Intelligent task management platform for multi-user collaborative charting

Abstract

The invention provides an intelligent task management platform for multi-user collaborative charting, which comprises a data management module, a member management module, a project management module and a task distribution and receiving module, realizes intelligent distribution and management of tasks by integrating advanced data management technology, and performs cloud storage on data in a charting process. Historical operation records can be traced, and meanwhile data safety is guaranteed. By establishing a dynamic adjustment mechanism, the cooperation efficiency is improved, and automatic quality control is realized, so that the efficiency and quality of high-precision map making are improved.

Description

Technical Field

[0001] The invention belongs to the technical field of map making management, and in particular relates to an intelligent task management platform for multi-person collaborative mapping. Background Art

[0002] High-precision map making is an important field in modern geographic information technology, which involves the accurate collection, processing and display of geospatial data. With the rapid development of technologies such as autonomous driving and smart cities, the demand for high-precision maps is growing. However, the production of high-precision maps is a complex and time-consuming process that requires a lot of data collection and processing. Therefore, it often requires multiple people to collaborate to improve mapping efficiency and accuracy.

[0003] In the process of making high-precision maps, data is often scattered among different departments or individuals, forming data islands. Traditional data management and task division generally rely on manual communication, which is inefficient and prone to problems such as poor information transmission and uncoordinated collaboration. On the other hand, the allocation of traditional tasks often relies on manual decision-making, lacks scientific analysis and prediction, and may lead to uneven task distribution. Some cartographers may have too much workload, while others may have too little workload, affecting the overall efficiency of the team. In addition, in traditional management methods, once tasks are assigned, they are rarely adjusted dynamically according to actual conditions. Faced with rapid changes in the geographical environment or urgent changes in mapping tasks, traditional methods lack flexible response strategies and make it difficult to adjust resource and task allocation in a timely manner.

[0004] To sum up, in the process of high-precision map production, traditional data management and task division do not support online data flow and automatic task allocation, it is difficult to adjust resources and task allocation in a timely manner, communication efficiency is low, and mapping efficiency and quality need to be further improved. Summary of the invention

[0005] The purpose of the present invention is to solve the above-mentioned technical problems and provide an intelligent task management platform for multi-person collaborative mapping. By integrating advanced data management technology, it can realize intelligent allocation and management of tasks, establish a dynamic adjustment mechanism, improve collaboration efficiency, and realize automated quality control, thereby improving the efficiency and quality of high-precision map production.

[0006] In order to solve the above problems, the present invention is implemented according to the following technical solutions:

[0007] The present invention provides an intelligent task management platform for multi-person collaborative mapping, comprising:

[0008] Data management module, the data management module is used to store and manage the original data and operation result data related to mapping, the original data is stored and managed through the MinIO cloud storage system, and the operation result data is stored and managed through the PostgreSQL database. The data management module includes a data packet production submodule, a data set creation submodule, a data management submodule, a query index establishment submodule and a database cluster construction submodule;

[0009] A member management module, in which different member roles have different menu permissions and data permissions;

[0010] A project management module, in which projects are created and managed by specific member roles in the member management module;

[0011] The task allocation and collection module includes a task pool creation submodule, a task acquisition submodule, a task collection submodule and a quality inspection submodule.

[0012] Preferably, the data packet production submodule includes dividing the original data in the mapping area into multiple sub-data based on the length of the data collection route; compressing the sub-data into a data packet in zip format as a clip package; wherein the original data is data stored in the MinIO cloud storage system; the data set creation submodule includes creating a project data set, uploading the clip package to the project data set, wherein one project data set corresponds to multiple clip packages.

[0013] Preferably, the data management submodule includes managing the business data in the project management module, the project member data in the member management module and the task data in the task allocation and collection module through a PostgreSQL database; storing the mapping relationship between tasks, members and original data through a PostgreSQL database; the query index establishment submodule includes establishing a query index of the PostgreSQL database to quickly query the mapping relationship between the tasks, members and original data; the database cluster construction submodule includes building a PostgreSQL database cluster to support high-concurrency business and support real-time parallel operations of multiple people and multiple teams.

[0014] Preferably, the member roles in the member management module include project leader, project team leader, operator and quality inspector; the project leader can create an account for the project team leader, and the project team leader can have multiple accounts; the project team leader can create accounts for operators and quality inspectors in the project.

[0015] Preferably, in the project management module, the overall project and sub-projects are created and managed by the project leader in the member management module. The project leader can associate a project team leader and a project data set for the sub-project, wherein the project data set is acquired through the data management module; the project team leader can associate operators and quality inspectors for the sub-project.

[0016] Preferably, the task pool creation submodule includes creating a task pool, wherein the amount of tasks in the task pool is consistent with the number of clip packages contained in the project data set in the data set creation submodule; and the task state of the task pool is initially set to an unallocated state.

[0017] Preferably, in the task pool creation submodule, when the number of clip packages in the project data set is updated, the data task volume of the task pool is also updated accordingly;

[0018] Preferably, the task acquisition submodule is used to complete the assignment of tasks, wherein the project leader to which the current project belongs assigns the tasks in the task pool to the operator to which the current project belongs to complete the assignment of the tasks, and the operator is an operator in an idle state; the task collection submodule is used for the operator to download the clip package of the corresponding task to perform the operation, and when the operation is completed, the operation result is uploaded to the MinIO cloud storage system, and correspondingly, the status of the task is updated to the pending quality inspection status.

[0019] Preferably, the quality inspection submodule is used to assign tasks in the state of awaiting quality inspection to quality inspectors in an idle state; the quality inspectors perform quality inspection on the tasks in the state of awaiting quality inspection; when the quality inspection is qualified, the status of the task is updated to the end status; when the quality inspection is unqualified, the status of the task is updated to the state of awaiting repair; when the task in the state of awaiting repair is repaired, the task in the state of awaiting repair is quality inspected again, and when the quality inspection is qualified, the status of the task in the state of awaiting repair is updated to the end status.

[0020] Compared with the prior art, the present invention has the following beneficial effects:

[0021] The present invention provides an intelligent task management platform for multi-person collaborative mapping, including a data management module, a member management module, a project management module, and a task allocation and collection module. By integrating advanced data management technology, the intelligent allocation and management of tasks are realized, the data of the mapping process is stored, and the historical operation records can be traced back, while ensuring the security of the data. By establishing a dynamic adjustment mechanism, the efficiency of collaboration is improved, and automated quality control is realized, thereby improving the efficiency and quality of high-precision map production.

[0022] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The specific embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings, wherein:

[0024] Figure 1 It is a technical flow chart of an intelligent task management platform for multi-person collaborative mapping according to an embodiment of the present invention;

[0025] Figure 2 is a task state flow chart of an embodiment of the present invention;

[0026] Figure 3 It is a flow chart for using the platform. DETAILED DESCRIPTION

[0027] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0028] The terms used in this application are only for the purpose of describing specific embodiments and are not intended to limit this application. Unless otherwise defined, the technical terms or scientific terms used in this specification should be understood by people with ordinary skills in the field to which this application belongs. The words "first", "second" and similar words used in this specification and claims do not indicate any order, quantity or importance, but are only used to distinguish different technical features.

[0029] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0030] In order to solve the technical problems in the prior art that, when making high-precision maps, traditional data management and task division do not support online data circulation and automatic task allocation, it is difficult to adjust resources and task allocation in time, the communication efficiency is low, and the mapping efficiency and mapping quality need to be further improved, the present invention provides an intelligent task management platform for multi-person collaborative mapping, which can improve collaboration efficiency and realize automated quality control, thereby improving the efficiency and quality of high-precision map production.

[0031] Figure 1It is a technical flow chart of an intelligent task management platform for multi-person collaborative mapping described in the present invention. Figure 2 It is a task status flow chart. Figure 3 It is a flow chart for using the platform.

[0032] As shown in the figure, the intelligent task management platform for multi-person collaborative mapping includes:

[0033] (1)Data management module.

[0034] Specifically, the data management module is used to store and manage the original data and operation result data related to mapping. The original data is stored and managed through the MinIO cloud storage system, and the operation result data is stored and managed through the PostgreSQL database.

[0035] It should be noted that the original data related to mapping includes original point cloud data, map tiles, and data collected by radar, cameras, etc. MinIO is a high-performance, distributed object storage system designed for cloud-native environments. It achieves a read speed of up to 55GB / s and a write speed of 35GB / s on standard hardware. It supports SSL / TLS encrypted transmission and multiple authentication mechanisms to ensure the security of data transmission and storage. The original data related to mapping is stored in the high-performance and secure MinIO cloud storage system for the addition, deletion, modification and query of original data files. Preferably, MinIO is connected to a local private cloud service to prevent data leakage and ensure data security.

[0036] It should be noted that PostgreSQL is a highly scalable open source object-relational database system. Storing and managing job results data through the PostgreSQL database has the characteristics of low cost, high scalability, and strong data integrity. It can realize the addition, deletion, modification, and query of job results data, and realize traceable historical job records.

[0037] Specifically, the data management module includes a data packet production submodule, a data set creation submodule, a data management submodule, a query index establishment submodule and a database cluster construction submodule.

[0038] In this embodiment, the data packet production submodule includes dividing the original data in the mapping area into multiple sub-data based on the length of the data collection route; compressing the sub-data into a data packet in zip format as a clip package; wherein the original data is the data stored in the MinIO cloud storage system.

[0039] It should be noted that in the process of making high-precision maps, cartographers need to plan routes for data collection, which cover the areas where high-precision maps need to be made. Along these routes, various sensors and devices (such as lidar, cameras, GPS, etc.) are used to collect geospatial data. The length of the collected routes is measured, which may involve actual physical distances, or the routes are converted into data lengths according to certain standards (for example, the number of data points is calculated based on the frequency of collection), so that the original data in the mapping area can be divided into multiple sub-data based on the length of the data collection route. The purpose of this is to refine the data management work so that the amount of data in each part is moderate, which is convenient for subsequent processing and analysis, and at the same time facilitates task allocation, so that each team can work independently on its own data segment, while maintaining communication and collaboration with other teams to ensure the consistency and coordination of the entire mapping process, and achieve the purpose of multi-person collaborative mapping.

[0040] In this embodiment, the data set creation submodule includes creating a project data set and uploading a clip package to the project data set, wherein one project data set corresponds to multiple clip packages. This is beneficial to subsequent task allocation.

[0041] In this embodiment, the data management submodule includes managing the business data in the project management module, the project member data in the member management module, and the task data in the task allocation and collection module through the PostgreSQL database; and storing the mapping relationship between tasks, members and original data through the PostgreSQL database.

[0042] It should be noted that PostgreSQL is a highly scalable open source object-relational database system. Managing data through the PostgreSQL database has the characteristics of low cost, high scalability, and strong data integrity.

[0043] It should be noted that the mapping relationship between tasks, members and raw data is stored in the PostgreSQL database. On the one hand, this helps to avoid data duplication and inconsistency. The PostgreSQL database management system has mechanisms to ensure data consistency, such as transaction control, which guarantees the accuracy and reliability of the mapping relationship. On the other hand, the database provides a powerful query language that can quickly retrieve and analyze the mapping relationship between tasks, members and raw data. In addition, storing mapping relationships helps to improve the efficiency, flexibility and security of data management, while supporting complex data analysis and business logic.

[0044] In this embodiment, the query index establishment submodule includes establishing a query index of the PostgreSQL database to quickly query the mapping relationship between tasks, members and original data, which is conducive to improving the accuracy of subsequent job task allocation.

[0045] In this embodiment, the database cluster building submodule includes building a PostgreSQL database cluster to support high-concurrency business and support real-time parallel operations of multiple people and multiple teams. This can improve the efficiency of task allocation and completion.

[0046] (2) Member management module.

[0047] Specifically, different member roles in the member management module have different menu permissions and data permissions.

[0048] In this embodiment, the member roles in the member management module include project leader, project team leader, operator and quality inspector.

[0049] The project leader can create an account for the project team leader, and the project team leader can have multiple accounts; the project team leader can create accounts for operators and quality inspectors in the project.

[0050] (3)Project management module.

[0051] Specifically, projects in the project management module are created and managed by specific members in the member management module.

[0052] Specifically, in the project management module, the project leader in the member management module creates and manages the overall project and sub-projects. The project leader can associate the project team leader and project data set for the sub-project, where the project data set is obtained through the data management module; the project team leader can associate operators and quality inspectors for the sub-project.

[0053] (4) Task allocation and collection module.

[0054] Specifically, the task allocation and collection module includes a task pool creation submodule, a task acquisition submodule, a task collection submodule and a quality inspection submodule.

[0055] In this embodiment, the task pool creation submodule includes creating a task pool and initially setting the task state of the task pool to an unassigned state, wherein the amount of tasks in the task pool is consistent with the number of clip packages included in the project data set in the data set creation submodule.

[0056] Furthermore, in the task pool creation submodule, when the number of clip packages in the project data set is updated, the data task volume of the task pool is also updated accordingly, ensuring information synchronization and facilitating unified management.

[0057] Preferably, the tasks in the task pool are arranged in order of priority, and the tasks with the highest priority are pushed first.

[0058] In this embodiment, the task acquisition submodule is used to complete the task allocation, wherein the project leader of the current project allocates the tasks in the task pool to the operators of the current project to complete the task allocation. The operators allocated are operators in an idle state.

[0059] In this embodiment, the task receiving submodule is used for the operator to download the clip package of the corresponding task to perform the operation. When the operation is completed, the operation result is uploaded to the MinIO cloud storage system. Correspondingly, the status of the task is updated to the pending quality inspection status.

[0060] In this embodiment, the quality inspection submodule is used to assign tasks in the state of waiting for quality inspection to quality inspectors in an idle state; the quality inspectors perform quality inspection on the tasks in the state of waiting for quality inspection. Performing quality inspection on the task completion results can improve the quality of mapping.

[0061] Furthermore, when the quality inspection is qualified, the status of the task is updated to the end status; when the quality inspection is unqualified, the status of the task is updated to the pending repair status.

[0062] Furthermore, after the task in the pending repair state is repaired, the task in the pending repair state is quality-checked again, and when the quality-check is qualified, the state of the task is updated to the end state.

[0063] Therefore, the intelligent task management platform for multi-person collaborative mapping provided by the present invention integrates advanced data management technology to realize intelligent task allocation and management, stores data of the mapping process in the cloud, realizes traceable historical operation records, and ensures data security. By establishing a dynamic adjustment mechanism, the efficiency of collaboration is improved and automated quality control is realized, thereby improving the efficiency and quality of high-precision map production.

[0064] In a specific embodiment, the platform administrator logs in to the platform to create a project leader (project manager) account. The project leader can log in to the account and create several sub-projects under the project on the platform and related project data sets associated with these sub-projects. The project leader can also create one or more project team leader accounts on the platform, and assign corresponding sub-projects to each project team leader account. The corresponding project team leader can log in to the account to view the project he is responsible for, including project information, progress, etc. Each project team leader can create one or more project member (including operators and quality inspectors) accounts for the project on the platform, and assign corresponding tasks to each project member. Correspondingly, operators and quality inspectors can log in to the account to view the tasks they are responsible for. After the operator completes his own task, the task will flow to the quality inspection task pool, and the quality inspector will conduct quality inspection on the task results. If the quality inspection is qualified, the task is terminated. If the quality inspection is unqualified, it is returned and the task is repaired again.

[0065] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0066] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. An intelligent task management platform for multi-person collaborative mapping, characterized in that: include: Data management module, the data management module is used to store and manage the original data and operation result data related to mapping, the original data is stored and managed through the MinIO cloud storage system, and the operation result data is stored and managed through the PostgreSQL database. The data management module includes a data packet production submodule, a data set creation submodule, a data management submodule, a query index establishment submodule and a database cluster construction submodule; A member management module, in which different member roles have different menu permissions and data permissions; A project management module, in which projects are created and managed by specific member roles in the member management module; The task allocation and collection module includes a task pool creation submodule, a task acquisition submodule, a task collection submodule and a quality inspection submodule.

2. The intelligent task management platform for multi-person collaborative mapping according to claim 1, characterized in that: The data packet making submodule includes dividing the original data in the mapping area into multiple sub-data based on the length of the data collection route; compressing the sub-data into a data packet in zip format as a clip packet; wherein the original data is the data stored in the MinIO cloud storage system; The data set creation submodule includes creating a project data set and uploading the clip package to the project data set, wherein one project data set corresponds to multiple clip packages.

3. The intelligent task management platform for multi-person collaborative mapping according to claim 2 is characterized by: The data management submodule manages the business data in the project management module, the project member data in the member management module and the task data in the task allocation and collection module through a PostgreSQL database; The mapping relationship between tasks, members and original data is stored in the PostgreSQL database; The query index establishment submodule includes establishing a query index of the PostgreSQL database to quickly query the mapping relationship between the tasks, members and original data; The database cluster building submodule includes building a PostgreSQL database cluster to support high-concurrency business and support real-time parallel operations of multiple people and multiple teams.

4. The intelligent task management platform for multi-person collaborative mapping according to claim 1 is characterized by: The member roles in the member management module include project leader, project team leader, operator and quality inspector; The project leader can create the account of the project team leader, and the project team leader can have multiple accounts; The project leader can create accounts for operators and quality inspectors in the project.

5. The intelligent task management platform for multi-person collaborative mapping according to claim 1 is characterized in that: In the project management module, the overall project and sub-projects are created and managed by the project leader in the member management module. The project leader can associate the project leader and the project data set for the sub-project, wherein the project data set is obtained through the data management module; the project leader can associate the operators and quality inspectors for the sub-project.

6. The intelligent task management platform for multi-person collaborative mapping according to claim 1 is characterized by: The task pool creation submodule includes creating a task pool, wherein the amount of tasks in the task pool is consistent with the number of clip packages contained in the project data set in the data set creation submodule; and the task state of the task pool is initially set to an unallocated state.

7. The intelligent task management platform for multi-person collaborative mapping according to claim 6 is characterized by: In the task pool creation submodule, when the number of clip packages in the project data set is updated, the data task volume of the task pool is also updated accordingly.

8. The intelligent task management platform for multi-person collaborative mapping according to claim 6 is characterized by: The task acquisition submodule is used to complete the assignment of tasks, wherein the project leader of the current project assigns the tasks in the task pool to the operators of the current project to complete the assignment of the tasks, and the operators are operators in an idle state; The task receiving submodule is used for the operator to download the clip package of the corresponding task to perform the operation. When the operation is completed, the operation result is uploaded to the MinIO cloud storage system. Correspondingly, the status of the task is updated to the pending quality inspection status.

9. The intelligent task management platform for multi-person collaborative mapping according to claim 6, characterized in that: The quality inspection submodule is used to assign the tasks in the state of waiting for quality inspection to the quality inspectors in the idle state; the quality inspectors perform quality inspection on the tasks in the state of waiting for quality inspection; When the quality inspection is qualified, the status of the task is updated to the completed status; when the quality inspection is unqualified, the status of the task is updated to the pending repair status; After the task in the pending repair state is repaired, the task in the pending repair state is quality-checked again. When the quality-check is qualified, the state of the task in the pending repair state is updated to the end state.

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