A precise construction control method and system for a booster station soil construction device foundation
By building a cloud-based sample library and comparing data in real time, the problem of lacking accurate benchmarks in the construction of substation equipment foundations was solved, realizing closed-loop control and data accumulation throughout the entire process, and improving construction accuracy and efficiency.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- 甘肃省安装建设集团有限公司
- Filing Date
- 2026-04-20
- Publication Date
- 2026-07-14
AI Technical Summary
In existing technologies, the construction of substation equipment foundations lacks quantifiable and precise benchmarks, monitoring and execution are separated, and data cannot be effectively integrated and reused, resulting in insufficient construction accuracy and low efficiency.
A cloud-based sample library is constructed, and construction data is collected in real time through sensors and 3D scanning equipment. The data is then dynamically compared with the cloud-based sample library to generate corrective control commands, thereby achieving closed-loop control and data accumulation throughout the entire process.
It enables data-driven precision construction, real-time correction, reduced rework costs, accumulation of construction knowledge, and improved construction accuracy and efficiency.
Smart Images
Figure CN122386685A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of power engineering construction technology, specifically relating to a method and system for precise construction control of civil equipment foundations for substations. Background Technology
[0002] As the core hub of new energy power plants such as wind power and photovoltaic power generation, as well as traditional substations, the accuracy of the equipment foundation construction of substations directly affects the installation quality and operational safety of core electrical equipment such as main transformers and GIS systems. Currently, the construction of substation equipment foundations mainly relies on traditional methods such as manual layout using total stations, static handover using BIM models, and rigid fixing with steel supports. However, existing technologies generally suffer from the following problems: First, insufficient data-driven approaches, with the construction process relying on experience-based judgment and lacking quantifiable and precise benchmarks as control standards; second, separation of monitoring and execution, with a time lag between measurement and construction, making it difficult to form real-time closed-loop control; and third, severe data silos, with the massive amounts of data generated during construction failing to be effectively integrated and utilized, and unable to be precipitated into a reusable construction knowledge base. Therefore, there is an urgent need for a method and system that can achieve precise control throughout the entire process, real-time correction, and effective accumulation of construction experience. Summary of the Invention
[0003] To address the problems mentioned in the background section, this invention provides a method and system for precise construction control of substation civil equipment foundations, solving the problems of insufficient data-driven approaches, separation of monitoring and execution, and ineffective data reuse in existing technologies.
[0004] To achieve the above objectives, the present invention provides the following technical solution: a method for precise construction control of foundation engineering for substations, comprising the following steps: Step S1: Construct a cloud-based sample library, which stores multiple precise construction samples for different equipment foundation types. Each precise construction sample includes at least a three-dimensional design model of the foundation structure, embedded part positioning parameters, and corresponding construction process parameters. Step S2: Acquire real-time data from the construction site. Through various sensors and 3D scanning equipment deployed at the construction site, collect data on the basic structure morphology, spatial location of embedded parts, and environmental parameters in real time during the construction process. Step S3: Perform dynamic comparison, dynamically compare the real-time data with the selected target precision construction sample in the cloud sample library, and calculate the deviation value between the current construction state and the standard state of the sample. Step S4: Generate and output control instructions. When the deviation value exceeds the preset threshold, generate correction control instructions based on the comparison results and push them to the execution terminal at the construction site to guide the construction personnel to make real-time adjustments.
[0005] Furthermore, the method for constructing precise construction samples in step S1 includes: collecting full-process data of equipment foundation construction from historical high-quality projects, and fusing and aligning the measured point cloud data with the design model using a nearest-point iteration algorithm. The nearest-point iteration algorithm minimizes the objective function. To achieve point cloud registration, among which For points in the measured point cloud, For the corresponding points in the standard model, extract the standard state parameters of key process nodes and associate them with the corresponding construction process information to form a standard sample.
[0006] Furthermore, the real-time data acquired in step S2 also includes the spatial coordinate data of the pre-embedded bolt group; the dynamic comparison in step S3 further includes using a least squares fitting algorithm to perform real-time fitting calculation of the overall flatness of the pre-embedded bolt group, wherein the least squares fitting algorithm fits the plane equation. The distance from the top point of each bolt to the fitted plane The maximum value is taken as the flatness deviation value and compared with the standard flatness in the sample.
[0007] Furthermore, the raw data collected by the sensor in step S2 is processed by the Kalman filter algorithm for noise reduction before participating in the dynamic comparison in step S3.
[0008] A precision construction control system for the foundation of a substation civil engineering equipment includes: The cloud-based sample library module is used to store and manage multiple precise construction samples for different equipment foundation types; The on-site sensing module includes various sensors and 3D scanning equipment deployed at the construction site, used to collect data on the basic structure morphology, spatial location of embedded parts, and environmental parameters in real time during the construction process. The data fusion and comparison module is used to dynamically compare the real-time data collected by the on-site perception module with the target precision construction sample selected in the cloud sample library module and calculate the deviation value. The feedback control module is used to generate a correction control command based on the comparison result when the deviation value exceeds a preset threshold, and push it to the execution terminal at the construction site.
[0009] Furthermore, the cloud sample library module also includes a sample learning and updating unit, which is used to extract the verified and qualified construction process data after the current construction is completed, and store it as a new sample in the cloud sample library through the nearest point iteration algorithm.
[0010] Furthermore, the field perception module also includes a data preprocessing unit, which uses a Kalman filter algorithm to filter and reduce noise in the raw data collected by the sensor.
[0011] Furthermore, the data fusion and comparison module uses the nearest point iteration algorithm to register three-dimensional spatial point clouds and uses the least squares fitting algorithm to calculate and compare the flatness of the pre-embedded bolt group.
[0012] Furthermore, the feedback control module also includes a visualization unit, which is used to overlay the deviation value on the basic three-dimensional model in the form of a color cloud map or arrow indication, so as to realize the visualization of the deviation status.
[0013] Furthermore, the various sensors include tilt sensors, displacement sensors, and temperature sensors.
[0014] Compared with the prior art, the present invention has the following beneficial effects: Data-driven precision construction: By building a cloud-based sample library, the construction experience of historical high-quality projects is transformed into quantifiable and precise construction samples, providing a scientific benchmark for current construction and changing the traditional subjective judgment method that relies on experience.
[0015] Closed-loop control throughout the entire process: By collecting construction site data in real time and dynamically comparing it with cloud samples, a closed-loop control of "monitoring-feedback-adjustment" is realized. Deviations can be detected and corrected in a timely manner during construction, avoiding the accumulation of problems until after construction is completed, and greatly reducing rework costs.
[0016] Effective reuse of data assets: The cloud-based sample library has the ability to learn and update. After each construction is completed, the verified construction data can be stored as new samples in the library, so that the sample library can be continuously enriched and optimized, and construction knowledge can be continuously accumulated and reused.
[0017] Multi-dimensional precise comparison: Combining the nearest point iteration algorithm to achieve overall morphological comparison in three-dimensional space, and combining the least squares fitting algorithm to achieve accurate calculation of the flatness of the pre-embedded bolt group, thus realizing multi-dimensional precise control from macro to micro. Attached Figure Description
[0018] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 This is a structural block diagram of the precision construction control system for the foundation of a booster station civil equipment provided in an embodiment of the present invention; Figure 2 This is a flowchart illustrating the precise construction control method for the foundation of a booster station's civil engineering equipment, provided in an embodiment of the present invention. Detailed Implementation
[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0020] Example 1 like Figure 1 As shown; This embodiment provides a precision construction control system for the foundation of civil engineering equipment in a booster station. For example... Figure 1 As shown, the system includes a cloud-based sample library module, a field sensing module, a data fusion and comparison module, and a feedback control module.
[0021] I. Cloud Sample Library Module The cloud-based sample library module is deployed on a cloud server to store and manage multiple precise construction samples for different equipment foundation types. Each precise construction sample includes at least a 3D design model of the foundation structure, embedded part positioning parameters, and corresponding construction process parameters.
[0022] The process of constructing a precise construction sample is as follows: Data from the entire construction process of equipment foundations in historical high-quality projects is collected, including design drawings, construction records, acceptance reports, and 3D laser scanning point cloud data. The nearest-point iterative algorithm is used to fuse and align the measured point cloud data with the design model. The objective function of this algorithm is... To achieve point cloud registration, among which For points in the measured point cloud, These are the points corresponding to the standard model. The objective function is minimized through iterative solving, achieving accurate registration between the point cloud and the model. After registration, standard state parameters for key process nodes (such as template installation completion, embedded part positioning completion, concrete pouring completion, etc.) are extracted, including 3D coordinates, flatness, verticality, elevation, temperature, etc., and associated with corresponding construction process information (such as concrete mix proportions, vibration parameters, curing conditions, etc.) to form standard samples.
[0023] The cloud-based sample library module also includes a sample learning and updating unit. Once the current construction is completed and passes on-site acceptance, the entire construction process data is extracted using the same nearest-point iterative algorithm and stored as new samples in the cloud-based sample library, enabling continuous enrichment and optimization of the sample library.
[0024] II. On-site Perception Module The on-site sensing module includes various sensors and 3D scanning equipment deployed at the construction site, used to collect data on the basic structure morphology, spatial location of embedded parts, and environmental parameters in real time during the construction process.
[0025] Multiple sensors are used, including: tilt sensors, deployed on the formwork surface to monitor the verticality and tilt angle of the formwork in real time; displacement sensors, deployed on the top of the embedded bolts or on the embedded plate to monitor the three-dimensional displacement of the embedded parts during concrete pouring in real time; and temperature sensors, embedded inside the concrete to monitor changes in the hydration heat and temperature of the concrete in real time. Three-dimensional scanning equipment includes fixed laser scanners or handheld 3D scanners, which perform 3D scanning of the construction site at key process nodes to obtain complete point cloud data of the foundation structure.
[0026] The on-site sensing module also includes a data preprocessing unit. Due to the complex environment of the construction site, the raw data collected by the sensors is subject to noise interference. The data preprocessing unit uses a Kalman filter algorithm to filter and reduce noise in the raw data, effectively eliminating instantaneous interference caused by factors such as concrete vibration and equipment vibration.
[0027] III. Data Fusion and Comparison Module The data fusion and comparison module is used to dynamically compare the real-time data collected and preprocessed by the on-site perception module with the target precision construction samples selected in the cloud sample library module, and calculate the deviation value.
[0028] The comparison includes two aspects: first, three-dimensional spatial comparison, which uses the nearest point iteration algorithm to register the real-time collected basic point cloud data with the three-dimensional design model in the sample, and calculates the overall deviation cloud map and the deviation of key feature points; second, flatness comparison of embedded parts, which uses the least squares fitting algorithm to calculate the overall flatness for embedded bolt groups or embedded plates, specifically: obtaining the spatial coordinate point set of the top of each embedded bolt. Fitting plane equation Calculate the distance from each point to the fitted plane. The maximum value of all distances is taken as the flatness deviation value, and compared with the standard flatness in the sample.
[0029] IV. Feedback Control Module The feedback control module generates a correction and adjustment command based on the comparison results when the deviation value exceeds a preset threshold, and pushes it to the execution terminal at the construction site. The preset thresholds are set according to different foundation types and construction stages, such as a horizontal displacement threshold of 2mm for pre-embedded bolts and a verticality threshold of 1‰.
[0030] The feedback control module also includes a visualization unit. This unit overlays deviation values onto the foundation's 3D model as color-coded cloud maps or arrow indicators, providing a visual representation of the deviation status. Correction and control commands, including template fine-tuning direction and displacement, pre-embedded bolt verticality correction values, and concrete pouring speed adjustment suggestions, are pushed to the terminal equipment at the construction site in a combination of text and graphics.
[0031] Example 2 This embodiment provides a method for precise construction control of the foundation of civil equipment in a booster station, which can be implemented based on the system described in Embodiment 1. Figure 2 As shown, the method includes the following steps: Step S1: Build a cloud-based sample library A cloud-based sample library was constructed to store multiple precise construction samples for different equipment foundation types. Taking the main transformer foundation of a 220kV step-up substation as an example, the flatness of the pre-embedded bolt group is required to be no more than ±2mm. Data from the entire construction process of this high-quality project was collected, and after registration using the nearest point iterative algorithm, the standard state parameters of key nodes were extracted to form a precise construction sample for the main transformer foundation.
[0032] Step S2: Obtain real-time data from the construction site At the substation construction site, tilt sensors, displacement sensors, temperature sensors, and 3D scanning equipment are deployed to collect data in real time during construction. Sensor data is uploaded in real time via a wireless transmission module, with the sampling frequency set according to the parameter type. The raw data undergoes noise reduction processing using a Kalman filter algorithm to effectively filter out instantaneous interference such as vibration.
[0033] Step S3: Perform dynamic comparison Real-time data is dynamically compared with selected target precision construction samples in a cloud-based sample library to calculate the deviation value. Taking the pre-embedded bolt positioning process as an example: the coordinate points of the top of each pre-embedded bolt are acquired in real time, and a least squares fitting algorithm is used to fit the plane to calculate the flatness deviation. When the measured flatness deviation exceeds a preset threshold, an early warning is triggered.
[0034] Step S4: Generate and output control commands When the deviation exceeds a preset threshold, a correction control command is generated based on the comparison results and pushed to the execution terminal at the construction site. The system calculates the deviation direction of each bolt, generates specific correction commands, and construction personnel make real-time adjustments according to the commands, then scan and compare again until the deviation value returns to the allowable range.
[0035] Step S5: Sample Learning and Update Once the current construction is completed and passes on-site inspection, the entire construction process data will be extracted using the nearest point iteration algorithm and stored as new samples in the cloud sample library to provide accurate construction benchmarks for subsequent similar projects.
[0036] The method and system for precise construction control of substation civil equipment foundations provided by this invention can be widely applied in power engineering fields such as wind power generation, photovoltaic power generation, thermal power generation, and substations. It can also be extended to other civil engineering projects such as industrial plants and equipment foundations that require high foundation construction precision. This invention effectively improves construction accuracy and efficiency through a cloud-based sample library and real-time monitoring feedback, demonstrating significant industrial practical value.
[0037] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.
Claims
1. A method for precise construction control of foundation engineering for a booster station, characterized in that, Includes the following steps: Step S1: Construct a cloud-based sample library, which stores multiple precise construction samples for different equipment foundation types. Each precise construction sample includes at least a three-dimensional design model of the foundation structure, embedded part positioning parameters, and corresponding construction process parameters. Step S2: Acquire real-time data from the construction site. Through various sensors and 3D scanning equipment deployed at the construction site, collect data on the basic structure morphology, spatial location of embedded parts, and environmental parameters in real time during the construction process. Step S3: Perform dynamic comparison, dynamically compare the real-time data with the selected target precision construction sample in the cloud sample library, and calculate the deviation value between the current construction state and the standard state of the sample. Step S4: Generate and output control instructions. When the deviation value exceeds the preset threshold, generate correction control instructions based on the comparison results and push them to the execution terminal at the construction site to guide the construction personnel to make real-time adjustments.
2. The method for precise construction control of substation civil equipment foundations according to claim 1, characterized in that, The method for constructing precise construction samples in step S1 includes: Data from the entire construction process of equipment foundations in historical high-quality projects is collected. The measured point cloud data is then fused and aligned with the design model using a nearest-point iterative algorithm, which minimizes the objective function. To achieve point cloud registration, among which For points in the measured point cloud, For the corresponding points in the standard model, extract the standard state parameters of key process nodes and associate them with the corresponding construction process information to form a standard sample.
3. The method for precise construction control of substation civil equipment foundations according to claim 1, characterized in that, The real-time data obtained in step S2 also includes the spatial coordinate data of the pre-embedded bolt group; the dynamic comparison in step S3 also includes using a least squares fitting algorithm to perform real-time fitting calculation of the overall flatness of the pre-embedded bolt group, wherein the least squares fitting algorithm fits the plane equation. The distance from the top point of each bolt to the fitted plane The maximum value is taken as the flatness deviation value and compared with the standard flatness in the sample.
4. The method for precise construction control of substation civil equipment foundations according to claim 1, characterized in that, The raw data collected by the sensor in step S2 is processed by the Kalman filter algorithm for noise reduction before participating in the dynamic comparison in step S3.
5. A precision construction control system for the foundation of a booster station civil engineering equipment, characterized in that, include: The cloud-based sample library module is used to store and manage multiple precise construction samples for different equipment base types; The on-site sensing module includes various sensors and 3D scanning equipment deployed at the construction site, used to collect data on the basic structure morphology, spatial location of embedded parts, and environmental parameters in real time during the construction process. The data fusion and comparison module is used to dynamically compare the real-time data collected by the on-site perception module with the target precision construction sample selected in the cloud sample library module and calculate the deviation value. The feedback control module is used to generate a correction control command based on the comparison result when the deviation value exceeds a preset threshold, and push it to the execution terminal at the construction site.
6. The precision construction control system for the foundation of the substation civil equipment according to claim 5, characterized in that, The cloud sample library module also includes a sample learning and updating unit, which is used to extract the verified and qualified construction process data after the current construction is completed and store it as a new sample in the cloud sample library through the nearest point iteration algorithm.
7. The precision construction control system for the foundation of the substation civil equipment according to claim 5, characterized in that, The field sensing module also includes a data preprocessing unit, which uses a Kalman filter algorithm to filter and reduce noise in the raw data collected by the sensor.
8. The precision construction control system for the foundation of the substation civil equipment according to claim 5, characterized in that, The data fusion and comparison module uses the nearest point iteration algorithm for three-dimensional spatial point cloud registration and the least squares fitting algorithm to calculate and compare the flatness of the pre-embedded bolt group.
9. The precision construction control system for the foundation of the substation civil equipment according to claim 5, characterized in that, The feedback control module also includes a visualization unit, which is used to overlay the deviation value on the basic three-dimensional model in the form of a color cloud map or arrow indication, so as to realize the visualization of the deviation status.
10. The precision construction control system for the foundation of the substation civil equipment according to claim 5, characterized in that, The various sensors include tilt sensors, displacement sensors, and temperature sensors.