Construction environment-oriented dynamic spectral feature basic database construction method
By collecting standardized spectral data at construction sites and performing quality grading and dynamic updates, the problem that existing spectral databases cannot reflect changes at construction sites in real time has been solved, achieving highly reliable construction and accuracy of the spectral database.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- SHENNONGJIA FOREST REGION POWER SUPPLY CO LTD HUBEI ELECTRIC POWER CO
- Filing Date
- 2025-12-08
- Publication Date
- 2026-05-12
AI Technical Summary
Existing spectral databases lack the ability to dynamically acquire and classify the spectra of ground objects disturbed at construction sites. They cannot reflect the continuous changes in the spectra of ground objects during construction in real time, resulting in discrepancies between the database and the actual situation on site, which affects the reliability of construction management and environmental monitoring.
By accessing the spectral acquisition rule base to obtain construction acquisition specification information, collecting native ground feature information and establishing an initial baseline spectral set, acquiring disturbed spectral data in real time during construction, performing quality inspection, generating spectral quality levels and associating them with construction event information, and dynamically updating the spectral database.
It enables continuous updating and highly reliable construction of the spectral database, ensuring that spectral features reflect real changes, possessing stable traceability, and improving the accuracy of construction management and environmental monitoring.
Smart Images
Figure CN122019495A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of data construction technology, and more specifically, to a method for constructing a dynamic spectral feature-based database for construction environments. Background Technology
[0002] In modern construction management and environmental monitoring, spectral technology is widely used for feature identification, environmental assessment, and construction impact analysis. Traditional spectral databases are usually built based on static acquisition or laboratory environmental data, lacking the ability to adapt dynamically to construction sites. During construction, factors such as mechanical disturbance, material handling, and changes in construction stages can cause real-time disturbances in the spectral characteristics of features, leading to discrepancies between existing spectral databases and actual site conditions.
[0003] The existing technology has the following shortcomings:
[0004] Currently, existing technologies mainly rely on static acquisition or spectral data in laboratory environments to build databases. They lack the ability to dynamically acquire, classify, and correlate the spectra of disturbed objects at construction sites with construction events. They cannot reflect the continuous changes in the spectra of objects during construction in real time and ensure the high reliability and traceability of the database. As a result, the spectral features in the database cannot accurately reflect the actual situation on site. In construction management and environmental monitoring applications, there are errors and insufficient reliability. Therefore, a method for constructing a dynamic spectral feature database for construction environments is proposed. Summary of the Invention
[0005] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a method for constructing a dynamic spectral feature database for construction environments. By employing standardized construction spectral acquisition, perturbation spectral quality classification, construction event correlation, and differentiated dynamic updates, the method achieves continuous updates and highly reliable construction of the spectral database, thereby solving the problems mentioned in the background art.
[0006] To achieve the above objectives, the present invention provides the following technical solution:
[0007] A method for constructing a dynamic spectral feature database for construction environments includes the following steps:
[0008] Step S1: Access the spectral acquisition rule base to retrieve construction acquisition specification information, and acquire native ground feature information of the construction area to be measured based on the construction acquisition specification information;
[0009] Step S2: Before construction, collect pure spectral data based on the native ground cover information of the construction area to be measured and establish an initial baseline spectral set. During construction, classify the construction stages and collect the disturbance spectral data of the native ground cover in real time at each construction stage.
[0010] Step S3: Perform quality inspection on the perturbation spectral data, set the spectral quality level of the perturbation spectral data according to the inspection results, and determine whether to associate the perturbation spectral data with construction event information and generate a perturbation spectral set based on the spectral quality level;
[0011] Step S4: Write the initial baseline spectral set into the spectral database, obtain the acquisition time sequence of the disturbance spectral set according to the construction event information and generate different version numbers, analyze the difference index between the disturbance spectral set and the initial baseline spectral set, and determine whether to trigger the dynamic update mechanism based on the difference index.
[0012] In a preferred embodiment, in step S1, the construction type of the construction area to be tested is matched with the spectral acquisition rule base to obtain the construction acquisition specification information of the construction area to be tested;
[0013] Construction data acquisition specifications are a set of standardized parameters and conditions, including a unified sampling band range, observation geometry, and basic instrument parameters;
[0014] Native feature information refers to the basic information obtained by collecting, recording and standardizing the intrinsic characteristics and environmental conditions of features within the construction area to be measured, including sampling time, geographic coordinates at the time of sampling and light intensity;
[0015] The sampling time is recorded using a high-precision time synchronization device;
[0016] The geographic coordinates at the time of sampling are obtained using a high-precision positioning device;
[0017] The light intensity at the time of sampling is obtained using a light sensor;
[0018] The sampling time, geographical coordinates at the time of sampling, and light intensity are integrated into a basic record template according to a preset order;
[0019] The above data collection process was carried out in accordance with the construction data collection specifications for the construction area to be tested.
[0020] In a preferred embodiment, in step S2, before construction, a hyperspectral imager is used to collect pure spectral data of the construction area to be tested according to the construction collection specifications of the construction area to be tested, based on the basic recording template, under the same sampling time, geographical coordinates and light intensity.
[0021] Repeat the above steps to obtain pure spectral data for each preset sampling point;
[0022] The pure spectral data from each preset sampling point are integrated into an initial baseline spectral set;
[0023] During construction, based on the construction schedule and key milestones, the continuous construction process is divided into several representative discrete construction stages.
[0024] In a preferred embodiment, in step S2, the construction stage classification adopts a two-level classification method based on the dominant environmental disturbance characteristics;
[0025] The specific division logic is as follows:
[0026] The primary classification, based on the main types of construction activities, divides the construction process into earthwork stage, structural engineering stage, and environmental restoration stage.
[0027] The secondary classification, based on the primary classification, further refines the classification according to the disturbance characteristics of construction activities on the surface and environment:
[0028] The earthwork engineering stage is further divided into the original surface state stage, the earthwork excavation stage, and the earthwork backfilling stage.
[0029] Within the structural engineering phase, it is further divided into the foundation construction phase and the main structure construction phase.
[0030] The environmental restoration phase is further divided into the site leveling phase and the vegetation restoration phase.
[0031] In a preferred embodiment, in step S2, after the key operation nodes of each construction stage are completed, the spectral data of the construction area to be tested is acquired by a hyperspectral imager according to the construction acquisition specification information of the construction area to be tested, based on the same sampling time, geographical coordinates and light intensity.
[0032] Repeat the above steps to obtain spectral data for each preset sampling point;
[0033] The spectral data of each preset sampling point in each construction stage are used as the perturbed spectral data of each preset sampling point in each construction stage.
[0034] In a preferred embodiment, in step S3, at the same construction stage and at the same preset sampling point, the perturbation spectral data of the preset sampling point are collected multiple times, and the data fluctuation characteristics of the preset sampling point are calculated according to the root mean square error calculation method.
[0035] Repeat the above steps to obtain the data fluctuation characteristics of each preset sampling point;
[0036] If the data fluctuation characteristics of each preset sampling point are less than or equal to the preset second fluctuation characteristic threshold, the spectral quality level of the perturbation spectral data of that preset sampling point is determined to be excellent.
[0037] If the data fluctuation characteristics of each preset sampling point are greater than the preset second fluctuation characteristic threshold and less than or equal to the preset first fluctuation characteristic threshold, then the spectral quality level of the perturbation spectral data of that preset sampling point is determined to be qualified.
[0038] If the data fluctuation characteristics of each preset sampling point are greater than the preset first fluctuation characteristic threshold, then the spectral quality level of the perturbation spectral data of that preset sampling point is determined to be unqualified.
[0039] In a preferred embodiment, in step S3, the perturbation spectral data of each preset sampling point with a spectral quality level of excellent or qualified are bound with the corresponding construction event information and integrated into a perturbation spectral set.
[0040] Construction event information refers to the collection of construction activities, construction stages, working conditions, and related parameters corresponding to each disturbed spectral data point recorded during the spectral acquisition process at the construction site.
[0041] In a preferred embodiment, in step S4, the initial baseline spectral set is written into the spectral database through the batch data interface provided by the database management system;
[0042] From the construction event information associated with the perturbation spectral set, the construction stage number and data collection timestamp are extracted, and the construction stage number and data collection timestamp are combined according to the preset coding rules to generate different version numbers;
[0043] The perturbation spectral set and the initial baseline spectral set are normalized to obtain the perturbation factor and the baseline factor;
[0044] The difference index between the perturbation spectral set and the initial baseline spectral set is calculated by combining the perturbation factor and the baseline factor. The calculation formula is as follows: ,in, For the construction area to be tested The perturbation factor for each preset sampling point. For the construction area to be tested Baseline factor for each preset sampling point, For the construction area to be tested The difference index between the perturbation spectral set at each preset sampling point and the initial baseline spectral set.
[0045] In a preferred embodiment, in step S4, if the difference index between the perturbation spectral set and the initial baseline spectral set at each preset sampling point is greater than or equal to a preset difference threshold, then the dynamic update mechanism is triggered.
[0046] If the difference index between the perturbation spectral set and the initial baseline spectral set at each preset sampling point is less than the preset difference threshold, it is determined that the dynamic update mechanism will not be triggered.
[0047] The dynamic update mechanism is a process that determines whether to update the spectral database structure or data content based on the changes in the spectral data caused by construction disturbance relative to the initial baseline spectral data, and then performs the update operation.
[0048] The technical effects and advantages of this invention are as follows:
[0049] This invention retrieves construction data acquisition specifications from a spectral acquisition rule base, and then collects native ground cover information of the construction area based on this specifications. Before construction, it collects clean spectral data based on the native ground cover information and establishes an initial baseline spectral set. During construction, it categorizes the construction stages and acquires perturbed spectral data for each stage in real time. It performs quality checks on the perturbed spectral data, generates spectral quality levels based on the check results, determines whether it is associated with construction event information based on the spectral quality levels, and forms a perturbed spectral set. The initial baseline spectral set is written into the spectral database. Based on the construction event information, it obtains the acquisition sequence of the perturbed spectral set and generates a corresponding version number. It analyzes the difference index between the perturbed spectral set and the initial baseline spectral set and determines whether to trigger a dynamic update mechanism. Through standardized acquisition, quality grading, data association, and differentiated dynamic management, it achieves continuous updates and highly reliable construction of the spectral database, ensuring that spectral characteristics reflect real changes and have stable traceability in the construction scenario. Attached Figure Description
[0050] Figure 1 This is a flowchart illustrating the implementation of the present invention;
[0051] Figure 2 This is a schematic diagram of the steps of the present invention. Detailed Implementation
[0052] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0053] This invention retrieves construction acquisition specification information by accessing a spectral acquisition rule base, and acquires native ground feature information of the construction area to be measured based on this specification information. Before construction, it collects pure spectral data based on the native ground feature information and establishes an initial baseline spectral set. During construction, it classifies the construction stages and acquires the perturbation spectral data of each stage in real time. It performs quality detection on the perturbation spectral data, generates spectral quality levels based on the detection results, determines whether it is associated with construction event information based on the spectral quality levels, and forms a perturbation spectral set. It writes the initial baseline spectral set into the spectral database, obtains the acquisition time sequence of the perturbation spectral set based on the construction event information, generates a corresponding version number, analyzes the difference index between the perturbation spectral set and the initial baseline spectral set, and determines whether to trigger a dynamic update mechanism based on this.
[0054] A method for constructing a dynamic spectral feature database for construction environments, such as... Figures 1 to 2 As shown, it includes the following steps:
[0055] Step S1: Access the spectral acquisition rule base to retrieve construction acquisition specification information, and acquire native ground feature information of the construction area to be measured based on the construction acquisition specification information;
[0056] Step S2: Before construction, collect pure spectral data based on the native ground cover information of the construction area to be measured and establish an initial baseline spectral set. During construction, classify the construction stages and collect the disturbance spectral data of the native ground cover in real time at each construction stage.
[0057] Step S3: Perform quality inspection on the perturbation spectral data, set the spectral quality level of the perturbation spectral data according to the inspection results, and determine whether to associate the perturbation spectral data with construction event information and generate a perturbation spectral set based on the spectral quality level;
[0058] Step S4: Write the initial baseline spectral set into the spectral database, obtain the acquisition time sequence of the disturbance spectral set according to the construction event information and generate different version numbers, analyze the difference index between the disturbance spectral set and the initial baseline spectral set, and determine whether to trigger the dynamic update mechanism based on the difference index.
[0059] The specific implementation is as follows:
[0060] In step S1, with the widespread application of remote sensing and spectral analysis technologies in fields such as engineering construction and environmental monitoring, it is necessary to obtain high-quality, traceable, and comparable spectral data in complex and ever-changing construction environments. Therefore, a standardized spectral acquisition specification system is constructed, which combines synchronization, positioning, and sensing technologies to achieve standardized, structured, and process-oriented management of the acquisition of ground object spectral information in the construction area, providing a reliable data foundation for subsequent data processing, model inversion, and construction supervision.
[0061] The construction type of the construction area to be tested is matched with the spectral acquisition rule base to obtain the construction acquisition specification information of the construction area to be tested.
[0062] Construction data acquisition specifications are a set of standardized parameters and conditions used to ensure that, in complex construction environments, native ground feature information collected by different batches and operators can provide a consistent and repeatable basis for subsequent spectral acquisition. These specifications mainly include a unified sampling band range, observation geometry, and basic instrument parameters.
[0063] Native feature information refers to the basic information obtained by collecting, recording and standardizing the intrinsic characteristics and environmental conditions of features within the construction area to be measured, including sampling time, geographic coordinates at the time of sampling and light intensity;
[0064] The sampling time is recorded using a high-precision time synchronization device;
[0065] The geographic coordinates at the time of sampling are obtained using a high-precision positioning device;
[0066] The light intensity at the time of sampling is obtained using a light sensor;
[0067] The sampling time, geographical coordinates at the time of sampling, and light intensity are integrated into a basic record template according to a preset order;
[0068] The above data collection process was carried out in accordance with the construction data collection specifications for the construction area to be tested.
[0069] It needs to be explained that the spectral acquisition rule base is a structured, predefined expert knowledge system or database. Its core function is to match and provide a standardized set of spectral data acquisition instructions and parameters for different types of construction areas and activities. A high-precision time synchronization device refers to a time recording device capable of providing millisecond-level or even microsecond-level time references during spectral sampling, used to record the sampling time. A high-precision positioning device refers to a positioning device capable of providing sub-meter to centimeter-level spatial positioning accuracy during spectral sampling, used to obtain the geographic coordinates during sampling. A light sensor refers to an optical measurement device that measures the ambient light intensity at the sampling point in real time during spectral sampling, used to obtain the light intensity during sampling. The preset sequence is... In the process of spectral acquisition, to ensure the structural uniformity of the basic recording template and the reproducibility of subsequent data processing, the system pre-sets a fixed order for various basic environmental parameters. Based on the actual process of spectral data acquisition, the priority of each field is determined as the basis for setting the preset order. By matching the construction type with the spectral acquisition rule base, the system achieves automatic acquisition and unified execution of construction acquisition specification information, improving the standardization and consistency of spectral data acquisition. Utilizing high-precision time synchronization, positioning devices, and light sensors, the system can accurately record key parameters such as sampling time, geographic coordinates, and ambient light. Based on the preset order, a structured basic recording template is constructed, enhancing the spatiotemporal traceability of the data and the reproducibility of the environmental state.
[0070] In step S2, during the refined process of engineering construction and management, it is necessary to quantitatively monitor the dynamic impact of construction activities on the surface and environment. Construction is a continuous, dynamic and phased process. Without a systematic framework for spectral data acquisition and analysis that matches the construction progress, the obtained data will be difficult to accurately reflect the unique disturbance characteristics of each construction stage, leading to the failure of dynamic monitoring. Therefore, a monitoring system is constructed with the construction stage as the framework, hyperspectral data as the objective, and strict adherence to specifications for comparing data before and after the construction phase.
[0071] Before construction, pure spectral data of the construction area to be tested were acquired using a hyperspectral imager according to the construction data collection specifications of the construction area to be tested, based on the basic recording template, under the same sampling time, geographical coordinates and light intensity.
[0072] Repeat the above steps to obtain pure spectral data for each preset sampling point;
[0073] The pure spectral data from each preset sampling point are integrated into an initial baseline spectral set;
[0074] During the construction process, based on the construction schedule and key nodes, the continuous construction process is divided into several representative discrete construction stages. The classification of construction stages adopts a two-level classification method based on the characteristics of the dominant environmental disturbance.
[0075] The specific division logic is as follows:
[0076] The primary classification, based on the main types of construction activities, divides the construction process into earthwork stage, structural engineering stage, and environmental restoration stage.
[0077] The secondary classification, based on the primary classification, further refines the classification according to the disturbance characteristics of construction activities on the surface and environment:
[0078] The earthwork engineering stage is further divided into the original surface state stage, the earthwork excavation stage, and the earthwork backfilling stage.
[0079] Within the structural engineering phase, it is further divided into the foundation construction phase and the main structure construction phase.
[0080] The environmental restoration phase is further divided into the site leveling phase and the vegetation restoration phase.
[0081] It should be noted that the original surface state period refers to the period before construction activities begin, when the original surface vegetation, soil, and other natural conditions remain intact; the earthwork excavation period refers to the period when surface soil and rock are removed by mechanical means, resulting in a significant change in surface cover; the earthwork backfilling period refers to the period when soil and rock are backfilled into the excavated area or a designated area to form a new surface cover; the foundation construction period refers to the period when foundation structure construction is carried out on the excavated work surface; the main structure construction period refers to the period when the main structure above ground is constructed; the site leveling period refers to the period when the construction area is leveled as a whole after the main project is completed; and the vegetation restoration period refers to the period when vegetation is planted and the ecology is restored on the leveled site.
[0082] After the key work nodes of each construction phase are completed, the spectral data of the construction area to be tested are acquired by a hyperspectral imager according to the construction data acquisition specifications of the construction area to be tested, based on the same sampling time, geographical coordinates and light intensity.
[0083] Repeat the above steps to obtain spectral data for each preset sampling point;
[0084] The spectral data of each preset sampling point in each construction stage are used as the perturbed spectral data of each preset sampling point in each construction stage.
[0085] It should be explained that a hyperspectral imager is a specialized observation device used to acquire the reflectance characteristics of a target area in a continuous, high-resolution spectral band, and is used to obtain pure spectral data of the construction area to be measured. The same sampling time refers to the spectral acquisition time being carried out within the same time window every day. The preset sampling points refer to the fixed coordinate positions in the construction area to be measured, which are determined in advance based on the construction scope, the distribution characteristics of ground features, and the spectral acquisition specifications, for performing spectral measurements. According to the overall layout and distribution of ground features in the construction area, the construction area to be measured is divided into different types of sampling sub-regions, and these different types of sampling sub-regions are used as preset sampling points. Through the initial baseline spectral set obtained before construction, a comparable and high-precision background data benchmark is provided for monitoring environmental changes throughout the entire construction process. The innovative use of a two-level classification method based on the dominant environmental disturbance characteristics to divide the construction stages allows continuous construction activities to be deconstructed into a series of discrete monitoring units with clear spectral characteristics, greatly enhancing the targeting of monitoring and the structure of time-series data.
[0086] In step S3, during the hyperspectral dynamic monitoring of the construction environment, the quality of the collected spectral data is easily affected by instantaneous environmental factors and instrument noise, which may cause a single measurement value to fail to stably reflect the true spectral attributes of the ground objects. If these data with random fluctuations or even gross errors are used directly for analysis, it will seriously affect the accuracy of subsequent change detection and ground object identification. Therefore, after obtaining the perturbation spectral data of each construction stage, a quantitative data quality assessment and screening mechanism is introduced.
[0087] During the same construction phase, at the same preset sampling point, the perturbation spectral data of the preset sampling point were collected multiple times, and the data fluctuation characteristics of the preset sampling point were calculated according to the root mean square error calculation method.
[0088] Repeat the above steps to obtain the data fluctuation characteristics of each preset sampling point;
[0089] The data fluctuation characteristics of each preset sampling point are compared with the preset first fluctuation characteristic threshold and the preset second fluctuation characteristic threshold for judgment:
[0090] If the data fluctuation characteristics of each preset sampling point are less than or equal to the preset second fluctuation characteristic threshold, the spectral quality level of the perturbation spectral data of that preset sampling point is determined to be excellent.
[0091] If the data fluctuation characteristics of each preset sampling point are greater than the preset second fluctuation characteristic threshold and less than or equal to the preset first fluctuation characteristic threshold, then the spectral quality level of the perturbation spectral data of that preset sampling point is determined to be qualified.
[0092] If the data fluctuation characteristics of each preset sampling point are greater than the preset first fluctuation characteristic threshold, the spectral quality level of the perturbation spectral data of that preset sampling point is determined to be unqualified.
[0093] The perturbation spectral data of each preset sampling point with a spectral quality level of excellent or qualified are bound with the corresponding construction event information and integrated into a perturbation spectral set.
[0094] It should be explained that the root mean square (RMS) error is a statistical indicator that measures the fluctuation or deviation of a set of data sequences. It evaluates the degree of deviation between the measured value and the reference value and is used to calculate the data fluctuation characteristics of each preset sampling point. The preset first fluctuation characteristic threshold and the preset second fluctuation characteristic threshold are important parameters used to determine the spectral quality level of the perturbed spectral data at each preset sampling point. The preset first fluctuation characteristic threshold is greater than the preset second fluctuation characteristic threshold. Based on historical data statistics, multiple samplings are performed on the native features of the construction area to be measured or similar scenarios to calculate the distribution of the root mean square error of the continuously sampled spectral data. The preset second fluctuation characteristic threshold can be set to the 25th percentile of the root mean square error distribution to ensure that the fluctuation of the superior data is minimized. The first fluctuation characteristic threshold can be set to the 75th percentile of the root mean square error distribution. Data exceeding this value is considered unqualified. Construction event information refers to the set of construction activities, construction stages, working conditions and related parameters corresponding to each perturbed spectral data during the spectral acquisition process at the construction site. It is used to construct the correlation between the spectrum and construction behavior. By continuously collecting and calculating the data fluctuation characteristics at the same point, a quantitative assessment of the reliability of a single spectral measurement data is achieved.
[0095] In step S4, as the construction progresses, the types and states of surface features continue to evolve, causing the spectral characteristics to change dynamically. If the spectral database only statically stores the initial baseline data before construction, it cannot truly reflect the dynamic surface information during the construction process, thereby weakening its applicability in progress monitoring and environmental impact assessment. Therefore, a spectral database that senses data changes and determines whether it needs to be updated is constructed.
[0096] The initial baseline spectral set is written into the spectral database through the batch data interface provided by the database management system;
[0097] Extract the construction stage number and data acquisition timestamp from the construction event information associated with the perturbation spectral set;
[0098] Different version numbers are generated by combining the construction phase number and the data collection timestamp according to the preset coding rules;
[0099] The perturbation factor and baseline factor are obtained by standardizing the data in the perturbation spectral set and the initial baseline spectral set.
[0100] The difference index between the perturbation spectral set and the initial baseline spectral set is calculated by combining the perturbation factor and the baseline factor. The calculation formula is as follows: ,in, For the construction area to be tested The perturbation factor for each preset sampling point. For the construction area to be tested Baseline factor for each preset sampling point, For the construction area to be tested The difference index between the perturbation spectral set at each preset sampling point and the initial baseline spectral set;
[0101] It should be noted that the larger the difference index, the more significant the difference between the disturbance spectrum and the baseline spectrum, the stronger the impact of construction activities on the ground feature, and the more obvious the changes in the spectral characteristics of the ground feature. The smaller the difference index, the more similar the disturbance spectrum is to the baseline spectrum, the smaller the impact of construction activities on the ground feature, the less obvious the changes in the spectral characteristics of the ground feature, and the closer to the original state.
[0102] The difference index between the perturbation spectral set and the initial baseline spectral set at each preset sampling point is compared with a preset difference threshold for judgment.
[0103] If the difference index between the perturbation spectral set and the initial baseline spectral set at each preset sampling point is greater than or equal to the preset difference threshold, the dynamic update mechanism is triggered.
[0104] If the difference index between the perturbation spectral set and the initial baseline spectral set at each preset sampling point is less than the preset difference threshold, it is determined that the dynamic update mechanism will not be triggered.
[0105] It needs to be explained that the batch data interface provided by the database management system refers to a standardized software interface used for large-scale data writing, updating, or reading, for interaction between the spectral database and external programs; the spectral database is a structured information management system used for long-term storage of collected spectral data; the preset coding rules refer to combining and encoding construction stage numbers and sampling times in a specific format to generate a unique identifier for each data entry or version of data in the spectral database. Specifically, each construction stage is numbered sequentially, the sampling time is recorded in the standard time format of year-month-day_hour:minute:second, and finally, the construction stage number and timestamp are concatenated with an underscore to form a coded string; the standardization processing methods include, but are not limited to, standard linear transformation based on interval scaling, statistical Z-Score standardization methods, or methods based on... The normalization method for nonlinear mapping functions, and the application of standardization processing, will not be elaborated here. The preset difference threshold is used to determine whether the change in the construction disturbance spectral data relative to the initial baseline spectral data reaches the standard for triggering the dynamic update mechanism. Through historical data analysis, disturbance spectral data and initial baseline spectral data at different stages under similar construction environments are collected, the difference index distribution of each sampling point is calculated, and the mean and standard deviation are statistically analyzed. The sum of the mean and standard deviation of the difference index is used as the preset difference threshold. The dynamic update mechanism is the core function of spectral database maintenance and evolution. It refers to the entire process in which the system automatically determines whether the database structure or data content needs to be updated based on the change in the construction disturbance spectral data relative to the initial baseline spectral data, and performs the update operation. Through a systematic process, the spectral database has been upgraded from static storage to dynamic sensing and response.
[0106] Finally, it should be noted that in this paper, relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations.
[0107] Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.
[0108] In this document, the singular forms “a,” “an,” and “the” may also include the plural forms unless the context clearly indicates otherwise. It should also be understood that terms such as “comprising / including” or “having” specify the presence of the stated features, integrals, steps, operations, components, parts, or combinations thereof, but do not preclude the possibility of the presence or addition of one or more other features, integrals, steps, operations, components, parts, or combinations thereof. Meanwhile, the term “and / or” as used in this specification includes any and all combinations of the associated listed items.
[0109] The various embodiments in this specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The various embodiments can be combined as needed, and the same or similar parts can be referred to each other.
[0110] The above description of the disclosed embodiments will enable those skilled in the art to make or use various modifications to these embodiments. It will be readily apparent to those skilled in the art that the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims
1. A method for constructing a dynamic spectral feature database for construction environments, characterized in that: Includes the following steps: Step S1: Access the spectral acquisition rule base to retrieve construction acquisition specification information, and acquire native ground feature information of the construction area to be measured based on the construction acquisition specification information; Step S2: Before construction, collect pure spectral data based on the native ground cover information of the construction area to be measured and establish an initial baseline spectral set. During construction, classify the construction stages and collect the disturbance spectral data of the native ground cover in real time at each construction stage. Step S3: Perform quality inspection on the perturbation spectral data, set the spectral quality level of the perturbation spectral data according to the inspection results, and determine whether to associate the perturbation spectral data with construction event information and generate a perturbation spectral set based on the spectral quality level; Step S4: Write the initial baseline spectral set into the spectral database, obtain the acquisition time sequence of the disturbance spectral set according to the construction event information and generate different version numbers, analyze the difference index between the disturbance spectral set and the initial baseline spectral set, and determine whether to trigger the dynamic update mechanism based on the difference index.
2. The method for constructing a dynamic spectral feature database for construction environments according to claim 1, characterized in that: In step S1, the construction type of the construction area to be tested is matched with the spectral acquisition rule base to obtain the construction acquisition specification information of the construction area to be tested; Construction data acquisition specifications are a set of standardized parameters and conditions, including a unified sampling band range, observation geometry, and basic instrument parameters; Native feature information refers to the basic information obtained by collecting, recording and standardizing the intrinsic characteristics and environmental conditions of features within the construction area to be measured, including sampling time, geographic coordinates at the time of sampling and light intensity; The sampling time is recorded using a high-precision time synchronization device; The geographic coordinates at the time of sampling are obtained using a high-precision positioning device; The light intensity at the time of sampling is obtained using a light sensor; The sampling time, geographical coordinates at the time of sampling, and light intensity are integrated into a basic record template according to a preset order; The above data collection process was carried out in accordance with the construction data collection specifications for the construction area to be tested.
3. The method for constructing a dynamic spectral feature database for construction environments according to claim 1, characterized in that: In step S2, before construction, the pure spectral data of the construction area to be tested is obtained by using a hyperspectral imager according to the construction data collection specifications of the construction area to be tested, based on the basic record template, under the same sampling time, geographical coordinates and light intensity. Repeat the above steps to obtain pure spectral data for each preset sampling point; The pure spectral data from each preset sampling point are integrated into an initial baseline spectral set; During construction, based on the construction schedule and key milestones, the continuous construction process is divided into several representative discrete construction stages.
4. The method for constructing a dynamic spectral feature database for construction environments according to claim 3, characterized in that: In step S2, the construction stage classification adopts a two-level classification method based on the dominant environmental disturbance characteristics; The specific division logic is as follows: The primary classification, based on the main types of construction activities, divides the construction process into earthwork stage, structural engineering stage, and environmental restoration stage. The secondary classification, based on the primary classification, further refines the classification according to the disturbance characteristics of construction activities on the surface and environment: The earthwork engineering stage is further divided into the original surface state stage, the earthwork excavation stage, and the earthwork backfilling stage. Within the structural engineering phase, it is further divided into the foundation construction phase and the main structure construction phase. The environmental restoration phase is further divided into the site leveling phase and the vegetation restoration phase.
5. The method for constructing a dynamic spectral feature database for construction environments according to claim 1, characterized in that: In step S2, after the key operation nodes of each construction stage are completed, the spectral data of the construction area to be tested is acquired by a hyperspectral imager according to the construction data acquisition specifications of the construction area to be tested, based on the same sampling time, geographical coordinates and light intensity. Repeat the above steps to obtain spectral data for each preset sampling point; The spectral data of each preset sampling point in each construction stage are used as the perturbed spectral data of each preset sampling point in each construction stage.
6. The method for constructing a dynamic spectral feature database for construction environments according to claim 1, characterized in that: In step S3, at the same construction stage and at the same preset sampling point, the perturbation spectral data of the preset sampling point are collected multiple times, and the data fluctuation characteristics of the preset sampling point are calculated according to the root mean square error calculation method. Repeat the above steps to obtain the data fluctuation characteristics of each preset sampling point; If the data fluctuation characteristics of each preset sampling point are less than or equal to the preset second fluctuation characteristic threshold, the spectral quality level of the perturbation spectral data of that preset sampling point is determined to be excellent. If the data fluctuation characteristics of each preset sampling point are greater than the preset second fluctuation characteristic threshold and less than or equal to the preset first fluctuation characteristic threshold, then the spectral quality level of the perturbation spectral data of that preset sampling point is determined to be qualified. If the data fluctuation characteristics of each preset sampling point are greater than the preset first fluctuation characteristic threshold, then the spectral quality level of the perturbation spectral data of that preset sampling point is determined to be unqualified.
7. The method for constructing a dynamic spectral feature database for construction environments according to claim 6, characterized in that: In step S3, the perturbation spectral data of each preset sampling point with a spectral quality level of excellent or qualified are bound with the corresponding construction event information and integrated into a perturbation spectral set. Construction event information refers to the collection of construction activities, construction stages, working conditions, and related parameters corresponding to each disturbed spectral data point recorded during the spectral acquisition process at the construction site.
8. The method for constructing a dynamic spectral feature database for construction environments according to claim 1, characterized in that: In step S4, the initial baseline spectral set is written into the spectral database through the batch data interface provided by the database management system; From the construction event information associated with the perturbation spectral set, the construction stage number and data collection timestamp are extracted, and the construction stage number and data collection timestamp are combined according to the preset coding rules to generate different version numbers; The perturbation spectral set and the initial baseline spectral set are normalized to obtain the perturbation factor and the baseline factor; The difference index between the perturbation spectral set and the initial baseline spectral set is calculated by combining the perturbation factor and the baseline factor. The calculation formula is as follows: ,in, For the construction area to be tested The perturbation factor of each preset sampling point. For the construction area to be tested Baseline factor for each preset sampling point, For the construction area to be tested The difference index between the perturbation spectral set at each preset sampling point and the initial baseline spectral set.
9. The method for constructing a dynamic spectral feature database for construction environments according to claim 8, characterized in that: In step S4, if the difference index between the perturbation spectral set and the initial baseline spectral set at each preset sampling point is greater than or equal to the preset difference threshold, then the dynamic update mechanism is triggered. If the difference index between the perturbation spectral set and the initial baseline spectral set at each preset sampling point is less than the preset difference threshold, it is determined that the dynamic update mechanism will not be triggered. The dynamic update mechanism is a process that determines whether to update the spectral database structure or data content based on the changes in the spectral data caused by construction disturbance relative to the initial baseline spectral data, and then performs the update operation.