Municipal asset data acquisition method

Abstract

The invention discloses a municipal asset data acquisition method. The method comprises the following steps: 1, field investigation: acquiring spatial coordinate data of municipal assets in real time through RTK positioning measurement equipment and acquiring other attribute information data of the municipal assets by using a mobile acquisition terminal; 2, interior work processing: uploading the space coordinate data and other attribute information data to a cloud management platform through an embedded software tool; and 3, data integration: the cloud management platform receives and processes the uploaded data, seamlessly splices and stores the data based on a spatial index algorithm, and a municipal asset database is dynamically updated. Through combination of the RTK positioning measurement equipment and the mobile acquisition terminal, space coordinate data and other attribute information data of municipal assets can be acquired in real time and can be quickly uploaded and updated to the cloud management platform, so that the operation process is effectively reduced, the number of workers is reduced, and the working efficiency is improved. And functions of municipal asset real-time acquisition, positioning, data uploading, system updating and management can be rapidly realized.

Description

Technical Field

[0001] The present invention relates to the technical field of electrical parameter measurement, and in particular to a municipal asset data collection method. Background Art

[0002] With the continuous advancement of urbanization, the number of municipal assets in cities is increasing, and their types are becoming increasingly complex and diverse. In order to effectively manage municipal assets, improve the operating efficiency and service level of municipal facilities, and ensure the safe and stable operation of cities, comprehensive, accurate, and real-time data collection and analysis of municipal assets is necessary.

[0003] Although there are many existing data collection technologies, such as using advanced surveying and remote sensing technologies to obtain basic geographic information data using digital surveying technology; using pipeline detectors, geological radars, total stations, GPS receivers, etc. to obtain urban municipal infrastructure data; using mobile measurement systems (MMS), portable mobile measurement systems (PMMS) drone collection systems, etc. to obtain road diseases, facility aging, damage, leakage and other underground infrastructure hidden danger data; although the final realization of urban municipal infrastructure basic data and hidden danger data collection, inspection, storage and update. However, the data collection process is like Figure 1 As shown, traditional methods suffer from cumbersome and lengthy workflows, requiring numerous people to collaborate on data collection, internal work organization, data import, and updates. This leads to delayed data updates and inefficient management. Furthermore, traditional methods struggle to achieve real-time collection, location, data upload, and rapid system updates for municipal assets, failing to meet the demands of modern urban management. Therefore, developing an efficient and accurate method for collecting municipal asset data is crucial. Summary of the Invention

[0004] In view of the above-mentioned deficiencies, the purpose of the present invention is to provide a municipal asset data collection method that realizes real-time collection, positioning and data uploading of municipal assets.

[0005] To achieve the above-mentioned purpose, the present invention provides the following technical solutions:

[0006] A municipal asset data collection method comprises the following steps:

[0007] (1) Field survey: Use RTK positioning measurement equipment to obtain real-time spatial coordinate data of municipal assets and use mobile data collection terminals to collect other attribute information data of municipal assets;

[0008] (2) Internal processing: uploading the spatial coordinate data and other attribute information data to the cloud management platform through embedded software tools;

[0009] (3) Data integration: The cloud management platform receives and processes the uploaded data, seamlessly connects the data and stores it in the database based on the spatial index algorithm, and dynamically updates the municipal asset database.

[0010] As a preferred solution of the present invention, the RTK positioning measurement equipment includes a base station receiver, a data link and a mobile station receiver, and achieves centimeter-level precision positioning through carrier phase observation.

[0011] As a preferred embodiment of the present invention, the mobile data collection terminal includes a drone, a mobile survey vehicle, and a portable handheld device. The drone is used to capture large-scale municipal assets, and the captured data is vectorized to assist RTK positioning and surveying equipment in data collection, improving the comprehensiveness and accuracy of data collection. The mobile survey vehicle is used to collect data on motorway facilities, while the portable data collection device is used to collect data in hard-to-reach areas such as non-motorized vehicle lanes, sidewalks, and bridge substructures, ensuring complete and accurate data collection.

[0012] As a preferred solution of the present invention, the embedded software tool integrates a GIS map module, a data encoding module and a real-time synchronization module, and supports offline collection and automatic synchronization with the cloud.

[0013] As a preferred embodiment of the present invention, step (1) includes a two-level quality inspection process after data collection:

[0014] 1.1 Process inspection: 100% verification of the integrity, format standardization and logical consistency of collected data;

[0015] 1.2 Final Acceptance: Ensure that the data is consistent with the actual scenario through field sampling verification with a sampling rate of no less than 30% and detailed inspection ratio of no less than 10% before storage. Strictly monitor the entire data collection process to ensure the accuracy and reliability of data collection and improve data quality.

[0016] As a preferred solution of the present invention, the spatial indexing algorithm uses multiple threads to access data and build indexes, improving data warehousing speed and query performance to meet the needs of large-scale data management. This algorithm incorporates technical measures such as data partitioning and sharding, concurrent index structure design, multi-threaded data warehousing optimization, and query parallelization to further optimize data warehousing and query efficiency.

[0017] As a preferred solution of the present invention, step (3) also performs data integration after data uploading, including steps such as data extraction, data transmission, data cleaning, data reorganization, data publishing and service reorganization, so as to achieve standardized and normalized management of data.

[0018] The beneficial effects of the present invention are as follows: the method provided by the present invention can obtain the spatial coordinate data of municipal assets and other attribute information data of municipal assets in real time by combining RTK positioning measurement equipment and mobile acquisition terminals, and can quickly upload and update them to the cloud management platform, effectively reducing the operation process and the number of manpower, and can quickly realize the real-time acquisition, positioning, data upload, system update and management functions of municipal assets. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is a flowchart of traditional data collection.

[0020] Figure 2 This is a flow chart of the municipal asset data collection method of the present invention.

[0021] Figure 3 This is a screenshot of the asset census software APP of the present invention.

[0022] Figure 4 Schematic diagram of municipal assets of the present invention.

[0023] Figure 5 Schematic diagram of the cloud management platform interface of the present invention. DETAILED DESCRIPTION

[0024] Example: See Figure 1 and Figure 5 , an embodiment of the present invention provides a municipal asset data collection method, which includes the following steps:

[0025] (1) Preparation phase: Before data collection, relevant preparation work needs to be done, such as the preparation and review of technical design plans, training, preparation of basic data, equipment, and human resources. After the above work is completed, the implementation phase can be entered, i.e., "starting work";

[0026] (2) Field survey: Field survey is conducted according to the plan and the requirements of the field survey of municipal facilities. The spatial coordinate data of municipal assets are obtained in real time through RTK positioning and measurement equipment, and other attribute information data of municipal assets are collected using mobile acquisition terminals. The RTK positioning and measurement equipment includes a base station receiver, a data link and a mobile station receiver, which achieves centimeter-level precision positioning through carrier phase observation. A receiver is placed on the base station as a reference station to continuously observe the satellite and send its observation data and station information to the mobile station in real time through radio transmission equipment. The mobile station GPS receiver receives the data transmitted by the base station through wireless receiving equipment while receiving the GPS satellite signal. Then, based on the principle of relative positioning, the three-dimensional coordinates of the mobile station and its accuracy are calculated in real time (i.e., the difference △X, △Y, △H between the base station and the mobile station coordinates, plus the WGS-84 coordinates of each point obtained by the base coordinates, and the plane coordinates X, Y and altitude H of each point on the mobile station are obtained through coordinate conversion parameters).

[0027] Mobile data collection terminals include drones, mobile survey vehicles, and portable handheld devices. Drones are used to capture large-scale images of municipal assets, and the captured data is vectorized to assist RTK positioning and survey equipment in data collection, improving the comprehensiveness and accuracy of data collection. Mobile survey vehicles are used to collect data on motorway facilities, while portable data collection devices are used to collect data in hard-to-reach areas such as non-motorized lanes, sidewalks, and bridge substructures, ensuring complete and comprehensive data collection.

[0028] Specifically, during the field work of the municipal survey, relevant workers use drones, total stations, mobile survey vehicles, portable handheld devices, RTK positioning survey equipment and other instruments and equipment to survey municipal roads and various urban components on both sides. For roads, bridges, and tunnels, drones are first used to take large-scale photos, and then the photographed data is vectorized. Combined with the marking of portable handheld devices and the video data of mobile survey vehicles, the surface work is completed and information such as road surface type, number of motor vehicle lanes, and pavement type is recorded. Roads and sidewalks are recorded by surveyors holding portable handheld devices, such as action cameras, to take videos of roads and sidewalks. Figure 3 During filming, the asset survey software app simultaneously records the start and end points and the locations of each segment, as well as the video time of each segment, which will serve as a basis for subsequent data entry and information compilation. For point assets, RTK positioning measurement equipment, mobile survey vehicles, and portable handheld devices such as backpack detectors are primarily used to record the asset's latitude and longitude information, as well as information such as material and shape, and measure dimensions and specifications to form point survey data.

[0029] During field exploration and survey, field exploration and survey personnel use portable handheld devices to conduct surveys. When they go to a specific road, the RTK positioning will automatically obtain the surveyor's location based on the current latitude and longitude, and then display the asset data and types around the location. The surveyor will check the displayed assets and the site to determine whether the assets exist and submit them for review. Incorrect items and missed items can be corrected in time, and data pictures can be collected and uploaded and saved at any time. The picture resources are automatically associated with the assets and finally compiled into an asset survey report.

[0030] After collecting data, each team conducted a self-inspection followed by a data review. This included project data collation, graphic data processing, attribute data processing (city component attribute data collation), geographic information processing (basic geographic information data, basic component data), and data quality checks (database construction, network database quality inspection, basic geographic information data quality inspection, geocoding data quality inspection, network atlas quality inspection, and component atlas quality inspection). This process involved a two-tiered quality inspection process, employing a two-tier inspection and one-tier acceptance system. In-process inspection and final acceptance were performed by the project collaborating unit's operations room (squadron) and institute (team), respectively. Acceptance was organized and conducted by the commissioning unit, or by a qualified inspection agency commissioned by the unit. All levels of inspection and acceptance must be conducted independently and may not be omitted or replaced. In-process inspections included 100% verification of the collected data's integrity, formatting, and logical consistency. Final acceptance involved field sampling with a sampling rate of at least 30% and a detailed pre-storage inspection of at least 10% to ensure data consistency with the actual scenario. Strictly monitor the entire data collection process to ensure accuracy and reliability, and improve data quality. During process inspections, submit inspection reports for each process outcome. During final acceptance, submit two phases of acceptance reports and a final acceptance report. Data is self-checked and mutually inspected by operators, and quality records are accurately filled in. Only when data meets acceptable standards can it be transferred to the next process. An internal quality audit system is established, and final inspection reports are compiled. Process inspections, final inspections, and quality assessments are conducted in accordance with relevant regulations and technical design specifications.

[0031] (3) Internal processing: The spatial coordinate data and other attribute information data are uploaded to the cloud management platform through an embedded software tool; the embedded software tool integrates a GIS map module, a data encoding module and a real-time synchronization module, and supports offline collection and automatic synchronization with the cloud.

[0032] (4) Data integration: The cloud management platform receives and processes the uploaded data. After the data is uploaded, it also performs data integration. Data integration includes steps such as data extraction, data transmission, data cleaning, data reorganization, data publishing, and service reorganization.

[0033] Specifically, data extraction: Data extraction is the first step in data integration, which is the process of selecting and extracting a specific subset of the data source. With data extraction, only relevant data can be accurately copied from a large amount of data.

[0034] Data transfer: Data transfer is the first step after data extraction, which is the process of sending the extracted specific data subset to the destination. Data transfer can automatically maintain the circulation and sharing of data.

[0035] Data cleaning: Directly transmitted data is processed according to cleaning rules in terms of data format, data encoding, data consistency, etc. Data cleaning can ensure the standardization of data in the central database.

[0036] Data reorganization: The cleaned data will be associated and processed according to the new data organization logic to strengthen the internal connection of the data.

[0037] Data publishing: Based on the needs of the subject database layer, a subset of data in the central database is regularly published to the subject database layer. Data publishing ensures timely updates of data in the subject database layer.

[0038] Service reorganization: Based on the data in the subject database, various data services are opened up to provide subject data services that can be used for various applications, thereby enhancing data reuse.

[0039] The method presented in this paper seamlessly integrates and stores data in a dynamically updated municipal asset database based on a spatial indexing algorithm. This spatial indexing algorithm utilizes multiple threads to access data and build indexes, improving data warehousing speed and query performance to meet the needs of large-scale data management. The spatial indexing algorithm incorporates technical approaches such as data partitioning and sharding, concurrent indexing structure design, multi-threaded data warehousing optimization, and query parallelization to further optimize data warehousing and query efficiency.

[0040] 1. Data partitioning and sharding:

[0041] 1.1 Spatial Partitioning: Divide data into multiple regions based on spatial location (such as grid, quadtree, or geo-hash). Each partition is managed by an independent thread or thread group to reduce lock contention.

[0042] Example: When using a grid index, the space is divided into uniform grid cells, and each thread is responsible for inserting and querying one or more adjacent cells.

[0043] 1.2 Sharding strategy: Use consistent hashing or range sharding to ensure even data distribution and avoid hot spots.

[0044] 2. Concurrent index structure design

[0045] 2.1 Read-Write Lock Optimization: Use read-write locks (such as pthread_rwlock or std::shared_mutex) on index nodes (such as R-tree nodes), allowing access by multiple readers or a single writer. Fine-grained locking: Lock each node independently, rather than globally, to improve concurrency. Fine-grained locking: Lock each node independently, rather than globally, to improve concurrency.

[0046] 2.2 Lock-free data structure: For high-frequency write scenarios, use lock-free queues (such as boost::lockfree) or CAS (Compare-And-Swap) operations to manage index updates.

[0047] 2.3 Version Control: Multi-version concurrency control (MVCC) is used to allow read operations to access the old version of the index and write operations to generate a new version to avoid read-write conflicts.

[0048] 3. Multi-threaded data storage optimization

[0049] 3.1 Batch Insertion and Buffer Queues: Producer-Consumer Model: Multiple producer threads place data into a thread-safe queue, and consumer threads retrieve data from the queue in batches and insert it into the index. Batch Submission: After accumulating a certain amount of data, the index is built in batches (such as the R-tree batch loading algorithm STR), reducing the overhead of frequent node splits.

[0050] 3.2. Parallel construction of local indexes: MapReduce mode: Divide the data into blocks, each thread builds a local index (Map phase), and finally merges them into a global index (Reduce phase).

[0051] Example: Use multiple threads to build subtrees of the R-tree separately and then merge them into the main tree.

[0052] 4. Query Parallelization

[0053] 4.1 Task decomposition: The query area is decomposed into multiple sub-areas, each sub-area is processed by a different thread, and the results are finally merged.

[0054] Example: When querying a large range, divide it into multiple grid cells, and each thread processes the data within one cell.

[0055] 4.2 Asynchronous query: Use thread pool to process concurrent query requests and return results in combination with Future / Promise mode.

[0056] Data entry means storing data into the database according to the comparison table. Before data entry, the data to be entered must be checked, including whether it is within the index range, whether it is within the map range, whether there is duplicate data in the database, whether the data structure is consistent with the database, etc., and then the data is imported into the database.

[0057] Furthermore, the seamless splicing method is used for automatic or semi-automatic storage, so that the component data of the entire city no longer has the concept of map sheets and blocks, and is truly continuous, so that users can freely roam the entire database. The method to achieve seamless splicing is as follows:

[0058] Municipal asset types include: roads, bridges, tunnels, lighting / lamp poles, traffic facilities, public facilities, urban environment facilities, landscaping facilities, mechanical and electrical facilities, and other components (civil air defense works, water area ancillary facilities, water area guardrails, flood control walls), etc., 10 major categories and 109 minor categories of urban "components".

[0059] During the asset survey, we used mobile survey vehicles, drones, manual walkthroughs, and data collection apps to obtain spatial geographic information, photos, videos, and other multi-source data on facilities and equipment. Field personnel conducted precise measurements and records, and on-site personnel processed the data with extremely high accuracy, with errors controlled within 3 centimeters. Layer by layer, the entire location was highly consistent and accurate. Each section was divided into 200-meter sections, each further divided into road, bridge, tunnel, culvert, garden, transportation, and other professional areas; a photo was taken every 100 meters; and we also recorded a video once a year, creating a health time machine for the road. This is filed like a physical examination, providing a reference for maintenance cost budgeting. Figure 4 .

[0060] See also Figure 5 , the spatial coordinate data of municipal assets can be obtained in real time through RTK positioning and measurement equipment, and other attribute information data of municipal assets can be collected using mobile collection terminals, and can be quickly uploaded and updated to the cloud management platform, realizing the digitalization of the entire process from field collection to internal management.

[0061] For example, a city needs to survey 100,000 manhole covers. Traditional methods take three months and have a high data error rate. Using the municipal asset data collection method presented in this invention, inspection teams carry RTK positioning measurement equipment and portable handheld devices for coordinated work. The portable handheld devices scan the manhole cover QR codes (bound to a unique ID), automatically obtain coordinates, take photos, and upload them. The data is then directly synchronized to the municipal asset management system. Field work is completed in two weeks with an error rate of less than 0.1%.

[0062] For example, a city needs to manage and maintain streetlight assets. The maintenance team uses RTK positioning and measurement equipment and portable handheld devices to coordinate the work. The portable handheld devices scan the QR codes (bound to a unique ID) on the light poles, automatically acquiring coordinates and simultaneously recording power and maintenance records. For subsequent management and maintenance, simply click on the GIS map to access all information, making operation simple and convenient.

[0063] Based on the disclosure and teachings of the above description, those skilled in the art to which the present invention belongs may also make changes and modifications to the above embodiments. Therefore, the present invention is not limited to the specific embodiments disclosed and described above, and some modifications and changes to the present invention should also fall within the scope of protection of the claims of the present invention. In addition, although some specific terms are used in this description, these terms are only for convenience of description and do not constitute any limitation to the present invention. As described in the above embodiments of the present invention, other methods obtained by using the same or similar structures are all within the scope of protection of the present invention.

Claims

1. A municipal asset data collection method, characterized in that: It includes the following steps: (1) Field survey: Use RTK positioning measurement equipment to obtain real-time spatial coordinate data of municipal assets and use mobile data collection terminals to collect other attribute information data of municipal assets; (2) Internal processing: uploading the spatial coordinate data and other attribute information data to the cloud management platform through embedded software tools; (3) Data integration: The cloud management platform receives and processes the uploaded data, seamlessly connects the data and stores it in the database based on the spatial index algorithm, and dynamically updates the municipal asset database.

2. The municipal asset data collection method according to claim 1, characterized in that: The RTK positioning measurement equipment includes a base station receiver, a data link and a mobile station receiver, and achieves centimeter-level precision positioning through carrier phase observation.

3. The municipal asset data collection method according to claim 1, characterized in that: The mobile acquisition terminal includes a drone, a mobile measurement vehicle and a portable handheld device.

4. The municipal asset data collection method according to claim 1, characterized in that: The embedded software tool integrates a GIS map module, a data encoding module, and a real-time synchronization module, and supports offline collection and automatic synchronization with the cloud.

5. The municipal asset data collection method according to any one of claims 1 to 4, characterized in that: Step (1) includes a two-level quality control process after data collection: 1.1 Process inspection: 100% verification of the integrity, format standardization and logical consistency of collected data; 1.2 Final acceptance: Ensure that the data is consistent with the actual scenario through field sampling verification with a sampling rate of no less than 30% and random inspection with a detailed inspection ratio of no less than 10% before storage.

6. The municipal asset data collection method according to claim 1, characterized in that: The spatial index algorithm uses multiple threads to access data and create indexes, including data partitioning and sharding, concurrent index structure design, multi-threaded data storage optimization and query parallelization.

7. The municipal asset data collection method according to claim 1, characterized in that: Step (3) After the data is uploaded, data integration is also performed, including data extraction, data transmission, data cleaning, data reorganization, data publishing and service reorganization.

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