), each target object corresponds to a track data point, and the main attributes of each track data point include its current (and historical) status information, such as position, speed, and direction of movement.

[0063] The track custom algorithm module is used for algorithms that reprocess tracks predefined and configured by the system, such as algorithms for statistical analysis of track attributes within a time window, or for formatting or mathematical transformations of track attributes.

[0064] The regional track management module is used when the perception data of a target object is distributed to the partition computing unit C for processing at time t, and its track data is also stored on D. When the perception data of the target object is distributed to the partition computing unit D for processing at time t+1, and the track data needs to be stored on D, the regional track management module needs to handle all the work related to the real-time migration of the target object's track data between different partition computing units.

[0065] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0066] Therefore, this application also proposes a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the methods described in any embodiment of this application.

[0067] Furthermore, this application also proposes an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method described in any embodiment of this application.

[0068] In the 1990s, improvements to a technology could be clearly distinguished as either hardware improvements (e.g., improvements to the circuit structure of diodes, transistors, switches, etc.) or software improvements (improvements to the methodology). However, with technological advancements, many methodological improvements today can be considered direct improvements to the hardware circuit structure. Designers almost always obtain the corresponding hardware circuit structure by programming the improved methodology into the hardware circuit. Therefore, it cannot be said that a methodological improvement cannot be implemented using hardware physical modules. For example, a Programmable Logic Device (PLD) (such as a Field Programmable Gate Array (FPGA)) is such an integrated circuit whose logic function is determined by the user programming the device. Designers can program and "integrate" a digital system onto a PLD themselves, without needing chip manufacturers to design and manufacture dedicated integrated circuit chips. Furthermore, nowadays, instead of manually manufacturing integrated circuit chips, this programming is mostly implemented using "logic compiler" software. Similar to the software compiler used in program development, the original code before compilation must be written in a specific programming language, called a Hardware Description Language (HDL). There are many HDLs, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, and RHDL (Ruby Hardware Description Language). Currently, the most commonly used are VHDL (Very-High-Speed ​​Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should understand that by simply performing some logic programming on the method flow using one of these hardware description languages ​​and programming it into an integrated circuit, the hardware circuit implementing the logical method flow can be easily obtained.

[0069] The controller can be implemented in any suitable manner. For example, it can take the form of a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro)processor, logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers, and embedded microcontrollers. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicon Labs C8051F320. A memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art will also recognize that, in addition to implementing the controller in purely computer-readable program code form, the same functionality can be achieved by logically programming the method steps to make the controller take the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers. Therefore, such a controller can be considered a hardware component, and the means included therein for implementing various functions can also be considered as structures within the hardware component. Alternatively, the means for implementing various functions can be considered as both software modules implementing the method and structures within the hardware component.

[0070] The systems, devices, modules, or units described in the embodiments of the above specification can be implemented by computer chips or physical entities, or by products with certain functions. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or any combination of these devices.

[0071] For ease of description, the above devices are described in terms of function, divided into various modules or units. Of course, in implementing this application, the functions of each module or unit can be implemented in one or more software and / or hardware.

[0072] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0073] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this specification. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0074] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0075] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a processing flow implemented by the computer, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0076] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0077] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory (NVM), like read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0078] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using 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 technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0079] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0080] This application can be described in the general context of computer-executable instructions, such as program modules, that are executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform a specific task or implement a specific abstract data type. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0081] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0082] The above description is merely an embodiment of the specification of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of this application should be included within the scope of the claims of this application.

Claims

1. A cloud-based method for processing flight track data, characterized in that, Includes the following steps: The first trajectory data of the target object at time T is obtained. Based on the first trajectory data, the first geographical region where the target object is located and the first node trajectory pool corresponding to the first geographical region are obtained. The node trajectory pool includes at least one data processing device. Each data processing device processes trajectory data of at least one geographical region. The trajectory data includes location data. Obtain the second track data of the target object at time T+1, and based on the second track data, obtain the second geographical region where the target object is located and the second node track pool corresponding to the second geographical region; Determine whether the first node track pool and the second node track pool are the same. If they are different, transfer the first track data in the first node track pool to the second node track pool. Based on the location data, the current geographic partition of the target object is obtained. It is determined whether the current geographic partition of the target object is using the current process. If so, the second track data is assigned to the process corresponding to the geographic partition. If not, the second track data is passed to the corresponding process to complete the selection of the running process. The geographic partition is one of several small areas divided by a geographic region, and each small area corresponds to at least one process.

2. The cloud-based trajectory data processing method according to claim 1, characterized in that, After completing the selection of the running process, the process further includes: Convert the second track data into system track data; A custom trajectory algorithm is completed using the system's trajectory data.

3. The cloud-based trajectory data processing method according to claim 2, characterized in that, The step of converting the second track data into system track data includes: Obtain the second track data, and based on the track attribute list, obtain the attribute values ​​in the track attribute list that correspond to those in the second track data; Create a temporary track object, assign attribute values ​​to the temporary track object, and generate standard track data in the track pool; The standard track data is converted into system track data.

4. The cloud-based trajectory data processing method according to claim 3, characterized in that, The process of converting the standard track data into system track data includes: Determine whether the standard track data is a new target; if so, mark the standard track data as system track data. If not, update the original system track data to form new system track data, so as to use the new system track data to complete the custom track algorithm.

5. The cloud-based trajectory data processing method according to claim 2, characterized in that, The process of using the system track data to complete the custom track algorithm then includes: Associate system track data with geographic regions and add system track data to the track set of the current geographic region.

6. The cloud-based trajectory data processing method according to claim 2, characterized in that, The step of associating system track data with a geographic region and adding the system track data to the track set of the current geographic region further includes: If the target object leaves the current geographical area, the system track data is transferred to the geographical area where the target object is located, and the system track data of the target object is removed from the track set of the original geographical area.

7. A cloud-based trajectory data processing device, characterized in that, include: The first data acquisition module is used to acquire the first trajectory data of the target object at time T, and based on the first trajectory data, acquire the first geographical region where the target object is located and the first node trajectory pool corresponding to the first geographical region. The node trajectory pool includes at least one data processing device, and each data processing device processes the trajectory data of at least one geographical region. The trajectory data includes location data. The second data acquisition module is used to acquire the second track data of the target object at time T+1, and based on the second track data, acquire the second geographical region where the target object is located and the second node track pool corresponding to the second geographical region. The judgment and execution module is used to determine whether the first node track pool and the second node track pool are the same. If they are different, the first track data in the first node track pool is transferred to the second node track pool. Based on the location data, the current geographic partition of the target object is obtained. It is determined whether the current geographic partition of the target object is using the current process. If so, the second track data is assigned to the process corresponding to the geographic partition. If not, the second track data is passed to the corresponding process to complete the selection of the running process. The geographic partition is one of several small areas divided by a geographic region, and each small area corresponds to at least one process.

8. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1-6.

Images