Project modularization processing method, device, equipment, medium and vehicle

Abstract

The invention relates to a project modularization processing method, device and equipment, a medium and a vehicle. The project modular processing method comprises the following steps: in response to a project processing request, obtaining three-dimensional point cloud data to be processed; determining a target algorithm module in a plurality of preset algorithm modules, wherein the target algorithm module is an algorithm module used for realizing the project processing request; performing combined configuration processing on the target algorithm module and the plurality of data blocks to obtain a project processing framework; and performing data processing on the to-be-processed three-dimensional point cloud data through the project processing framework to obtain a corresponding processing result. According to the embodiment of the invention, the redundancy of repeated development of similar algorithm modules can be reduced, and the development efficiency is improved.

Description

Technical Field

[0001] This disclosure relates to the field of computer technology, and in particular to a modular processing method, apparatus, equipment, medium, and vehicle for projects. Background Technology

[0002] 3D algorithms refer to algorithms that are frequently used in fields such as industry and robotics, which primarily process 3D object data. These include, but are not limited to, point cloud downsampling algorithms, point cloud filtering algorithms, point cloud matching algorithms, shape fitting algorithms, and robotic arm pose transformation algorithms.

[0003] In related technologies, when robotic arms perform tasks such as grasping, measuring, and guiding target objects in fields such as intelligent manufacturing and intelligent services, it is necessary to collect three-dimensional information of the target object and then perform subsequent processing based on the three-dimensional information of the target object. Different processing flows are set up for different processing tasks. In the processing flow, multiple three-dimensional algorithms are often used one or more times. The number of times and the order of calling three-dimensional algorithms are often different in different processing flows. If each processing task is developed separately and then processed separately, it will cause development redundancy and will not be conducive to the rapid completion of processing tasks. Summary of the Invention

[0004] To address the aforementioned technical problems, this disclosure provides a modular processing method, apparatus, equipment, medium, and vehicle for projects.

[0005] Firstly, this disclosure provides a modular project processing method, including:

[0006] In response to a project processing request, obtain the 3D point cloud data to be processed;

[0007] A target algorithm module is determined from a set of preset algorithm modules. The target algorithm module is the algorithm module used to implement the project's request processing.

[0008] The target algorithm module and multiple data blocks are combined and configured to obtain the project processing framework;

[0009] The project processing framework is used to process the 3D point cloud data to be processed, and the corresponding processing results are obtained.

[0010] Secondly, this disclosure provides a modular project processing apparatus, including:

[0011] The data acquisition unit is used to acquire the 3D point cloud data to be processed in response to project processing requests;

[0012] A module determination unit is used to determine a target algorithm module from a set of preset algorithm modules, wherein the target algorithm module is an algorithm module used to implement the project processing request;

[0013] The first processing unit is used to combine and configure the target algorithm module and multiple data blocks to obtain the project processing framework.

[0014] The second processing unit is used to process the 3D point cloud data to be processed through the project processing framework to obtain the corresponding processing results.

[0015] Thirdly, this disclosure provides a modular processing device for projects, including:

[0016] processor;

[0017] Memory, used to store executable instructions;

[0018] The processor is used to read executable instructions from memory and execute the executable instructions to implement the modular processing method of the first aspect.

[0019] Fourthly, this disclosure provides a computer-readable storage medium storing a computer program that, when executed by a processor, causes the processor to implement the modular processing method of the first aspect.

[0020] Fifthly, this disclosure provides a vehicle including the above-described modular processing device.

[0021] The technical solution provided in this disclosure has the following advantages compared with the prior art:

[0022] The modular processing method, apparatus, device, medium, and vehicle of this disclosure are capable of responding to a project processing request by acquiring 3D point cloud data to be processed, then determining a target algorithm module from a set of preset algorithm modules. The target algorithm module is an algorithm module used to implement the project processing request. The target algorithm module and multiple data blocks are then combined and configured to obtain a project processing framework. Finally, the project processing framework is used to process the 3D point cloud data to obtain the corresponding processing result. Thus, a target algorithm module can be determined from a set of preset algorithm modules according to the project processing request, and a project processing framework can be obtained by combining the target algorithm module and multiple data blocks. Data processing is then performed using the project processing framework, allowing for free combination of algorithm modules according to user needs. This reduces redundancy from repetitive development of similar algorithm modules and improves development efficiency. Attached Figure Description

[0023] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and the originals and elements are not necessarily drawn to scale.

[0024] Figure 1 A flowchart illustrating a modular project processing method provided in this embodiment of the disclosure;

[0025] Figure 2 A flowchart illustrating another modular project processing method provided in this disclosure embodiment;

[0026] Figure 3 This is a schematic diagram of the structure of a modular processing device for a project provided in an embodiment of the present disclosure;

[0027] Figure 4 This is a schematic diagram of the structure of a modular processing device for a project provided in an embodiment of this disclosure. Detailed Implementation

[0028] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0029] It should be understood that the steps described in the method embodiments of this disclosure may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of this disclosure is not limited in this respect.

[0030] The term "comprising" and its variations as used herein are open-ended inclusions, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Definitions of other terms will be given in the description below.

[0031] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are used only to distinguish different devices, modules or units, and are not used to limit the order of functions performed by these devices, modules or units or their interdependencies.

[0032] It should be noted that the terms "a" and "a plurality of" used in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0033] The names of messages or information exchanged between multiple devices in the embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

[0034] To address the aforementioned problems, this disclosure provides a modular project processing method, apparatus, equipment, medium, and vehicle. The following will first combine... Figures 1 to 2 The modular processing method for projects provided in the embodiments of this disclosure will be described in detail.

[0035] Figure 1 A flowchart illustrating a modular project processing method provided in an embodiment of this disclosure is shown.

[0036] In this embodiment of the disclosure, the modular processing method of the project can be executed by an electronic device. Specifically, the electronic device may include, but is not limited to, mobile terminals such as computer devices, mobile phones, in-vehicle devices, vehicle controllers, tablet computers, and wearable devices.

[0037] like Figure 1 As shown, the modular processing method for this project may include the following steps.

[0038] S110. In response to a project processing request, obtain the 3D point cloud data to be processed.

[0039] In this embodiment of the disclosure, the electronic device can respond to a project processing request and acquire the three-dimensional point cloud data to be processed corresponding to the target object.

[0040] Optionally, a project processing request can handle some project tasks for a user request.

[0041] Optionally, the target object can be the object to be processed.

[0042] Optionally, the 3D point cloud data to be processed can be a set of points consisting of a large number of points in 3D spatial coordinates.

[0043] Specifically, when a user wants to process a target object, they can send a project processing request to the electronic device. The electronic device can receive and respond to the project processing request and obtain the 3D point cloud data to be processed corresponding to the target object, such as point cloud data with color normals transmitted from the sensor.

[0044] S120. Determine a target algorithm module from a set of preset algorithm modules. The target algorithm module is an algorithm module used to implement the project processing request.

[0045] In this embodiment of the disclosure, the electronic device can determine the target algorithm module from a set of preset algorithm modules.

[0046] Optionally, the algorithm module can be a pre-defined module with different algorithm functions.

[0047] Optionally, the target algorithm module can be an algorithm module used to implement the project processing request.

[0048] Specifically, after receiving a project processing request, the electronic device can determine the target algorithm module from a set of preset algorithm modules, that is, determine the algorithm module used to implement the project processing request from a set of preset algorithm modules.

[0049] S130. Combine and configure the target algorithm module and multiple data blocks to obtain the project processing framework.

[0050] In this embodiment of the disclosure, the electronic device can combine and configure the target algorithm module and multiple data blocks to obtain a project processing framework.

[0051] Alternatively, the data clumps can be clumps used to store various types of data.

[0052] Alternatively, the combined configuration processing can be a configuration that allows for free combination of the target algorithm module and multiple data blocks.

[0053] Alternatively, the project processing framework can be a pre-configured framework system for performing project processing.

[0054] Specifically, the electronic device can freely combine and configure the target algorithm module and multiple data blocks, that is, perform combination configuration processing to obtain the project processing framework.

[0055] S140. The three-dimensional point cloud data to be processed is processed through the project processing framework to obtain the corresponding processing results.

[0056] In this embodiment of the disclosure, the electronic device can perform data processing on the three-dimensional point cloud data to be processed through the project processing framework to obtain the corresponding processing results.

[0057] Specifically, after obtaining the project processing framework, the electronic device can process the 3D point cloud data to be processed through the project processing framework to obtain the corresponding processing results.

[0058] Therefore, in this embodiment of the present disclosure, in response to a project processing request, the system can acquire the 3D point cloud data to be processed, then determine a target algorithm module from a set of preset algorithm modules. The target algorithm module is an algorithm module used to implement the project processing request. Then, the target algorithm module and multiple data blocks are combined and configured to obtain a project processing framework. Finally, the project processing framework is used to process the 3D point cloud data to be processed to obtain the corresponding processing result. Thus, a target algorithm module can be determined from a set of preset algorithm modules according to the project processing request, and a project processing framework can be obtained by combining the target algorithm module and multiple data blocks. Data processing is performed through the project processing framework, that is, the algorithm modules can be freely combined according to user needs, thereby reducing the redundancy of repeated development of similar algorithm modules and improving development efficiency.

[0059] Optionally, the target algorithm module can be at least one of a sampling module, a filtering module, a fitting module, a clustering module, and a matching module. Specifically, the sampling module can perform point cloud sampling on the 3D point cloud data to be processed; the filtering module can perform point cloud filtering on the 3D point cloud data to be processed; the fitting module can perform point cloud fitting on the 3D point cloud data to be processed; the clustering module can perform point cloud clustering on the 3D point cloud data to be processed; and the matching module can perform point cloud matching on the 3D point cloud data to be processed.

[0060] Optionally, prior to S120, the modular processing method of this project may further include: performing algorithm modular processing on multiple three-dimensional algorithms to obtain multiple preset algorithm modules.

[0061] Optionally, a 3D algorithm can refer to an algorithm that is frequently used in fields such as industry and robotics, and that primarily processes 3D point cloud data.

[0062] Alternatively, algorithm modularization can be the process of abstracting 3D algorithms into modules.

[0063] Optionally, the algorithm module may include static parameters, dynamic parameters, connection parameters, visualization parameters, and process parameters.

[0064] Specifically, the electronic device can first perform algorithm modularization on multiple 3D algorithms, that is, abstract the 3D algorithms into modules to obtain multiple preset algorithm modules. These algorithm modules can include static parameters, dynamic parameters, connection parameters, visualization parameters, and process parameters. For example, static parameters can be assigned to the corresponding algorithm module only during the overall framework initialization phase to perform the module's initialization work. These parameters cannot be changed during framework runtime; for some algorithm modules that do not require initialization, these parameters can be empty. Dynamic parameters can be assigned to the corresponding algorithm module only when the framework executes the algorithm module. These parameters need to be assigned each time the system is called and can be dynamically modified during framework runtime. For some algorithm modules that do not require dynamic parameters, these parameters can be empty. Connection parameters can specify the preceding and following data block modules of the current algorithm module using numerical indices to configure the execution order of each algorithm module within the overall framework. Algorithm modules with the same function are allowed to appear multiple times, distinguished by suffixes in their names. Visualization parameters can configure whether the execution results of the current algorithm module are displayed or saved, etc., providing visualization functionality. Process parameters can configure some process-related parameters of the current algorithm module, such as the module's index among all modules and the number of times it is allowed to be executed.

[0065] Optionally, prior to S130, the modular processing method of this project may further include: pre-designing multiple data blocks, the data blocks being used to store module input data and module output data, the data blocks including data identifiers for indicating data types.

[0066] In this embodiment of the disclosure, the electronic device may be pre-designed with multiple data blocks.

[0067] Optionally, the data block can be used to store module input data and module output data. The module input data can be data used to input the algorithm module, such as the 3D point cloud data to be processed or the output data of the previous algorithm module. The module output data can be the data output by the algorithm module after processing the input data.

[0068] Optionally, the data block may include a data identifier to indicate the data type.

[0069] Specifically, the electronic device can be pre-designed with multiple data blocks, which can be used to store module input data and module output data, and also include data identifiers to indicate the data type.

[0070] Optionally, S130 may specifically include: combining multiple target algorithm modules and multiple data blocks to determine corresponding sequential connection relationships; configuring connection parameters of multiple target algorithm modules according to the sequential connection relationships and generating corresponding configuration files; and generating the project processing framework according to the configuration files.

[0071] In this embodiment of the disclosure, the electronic device can combine multiple target algorithm modules and multiple data blocks to determine the corresponding sequential connection relationship.

[0072] Optionally, the sequential connection relationship can be a relationship used to characterize the connection order between the multiple target algorithm modules and the multiple data blocks.

[0073] Specifically, the electronic device can combine multiple target algorithm modules and multiple data blocks according to the project processing request, and determine the corresponding sequential connection relationship. For example, the electronic device can combine a data block for the input and output of each target algorithm module, thereby storing the data before and after the algorithm processing, while allowing the algorithm module itself to focus on data processing. Two adjacent algorithm modules will share a data block.

[0074] Furthermore, the electronic device can configure the connection parameters of multiple target algorithm modules according to the sequential connection relationship and generate corresponding configuration files.

[0075] Optionally, the configuration file can be a file that configures the project's processing framework.

[0076] Specifically, after combining multiple target algorithm modules and multiple data blocks to obtain corresponding sequential connection relationships, the electronic device can configure the connection parameters of multiple target algorithm modules according to the sequential connection relationships and generate corresponding configuration files.

[0077] Furthermore, the electronic device can generate the project processing framework based on the configuration file.

[0078] Figure 2 A flowchart illustrating another modular project processing method provided in an embodiment of this disclosure is shown.

[0079] like Figure 2As shown, after receiving a project processing request, the electronic device can determine the target algorithm module from among multiple algorithm modules. This target module could be a data module (data_module:000), a sampling module (sampling_module:001), a clustering module (clustering_module:002), or a fitting module (fitting_module:003). The connection parameters (connection_configurations), visualization parameters (visualization_configurations), and pipeline parameters (pipeline_configurations) of these four algorithm modules are identical. Static and dynamic parameters vary depending on the function of each algorithm module. Taking fitting_module:003 as an example, its input data block is data block number 3 output from the preceding algorithm module clustering_module:002, and its output data block number 4 is the final result, the ground 3D pose result given by the fitting_module:003 algorithm module. The input_types and output_types specify the data types of the corresponding data blocks. The `visualization_configurations` directive provides parameters such as display duration and display policy (0 for ignore, 1 for display point cloud, 2 for save point cloud). The `pipeline_configurations` directive provides the index and execution number of this algorithm module within the system; a value of -1 indicates execution on every attempt. The `fitting_module:003` algorithm module does not require initialization, therefore its static parameters are empty.

[0080] Optionally, S140 may specifically include: inputting the three-dimensional point cloud data to be processed into the project processing framework, so that the project processing framework processes the data sequentially according to the sequential connection relationship of the multiple target algorithm modules and the multiple data blocks, and obtains the corresponding processing result.

[0081] Specifically, after obtaining the project processing framework, the electronic device can input the 3D point cloud data to be processed into the project processing framework, so that the project processing framework processes the data sequentially according to the sequential connection relationship of multiple target algorithm modules and multiple data blocks, and obtains the corresponding processing results.

[0082] Figure 3A schematic diagram of the structure of a modular processing apparatus for a project provided in an embodiment of this disclosure is shown.

[0083] In some embodiments of this disclosure, Figure 3 The modular processing device shown can be housed in an electronic device. Specifically, this electronic device may include, but is not limited to, mobile terminals such as computer equipment, mobile phones, in-vehicle equipment, vehicle controllers, tablet computers, and wearable devices.

[0084] like Figure 3 As shown, the modular processing device 300 of this project may include a data acquisition unit 310, a module determination unit 320, a first processing unit 330, and a second processing unit 340.

[0085] The data acquisition unit 310 can be used to acquire 3D point cloud data to be processed in response to a project processing request.

[0086] The module determination unit 320 can be used to determine a target algorithm module among a plurality of preset algorithm modules, wherein the target algorithm module is an algorithm module used to implement the project processing request.

[0087] The first processing unit 330 can be used to combine and configure the target algorithm module and multiple data blocks to obtain a project processing framework.

[0088] The second processing unit 340 can be used to process the three-dimensional point cloud data to be processed through the project processing framework to obtain the corresponding processing results.

[0089] Therefore, in this embodiment of the present disclosure, in response to a project processing request, the system can acquire the 3D point cloud data to be processed, then determine a target algorithm module from a set of preset algorithm modules. The target algorithm module is an algorithm module used to implement the project processing request. Then, the target algorithm module and multiple data blocks are combined and configured to obtain a project processing framework. Finally, the project processing framework is used to process the 3D point cloud data to be processed to obtain the corresponding processing result. Thus, a target algorithm module can be determined from a set of preset algorithm modules according to the project processing request, and a project processing framework can be obtained by combining the target algorithm module and multiple data blocks. Data processing is performed through the project processing framework, that is, the algorithm modules can be freely combined according to user needs, thereby reducing the redundancy of repeated development of similar algorithm modules and improving development efficiency.

[0090] In some embodiments of this disclosure, the target algorithm module may be at least one of a sampling module, a filtering module, a fitting module, a clustering module, and a matching module.

[0091] In some embodiments of this disclosure, the modular processing apparatus 300 may include a third processing unit.

[0092] The third processing unit can be used to perform algorithm modularization processing on multiple three-dimensional algorithms before determining the target algorithm module from the preset multiple algorithm modules, to obtain the preset multiple algorithm modules, wherein the algorithm modules include static parameters, dynamic parameters, connection parameters, visualization parameters and process parameters.

[0093] In some embodiments of this disclosure, the modular processing apparatus 300 may include a fourth processing unit.

[0094] The fourth processing unit can be used to pre-design multiple data blocks before combining and configuring the target algorithm module and multiple data blocks. The data blocks are used to store module input data and module output data, and the data blocks include data identifiers for indicating data types.

[0095] In some embodiments of this disclosure, the first processing unit 330 may specifically include a module combination subunit, a file generation subunit, and a framework generation subunit.

[0096] This module combination subunit can be used to combine multiple target algorithm modules and multiple data blocks to determine the corresponding sequential connection relationship.

[0097] This file generation subunit can be used to configure the connection parameters of multiple target algorithm modules according to the sequential connection relationship, and generate the corresponding configuration file.

[0098] The framework generation subunit can be used to generate the project processing framework based on the configuration file.

[0099] In some embodiments of this disclosure, the second processing unit 340 may be specifically used to input the three-dimensional point cloud data to be processed into the project processing framework, so that the project processing framework performs data processing sequentially according to the sequential connection relationship of multiple target algorithm modules and multiple data blocks to obtain the corresponding processing results.

[0100] It should be noted that, Figure 3 The modular processing unit 300 shown can perform... Figures 1 to 2 The various steps in the method embodiment shown are implemented. Figures 1 to 2 The processes and effects in the method embodiments shown are not described in detail here.

[0101] Figure 4 A schematic diagram of the structure of a modular processing device for a project provided in an embodiment of this disclosure is shown.

[0102] In some embodiments of this disclosure, Figure 4 The modular processing device shown can be an electronic device that the user wants to modularize. Specifically, the electronic device can include, but is not limited to, mobile terminals such as mobile phones, in-vehicle devices, vehicle controllers, tablets, wearable devices, and smart home devices.

[0103] like Figure 4 As shown, the modular processing device of this project may include a processor 401 and a memory 402 storing computer program instructions.

[0104] Specifically, the processor 401 may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0105] Memory 402 may include a large-capacity storage for information or instructions. For example, and not limitingly, memory 402 may include a hard disk drive (HDD), a floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or a Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 402 may include removable or non-removable (or fixed) media. Where appropriate, memory 402 may be internal or external to the integrated gateway device. In a particular embodiment, memory 402 is a non-volatile solid-state memory. In a particular embodiment, memory 402 includes read-only memory (ROM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (Electrically Programmable ROM, EPROM), an electrically erasable programmable PROM (EEPROM), an electrically alterable ROM (EAROM), or flash memory, or a combination of two or more of these.

[0106] The processor 401 performs the steps of the modular processing method for projects provided in this disclosure by reading and executing computer program instructions stored in the memory 402.

[0107] In one example, the modular processing device of this project may also include a transceiver 403 and a bus 404. For example, Figure 4As shown, the processor 401, memory 402 and transceiver 403 are connected via bus 404 and communicate with each other.

[0108] Bus 404 includes hardware, software, or both. For example, and not limitingly, a bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industrial Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a MicroChannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Bus (VLB) bus, or other suitable buses, or a combination of two or more of these. Where appropriate, bus 404 may include one or more buses. Although specific buses are described and illustrated in the embodiments of this application, this application considers any suitable bus or interconnection.

[0109] This disclosure also provides a computer-readable storage medium that can store a computer program that, when executed by a processor, enables the processor to implement the modular project processing method provided in this disclosure.

[0110] The aforementioned storage medium may, for example, include a memory 402 containing computer program instructions, which can be executed by a processor 401 of the project modular processing device to complete the project modular processing method provided in this embodiment. Optionally, the storage medium may be a non-transitory computer-readable storage medium, such as a ROM, random access memory (RAM), compact disc ROM (CD-ROM), magnetic tape, floppy disk, and optical data storage device.

[0111] This disclosure also provides a vehicle that includes the project modular processing apparatus as described above. It is understood that the vehicle may also include a processor, a memory, and a computer program. The computer program is stored in the memory and configured to be executed by the processor to implement the project modular processing method provided in this disclosure. The processor and memory are already... Figure 4 The parts of the illustrated embodiments will not be repeated here.

[0112] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the term "comprising" is 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.

[0113] The above description is merely a specific embodiment of this disclosure, enabling those skilled in the art to understand or implement it. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this disclosure. Therefore, this disclosure is not to be limited to the embodiments described herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A modular project processing method, characterized in that, include: In response to a project processing request, obtain the 3D point cloud data to be processed; A target algorithm module is determined from a set of preset algorithm modules. The target algorithm module is the algorithm module used to implement the project's request processing. The target algorithm module and multiple data blocks are combined and configured to obtain the project processing framework; The project processing framework is used to process the 3D point cloud data to be processed, and the corresponding processing results are obtained.

2. The method according to claim 1, characterized in that, The target algorithm module is at least one of the following: sampling module, filtering module, fitting module, clustering module, and matching module.

3. The method according to claim 1, characterized in that, Before determining the target algorithm module from a set of preset algorithm modules, the method further includes: Multiple 3D algorithms are modularized to obtain multiple preset algorithm modules, which include static parameters, dynamic parameters, connection parameters, visualization parameters, and process parameters.

4. The method according to claim 1, characterized in that, Before combining and configuring the target algorithm module and multiple data blocks, the method further includes: Multiple data blocks are pre-designed to store module input data and module output data, and each data block includes a data identifier to indicate the data type.

5. The method according to claim 4, characterized in that, The process of combining and configuring the target algorithm module and multiple data blocks to obtain the project processing framework includes: The target algorithm modules and data blocks are combined to determine the corresponding sequential connection relationships; Configure the connection parameters of multiple target algorithm modules according to the sequential connection relationship, and generate corresponding configuration files; The project processing framework is generated based on the configuration file.

6. The method according to claim 5, characterized in that, The process of processing the 3D point cloud data to be processed through the project processing framework to obtain the corresponding processing results includes: The 3D point cloud data to be processed is input into the project processing framework, so that the project processing framework processes the data sequentially according to the sequential connection relationship of multiple target algorithm modules and multiple data blocks to obtain the corresponding processing results.

7. A modular project processing device, characterized in that, include: The data acquisition unit is used to acquire the 3D point cloud data to be processed in response to project processing requests; A module determination unit is used to determine a target algorithm module from a set of preset algorithm modules, wherein the target algorithm module is an algorithm module used to implement the project processing request; The first processing unit is used to combine and configure the target algorithm module and multiple data blocks to obtain the project processing framework. The second processing unit is used to process the 3D point cloud data to be processed through the project processing framework to obtain the corresponding processing results.

8. A modular processing device for projects, characterized in that, include: processor; Memory, used to store executable instructions; The processor is configured to read the executable instructions from the memory and execute the executable instructions to implement the project modular processing method according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The storage medium stores a computer program that, when executed by a processor, causes the processor to implement the modular processing method for projects according to any one of claims 1-6.

10. A vehicle, characterized in that, Includes the project modular processing apparatus as described in claim 7, the project modular processing device as described in claim 8, or the computer-readable storage medium as described in claim 9.

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