A method for quickly constructing a millimeter-level virtual dynamic domain field and a real dynamic domain field by using a base station and a laser radar reference array

Abstract

The application discloses a method for quickly constructing a millimeter-level virtual dynamic domain field and a real dynamic domain field by adopting a base station and a laser radar benchmark array, and belongs to the technical field of intelligent manufacturing, intelligent construction and industrial automation. The method deploys the base station and the laser radar benchmark array, so that the observation data has a unified timestamp; sufficient ranging samples are repeatedly collected for the same measured point, random errors are removed, and only one ranging value is output; a hyperstatic equation set is constructed based on the ranging value to complete calculation; a virtual dynamic domain field is generated based on the calculation result, three non-collinear coordinate control points are selected, a real three-dimensional coordinate of a global observation point is obtained in combination with the relative position relationship of the virtual domain field, and a real dynamic domain field with geographical coordinates is generated. The application adopts a statistical filtering and hyperstatic equation set layered error processing mechanism, breaks away from the dependence on external benchmarks and artificial targets, and realizes target-free autonomous field construction and dynamic calibration.

Description

Technical Field

[0001] This invention belongs to the field of intelligent manufacturing, intelligent construction and industrial automation technology, and specifically relates to a method for rapidly constructing millimeter-level dynamic field using base stations and lidar reference arrays. Background Technology

[0002] Current high-precision dynamic domain field construction technologies largely rely on external geodetic benchmarks, satellite positioning signals, and artificial targets to complete coordinate calibration and benchmark establishment. Mainstream solutions generally depend on a small number of observation devices coupled with fixed compensation algorithms to achieve spatial positioning, and the overall architecture is highly dependent on external auxiliary conditions and manual calibration. Various traditional positioning systems typically involve complex preprocessing procedures such as attitude calibration, extrinsic parameter calibration, and component error compensation. This is currently the mainstream implementation method for high-precision dynamic spatial domain field construction and is widely used in various high-precision spatial positioning and dynamic monitoring scenarios.

[0003] Existing technologies have significant technical shortcomings. First, traditional solutions rely heavily on manual calibration and various specialized error compensations, resulting in cumbersome system architecture, complex deployment processes, low operational efficiency, and susceptibility to environmental disturbances, equipment micro-vibrations, and atmospheric interference, making it difficult to maintain stable millimeter-level dynamic accuracy over long periods. Second, traditional observation networks lack sufficient redundancy, failing to form effective statically indeterminate constraints. The solution process is prone to ambiguous solutions, and there is no self-healing reconstruction capability in case of equipment malfunction, leading to poor fault tolerance. Furthermore, they are highly dependent on external reference signals, significantly limiting their applicability and robustness in complex obstruction and signal loss scenarios.

[0004] Third, existing technologies cannot construct millimeter-level virtual dynamic field fields or real dynamic field fields. Summary of the Invention

[0005] The purpose of this section is to outline some aspects of embodiments of the present invention and to briefly describe some preferred embodiments. Simplifications or omissions may be made in this section, as well as in the abstract and title of this application, to avoid obscuring the purpose of these documents; however, such simplifications or omissions should not be construed as limiting the scope of the invention.

[0006] The problem that this invention aims to solve is that the existing observation network has insufficient redundancy, cannot form effective statically indeterminate constraints, and is extremely susceptible to environmental disturbances.

[0007] In view of the problems existing in the above and / or existing methods for constructing millimeter-level virtual dynamic field and real dynamic field, the present invention is proposed.

[0008] To address the aforementioned technical problems, this invention provides the following technical solution: a method for rapidly constructing millimeter-level virtual dynamic domain and real dynamic domain using a base station and a lidar reference array. This method involves classifying the error in a single refresh cycle of lidar single-point ranging into two categories: purely random error and single-machine subsystem deviation corresponding to a single lidar. For purely random error, within a single refresh cycle, the same lidar repeatedly collects sufficient ranging samples from the same measured point, and combines this with a statistical filtering algorithm to achieve error convergence and suppression, controlling the remaining random residual within the allowable range of millimeter-level positioning accuracy. Finally, the unique value of the lidar's ranging at that point after removing random errors is output. The single-machine subsystem deviation is the systematic error of a single lidar eliminated by a unified solution of the statically indeterminate equations. The deviation of a single-machine subsystem includes inherent subsystem deviation, overall common-mode deviation of the refresh cycle, local environmental system deviation, and global coupling deviation. This method configures a single refresh cycle data refresh period. Within a single refresh cycle, the equipment operating conditions, the overall atmospheric environment, and various external interference factors change gradually, and the overall deviation of each lidar single-machine subsystem is approximately constant, with only fluctuations within the allowable accuracy range. Based on the single-point ranging error processing mechanism and the steady-state characteristics of the single refresh cycle data refresh period, the deviation of each lidar single-machine subsystem is set as an independent unknown. A statically indeterminate equation system is constructed using redundant observations from multiple lidars to uniformly solve and eliminate all single-machine subsystem deviations, achieving a high-precision dynamic domain field construction across the entire domain. Specifically, this includes the following steps: S1: Deploy base stations and lidar reference array equipment. The base stations are located in or around the proposed domain area. The lidar reference array is deployed within the proposed domain area, around the perimeter, or in a distributed manner combining both. At any grid point within a given area and with a given accuracy, at least four non-coplanar lidars can simultaneously form effective observation coverage within the effective ranging range, thus constructing multi-dimensional redundant observation conditions. The base station coordinates the signal acquisition, data transmission, and operational status monitoring of all lidars within a single refresh cycle.

[0009] S2: Establish communication between the base station and the laser array, so that all radar observation data have a unified aligned timestamp, and initialize the statistical filtering module and the statically indeterminate equation solving module;

[0010] S3: The ranging samples of the laser reference array timestamp are based on the statistical regularity of the ranging samples. Within one refresh cycle, the same radar repeatedly collects a sufficient number of ranging samples for the same measured point and removes abnormal data.

[0011] S4: The ranging samples are processed using a statistical filtering method to suppress random errors and control the residual value to meet the millimeter-level accuracy requirements. Each radar outputs a unique ranging value for the same measured point.

[0012] S5: Based on the ranging values, construct a set of statically indeterminate equations, eliminate all subsystem deviations, fit and solve for the coordinates of the measured point and the radar coordinates, and generate the observation results;

[0013] S6: Based on the observation results, a virtual dynamic field is generated. Preferably, three non-collinear real coordinate control points are selected and deployed in the proposed field area that can be covered by the radar. The three-dimensional positions of the control points are directly assigned to the virtual coordinates of the virtual dynamic field. The virtual coordinates are established as a reference. Based on the relative positional relationship of the virtual dynamic field, the real three-dimensional positions of all the observation points in the proposed field area are obtained, and a real dynamic field with geographic coordinates is generated.

[0014] As a preferred embodiment of the method for rapidly constructing millimeter-level virtual dynamic field and real dynamic field using base station and lidar reference array as described in this invention, wherein: in step S1, the base station is configured with a global computing power unit, which is used to perform field modeling, multi-radar data fusion, statistical filtering, solving of statically indeterminate equations and global real-time high-frequency refresh tasks, and has a residual self-checking function.

[0015] The refresh cycle refers to the complete cycle of a millimeter-level dynamic domain field completing one full-domain data acquisition, error processing, statically indeterminate fitting solution and domain field parameter update, which is also the time interval for the radar to complete a single round of full-quantity ranging sample acquisition.

[0016] The sufficient range measurement sample refers to all range measurement samples required to achieve a complete cycle and output the unique range measurement value.

[0017] The operational status monitoring includes collecting sufficient ranging samples from the same measured point within a single refresh cycle to meet the requirements of statistical filtering analysis, and ensuring that any measured point within the proposed domain area can be covered by no fewer than four non-coplanar radars within an effective ranging distance.

[0018] As a preferred embodiment of the method for rapidly constructing millimeter-level virtual dynamic field and real dynamic field using base station and lidar reference array as described in the present invention, wherein: the lidar reference array in step S1 adopts a form including but not limited to fixed installation, mechanical equipment integration, or aerial drone deployment;

[0019] The following conditions must be met within the single refresh cycle:

[0020] 1) The lidar array completes the ranging sample collection of all grid points within a given area and with a given accuracy.

[0021] 2) All grid measurement points across the entire area are collected through a unified radar array collaborative data acquisition process;

[0022] 3) Disturbances generated during the acquisition process are classified as random errors; or the influence of disturbances generated during the acquisition process can be eliminated in advance, and the observed samples after eliminating disturbances still conform to the random error distribution.

[0023] 4) At any grid measurement point, within the effective ranging range, at least four non-coplanar radars can simultaneously form effective observation coverage, thus constructing multi-dimensional redundant observation conditions.

[0024] 5) Within the effective ranging range, each LiDAR continuously and repeatedly collects sufficient ranging samples for the same grid point within the cycle to meet the conditions required for the statistical filtering analysis of ranging for the corresponding grid point by the LiDAR.

[0025] 6) Within the scope of periodic operations, there is no need to add special compensation strategies for disturbances, and random errors are uniformly suppressed and eliminated by statistical filtering.

[0026] As a preferred embodiment of the method for rapidly constructing millimeter-level virtual dynamic field and real dynamic field using base station and lidar reference array as described in this invention, the on-site global computing power unit includes, but is not limited to, industrial-grade GPUs, on-site computing power modules, edge computing units, cloud-based backend computing power units, and hardware-software collaborative computing power combination structures, which only need to meet the requirements of field modeling, multi-radar data fusion, statistical filtering operations, solving of statically indeterminate equations, and real-time high-frequency refresh of the entire field.

[0027] The base station relies on the built-in on-site global computing power unit to build a unified global time synchronization system. The time synchronization system is used to complete the clock alignment of all radar devices, so that the observation data collected by the radar in the whole domain has a unified timestamp.

[0028] As a preferred embodiment of the method for rapidly constructing millimeter-level virtual dynamic field and real dynamic field using base station and lidar reference array as described in this invention, in step S3, before synchronously collecting ranging samples with a unified timestamp, it is necessary to ensure that the number of ranging samples collected by each radar for the same measured point within the effective ranging distance in a single refresh cycle meets the principle of sufficient sampling.

[0029] The principle of sufficient sampling includes:

[0030] The number of measured point samples is sufficient to form a sample set that conforms to statistical laws, and the random error is suppressed to a level that does not affect millimeter-level accuracy through the statistical filtering, and the sampling process is completed within a time less than or equal to the planned refresh interval.

[0031] As a preferred embodiment of the method for rapidly constructing millimeter-level virtual dynamic field and real dynamic field using a base station and lidar reference array as described in this invention, the core logic for constructing the statically indeterminate equation system in step S5 is as follows:

[0032] Suppose that the laser radar reference array has a total of k laser radars, where k is a positive integer, and the total number of unknowns in a single refresh cycle is 3n + k × (3 + 1).

[0033] Where 3n represents the unknown three-dimensional coordinates corresponding to n measured points, and each radar corresponds to 3 position parameters and 1 single-unit subsystem deviation parameter, totaling k×4 unknowns. The single-unit subsystem deviation of a single radar includes four categories of deviations: the first category is inherent subsystem deviation, generated by equipment processing, assembly, installation, and sensor inherent properties; the second category is overall common-mode deviation during the refresh cycle, caused by changes in overall temperature, humidity, and air pressure across the entire domain; the third category is local environmental system deviation, including local operating condition interference such as refractive index changes, airflow, dust, and local thermal disturbances; and the fourth category is global coupling deviation, encompassing all composite disturbances derived from the interweaving of various errors.

[0034] The number of observation equations in the statically indeterminate equation set is determined by the number of measured points n. Any measured point is covered by no less than 4 non-coplanar lidars within the effective range. A single measured point can form 4n independent observation equations.

[0035] When n is large enough, the number of observation equations is much greater than the number of unknowns, that is, 4n > 3n + k × (3 + 1) constitutes a strong statically indeterminate constraint.

[0036] The system of statically indeterminate equations is used to complete the fitting solution, simultaneously calculating the coordinates of the measured point and the radar's own deployment coordinates, while automatically offsetting the deviations of individual subsystems of each lidar in the lidar reference array.

[0037] As a preferred embodiment of the method for rapidly constructing millimeter-level virtual dynamic field and real dynamic field using base station and lidar reference array as described in this invention, in step S4, the statistical filtering method used includes, but is not limited to, various noise reduction methods such as time-series statistical filtering, mean filtering, and Gaussian filtering, which can be adaptively matched according to the on-site working environment.

[0038] The suppression of random errors includes:

[0039] The ranging samples within a single refresh cycle are subjected to integrated noise reduction processing through a multi-dimensional statistical filtering algorithm, which suppresses random errors including but not limited to equipment and instrument noise, atmospheric disturbances and on-site mechanical micro-vibrations. The residual value of the random error is controlled within a range that does not affect the millimeter-level positioning accuracy. Furthermore, each radar outputs only one unique ranging value for the same measured point, which serves as the basic data for constructing the statically indeterminate equation set.

[0040] As a preferred embodiment of the method for rapidly constructing millimeter-level virtual dynamic field and real dynamic field using base station and lidar reference array as described in this invention, in step S6 after generating the virtual dynamic field, the virtual dynamic field does not need to rely on external geodetic coordinates, global navigation satellite system signals and external targets, and can directly perform dynamic high-precision operation.

[0041] In step S1, during the operational status monitoring, at any grid point in the predetermined area and with predetermined accuracy, four radars are simultaneously set up within the effective ranging range and arranged in a non-coplanar spatial manner, which can form a stable and reliable statically indeterminate redundant observation constraint condition.

[0042] If only three non-collinear radars are deployed at some or all of the measured points in the field within the effective ranging distance, the observation constraints need to be supplemented by equivalent redundancy supplementation methods such as radar attitude and rotation angle parameter calculation, spatial position association matching, and temporal continuous constraint binding.

[0043] If more than four non-coplanar radars are deployed at some or all of the measured points within the effective ranging distance, the higher the redundancy of the observation equation, the faster the convergence speed of the statically indeterminate fitting, and the higher the accuracy of the solution result. When individual radars experience settlement, tilting, or loosening faults during operation, the faulty radars can be downweighted and isolated. After the faulty radars have been corrected and their operating status has stabilized, they can be directly rejoined to the array and continue to participate in the synchronous acquisition of all measurement points in the entire area, so as to achieve uninterrupted operation.

[0044] As a preferred embodiment of the method for rapidly constructing millimeter-level virtual dynamic field and real dynamic field using base station and lidar reference array as described in this invention, in step S4, the statistical filtering method is used to process the ranging sample without the need for radar attitude calibration, rotation angle and angular velocity measurement, manual external parameter calibration, vibration-specific compensation, various common mode error sub-item compensation, and geodetic reference and artificial target required by traditional positioning.

[0045] As a preferred embodiment of the method for rapidly constructing millimeter-level virtual dynamic field and real dynamic field using base station and lidar reference array as described in this invention, wherein: the time continuity constraint in step S5 is that there are no sudden changes in radar position, measured point position and system error within adjacent refresh cycles;

[0046] The spatial continuity constraint ensures that the spatial distribution of the measured points has no geometric jumps, in order to avoid ambiguous solutions in the fitting solution.

[0047] As a preferred embodiment of the method for rapidly constructing millimeter-level virtual dynamic field and real dynamic field using a base station and a lidar reference array as described in this invention, the base station and lidar can interact with each other by using wired transmission, wireless transmission or a hybrid wired-wireless transmission method, depending on the on-site operating environment.

[0048] After generating the virtual dynamic field and the real dynamic field, the base station outputs the full-domain dynamic three-dimensional coordinate data and field model data to the outside world in real time according to the set refresh frequency.

[0049] The base station persistently stores the raw data of the time domain field and the calculated three-dimensional domain field data locally, allowing staff to retrieve the corresponding historical domain field data according to the time node, realize historical working condition backtracking and time domain data comparison and analysis, and distribute the domain field data to the outside world through wired or wireless communication.

[0050] As a preferred embodiment of the method for rapidly constructing millimeter-level virtual dynamic field and real dynamic field using a base station and lidar reference array as described in this invention, the invention defines a dedicated progressive operation logic:

[0051] The preferred approach is to use a fixed process of "outlier removal - statistical filtering - statically indeterminate fitting solution" to complete multi-level error elimination and coordinate calculation.

[0052] Any implementation method that circumvents this technical solution by relying on the core technical principles of the error classification mechanism, multi-radar redundant observation, and solution of statically indeterminate equations of this invention, through error classification reorganization, fine-tuning the order of steps, replacing equivalent algorithms, or adding auxiliary software and hardware components, falls within the protection scope of this invention.

[0053] As a preferred embodiment of the method for rapidly constructing millimeter-level virtual dynamic field and real dynamic field using base station and lidar reference array as described in this invention, the on-site sampling operation must simultaneously meet the two conditions of sufficient sample quantity and statistical regularity of data distribution, and the duration of a single sampling session must not exceed the preset refresh interval.

[0054] This invention is compatible with various raw data processing methods, including data integration, outlier screening, multi-level filtering and noise reduction, and classification and summary output. At the same time, it does not limit the output form of ranging data, and all equivalent modes such as single-cycle single-value output, single-cycle multi-value output, and multi-cycle data fusion and summary output are included. As long as they are compatible with the redundant observation and statically indeterminate solution system of this invention, they are all within the scope of protection of this invention.

[0055] As a preferred embodiment of the method for rapidly constructing millimeter-level virtual dynamic field and real dynamic field using base station and lidar reference array as described in this invention, sub-millimeter level and higher precision dynamic field can be constructed by increasing the number of redundant observation radar devices in the field, upgrading lidar hardware configuration, and optimizing base station computing unit configuration.

[0056] Any implementation method that adopts the core field-building logic of the error processing mechanism, redundant observation mode, and unified solution of the statically indeterminate equation system of this invention to achieve higher precision field construction falls within the protection scope of this invention.

[0057] This invention relies on a redundant observation and global time synchronization architecture of a lidar reference array, combined with statistical filtering and a hierarchical error processing mechanism for overstatic fitting, to completely eliminate dependence on external references, satellite signals, and artificial targets, achieving targetless autonomous field construction and dynamic calibration. This invention abandons various complex calibration and component error compensation processes, simplifying the system architecture and improving deployment efficiency. Redundant observation constraints avoid ambiguity in the solution. It also possesses the ability to identify and isolate equipment anomalies and reconstruct the reference self-healing mechanism, adaptively offsetting various system errors and random disturbances. It exhibits strong resistance to environmental interference, maintaining long-term stable millimeter-level dynamic positioning accuracy, and is adaptable to various complex indoor and outdoor operating scenarios, significantly improving its versatility and engineering practicality. Attached Figure Description

[0058] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein:

[0059] Figure 1 This is a flowchart illustrating the method for rapidly constructing millimeter-level virtual dynamic field and real dynamic field using a base station and lidar reference array in Example 1.

[0060] Figure 2 This is a schematic diagram illustrating the radar operation quantity determination method in Example 1, which uses a base station and a lidar reference array to rapidly construct millimeter-level virtual dynamic field and real dynamic field. Detailed Implementation

[0061] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0062] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0063] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0064] Example 1

[0065] Reference Figure 1 and Figure 2 This is the first embodiment of the present invention, which provides a method for rapidly constructing millimeter-level virtual dynamic field and real dynamic field using a base station and a lidar reference array, comprising:

[0066] This method classifies single-point ranging errors of lidar into two categories: purely random errors and single-unit subsystem deviations corresponding to a single lidar. For purely random errors, the same lidar repeatedly collects sufficient ranging samples from the same measured point within a single refresh cycle, and combines statistical filtering algorithms to achieve error convergence and suppression, controlling the remaining random residuals within the millimeter-level positioning accuracy tolerance range. Finally, the unique value of the lidar's ranging at that point after removing random errors is output. The single-unit subsystem deviation is the systematic deviation of a single lidar eliminated by the unified solution of the statically indeterminate equations. The single-unit subsystem deviation includes the subsystem's inherent deviation, the overall common-mode deviation of the refresh cycle, and local deviations. The method addresses environmental system deviations and global coupling deviations. It configures a single refresh cycle for data, within which the equipment operating conditions, global atmospheric environment, and various external interference factors change gradually. The overall deviations of each lidar subsystem are approximately constant, with only fluctuations within the allowable accuracy range. Based on the single-point ranging error processing mechanism and the steady-state characteristics of the single refresh cycle, the deviation of each lidar subsystem is set as an independent unknown. A statically indeterminate equation system is constructed using redundant observations from multiple lidars to uniformly solve and eliminate all subsystem deviations, achieving a high-precision dynamic domain field construction across the entire area. The specific steps include: S1: Deploy base stations and lidar reference array equipment. The base stations are located in or around the proposed domain area. The lidar reference array is deployed within the proposed domain area, around the perimeter, or in a distributed manner combining both. At any grid point within a given area and with a given accuracy, at least four non-coplanar lidars can simultaneously form effective observation coverage within the effective ranging range, thus constructing multi-dimensional redundant observation conditions. The base station coordinates the signal acquisition, data transmission, and operational status monitoring of all lidars within a single refresh cycle.

[0067] The base station described in step S1 is configured with a global computing power unit. The computing power unit is used to perform domain field modeling, multi-radar data fusion, statistical filtering, solving of statically indeterminate equations, and global real-time high-frequency refresh tasks, and has residual self-checking function.

[0068] The refresh cycle refers to the complete cycle of a millimeter-level dynamic domain field completing one full-domain data acquisition, error processing, statically indeterminate fitting solution and domain field parameter update, which is also the time interval for the radar to complete a single round of full-quantity ranging sample acquisition.

[0069] The sufficient range measurement sample refers to all range measurement samples required to achieve a complete cycle and output the unique range measurement value.

[0070] The operational status monitoring includes collecting sufficient ranging samples from the same measured point within a single refresh cycle to meet the requirements of statistical filtering analysis, and ensuring that any measured point within the proposed domain area can be covered by no fewer than four non-coplanar radars within an effective ranging distance.

[0071] The lidar reference array mentioned in step S1 can be deployed in various forms, including but not limited to fixed installation, integrated mechanical equipment mounting, or aerial drone mounting.

[0072] The following conditions must be met within the single refresh cycle:

[0073] 1) The lidar array completes the ranging sample collection of all grid points within a given area and with a given accuracy.

[0074] 2) All grid measurement points across the entire area are collected through a unified radar array collaborative data acquisition process;

[0075] 3) Disturbances generated during the acquisition process are classified as random errors; or the influence of disturbances generated during the acquisition process can be eliminated in advance, and the observed samples after eliminating disturbances still conform to the random error distribution.

[0076] 4) At any grid measurement point, within the effective ranging range, at least four non-coplanar radars can simultaneously form effective observation coverage, thus constructing multi-dimensional redundant observation conditions.

[0077] 5) Within the effective ranging range, each LiDAR continuously and repeatedly collects sufficient ranging samples for the same grid point within the cycle to meet the conditions required for the statistical filtering analysis of ranging for the corresponding grid point by the LiDAR.

[0078] 6) Within the scope of periodic operations, there is no need to add special compensation strategies for disturbances, and random errors are uniformly suppressed and eliminated by statistical filtering.

[0079] The on-site, all-domain integrated computing power unit includes, but is not limited to, industrial-grade GPUs, on-site computing power modules, edge computing units, cloud-based backend computing power units, and hardware-software collaborative computing power combination structures. It only needs to meet the requirements of domain field modeling, multi-radar data fusion, statistical filtering operations, solving of statically indeterminate equations, and real-time high-frequency refresh of the entire domain.

[0080] The base station relies on the built-in on-site global computing power unit to build a unified global time synchronization system. The time synchronization system is used to complete the clock alignment of all radar devices, so that the observation data collected by the radar in the whole domain has a unified timestamp.

[0081] S2: Establish communication between the base station and the laser array, so that all radar observation data have a unified aligned timestamp, and initialize the statistical filtering module and the statically indeterminate equation solving module;

[0082] S3: The ranging samples of the laser reference array timestamp are based on the statistical regularity of the ranging samples. Within one refresh cycle, the same radar repeatedly collects a sufficient number of ranging samples for the same measured point and removes abnormal data.

[0083] Before synchronously collecting ranging samples with a unified timestamp in step S3, it is necessary to ensure that the number of ranging samples collected by each radar for the same measured point within the effective ranging distance in a single refresh cycle meets the principle of sufficient sampling.

[0084] The principle of sufficient sampling includes:

[0085] The number of measured point samples is sufficient to form a sample set that conforms to statistical laws, and the random error is suppressed to a level that does not affect millimeter-level accuracy through the statistical filtering, and the sampling process is completed within a time less than or equal to the planned refresh interval.

[0086] S4: The ranging samples are processed using a statistical filtering method to suppress random errors and control the residual value to meet the millimeter-level accuracy requirements. Each radar outputs a unique ranging value for the same measured point.

[0087] In step S4, the statistical filtering method used includes, but is not limited to, various noise reduction methods such as time-series statistical filtering, mean filtering, and Gaussian filtering, which can be adaptively matched according to the on-site working environment.

[0088] The suppression of random errors includes:

[0089] The ranging samples within a single refresh cycle are subjected to integrated noise reduction processing through a multi-dimensional statistical filtering algorithm, which suppresses random errors including but not limited to equipment and instrument noise, atmospheric disturbances and on-site mechanical micro-vibrations. The residual value of the random error is controlled within a range that does not affect the millimeter-level positioning accuracy. Furthermore, each radar outputs only one unique ranging value for the same measured point, which serves as the basic data for constructing the statically indeterminate equation set.

[0090] In step S4, the statistical filtering method is used to process the ranging sample without the need for radar attitude calibration, rotation angle and angular velocity measurement, manual external parameter calibration, vibration-specific compensation, various common mode error sub-item compensation, and geodetic benchmark and artificial target required by traditional positioning.

[0091] S5: Based on the ranging values, construct a set of statically indeterminate equations, eliminate all subsystem deviations, fit and solve for the coordinates of the measured point and the radar coordinates, and generate the observation results;

[0092] The core logic for constructing the statically indeterminate equation system in step S5 is as follows:

[0093] Suppose that the laser radar reference array has a total of k laser radars, where k is a positive integer, and the total number of unknowns in a single refresh cycle is 3n + k × (3 + 1).

[0094] Where 3n represents the unknown three-dimensional coordinates corresponding to n measured points, and each radar corresponds to 3 position parameters and 1 single-unit subsystem deviation parameter, totaling k×4 unknowns. The single-unit subsystem deviation of a single radar includes four categories of deviations: the first category is inherent subsystem deviation, generated by equipment processing, assembly, installation, and sensor inherent properties; the second category is overall common-mode deviation during the refresh cycle, caused by changes in overall temperature, humidity, and air pressure across the entire domain; the third category is local environmental system deviation, including local operating condition interference such as refractive index changes, airflow, dust, and local thermal disturbances; and the fourth category is global coupling deviation, encompassing all composite disturbances derived from the interweaving of various errors.

[0095] The time continuity constraint mentioned in step S5 means that there are no sudden changes in radar position, measured point position and system error within adjacent refresh cycles;

[0096] The spatial continuity constraint ensures that the spatial distribution of the measured points has no geometric jumps, in order to avoid ambiguous solutions in the fitting solution.

[0097] The base station and the lidar can communicate with each other using wired transmission, wireless transmission, or a hybrid wired-wireless transmission method, depending on the on-site working environment.

[0098] After generating the virtual dynamic field and the real dynamic field, the base station outputs the full-domain dynamic three-dimensional coordinate data and field model data to the outside world in real time according to the set refresh frequency.

[0099] The base station persistently stores the raw data of the time domain field and the calculated three-dimensional domain field data locally, allowing staff to retrieve the corresponding historical domain field data according to the time node, realize historical working condition backtracking and time domain data comparison and analysis, and distribute the domain field data to the outside world through wired or wireless communication.

[0100] The number of observation equations in the statically indeterminate equation set is determined by the number of measured points n. Any measured point is covered by no less than 4 non-coplanar lidars within the effective range. A single measured point can form 4n independent observation equations.

[0101] When n is large enough, the number of observation equations is much greater than the number of unknowns, that is, 4n > 3n + k × (3 + 1) constitutes a strong statically indeterminate constraint.

[0102] The system of statically indeterminate equations is used to complete the fitting solution, simultaneously calculating the coordinates of the measured point and the radar's own deployment coordinates, while automatically offsetting the deviations of individual subsystems of each lidar in the lidar reference array.

[0103] S6: Based on the observation results, a virtual dynamic field is generated. Preferably, three non-collinear real coordinate control points are selected and deployed in the proposed field area that can be covered by the radar. The three-dimensional positions of the control points are directly assigned to the virtual coordinates of the virtual dynamic field. The virtual coordinates are established as a reference. Based on the relative positional relationship of the virtual dynamic field, the real three-dimensional positions of all the observation points in the proposed field area are obtained, and a real dynamic field with geographic coordinates is generated.

[0104] After the virtual dynamic field is generated in step S6, the virtual dynamic field does not need to rely on external geodetic coordinates, global navigation satellite system signals and external targets, and can directly perform dynamic high-precision operations.

[0105] In step S1, during the operational status monitoring, at any grid point in the predetermined area and with predetermined accuracy, four radars are simultaneously set up within the effective ranging range and arranged in a non-coplanar spatial manner, which can form a stable and reliable statically indeterminate redundant observation constraint condition.

[0106] If only three non-collinear radars are deployed at some or all of the measured points in the field within the effective ranging distance, the observation constraints need to be supplemented by equivalent redundancy supplementation methods such as radar attitude and rotation angle parameter calculation, spatial position association matching, and temporal continuous constraint binding.

[0107] If more than four non-coplanar radars are deployed at some or all of the measured points within the effective ranging distance, the higher the redundancy of the observation equation, the faster the convergence speed of the statically indeterminate fitting, and the higher the accuracy of the solution result. When individual radars experience settlement, tilting, or loosening faults during operation, the faulty radars can be downweighted and isolated. After the faulty radars have been corrected and their operating status has stabilized, they can be directly rejoined to the array and continue to participate in the synchronous acquisition of all measurement points in the entire area, so as to achieve uninterrupted operation.

[0108] This invention defines a proprietary progressive operation logic:

[0109] The preferred approach is to use a fixed process of "outlier removal - statistical filtering - statically indeterminate fitting solution" to complete multi-level error elimination and coordinate calculation.

[0110] Any implementation method that circumvents this technical solution by relying on the core technical principles of the error classification mechanism, multi-radar redundant observation, and solution of statically indeterminate equations of this invention, through error classification reorganization, fine-tuning the order of steps, replacing equivalent algorithms, or adding auxiliary software and hardware components, falls within the protection scope of this invention.

[0111] On-site sampling operations must simultaneously meet two conditions: sufficient sample quantity and statistical regularity of data distribution, and the sampling time for a single batch must not exceed the preset refresh interval;

[0112] This invention is compatible with various raw data processing methods, including data integration, outlier screening, multi-level filtering and noise reduction, and classification and summary output. At the same time, it does not limit the output form of ranging data, and all equivalent modes such as single-cycle single-value output, single-cycle multi-value output, and multi-cycle data fusion and summary output are included. As long as they are compatible with the redundant observation and statically indeterminate solution system of this invention, they are all within the scope of protection of this invention.

[0113] Dynamic domain fields with sub-millimeter and higher precision levels can be built by increasing the number of redundant observation radar devices on site, upgrading lidar hardware configuration, and optimizing base station computing unit configuration.

[0114] Any implementation method that adopts the core field-building logic of the error processing mechanism, redundant observation mode, and unified solution of the statically indeterminate equation system of this invention to achieve higher precision field construction falls within the protection scope of this invention.

[0115] Example 2

[0116] The second embodiment of the present invention differs from the first embodiment in that it further includes:

[0117] Step 1 Equipment Deployment: The base station, which uses a base station and a lidar reference array to quickly construct a millimeter-level virtual dynamic field and a real dynamic field, is deployed in the center of the area where the field to be constructed is to be located, ensuring unobstructed signal transmission.

[0118] The lidar reference array, which rapidly constructs millimeter-level virtual and real dynamic domain fields using base stations and lidar reference arrays, is deployed within, around, or in combination with the domain field to be constructed. The lidar deployment method is unrestricted, including but not limited to fixed installation, integration onto mechanical equipment, and deployment on UAVs. It only needs to meet the following requirements: within a single refresh cycle, the disturbances experienced by the radar can be categorized as lidar random errors or their impact can be eliminated in advance; after eliminating the disturbances, the resulting observation samples still conform to the random error distribution; and within a single refresh cycle, sufficient ranging samples can be collected from the same measured point to meet the requirements of statistical filtering analysis. No special compensation strategy is needed for such disturbances; they can all be uniformly suppressed and eliminated through statistical filtering. The system provides coverage for any point by having ≥4 non-coplanar radars within the effective ranging range. Four or more non-coplanar radars can naturally form a statically indeterminate constraint. Three non-collinear radars cannot form a statically indeterminate equation system on their own. If stable redundant observation and millimeter-level dynamic accuracy are required, additional redundant operations such as radar rotation angle, radar angular velocity, rotation velocity, and attitude calibration are needed. Such implementation methods still fall within the scope of this protection.

[0119] The method of rapidly constructing millimeter-level virtual and real dynamic domain fields using base stations and lidar reference arrays requires no attitude calibration, no angle measurement, no external reference, and no vibration sensor for the measured point. The selected radar has no special hardware restrictions, only that the ranging samples collected by it for any measured point in the proposed domain field area within one refresh cycle have normal statistical dispersion and follow statistical laws, and that it can collect a sufficient number of ranging samples for the same measured point within a single refresh cycle to meet the requirements of statistical filtering analysis. At the same time, the number of samples must meet the core principle of sample quantity mentioned above.

[0120] Step 2 System Initialization: The system uses a base station and a lidar reference array to quickly construct a millimeter-level virtual dynamic field and a real dynamic field startup device, and establishes communication.

[0121] Using base stations and lidar reference arrays, millimeter-level virtual dynamic field and real dynamic field are quickly constructed to achieve global time synchronization across the entire domain, with all radar outputs aligned with timestamp data;

[0122] The parameters for rapidly constructing millimeter-level virtual dynamic field and real dynamic field using base stations and lidar reference arrays are set: the area to be constructed, the accuracy threshold, and the refresh rate.

[0123] A module for initializing statistical filtering and solving statically indeterminate equations is used to quickly construct millimeter-level virtual and real dynamic domain fields using base stations and lidar reference arrays, without the need to initialize error compensation parameters.

[0124] Step 3 Self-check and targetless fully automatic calibration: Self-check of the computing power unit for rapidly constructing millimeter-level virtual dynamic field and real dynamic field using base station and lidar reference array: observation residual, redundant coverage to ensure coverage of ≥4 non-coplanar radars within the effective ranging distance at any point, time synchronization status, whether the sample dispersion of any measured point in the proposed field area meets the requirements in one refresh cycle, and whether the number of samples meets the core principles.

[0125] The system employs base stations and lidar reference arrays to rapidly construct millimeter-level virtual dynamic field and real dynamic field for targetless, fully automated, and rapid calibration: it automatically completes the initial radar cooperative adaptation, establishes a unified reference within the field, and completes the entire process within 3 minutes.

[0126] The method of rapidly constructing millimeter-level virtual dynamic field and real dynamic field using base stations and lidar reference arrays requires no manual intervention, no external instruments, and no calibration board.

[0127] Step 4: Rapid Construction of Dynamic Domain Field: Data acquisition for rapidly constructing millimeter-level virtual and real dynamic domain fields using base stations and lidar reference arrays: Time-stamped ranging data is collected synchronously across the entire domain. Each radar scans and collects a large number of ranging samples for any measured point within the proposed domain field area within one refresh cycle, ensuring that the samples corresponding to each measured point have normal statistical dispersion and follow statistical laws. If the radar is a multi-line type, its multi-line scanning can cover multiple measured points. The ranging samples collected within one refresh cycle for each measured point must meet the above requirements, and the number of samples must meet the core principle.

[0128] The method employs base stations and lidar reference arrays to rapidly construct millimeter-level virtual dynamic domain fields and remove outliers from real dynamic domain fields: outliers that exceed the overall statistical regularity of the samples, i.e., abnormal data that deviate from the normal distribution range of the samples and have a large deviation from most samples, are automatically removed. No fixed deviation threshold is set. Based on the actual ranging conditions of the radar, as long as the data deviates from the overall statistical regularity of the samples collected by the radar for any measured point in the area to be constructed within one refresh cycle, it is judged as an outlier to avoid affecting subsequent processing.

[0129] Random error elimination is achieved by rapidly constructing millimeter-level virtual dynamic domain field and real dynamic domain field using base station and lidar reference array: statistical filtering mathematical methods, including but not limited to time series statistical filtering, mean filtering, Gaussian filtering, etc., are used to process a large number of ranging samples collected by the radar in one refresh cycle, and the random error residual is suppressed to a range that does not affect the millimeter-level positioning accuracy based on the statistical law of the samples.

[0130] A system of statically indeterminate equations for rapidly constructing millimeter-level virtual and real dynamic domain fields using base stations and lidar reference arrays is employed. This system relies on a large amount of redundant observation data to construct the statically indeterminate equations. Each radar collects sufficient samples from a single measurement point within one refresh cycle. After statistical filtering to eliminate random errors, a precise ranging value is output. Any measurement point is covered by at least four radars, yielding at least four independent precise ranging values. The coordinates of the measured point and the radar itself are obtained through equation fitting and solving, simultaneously eliminating all single-unit subsystem biases without requiring additional compensation for common-mode errors and subsystem biases. Single-unit subsystem biases include, but are not limited to: ① Inherent biases: radar component manufacturing tolerances, overall assembly and installation errors, and inherent drift of the components; ② Periodic common-mode biases: overall uniform biases caused by large-scale synchronous changes in temperature and air pressure across the entire domain during a single refresh cycle; ③ Local environmental biases: local airflow, dust, local temperature changes, and optical path refraction disturbances; ④ Global coupling biases: mutual interference between multiple radar operating conditions and the superposition of various errors forming composite disturbance errors.

[0131] The virtual dynamic field generation method uses base stations and lidar reference arrays to quickly construct millimeter-level virtual dynamic field and real dynamic field: it constructs a high-frequency real-time refreshed millimeter-level virtual dynamic field that is independent, self-consistent, has no external reference dependency, is dynamically stable, and can be used directly in operation scenarios that do not require a real dynamic field.

[0132] The optional steps for rapidly constructing millimeter-level virtual dynamic field and real dynamic field using base stations and lidar reference arrays are as follows: Real dynamic field conversion: If a real dynamic field with real geographic coordinates is required, it is preferable to select three non-collinear known real three-dimensional coordinate scattered points (control points), place them in the area to be constructed field and ensure they are effectively covered by the radar. After collecting the virtual coordinates of the scattered points, the real three-dimensional positions of the scattered points are directly assigned to their corresponding virtual coordinates to establish a coordinate correspondence benchmark. The real three-dimensional positions of all other measuring points in the area to be constructed field can be automatically derived, thus completing the generation of the real dynamic field. This step does not change the core structure and accuracy of the original virtual dynamic field.

[0133] Step 5: Dynamic Maintenance and Redundancy Self-Healing: Accuracy maintenance of the millimeter-level virtual dynamic field and real dynamic field is rapidly constructed using base station and lidar reference array. Statistical filtering and fitting of statically indeterminate equations are continuously performed. Based on the core principle of sample quantity, sampling is continuously performed to continuously suppress random error residuals to a range that does not affect millimeter-level positioning accuracy. Common-mode errors, including but not limited to temperature drift, zero bias, installation residuals, atmospheric refraction and radar's own common-mode correlation errors, are continuously offset and subsystem inherent biases are eliminated to maintain stable field accuracy.

[0134] The system rapidly constructs millimeter-level virtual dynamic field and real-time refreshes the actual dynamic field using base stations and lidar reference arrays: high-frequency dynamic updates across the entire domain to match the movement of the operating equipment;

[0135] Fault self-healing is achieved by rapidly constructing millimeter-level virtual dynamic field and real dynamic field using base station and lidar reference array: the field is automatically reconstructed when a single radar malfunctions, and the operation is not interrupted;

[0136] The timing calibration of millimeter-level virtual dynamic field and real dynamic field is rapidly constructed using base station and lidar reference array: it automatically maintains global time synchronization without drift or disorder; at the same time, it continuously monitors whether the radar sample dispersion and sample quantity meet the core principles, ensuring that the data conforms to statistical laws and can suppress random error residuals to the specified range.

[0137] Example 3

[0138] The third embodiment of the present invention provides a method for rapidly constructing millimeter-level virtual dynamic field and real dynamic field using a base station and a lidar reference array, comprising:

[0139] This illustrative embodiment is used to fully explain the error hierarchical processing mechanism, redundant observation network mode, sufficient sampling constraint conditions, and targetless autonomous dynamic field construction principle proposed in this invention, using the automated welding operation scenario of a large steel structure bridge as the simulation and deduction object.

[0140] The design accuracy target of this embodiment is: the relative deviation of the measuring points inside the entire dynamic field is ≤2.0mm, and the absolute positioning accuracy of a single point in the real dynamic field after geodetic coordinate mapping is ≤2.0mm. This accuracy matches the performance limit of 1mm×1mm high-density grid sampling points and meets the millimeter-level control requirements of precision construction operations such as steel structure welding and large component assembly.

[0141] It should be clearly stated that the configuration parameters used in this embodiment, such as 1mm×1mm full-area high-density grid sampling and 0.5s high-frequency full-area dynamic refresh, are forward-looking simulation parameters based on the industry's hardware iteration plan, and are not physical parameters that can be directly implemented at this stage.

[0142] Currently, mass-produced area-array LiDAR is limited by SPAD pixel density and optical crosstalk, while edge industrial control GPUs are limited by the computing power required to solve large-scale statically indeterminate equations, thus hindering the engineering implementation of this solution. However, based on the iterative plans of leading industry manufacturers, ultra-high pixel area-array Flash radar and high-end industrial edge computing modules are expected to achieve mass production within two years. At that time, this solution can be directly transformed into an engineering solution. This embodiment is only used to explain the optimal working principle and performance ceiling of the invention and does not constitute an arbitrary idealization of parameters.

[0143] The simulation operation field was set to be 30m long, 15m wide, and 8m high. It simulated and replicated common construction scene disturbances such as ambient temperature and humidity micro-disturbances, equipment mechanical micro-vibrations, and partial obstruction by obstacles in a single refresh cycle, thus simulating complex indoor and outdoor integrated construction conditions.

[0144] The simulation system is configured with a virtual base station to coordinate all computing tasks, including global device clock synchronization, multi-radar ranging sample collection, outlier removal, composite filtering and noise reduction, iterative solution of statically indeterminate equations, 3D domain modeling, and periodic refresh. Six forward-looking, ultra-high pixel array Flash LiDARs are distributed across the work area, with the following deployment rules:

[0145] Any grid measurement point within the area is synchronously covered by at least 4 spatial non-coplanar radars; all radars synchronize time via a hardware clock synchronization protocol, with a global time synchronization accuracy of ≤0.1ms.

[0146] In this embodiment, the radar adopts a fully fixed, static installation method, without mechanical rotation or dynamic elevation adjustment. The accuracy and ranging stability of the full-range 1mm×1mm high-density grid sampling are entirely guaranteed by the radar's native optoelectronic hardware performance. The specific core hardware parameters of the radar used in this embodiment are as follows:

[0147] 1) Effective Ranging Performance: In line with industry hardware iteration trends, the radar is pre-configured to have a steady-state effective ranging range of 0m to 50m. This parameter fully covers all high and low position measuring points in the 30m×15m×8m work area of ​​this embodiment, and is compatible with the actual working ranging range of 28.6m to 45.7m. Sufficient engineering redundancy is reserved, with no ranging blind spots, no long-range accuracy attenuation, and no signal-to-noise ratio failure issues at the field of view edges. Compared to mainstream 30m-class commercial area array radars on the market, the ranging coverage is significantly improved, the parameters closely match real engineering conditions, and it balances practicality with industry foresight.

[0148] 2) Single-shot ranging accuracy performance: The default single-shot ranging random error of the next-generation radar hardware is ≤20mm. This hardware specification is adapted to the error layering processing mechanism of this solution. Combined with 1000Hz ultra-high frequency large-sample sampling and multi-level composite time-series filtering algorithm, the original 20.0mm hardware random error can be stably suppressed to within 1.0mm. Sufficient accuracy redundancy is reserved to strictly ensure the final 2.0mm absolute positioning accuracy across the entire domain. This fully highlights the core technical advantages of the integrated architecture of large-sample filtering, redundant networking, and ultra-static iterative solution of this invention. Unlike conventional single-radar detection and simple parallel hardware solutions of multiple radars on the market, this solution is not a simple stacking of devices. Relying on a systematic error control algorithm, it effectively solves the defects of traditional solutions such as inconsistent timing, easy accumulation of errors, and insufficient stability. It can accurately suppress the original 20mm hardware error of the radar to the millimeter level. The overall positioning accuracy, anti-interference ability, and operational stability are far superior to traditional single-radar and simple parallel radar detection systems.

[0149] 3) Extreme Angular Resolution Performance: Considering the hardware characteristics of fixed radar without dynamic scanning compensation, and to meet the high-precision field construction requirement of 1mm×1mm grid fidelity at the farthest working distance in this embodiment, the radar's extreme angular resolution is preset to ≤0.012°. This ultra-high angular resolution can completely avoid the problems of aliasing at long-distance measurement points and spatial resolution degradation, ensuring that it still maintains millimeter-level fine resolution under extreme ranging conditions. It is the core hardware prerequisite for realizing small-scene, high-precision, and high-density three-dimensional field construction.

[0150] 4) Array Pixel Scale: Matching the aforementioned high-precision resolution specifications and mature industrial large field-of-view specifications (horizontal FOV=120°, vertical FOV=90°), and considering future mass-producible hardware capabilities, the preset total detection pixel scale of the entire array is 15 million pixels, corresponding to ≥3870 horizontal pixels and ≥3876 vertical pixels. This 10-million-pixel configuration overcomes the shortcomings of insufficient pixel density and poor fine sampling capabilities in existing commercial radars, while also avoiding the idealistic defects of ultra-high pixel architectures that cannot be mass-produced. It represents the mainstream high-end configuration for hardware iteration in the next two years. It ensures that, under fixed and static working conditions, all 1mm×1mm grid points across the entire area are independently and accurately sampled, with no missed samples, no optical crosstalk, and no resolution degradation, accurately capturing millimeter-level assembly deviations and minute deformations in steel structure construction.

[0151] This invention divides lidar ranging errors into two independent categories: pure random errors and single-machine subsystem biases.

[0152] Within a 0.5s single-cycle steady-state time window, the deviation of all radar subsystems remains constant, with only pure random errors exhibiting instantaneous and irregular fluctuations; this characteristic is also the core foundation of the error hierarchical processing architecture of this invention.

[0153] The deviations of a single subsystem can be categorized into four types: inherent radar installation deviations, periodic common-mode environment deviations, local area environment deviations, and multi-device global coupling deviations.

[0154] Pure random errors mainly originate from sensor electrical noise, atmospheric micro-disturbances, and micro-vibrations of construction machinery. These errors have no fixed variation pattern and cannot be eliminated by pre-calibration or hardware compensation. They can only be suppressed by high-frequency large-sample statistical filtering.

[0155] The deviation of a single subsystem is the steady-state deviation within a single refresh cycle. There is no need to add external modules such as temperature compensation, vibration compensation, and refraction correction. All of them are uniformly included as unknowns in the statically indeterminate observation equation set. The overall offset is completed through iterative solution, and the final global comprehensive steady-state deviation can be stably controlled within 2.0 mm.

[0156] This embodiment sets a global refresh cycle of 0.5s (2Hz). On the one hand, it can shorten the steady-state time window, isolate the problem of large fluctuations in environmental parameters under long cycles, and ensure that the deviation of the single-machine subsystem remains constant. On the other hand, it can update the coordinates of the global measuring points at high frequency, adapt to the small dynamic changes in the construction scenario, and take into account both positioning accuracy and dynamic adaptability.

[0157] To match the hardware performance of next-generation area array radar, a single-point sampling frequency of 1000Hz was set. Within a 0.5s refresh cycle, a single radar continuously collected 500 sets of raw ranging samples from the same measurement point. The large sample data satisfies the normal distribution characteristics, and pure random errors can be suppressed to the greatest extent through a composite filtering algorithm, providing noise-free, high-precision ranging data for statically indeterminate solutions.

[0158] Formula for calculating the sample mean: .

[0159] Formula for calculating sample standard deviation: .

[0160] After large-sample convergence, the residual random error of the filter satisfies: .

[0161] The effectiveness of full-area coverage was verified by combining the radar's 50m effective ranging range: In this embodiment, the farthest measured slant range in the simulated full field was only 45.7m. The spatial slant range of all measuring points in the entire field was less than the 50m effective ranging threshold. All measuring points in the entire field were within the effective ranging range of all radars, with no issues of overrange failure, signal attenuation, or loss of effective data. The maximum diagonal slant range in the site space was only 34.7m, providing sufficient ranging safety redundancy. In terms of network coverage, all measuring points at the site edge and the four corner reference areas stably met the effective coverage of 4 or more non-coplanar radars, strictly meeting the minimum observation constraints of this invention. The core construction area and key welding and assembly areas of the site could achieve effective coverage by overlapping 5 to 6 radars, forming a strong statically indeterminate observation constraint, significantly improving the solution robustness and anti-interference capability. After full-area traversal verification, the 6-radar network scheme can achieve 100% area coverage that meets the effective ranging coverage of ≥4 non-coplanar radars, fully adapting to the statically indeterminate solution architecture and millimeter-level field construction requirements of this invention.

[0162] Step S1: Establish a full-area simulation operation environment by placing the virtual base station in an unobstructed location in the center of the area; deploy 6 lidar units at the four corners and the center of the operation area to complete the full-area network configuration. The base station centrally manages the data acquisition, bidirectional transmission, and operational status monitoring of all lidar units.

[0163] Step S2: Establish a communication link between the base station and the radar cluster to complete global clock synchronization; initialize the LM nonlinear iterative solver and set the iteration convergence threshold to 1.0 mm. This threshold is lower than the global accuracy limit of 2.0 mm, leaving sufficient accuracy redundancy.

[0164] Step S3: Within a single refresh cycle, data is simultaneously collected by six radars. The core welding measurement point P is selected as the analysis object. Measurement point P is covered by radars 1-4, with original ranges of 8.5m, 9.2m, 7.8m, and 10.1m, respectively. 500 sets of original samples are collected by each radar. The 3σ criterion is used to remove outliers, avoiding abnormal data caused by instantaneous obstruction and sudden disturbances. After removing outliers, the number of valid samples per measurement point is ≥480 sets.

[0165] Step S4: A composite noise reduction algorithm combining time-series filtering and mean filtering is used to process the effective sample set in one go, completely eliminating all purely random errors. After filtering, the residual random error of the measurement point is controlled within 1.0 mm.

[0166] Step S5: Based on the filtered high-precision ranging values, a set of statically indeterminate observation equations is constructed. All systematic deviations in this invention are uniformly aggregated into radar single-unit subsystem deviations, without adding additional unknown error quantities, fully matching the protection architecture of the claims. The observation equations are as follows:

[0167]

[0168] In the formula: The three-dimensional coordinates of the measurement point to be measured; The three-dimensional installation coordinates of the i-th radar; This corresponds to the deviation of a single subsystem of the radar; This is the accurate distance measurement value after filtering.

[0169] This scheme adopts a partitioned gridded solution strategy, dividing the 30m×15m×8m working area into independent sub-blocks of 0.5m×0.5m each. Within each sub-block, 1mm×1mm grid measuring points are arranged. A single sub-block contains a total of 250,000 independent grid measuring points. The system uses the constraint that each measuring point must be covered by at least four non-coplanar radars as the solution constraint. A single radar can construct one set of ranging observation equations for a single measuring point, resulting in four sets of independent observation equations per measuring point. The unknowns to be solved include two types of complete parameters: first, the three-dimensional coordinate unknowns of the measuring points, with each grid measuring point containing three coordinate unknowns (x, y, z), totaling 750,000 coordinate unknowns across the 250,000 measuring points in the sub-block; second, radar unknowns, with three installation coordinate unknowns and one single-unit subsystem deviation unknown corresponding to each radar, totaling four unknowns. In this embodiment, six radars are deployed, resulting in a total of 24 radar-related unknowns. In summary, under the single 0.5m×0.5m sub-block solution system, the total number of unknowns is 750,024, corresponding to 1 million sets of observation equations within the sub-block. The number of equations significantly exceeds the number of unknowns, forming extremely strong statically indeterminate redundancy constraints. Iterative solving using the LM algorithm can accurately offset all radar system biases. Combined with spatial and temporal constraints, it effectively avoids coordinate jumps and data mutations, significantly improving the stability and robustness of the overall solution.

[0170] After iterative solving, the optimal three-dimensional coordinates of measuring point P are (2.5015m, 3.2012m, 1.5008m). The base station aggregates and stitches together the solution data of all sub-blocks to generate a complete global three-dimensional virtual dynamic coordinate field.

[0171] Step S6: To convert the virtual field to geodetic coordinates, select three unobstructed, high-recognition control points to complete coordinate binding. Pre-assign real geographic coordinates: A (1.0000m, 1.0000m, 0.5000m), B (10.0000m, 1.0000m, 0.5000m), C (5.5000m, 8.0000m, 0.5000m).

[0172] Virtual coordinates of control points are collected and bound one-to-one with their real coordinates to establish a unified mapping benchmark for the entire region. Based on the relative positional relationships within the domain field, the real geographic coordinates of all grid measurement points are calculated, ultimately generating a real dynamic domain field with a geodetic benchmark. The base station continuously refreshes and outputs the domain field data at a frequency of 2Hz, and simultaneously completes local data storage.

[0173] To fully and rigorously explain the implementation principle of this invention, the data tables in this chapter are divided into two layers: the first layer is the preliminary basic appendix, which is the underlying constraint and operating foundation of this simulation case. All experimental data and main table conclusions are generated based on this condition; the second layer consists of six core main tables, which comprehensively verify the technical advantages of this invention from six dimensions: sampling filtering, network constraints, error suppression, refresh characteristics, horizontal comparison, and long-term stability.

[0174] Table 1 System Core Operating Parameter Configuration Table

[0175] Parameter Items Configuration values Operating area size 30m×15m×8m Radar hardware type Ultra-high pixel SPAD array Flash LiDAR (forward-looking) Total number of radars deployed 6 units Single-point sampling frequency 1000Hz Global refresh cycle 0.5s Dynamic refresh rate 2Hz Number of original samples per cycle 500 sets Lower limit of effective samples at a single measurement point ≥480 groups Grid layout specifications 1mm×1mm Iterative convergence threshold 1.0mm Upper limit of global field accuracy ≤2.0mm Time synchronization accuracy ≤0.1ms

[0176] Table 2 Comparison of Error Hierarchy Processing Mechanisms

[0177] Error categories Sources of error Handling method Control objectives Pure random error Instantaneous irregular errors such as equipment and instrument noise, atmospheric disturbances, and on-site mechanical micro-vibrations. Large-sample statistical composite filtering to suppress fluctuation noise in batches. Residual error < 1.0 mm Subsystem deviation Radar installation deviation, ambient temperature drift, atmospheric refraction, global coupling steady-state deviation Incorporating the statically indeterminate equations, and unifying the cancellation through LM iteration. Overall deviation ≤2.0mm

[0178] Table 3. Comparison of Control Point Coordinate Mapping Accuracy

[0179] Control point numbering Actual geographic coordinates (m) Virtual field coordinates (m) Coordinate deviation (mm) A (1.0000,1.0000,0.5000) (1.0003,0.9998,0.5001) ≤2.0 B (10.0000,1.0000,0.5000) (9.9995,1.0002,0.5000) ≤2.0 C (5.5000,8.0000,0.5000) (5.5002,7.9997,0.5002) ≤2.0

[0180] Table 4 Comparison of LiDAR ranging sample acquisition and filtering data within a single cycle.

[0181] Radar number Effective ranging distance (m) Original sample (group) Valid samples (groups) Filtered distance measurement (m) Filtering residual error (mm) Radar No. 1 8.5 500 486 8.5012 ＜1.0 Radar No. 2 9.2 500 482 9.2015 ＜1.0 Radar No. 3 7.8 500 490 7.7998 ＜1.0 Radar No. 4 10.1 500 485 10.1005 ＜1.0 Radar No. 5 12.5 500 488 12.5021 ＜1.0 Radar No. 6 11.3 500 484 11.3017 ＜1.0

[0182] Table 5 Comparison of statically indeterminate constraint conditions under different radar deployment numbers

[0183] Deployment method Number of radar units Number of radars covering the measured points Whether it forms statically indeterminate Number of observation equations Number of unknowns System redundancy Traditional triangular intersection 3 3 no 3n 3n+12 Negative value (underdetermined) Four units deployed in the same direction 4 4 no 4n 3n+16 Insufficient, coplanar rank deficiency invalid Four non-coplanar units (lower limit of this scheme) 4 4 yes 4n 3n+16 n-16 Six units deployed in a mixed configuration (in this embodiment) 6 5 yes 5n 3n+24 2n-24

[0184] Table 6 Comparison of various error suppression effects

[0185] Error Components Traditional processing methods Processing method of the present invention Final residual error Random noise of equipment and instruments Single hardware filtering and noise reduction Large-sample temporal composite filtering <1.0mm Radar installation fixed residual Manual repeated calibration Incorporating iterative offsetting of deviations in single-machine subsystems ≤1.7mm Ambient temperature drift error External sensor special compensation Incorporating iterative offsetting of deviations in single-machine subsystems ≤1.7mm Atmospheric refraction disturbance deviation Passive correction of empirical formulas Incorporating iterative offsetting of deviations in single-machine subsystems ≤1.7mm Micro-vibration at the construction site Add mechanical vibration damping structure Suppressing by pure random error filtering <1.0mm

[0186] Table 7 Relationship between Dynamic Domain Field Refresh Frequency and Positioning Accuracy

[0187] Refresh cycle (ms) refresh rate per second Number of valid sampling points per cycle Positioning accuracy after filtering (mm) Single-frame computation time (ms) 1000 1 200 1.86 165 500 2 500 1.42 192 200 5 800 1.15 246 100 10 1000 0.95 315

[0188] Table 8 Comparison of construction efficiency between the present invention and traditional methods

[0189] Comparison Projects Traditional static calibration / intersection techniques The dynamic domain field construction scheme of this invention Technical Differences Explanation Domain field construction capability It only supports static calibration of discrete measurement points and cannot construct a full-domain three-dimensional dynamic field. It can autonomously generate a complete global three-dimensional dynamic coordinate field. There is a generational gap in technology. Coordinate update format One-time static calibration, cannot be dynamically updated. 2Hz high-frequency global synchronous refresh Adapt to dynamic construction conditions Peripheral Dependency It is necessary to rely on a total station, a dedicated target, and reference control points. No target, no peripherals, no GNSS geodetic reference required Lowering deployment barriers and costs Environmental interference resistance Unable to withstand temperature drift and micro-vibrations, calibration becomes invalid after disturbance. Error stratification processing to adapt to complex construction interference The project is more feasible to implement. Fault tolerance mechanism A single device failure results in overall failure, requiring complete recalibration of the entire site. Redundant networking, automatic self-healing and replacement of equipment failures. Possesses engineering fault tolerance capability

[0190] Table 9. Positioning accuracy stability test over 100 consecutive refresh cycles.

[0191] Test time period (s) Average positioning error (mm) Maximum positioning error (mm) Standard deviation of error (mm) Valid refresh count 0-5 1.31 1.76 0.09 100 5-10 1.28 1.72 0.08 100 10-15 1.30 1.81 0.10 100 15-20 1.27 1.70 0.08 100 20-25 1.29 1.78 0.09 100

[0192] This embodiment is a forward-looking simulation principle case for next-generation hardware, and its overall technical logic is closed-loop and self-consistent:

[0193] Based on the system parameters, error stratification rules, and coordinate mapping benchmarks specified in the three pre-appendice tables (Tables 1, 2, and 3), the system distinguishes between two types of errors using a 0.5s steady-state time window. Pure random errors are suppressed through large-sample composite filtering. A strongly statically indeterminate set of equations is then constructed based on a multi-radar non-coplanar redundant network to iteratively and uniformly offset all single-machine subsystem deviations. Finally, a millimeter-level three-dimensional dynamic domain field with a geodetic benchmark is generated by combining the control point binding algorithm.

[0194] The absolute positioning error of the full-area measuring points is stably controlled within 2.0mm, meeting the tolerance control standards for precision construction such as welding of large steel structures and assembly of components.

[0195] By analyzing the entire set of data tables layer by layer: the preliminary appendix clearly defines the simulation boundaries and operating rules, and all the experimental data in the main table do not have any idealized invalid settings, which fully match the hardware performance limits of future array radar and edge computing power.

[0196] Tables 4 and 6 verify the effectiveness of the dual-layer error control mechanism. This invention can suppress random errors to within 1.0 mm and constrain the overall systematic deviation of a single machine to within 1.7 mm. Table 5 proves that only non-coplanar redundant networking can form effective statically indeterminate constraints, solving the underdetermined solution problem from the root. Table 7 shows that the refresh rate test in this embodiment can achieve the optimal engineering balance between positioning accuracy and computational power overhead. Table 9 provides evidence that the system has no accuracy drift, no frame loss failure, and excellent reliability during long-term continuous operation.

[0197] Regarding the horizontal comparison in Table 8, it is important to emphasize that traditional triangulation, static calibration, and other related technologies are essentially only capable of performing one-time static calibration of discrete spatial measurement points. Their underlying principles determine that they cannot establish, let alone update, the global three-dimensional dynamic coordinate field.

[0198] These two technologies do not belong to the same technical approach, therefore the notion of "higher efficiency" versus "lower efficiency" in traditional solutions does not apply. This invention fills the technological gap in the autonomous construction of dynamic millimeter-level domain fields. Currently, it cannot be implemented due to limitations in mass-production hardware performance. However, once ultra-high pixel area array radar and high-end industrial edge computing modules achieve large-scale mass production, this embodiment can be directly replicated for engineering transformation, adapting to the vast majority of complex and precise indoor and outdoor construction scenarios.

[0199] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for rapidly constructing millimeter-level virtual dynamic field and real dynamic field using a base station and a lidar reference array, characterized in that, Within a single refresh cycle, the ranging error of the lidar is divided into pure random error and single-machine subsystem deviation. For pure random error, the same radar repeatedly samples the same measurement point in a single refresh cycle and performs statistical filtering to suppress the error to the millimeter accuracy range. Deviations in each individual subsystem are eliminated by solving a set of statically indeterminate equations constructed using redundant observations from multiple radars. Based on this, millimeter-level virtual dynamic field and real dynamic field are rapidly constructed, including the following steps: S1: Deploy base stations and lidar reference array equipment. The base stations are located in or around the proposed domain area. The lidar reference array is deployed within the proposed domain area, around the perimeter, or in a distributed manner combining both. At any grid point within a given area and with a given accuracy, at least four non-coplanar lidars can simultaneously form effective observation coverage within the effective ranging range, thus constructing multi-dimensional redundant observation conditions. The base station coordinates the signal acquisition, data transmission, and operational status monitoring of all lidars within a single refresh cycle. S2: Establish communication between the base station and the laser array, so that all radar observation data have a unified aligned timestamp, and initialize the statistical filtering module and the statically indeterminate equation solving module; S3: The ranging samples of the laser reference array with a unified timestamp are used as a reference to repeatedly collect a sufficient number of ranging samples for the same measured point by the same radar within a refresh cycle, based on the statistical regularity of the ranging samples, and abnormal data is eliminated. S4: The ranging samples are processed using a statistical filtering method to suppress random errors and control the residual value to meet the millimeter-level accuracy requirements. Each radar outputs a unique ranging value for the same measured point. S5: Based on the ranging values, construct a set of statically indeterminate equations, eliminate all deviations of single-machine subsystems, fit and solve for the coordinates of the measured point and radar coordinates, and generate the observation results; S6: Based on the observation results, a virtual dynamic field is generated. Three non-collinear real coordinate control points are preferably selected. The three-dimensional positions of the control points are directly assigned to the virtual coordinates of the corresponding virtual dynamic field. The virtual coordinates are established as a reference. Based on the relative positional relationship of the virtual dynamic field, the real three-dimensional positions of all the observation points in the proposed field area are obtained, and a real dynamic field with geographic coordinates is generated.

2. The method for rapidly constructing millimeter-level virtual dynamic field and real dynamic field using a base station and a lidar reference array according to claim 1, characterized in that, The deviation of the single-machine subsystem includes four types of deviations within a single refresh cycle of a single lidar: inherent deviation, periodic common mode deviation, local environment deviation, and global coupling deviation. Within a single refresh cycle, the equipment operating conditions, global atmospheric environment and various external interference factors change gradually, and the overall deviation of each lidar single-machine subsystem is approximately constant. The base station described in step S1 is configured with a global computing power unit. The computing power unit is used to perform domain field modeling, multi-radar data fusion, statistical filtering, solving of statically indeterminate equations, and global real-time high-frequency refresh tasks, and has residual self-checking function. The refresh cycle refers to the complete cycle of a millimeter-level dynamic domain field completing one full-domain data acquisition, error processing, statically indeterminate fitting solution and domain field parameter update, which is also the time interval for the radar to complete a single round of full-quantity ranging sample acquisition. The sufficient range measurement sample refers to all range measurement samples required to achieve a complete cycle and output the unique range measurement value. The operational status monitoring includes collecting sufficient ranging samples from the same measured point within a single refresh cycle to meet the requirements of statistical filtering analysis, and ensuring that any measured point within the proposed domain area can be covered by no fewer than four non-coplanar radars within an effective ranging distance.

3. The method for rapidly constructing millimeter-level virtual dynamic field and real dynamic field using a base station and a lidar reference array according to claim 1, characterized in that, The lidar reference array mentioned in step S1 can be deployed in various forms, including but not limited to fixed installation, integrated mechanical equipment mounting, or aerial drone mounting. The following conditions must be met within the single refresh cycle: 1) The lidar array completes the ranging sample collection of all grid points within a given area and with a given accuracy. 2) All grid measurement points across the entire area are collected through a unified radar array collaborative data acquisition process; 3) Disturbances generated during the acquisition process are classified as random errors; or the influence of disturbances generated during the acquisition process can be eliminated in advance, and the observed samples after eliminating disturbances still conform to the random error distribution. 4) At any grid measurement point, within the effective ranging range, at least four non-coplanar radars can simultaneously form effective observation coverage, thus constructing multi-dimensional redundant observation conditions. 5) Within the effective ranging range, each LiDAR continuously and repeatedly collects sufficient ranging samples for the same grid point within the cycle to meet the conditions required for the statistical filtering analysis of ranging for the corresponding grid point by the LiDAR. 6) Within the scope of periodic operations, there is no need to add special compensation strategies for disturbances, and random errors are uniformly suppressed and eliminated by statistical filtering.

4. The method for rapidly constructing millimeter-level virtual dynamic field and real dynamic field using a base station and a lidar reference array according to claim 2, characterized in that, The global computing power unit includes, but is not limited to, industrial-grade GPUs, on-site computing power modules, edge computing units, cloud-based backend computing power units, and hardware-software collaborative computing power combination structures. It only needs to meet the requirements of domain field modeling, multi-radar data fusion, statistical filtering operations, solving of statically indeterminate equations, and global real-time high-frequency refresh. The base station relies on the built-in on-site global computing power unit to build a unified global time synchronization system. The time synchronization system is used to complete the clock alignment of all radar devices, so that the observation data collected by the radar in the whole domain has a unified timestamp.

5. The method for rapidly constructing millimeter-level virtual dynamic field and real dynamic field using a base station and a lidar reference array according to claim 1, characterized in that, Before synchronously collecting ranging samples with a unified timestamp in step S3, it is necessary to ensure that the number of ranging samples collected by each radar for the same measured point within the effective ranging distance in a single refresh cycle meets the principle of sufficient sampling. The principle of sufficient sampling includes: The number of measured point samples is sufficient to form a sample set that conforms to statistical laws, and the random error is suppressed to a level that does not affect millimeter-level accuracy through the statistical filtering, and the sampling process is completed within a time less than or equal to the planned refresh interval.

6. The method for rapidly constructing millimeter-level virtual dynamic field and real dynamic field using a base station and a lidar reference array according to claim 1, characterized in that, The core logic for constructing the statically indeterminate equation system in step S5 is as follows: Suppose that the laser radar reference array has a total of k laser radars, where k is a positive integer, and the total number of unknowns in a single refresh cycle is 3n + k × (3 + 1). Where 3n represents the unknown three-dimensional coordinates of n measured points, and each radar has 3 position parameters and 1 subsystem deviation parameter, totaling k×4 unknowns. The single-unit subsystem deviation of a single radar includes four categories of deviations: the first category is the inherent deviation of the subsystem, which is generated by equipment processing, assembly and installation, and the sensor's own properties; the second category is the overall common-mode deviation of the refresh cycle, which is caused by changes in the overall temperature, humidity and air pressure of the entire domain; the third category is the local environmental system deviation, which includes changes in refractive index, airflow, dust, local thermal disturbances and local operating condition interference; and the fourth category is the global coupling deviation, which includes all the complex disturbances derived from the interweaving of various errors. The number of observation equations in the statically indeterminate equation set is determined by the number of measured points n. Any measured point is covered by no less than 4 non-coplanar lidars within the effective range. A single measured point can form 4n independent observation equations. When n is large enough, the number of observation equations is much greater than the number of unknowns, that is, 4n > 3n + k × (3 + 1) constitutes a strong statically indeterminate constraint. The system of statically indeterminate equations is used to complete the fitting solution, simultaneously calculating the coordinates of the measured point and the radar's own deployment coordinates, while automatically offsetting the deviations of individual subsystems of each lidar in the lidar reference array.

7. The method for rapidly constructing millimeter-level virtual dynamic field and real dynamic field using a base station and a lidar reference array according to claim 1, characterized in that, In step S4, the statistical filtering method used includes, but is not limited to, various noise reduction methods such as time-series statistical filtering, mean filtering, and Gaussian filtering, which can be adaptively matched according to the on-site working environment. The suppression of random errors includes: The ranging samples within a single refresh cycle are subjected to integrated noise reduction processing through a multi-dimensional statistical filtering algorithm, which suppresses random errors including but not limited to equipment and instrument noise, atmospheric disturbances and on-site mechanical micro-vibrations. The residual value of the random error is controlled within a range that does not affect the millimeter-level positioning accuracy. Furthermore, each radar outputs only one unique ranging value for the same measured point, which serves as the basic data for constructing the statically indeterminate equation set.

8. A method for rapidly constructing millimeter-level virtual dynamic field and real dynamic field using a base station and a lidar reference array according to claim 1 or 2, characterized in that, After the virtual dynamic field is generated in step S6, the virtual dynamic field does not need to rely on external geodetic coordinates, global navigation satellite system signals and external targets, and can directly perform dynamic high-precision operations. In step S1, during the operational status monitoring, at any grid point in the predetermined area and with predetermined accuracy, four radars are simultaneously set up within the effective ranging range and arranged in a non-coplanar spatial manner, which can form a stable and reliable statically indeterminate redundant observation constraint condition. If only three non-collinear radars are deployed at some or all of the measured points in the field within the effective ranging distance, the observation constraints need to be supplemented by equivalent redundancy supplementation methods such as radar attitude angle parameter calculation, spatial position association matching, and temporal continuous constraint binding. If more than four non-coplanar radars are deployed at some or all of the measured points within the effective ranging distance, the higher the redundancy of the observation equation, the faster the convergence speed of the statically indeterminate fitting, and the higher the accuracy of the solution result. When individual radars experience settlement, tilting, or loosening faults during operation, the faulty radars can be downweighted and isolated. After the faulty radars have been corrected and their operating status has stabilized, they can be directly rejoined to the array and continue to participate in the synchronous acquisition of all measurement points in the entire area, so as to achieve uninterrupted operation.

9. A method for rapidly constructing millimeter-level virtual dynamic field and real dynamic field using a base station and a lidar reference array according to claim 7, characterized in that, In step S4, the statistical filtering method is used to process the ranging sample without the need for radar attitude calibration, rotation angle and angular velocity measurement, manual external parameter calibration, vibration-specific compensation, various common mode error sub-item compensation, and geodetic benchmark and artificial target required by traditional positioning.

10. The method for rapidly constructing millimeter-level virtual dynamic field and real dynamic field using a base station and a lidar reference array according to claim 1, characterized in that, The time continuity constraint mentioned in step S5 means that there are no sudden changes in radar position, measured point position and system error within adjacent refresh cycles; The spatial continuity constraint ensures that the spatial distribution of the measured points has no geometric jumps, in order to avoid ambiguous solutions in the fitting solution.

11. The method for rapidly constructing millimeter-level virtual dynamic field and real dynamic field using a base station and a lidar reference array according to claim 1, characterized in that, The base station and the lidar can communicate with each other using wired transmission, wireless transmission, or a hybrid wired-wireless transmission method, depending on the on-site working environment. After generating the virtual dynamic field and the real dynamic field, the base station outputs the full-domain dynamic three-dimensional coordinate data and field model data to the outside world in real time according to the set refresh frequency. The base station persistently stores the raw data of the time domain field and the calculated three-dimensional domain field data locally, allowing staff to retrieve the corresponding historical domain field data according to the time node, realize historical working condition backtracking and time domain data comparison and analysis, and distribute the domain field data to the outside world through wired or wireless communication.

12. The method for rapidly constructing millimeter-level virtual dynamic field and real dynamic field using a base station and a lidar reference array according to claim 1, characterized in that, This invention defines a proprietary progressive operation logic: The preferred approach is to use a fixed process of "outlier removal - statistical filtering - statically indeterminate fitting solution" to complete multi-level error elimination and coordinate calculation. Any implementation method that circumvents this technical solution by relying on the core technical principles of the error classification mechanism, multi-radar redundant observation, and solution of statically indeterminate equations of this invention, through error classification reorganization, fine-tuning the order of steps, replacing equivalent algorithms, or adding auxiliary software and hardware components, falls within the protection scope of this invention.

13. The method for rapidly constructing millimeter-level virtual dynamic field and real dynamic field using a base station and a lidar reference array according to claim 1, characterized in that, On-site sampling operations must simultaneously meet two conditions: sufficient sample quantity and statistical regularity of data distribution, and the duration of a single sampling session must not exceed the preset refresh interval; This invention is compatible with various raw data processing methods, including data integration, outlier screening, multi-level filtering and noise reduction, and classification and summary output. At the same time, it does not limit the output form of ranging data. Single-cycle single-value output, single-cycle multi-value output, and multi-cycle data fusion and summary output are all equivalent modes. As long as they are compatible with the redundant observation and statically indeterminate solution system of this invention, they are all within the scope of protection of this invention.

14. The method for rapidly constructing millimeter-level virtual dynamic field and real dynamic field using a base station and a lidar reference array according to claim 1, characterized in that, Dynamic domain fields with sub-millimeter and higher precision levels can be built by increasing the number of redundant observation radar devices on site, upgrading lidar hardware configuration, and optimizing base station computing unit configuration. Any implementation method that adopts the core field-building logic of the error processing mechanism, redundant observation mode, and unified solution of the statically indeterminate equation system of this invention to achieve higher precision field construction falls within the protection scope of this invention.

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