Construction quality data management and dispatching system based on deck flatness of pcc roll-on / roll-off ship

Abstract

The application discloses a construction quality data management and scheduling system based on PCTC roll-on / roll-off ship deck flatness, which comprises a data access unit, an association construction unit, a state analysis unit and a management and scheduling unit. The data access unit is used for uniformly marking and format-converting deck flatness measurement data, construction process parameters and segmented space position information of each working procedure, forming structured precision data with time markers and space coordinates and storing the structured precision data into a precision database. The association construction unit is used for associating and mapping the structured precision data based on segmented assembly relationships and construction sequences, and constructing a precision association data set. The state analysis unit is used for associating and analyzing flatness deviation and grading based on segmented closing sequences and vehicle deck interlayer relationships, and generating quality evaluation indexes. The management and scheduling unit is used for generating working procedure scheduling schemes, resource allocation schemes and quality management and control decisions based on the quality evaluation indexes and construction resource constraint conditions, and realizing dynamic optimization and quality tracing of the construction process, so as to improve deck flatness control level and construction management efficiency.

Description

Technical Field

[0001] This invention relates to the field of computer-based engineering construction quality management and production scheduling optimization technology, and in particular to a construction quality data management and scheduling system based on the flatness of the PCTC roll-on / roll-off ship deck. Background Technology

[0002] A PCTC (Pure Car and Truck Carrier) is a multi-deck vessel specifically designed for transporting cars and heavy vehicles. Its decks are typically constructed in sections and assembled gradually. In existing technologies, during the section construction of a PCTC, deck flatness data is usually obtained through methods such as total stations, laser scanners, or manual measurements. This data is then recorded in conjunction with construction process parameters and section location information. Various types of data are typically stored in different measuring devices, construction record systems, or quality management systems. Some companies use simple information systems to archive and retrieve data to support construction quality management and subsequent inspections.

[0003] However, in the above-mentioned technical solutions, there is a lack of a unified data organization method between the flatness data collected in each process and the construction process parameters. It is difficult to effectively correlate them based on the segmented assembly relationship and construction sequence. Data from different times and different segments are isolated, which makes it impossible to accurately reflect the transmission relationship and cumulative impact of flatness deviation in the segmented assembly process. At the same time, existing systems can usually only provide static data records or single-point analysis results, which makes it difficult to form quality evaluation indicators for the construction process. Furthermore, they cannot combine construction resource constraints and process rules to generate effective scheduling decisions, thus showing significant deficiencies in quality control, construction optimization, and problem tracing.

[0004] Therefore, it is necessary to propose a new technical solution for construction quality data management and scheduling. Summary of the Invention

[0005] This application provides a construction quality data management and scheduling system based on the flatness of the PCTC roll-on / roll-off ship deck, in order to improve the level of deck flatness control and construction management efficiency.

[0006] This application provides a construction quality data management and scheduling system based on PCTC (Panel Coordination Theorem) of roll-on / roll-off (Ro-Ro) ships, including: The data access unit is used to collect deck flatness measurement data, construction process parameters and section spatial location information corresponding to each process during the construction of the roll-on / roll-off ship sections. It also performs unified identification and format conversion processing on the data to generate structured precision data with time stamps and spatial coordinates, and stores the structured precision data in the precision database. The associated construction unit is used to read structured accuracy data from the accuracy database, perform association mapping on accuracy data of different times and different segments based on segmented assembly relationships and construction sequence, construct accuracy association datasets and merge them back into the accuracy database; The status analysis unit is used to perform correlation analysis and deviation level assessment on the flatness deviation of each segment based on the accuracy correlation data set, according to the segment assembly sequence and the interlayer relationship of the vehicle deck, and generate quality evaluation indicators that characterize the quality status and deviation risk level of each process. The control and scheduling unit receives quality evaluation indicators, combines them with preset construction resource constraints, process execution rules and flatness control thresholds, generates management instruction information that includes process scheduling plan, construction resource allocation plan and quality control decision, and stores the management instruction information and corresponding quality evaluation indicators in the accuracy database to achieve quality traceability.

[0007] The beneficial effects of this application mainly include: (1) By uniformly identifying and converting the deck flatness measurement data, construction process parameters and segment spatial location information, and constructing structured precision data with time stamps and spatial coordinates, the standardized integration and centralized storage of multi-source heterogeneous data are realized, significantly improving the consistency and usability of the data and avoiding the information fragmentation problem caused by data dispersion. (2) By mapping the precision data of different times and different segments based on the segment assembly relationship and construction sequence, a precision correlation data set is constructed, so that the precision data between each process forms a traceable correlation system, thereby reflecting the mutual influence relationship and historical evolution process between segments, and improving the overall correlation and analysis depth of the construction process data. (3) By combining the segment assembly sequence and the vehicle deck layer relationship to conduct correlation analysis and deviation level assessment of flatness deviation, quality evaluation indicators are generated, realizing the quantitative expression of construction quality status and deviation risk, and transforming construction quality from traditional post-event inspection to process evaluation, which is conducive to identifying potential quality problems in advance and reducing the risk of error accumulation. (4) By combining construction resource constraints and process execution rules with quality evaluation indicators to generate process scheduling schemes, resource allocation schemes and quality control decisions, the collaborative optimization of construction quality management and production scheduling can be achieved. At the same time, by linking and storing management instruction information with quality evaluation indicators, a complete data closed loop and quality traceability chain can be formed, thereby improving the efficiency of construction organization and the controllability and reliability of quality management. Attached Figure Description

[0008] Figure 1 This is a schematic diagram of a construction quality data management and scheduling system based on the flatness of the PCTC roll-on / roll-off ship deck, provided in the first embodiment of this application. Detailed Implementation

[0009] Many specific details are set forth in the following description to provide a full understanding of this application. However, this application can be implemented in many other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of this application; therefore, this application is not limited to the specific embodiments disclosed below.

[0010] The first embodiment of this application provides a construction quality data management and scheduling system based on the flatness of the PCTC (Planetary Control Center) ship deck. Please refer to... Figure 1 This figure is a schematic diagram of the first embodiment of this application. The following is in conjunction with... Figure 1 This application provides a detailed description of a construction quality data management and scheduling system based on the flatness of the deck of a roll-on / roll-off ship, according to the first embodiment of the present application.

[0011] The construction quality data management and scheduling system based on PCTC roll-on / roll-off ship deck flatness includes a data access unit 101, an association construction unit 102, a status analysis unit 103, and a control and scheduling unit 104.

[0012] The data access unit 101 is used to collect deck flatness measurement data, construction process parameters and section spatial location information corresponding to each process during the construction of the roll-on / roll-off ship sections, and to perform unified identification and format conversion processing on the data to generate structured precision data with time stamps and spatial coordinates, and to store the structured precision data in the precision database.

[0013] In this embodiment, the data access unit 101 is used to collect, uniformly identify, convert formats, and structure data from multiple sources during the segmented construction of the roll-on / roll-off (Ro-Ro) vessel, generating structured, high-precision data that can be used for subsequent correlation analysis and scheduling decisions. The PCTC Ro-Ro vessel is a Pure Car and Truck Carrier, whose deck is typically a multi-layered structure and is constructed using a segmented manufacturing and assembly method. Therefore, various types of data related to deck flatness are continuously generated in different segments and construction processes.

[0014] In practical implementation, the data access unit 101 first establishes a data interface with the on-site measurement equipment and construction management system to achieve synchronous acquisition of multi-source data. The measurement equipment can be a total station, a 3D laser scanner, or a structured light measurement device, used to acquire spatial position information of multiple measurement points on the deck surface. Each measurement point contains at least spatial position data under a unified ship coordinate system, where the planar position is used to determine the specific area of ​​the measurement point on the deck, and the height information reflects the height of the measurement point relative to a reference datum plane. The construction process parameters can be obtained from the construction management system or manually entered, specifically including welding sequence, construction time, heating or straightening process parameters, and fixture arrangement status. The segmented spatial position information is determined through a preset segment numbering rule and a mapping relationship with the ship coordinate system, ensuring that data from different segments can be expressed under the same spatial reference system.

[0015] After data acquisition is completed, the data access unit 101 performs unified identification processing on all data. Unified identification refers to attaching a set of identification information to each piece of data to uniquely determine its source and status. This identification information includes at least a segment number, a process identifier, and a time stamp. The segment number distinguishes different hull sections, the process identifier indicates the construction stage to which the data belongs, and the time stamp records the specific time of data acquisition or generation. This combination of identifiers ensures the uniqueness of each piece of data within the construction process and provides a basis for subsequent data traceability by time or process.

[0016] Subsequently, the data access unit 101 performs a unified format conversion process on data from different sources. Since data output from different devices and systems may differ in units, data structures, and representations—for example, height data output by measuring devices may use different units or precision—it is necessary to uniformly convert all data related to flatness judgment to the same unit of measurement and unify the data field structure to meet the preset data storage format requirements. Simultaneously, missing fields are filled in; for example, the current system time is added to data lacking time information, and the segment to which data belongs is deduced from its spatial location, thus ensuring that all data has a complete structure.

[0017] After completing the unified identification and format conversion, the data access unit 101 generates structured precision data. The structured precision data refers to a data set organized in a unified field format, which at least includes segmentation information, process information, time stamps, and spatial location data of measurement points. In specific implementations, multiple measurement point data collected in the same segment and under the same process can be organized into a single data set and stored according to the measurement point location order or the collection order.

[0018] To further reflect the deck flatness status, in this embodiment, the data access unit 101 also performs deviation determination processing on each measuring point. Specifically, it compares the actual measured height of the measuring point with the corresponding height in the design reference model, determines whether the measuring point is above or below the reference plane based on the difference between the two, and records the degree of deviation. For example, when the actual height of a measuring point is significantly higher than the design value, it can be determined as a positive convex deviation; when it is lower than the design value, it can be determined as a concave deviation. This deviation information is stored as part of the structured accuracy data, so that subsequent modules can use it directly without having to perform basic calculations again.

[0019] After the above processing is completed, the data access unit 101 writes the structured precision data into the precision database for unified management. The precision database can be implemented using a relational database or a distributed database. Internally, it establishes an index structure according to segment number, process identifier, and time sequence, enabling continuous storage of data for the same segment at different construction stages and supporting fast retrieval. Simultaneously, during storage, historical data for the same segment is archived to form a complete construction process data chain.

[0020] Through the above methods, the data access unit 101 realizes the transformation from raw, scattered data to unified, structured, and precise data, enabling data from different sources, at different times, and in different segments to be expressed and stored in the same system, providing a consistent and reliable data foundation for subsequent correlation construction and quality analysis.

[0021] Furthermore, the data access unit is specifically used for: Based on the spatial location data of deck measuring points collected by the measuring equipment and the construction process parameters, coordinate transformation is performed on each measuring point according to the unified ship coordinate system, and the measuring point data obtained by different equipment are mapped to the same coordinate reference to generate unified coordinate measuring point data. Based on unified coordinate measurement point data and preset segmented spatial boundary information, the segmentation and affiliation of each measurement point are determined, and a unique location identifier is assigned to each measurement point according to its spatial projection position within the segment, so that the corresponding positions of the same segment at different acquisition times have consistent identifiers, and initial identifier data with location identifiers is generated. Based on the initial identification data, the actual measured height of each measuring point is compared with the corresponding design reference height to determine the deviation direction and deviation value of each measuring point. For measuring points with abnormal deviation changes, a consistency verification process is performed to generate valid deviation data. Based on the effective deviation data and location identifiers, the measuring points in the same segment and at the same process stage are fixedly sorted according to the location identifiers to generate structured measuring point sequence data with a unique spatial sequence. Based on structured measurement point sequence data, the measurement point data, segment number, process identifier, time marker and location identifier are combined to generate structured precision data with unique spatial correspondence, and the structured precision data is stored in the precision database.

[0022] In this embodiment, the data access unit is used to transform raw data from different measuring devices and construction systems into structured precision data with a unified spatial reference, unique positional correspondence, and stable data structure, thereby providing a reliable and consistent data foundation for subsequent correlation construction and status analysis. The PCTC roll-on / roll-off ship is a multi-deck vehicle transport vessel, constructed using a segmented manufacturing and gradual assembly method. Data collected by different measuring devices at different times and stages of the process typically differ in spatial reference and data format, thus requiring unification through the processing flow described in this embodiment.

[0023] In practice, the first step is to perform coordinate transformation on each measuring point based on the spatial location data of the deck measuring points collected by the measuring equipment and the construction process parameters. The measuring equipment can be a total station, laser scanner, or other 3D measuring equipment. Different equipment may use different coordinate reference systems, such as a local coordinate system with the equipment installation point as the origin or a temporary coordinate system with the measuring station as the reference. To achieve data unification, all measuring point data needs to be transformed to a preset unified ship coordinate system. This ship coordinate system is usually established based on the ship design model, and its origin and axial direction are determined during the design phase. In this embodiment, by obtaining the transformation relationship between the measuring equipment coordinate system and the ship coordinate system, the coordinates of the measuring points in the equipment coordinate system are mapped to the ship coordinate system. For example, when the position of the origin of a measuring equipment in the ship coordinate system and its coordinate axis direction are known, the relative position of the measuring point in the equipment coordinate system can be converted to the absolute position in the ship coordinate system, thereby obtaining unified coordinate measuring point data. This step ensures that data from different equipment are expressed under the same spatial reference.

[0024] After coordinate unification, based on the unified coordinate measurement point data and preset segmented spatial boundary information, the segment affiliation of each measurement point is determined. The segmented spatial boundary information originates from the ship's structural design model, where each segment corresponds to a spatial region in the ship's coordinate system, which can be defined by boundary lines or boundary surfaces. In practice, for each measurement point, its segment number is determined by whether its spatial position in the ship's coordinate system falls within the boundary range of a certain segment. If a measurement point is located near the boundaries of two segments, its affiliation is determined based on its projected position or the principle of closest proximity.

[0025] After segmentation and assignment, a unique location identifier is further assigned to each measuring point. A unique location identifier is a code used to uniquely identify a specific spatial location within a segment. This identifier remains consistent across different data acquisition times, ensuring that measuring points at the same physical location have the same identifier in data from different time periods. In this embodiment, location identifiers can be assigned based on the spatial projection position of the measuring point within the segment. For example, the segmented area can be divided into several regular grids, each grid corresponding to a fixed number. When a measuring point falls into a grid, that grid number is assigned as its location identifier. Thus, when measuring the same location at different times, even if the actual coordinates of the measuring point change slightly, it can still be mapped to the same grid, thereby obtaining the same location identifier. This method generates initial identifier data with location identifiers, enabling subsequent data processing to perform one-to-one correspondence based on location.

[0026] After obtaining the initial identification data, the actual measured height of each measuring point is compared with the design reference height to determine the direction and value of the deviation for each measuring point. The design reference height is derived from the theoretical height value of the corresponding position in the hull design model. In practice, for each measuring point, the reference height at the corresponding position is found in the design model based on its location identification, and then the actual measured height is compared with the reference height. When the actual height is higher than the reference height, it is determined to be a positive deviation; when it is lower than the reference height, it is determined to be a negative deviation, and the deviation value is recorded. To ensure data reliability, consistency verification processing is also required for measuring points with abnormal deviation changes. For example, when the measurement result of a certain measuring point shows a significantly unreasonable abrupt change in adjacent time intervals, it can be compared with its surrounding measuring points to determine whether it is an outlier. If the deviation of this measuring point differs too much from that of the surrounding measuring points, it can be marked as abnormal data and removed or corrected, thereby generating valid deviation data.

[0027] After obtaining valid deviation data, the measuring points in the same segment and at the same process stage are fixedly sorted based on their location identifiers. Fixed sorting means arranging the measuring points according to the order of their location identifiers, ensuring that the sequence of measuring points for the same segment remains consistent across different times. In practice, the measuring points can be sorted according to the numerical order of their location identifiers, for example, by arranging them from smallest to largest grid number. This method creates a structured measuring point sequence data with a unique spatial sequence. The characteristic of this sequence is that its order is determined solely by spatial location, independent of measurement time or data source, thus ensuring direct comparison between data from different times.

[0028] Finally, based on the structured measurement point sequence data, the measurement point data, segment numbers, process identifiers, time stamps, and location identifiers are combined to generate structured precision data. This structured precision data consists of data records with a unified field structure. Each record contains information such as the spatial location of the corresponding measurement point, deviation value, segment, construction stage, and unique location identifier. This combination ensures that the data has clear identification in three dimensions: time, space, and construction process. Subsequently, this structured precision data is stored in a precision database. During storage, an index can be created according to the segment number and time stamp, allowing for rapid retrieval of data from the same segment at different construction stages in chronological order. Furthermore, the introduction of location identifiers allows data from different times to be directly correlated based on the same location, thus providing a stable data foundation for subsequent association construction and status analysis.

[0029] The association construction unit 102 is used to read structured accuracy data from the accuracy database, perform association mapping on accuracy data of different times and different segments based on segmented assembly relationships and construction sequence, and construct an accuracy association dataset which is then merged and stored back into the accuracy database.

[0030] In this embodiment, the association construction unit 102 is used to establish inherent relationships between different segments, different processes, and different times based on the structured precision data that has been uniformly processed and stored in the precision database, thereby forming a precision-related data set that can be used for subsequent quality analysis and scheduling decisions. The core function of this unit is to transform the originally independently stored data into a data system with clear association logic, so that the data not only has "queryability" but also "interpretability" and "traceability".

[0031] In specific implementation, the association construction unit 102 first reads structured precision data from the precision database. The reading method can be filtered based on segment number, process identifier, and time stamp. For example, when it is necessary to analyze multiple adjacent segments of a certain deck in a certain construction stage, all data records of these segments in that stage and the stages before and after can be read at the same time to form a basic set of data to be associated.

[0032] After reading the data, the association construction unit 102 establishes spatial associations based on preset segmented assembly relationships. Segmented assembly relationships refer to the spatial connections and adjacencies of various segments during the construction of a roll-on / roll-off ship. These relationships are typically derived from the hull structure design model or construction assembly drawings. In this embodiment, each segment can be considered an independent entity, and its set of adjacent segments is determined based on the connection relationships between adjacent segments in the design drawings. For example, for a segment A, which is directly connected to segments B and C during actual assembly, B and C can be identified as adjacent segments of A. In a specific implementation, this can be described by establishing a segmented adjacency table, which records the numbers of all adjacent segments corresponding to each segment, thereby achieving a standardized expression of spatial relationships.

[0033] Building upon spatial association, the association construction unit 102 further establishes temporal association based on the construction sequence. The construction sequence refers to the order in which segments are installed, welded, and adjusted during the actual construction process. This sequence can be obtained through a construction planning system or inferred from time stamps. In this embodiment, for the same segment, data collected at different times can be arranged chronologically to form the segment's time-series data. Between different segments, their sequential relationship can be determined based on the construction sequence. For example, if segment A is joined before segment B, then the data of A can be considered to have a preceding influence on B in time. Through this method, a cross-segment temporal association chain can be constructed.

[0034] After completing spatial and temporal association, the association construction unit 102 performs association mapping processing on the data. Association mapping refers to combining data from different sources, times, and segments according to preset rules to form a logically connected data set. In specific implementations, a "target segment" can be used as the center, with its own data, adjacent segment data, and its historical data forming a subset of associated data. For example, for the data of a segment A at a certain process stage, it can simultaneously associate with the data of its previous process stage and the data of adjacent segments B and C at the same or adjacent process stages, thereby forming a comprehensive data set containing multi-segment, multi-time information.

[0035] To make the above associations operable, in this embodiment, association identification information can be added to each data record to mark its association set and its relationship type within the set. For example, a certain data can be marked as "current segment data," "adjacent segment data," or "historical stage data," so that subsequent analysis units can execute different processing strategies based on different types of data.

[0036] Furthermore, during the association construction process, the influence relationships between data can be preliminarily quantified. Specifically, for a given data segment, the degree of influence of adjacent segments can be determined based on its spatial distance or connection boundary with neighboring segments. For example, when two segments have a large contact boundary, their mutual influence is high; when the contact boundary is small or they are only connected through an indirect structure, their influence is low. In practical implementation, the degree of influence can be divided into three levels—high, medium, and low—based on the length or area of ​​the segment contact boundary, and written as an additional attribute into the associated data, thus providing a basis for subsequent analysis.

[0037] After completing the aforementioned association mapping and relationship labeling, the association construction unit 102 generates a precision association data set. This data set can be understood as an overall data structure composed of multiple association subsets. Each subset corresponds to a segment's comprehensive data environment under specific process and time conditions, including the segment's own data, adjacent segment data, and historical data. This organization method can fully reflect the multidimensional influencing factors of a certain segment during the construction process.

[0038] Finally, the association construction unit 102 stores the completed precision association data set back to the precision database. During storage, a unique identifier can be assigned to each association data set, and its corresponding segment number, process identifier, and time range can be recorded, so that the subsequent status analysis unit can directly call the set without performing complex association operations again. At the same time, by persistently storing the association results, subsequent historical backtracking and quality tracking can also be supported.

[0039] Through the above specific implementation methods, the association construction unit 102 realizes the transformation from structured precision data to a multidimensional association data set, enabling the spatial, temporal, and influence relationships between data to be systematically expressed, thereby providing a reliable data foundation for subsequent flatness deviation analysis and construction scheduling.

[0040] Based on the above implementation methods, the workflow of the association construction unit 102 will be further described in detail with reference to a specific embodiment, so that those skilled in the art can more clearly understand its specific implementation.

[0041] Suppose that during the construction of a PCTC roll-on / roll-off ship, the current stage is the assembly of the third vehicle deck section, and it is necessary to analyze the construction quality of adjacent sections D3-S10, D3-S11, and D3-S12. The accuracy database already stores structured accuracy data of the above sections at multiple process stages, including data records from the assembly completion stage, welding completion stage, and initial correction stage.

[0042] In the specific processing, the association construction unit 102 first reads all the structured accuracy data of segments D3-S10, D3-S11, and D3-S12 in the current stage and the two previous stages from the accuracy database according to the segment number and process identifier. For example, for segment D3-S11, it reads all the measurement point data in the welding completion stage and the initial correction stage, and at the same time reads its historical data in the assembly completion stage, thereby forming a time series data set for this segment.

[0043] After completing data reading, the system establishes spatial associations based on preset segmented assembly relationships. By querying the segment adjacency table, it is determined that segment D3-S11 is directly connected to D3-S10 and D3-S12, with D3-S10 located on its left boundary and D3-S12 on its right boundary. Based on this, the system marks D3-S10 and D3-S12 as adjacent segments of D3-S11 and uses the connection relationship among these three segments as the basis for spatial association.

[0044] Building upon this foundation, a temporal correlation is further established. According to construction records, it is determined that D3-S10 was completed before D3-S11, and D3-S11 was assembled before D3-S12. Therefore, in terms of time, the data from D3-S10 has a preceding influence on D3-S11, while the data from D3-S11 subsequently influences D3-S12. Based on this, the system constructs a cross-segment temporal correlation chain, ensuring an ordered temporal relationship between data from different segments.

[0045] Subsequently, the association construction unit 102 performs association mapping processing with segment D3-S11 as the target segment. Specifically, it uses the data of D3-S11 in the current process stage as the core data, while also incorporating historical data from the previous and two previous stages, and includes the data of adjacent segments D3-S10 and D3-S12 in their corresponding process stages, thus forming a subset of associated data containing multi-segment and multi-time information. For example, in the analysis of the welding completion stage, this subset includes the measurement point deviation data of D3-S11 in this stage, its data in the assembly stage, and the data of D3-S10 in the welding stage and D3-S12 in the assembly stage.

[0046] During the construction of this associated data subset, the system adds association identification information to each data record. For example, the data from the current stage of D3-S11 is labeled as "current segment data," its historical data is labeled as "historical stage data," and the data from D3-S10 and D3-S12 are labeled as "adjacent segment data." This labeling method allows data from different sources to be clearly distinguished within the same set, thus providing a basis for subsequent analysis.

[0047] Furthermore, within this subset of related data, the influence relationships between segments are preliminarily quantified. For example, by analyzing the connection boundary length between D3-S11 and D3-S10, it is found that the contact boundary between them is relatively long, so its influence level is marked as high; while the contact boundary between D3-S11 and D3-S12 is relatively short, so it is marked as medium influence. This influence level is written as an additional attribute into the corresponding data record.

[0048] After completing the above processing, a precision-related data subset is formed for segment D3-S11. This subset fully reflects the quality status of the segment in the current stage and its relationship with adjacent segments and historical stages. Similarly, the system can construct separate related data subsets for D3-S10 and D3-S12 as target segments, thereby forming a complete precision-related data set.

[0049] Finally, the association construction unit 102 stores the aforementioned precision association data set back to the precision database. During storage, a unique identifier is assigned to each subset of association data, for example, using a combination of "segment number + process stage" as an index, thus facilitating rapid retrieval later. For instance, when the status analysis unit needs to analyze the deviation of D3-S11 in the welding stage, it can directly read the corresponding subset of association data without having to reconstruct the data association.

[0050] As can be seen from the above specific embodiments, the association construction unit 102 can establish clear spatial and temporal relationships between multi-segment and multi-stage data, and organize them through a unified data structure, so that the originally scattered data can be transformed into a data set with clear logical relationships.

[0051] Furthermore, the associated building unit is specifically used for: Based on the structured precision data stored in the precision database, the spatial position and deviation data of each segment measurement point are extracted according to the segment number, and the direct connection relationship between segments is determined according to the spatial boundary information of the segments in the ship coordinate system, thus generating segment adjacency relationship data. Based on the segmented adjacency relationship data, the set of measurement points in the corresponding area of ​​each adjacent segment in the connection boundary is extracted. According to the spatial position of the measurement points on the connection boundary, the target segment measurement points are matched with the adjacent segment measurement points one by one. For each target measurement point, the adjacent measurement point with the smallest spatial distance and within the same boundary position range is selected as the unique matching object, and unique boundary matching data is generated. Based on the time stamps and process identifiers in the unique boundary matching data and structured precision data, the matching measurement points are arranged according to the construction sequence. The matching measurement point data of the same connection boundary at different process stages are combined according to the time sequence to generate boundary association sequence data containing unique spatial correspondence and temporal evolution relationship. Based on boundary-related sequence data, a subset of related data is constructed for each target segment, which includes its own measurement point data, unique matching data of adjacent segments and data of its preceding process stages. The source type and boundary position of each data in the subset of related data are marked to generate a precision-related data set. The accuracy-related data set is stored back into the accuracy database according to segment number and process stage.

[0052] In this embodiment, the association construction unit is used to establish deterministic associations between segments in the spatial and temporal dimensions based on the existing structured precision data in the precision database, and to form a precision association data set that can be directly called by the subsequent state analysis unit. The core of this unit lies in transforming the originally discrete measurement point data into a data structure with clear correspondences through segment boundary constraints and a unique matching mechanism, thereby ensuring the accuracy and consistency of subsequent analysis results.

[0053] In practice, the spatial location and deviation data of the measuring points for each segment are first extracted from the accuracy database according to the segment number. The spatial location of the measuring point is a three-dimensional position represented in a unified hull coordinate system, where the planar coordinates are used to determine the position of the measuring point on the deck, and the height information is used to reflect the deviation of the measuring point from the design reference plane. The deviation data is the deviation value of the measuring point relative to the corresponding position in the design model. After extracting the data, the direct connection relationship between the segments is determined by combining the boundary definition information of each segment in the hull structure design model. The so-called direct connection relationship refers to the physical connection between two segments formed by welding or structural splicing during the actual assembly process. This relationship can be determined by the contact situation of the segment boundaries in the design drawings. In practice, the boundary of each segment can be represented as a set of continuous spatial boundary points or boundary line segments. When the boundaries of two segments coincide or are adjacent in space, it is determined that there is a direct connection relationship, and it is recorded as segment adjacency relationship data.

[0054] After determining the segmental adjacency relationships, the measurement points within the connection boundary region are further extracted. The connection boundary region refers to the set of measurement points located near the segment boundary that reflects the segmental connection status. In practice, a boundary region can be formed by extending a certain range into the segment from the segmental boundary line; for example, all measurement points within a certain distance from the boundary can be selected as a candidate set. Subsequently, the set of measurement points within this region is extracted from each of the two adjacent segments.

[0055] After obtaining the boundary measurement point sets of two adjacent segments, each measurement point needs to be matched. To ensure the uniqueness and stability of the matching relationship, this implementation introduces a unique matching rule. Specifically, for each measurement point in the target segment, the measurement point whose spatial position is closest to it is found in the boundary measurement point set of the adjacent segments as the matching object. In the search process, the spatial distance between the target measurement point and each candidate measurement point of the adjacent segment is first calculated. This spatial distance can be determined by the three-dimensional distance between the two points, that is, it is calculated based on the difference in position between the two points in the ship's coordinate system. For example, if the difference between the target measurement point and the candidate measurement point in terms of planar position and height is small, then their spatial distance is small. Then, the measurement point with the smallest distance is selected from all candidate measurement points as the matching object. At the same time, to avoid cross-regional incorrect matching, it is also required that the selected measurement point must be located within the same connecting boundary position range, that is, its projected position should be within the same boundary area. Through the above constraints, each target measurement point can obtain a unique corresponding adjacent measurement point, thereby forming unique boundary matching data.

[0056] After completing the measurement point matching, time and process information are further incorporated to organize the matching results. Specifically, the time stamps and process identifiers contained in the structured precision data are used to sort the data of each matched measurement point at different construction stages. The construction sequence refers to the order in which segments are assembled, welded, and adjusted during the actual construction process, and this sequence can be determined by time stamps or construction plans. In this embodiment, the matching measurement point data collected at different construction stages on the same connection boundary are arranged chronologically and grouped by process stage to form boundary association sequence data. This sequence data not only contains spatially unique corresponding measurement point pairs but also reflects the change trajectory of these measurement points during the construction process, that is, the evolution of deviation at the same location at different times.

[0057] After obtaining the boundary association sequence data, a subset of associated data is constructed centered on the target segment. This subset includes not only the measurement point data of the target segment itself, but also the measurement point data of its matching adjacent segments, as well as the historical data of the target segment from previous processes. During the construction process, each data record is marked with its source, such as "current segment data," "adjacent segment matching data," or "historical stage data," and its corresponding connection boundary position is recorded. In this way, data from different sources can be clearly distinguished within the same data subset while maintaining their correspondence.

[0058] Furthermore, to ensure data traceability and consistency, when constructing the precision-related data set, each subset of related data is bound to its corresponding segment number and process stage, and a unique identifier is assigned to it. This way, in subsequent analysis, the corresponding related data can be directly retrieved based on the segment number and construction stage without requiring repeated matching and construction operations.

[0059] Finally, the generated precision-related data set is stored back into the precision database. During storage, indexes can be created according to segment numbers and process stages to support fast queries. Simultaneously, the related data of the same segment at different process stages are organized chronologically to form a complete construction process data chain.

[0060] The status analysis unit 103 is used to perform correlation analysis and deviation level assessment on the flatness deviation of each segment based on the accuracy correlation data set, according to the segment assembly sequence and the interlayer relationship of the vehicle deck, and generate quality evaluation indicators that characterize the quality status and deviation risk level of each process.

[0061] In this embodiment, the state analysis unit 103 is used to systematically analyze the flatness deviation of each segment at different construction stages based on the accuracy correlation data set generated by the correlation construction unit, and to form quality evaluation indicators that can be directly used for construction management and scheduling decisions. The core function of this unit is to further transform data with spatial and temporal correlation into a quality state description with clear engineering significance, so that the original measurement data can reflect the distribution characteristics, evolution trend and potential risks of errors during construction.

[0062] In specific implementation, the state analysis unit 103 first retrieves the accuracy-related data set of the corresponding segment under the target construction stage from the accuracy database. This data set already includes the measurement data of the current segment itself, data of adjacent segments, and historical stage data, thus reflecting the multi-source influences on the segment in the current state. After reading the data, a basic analysis of the flatness state of the current segment is first performed. The so-called flatness deviation refers to the deviation of each measuring point on a segment from the design reference surface. This deviation has been recorded in the form of deviation during the data access stage. In this embodiment, the overall flatness state of a segment can be obtained by statistically describing the deviation of all measuring points in the segment, such as counting the number of measuring points with large deviations, whether the deviation directions are consistent, and whether the deviation distribution is concentrated in a certain area.

[0063] Based on this, the state analysis unit 103 performs correlation analysis on the deviations according to the segment closure sequence. The segment closure sequence refers to the order in which the segments are installed and fixed during construction, and this sequence determines the transmission path of errors within the structure. For example, if segment A is installed first, and then segment B is connected to it, the flatness of segment A will directly affect the installation of segment B. In practice, the deviation of the current segment can be compared with the deviation of its predecessor segment during closure to determine whether the current deviation is consistent with or superimposed on the previous segment. For example, when two adjacent segments show deviations in the same direction at the connection boundary, it can be considered that there is an error continuation phenomenon; when the deviations are in opposite directions but close in value, they may cancel each other out. In this way, the transmission characteristics of errors during construction can be identified.

[0064] Furthermore, the state analysis unit 103 performs cross-layer correlation analysis on the deviation in conjunction with the inter-layer relationships of the vehicle deck. Inter-layer relationships refer to the vertical connections between multiple decks formed by columns, supporting components, or frame structures. In PCTC roll-on / roll-off ships, deformation of the upper deck may be transmitted to the lower deck through the supporting structure, and vice versa. Therefore, when analyzing the flatness deviation of a certain deck segment, it is necessary to consider the segment status of the corresponding areas above and below it simultaneously. In specific implementations, the corresponding segments above or below the current segment can be identified according to a preset inter-layer mapping relationship, and their deviation data can be included in the analysis scope. For example, when a segment in the upper layer shows a significant concave deviation, and its corresponding segment in the lower layer shows a similar trend, it can be determined that the deviation has cross-layer transmission characteristics, thereby improving the accuracy of risk assessment.

[0065] After completing the aforementioned correlation analysis, the state analysis unit 103 assesses the flatness deviation of each segment. The deviation level refers to the result of classifying the quality status based on the degree of deviation. In this embodiment, several level ranges can be preset according to actual engineering requirements. For example, a state where the deviation is controlled within a small range is defined as a normal state; a state where the deviation reaches a certain threshold but can still be corrected through simple adjustments is defined as a warning state; and a state where the deviation exceeds the allowable range and may affect the structural quality is defined as a risk state. In the specific judgment process, the maximum deviation value of the segment's measuring points, the area of ​​concentrated deviation, and the trend of deviation over time can be comprehensively considered. For example, if the deviation of a segment continues to increase in two consecutive construction stages, even if the current deviation has not yet exceeded the control threshold, it can still be judged as a high-risk level.

[0066] Based on this, the status analysis unit 103 generates quality evaluation indicators. These indicators are a comprehensive expression of the construction quality status of each segment, including not only deviation levels but also descriptions of deviation trends and the scope of influence. For example, for a specific segment, an evaluation result can be generated stating that "the current state is under warning, deviations are mainly concentrated in the boundary area, and have shown a continuous increasing trend in the recent construction phase." These evaluation indicators can be represented in a structured form, such as recording deviation levels, trends, and affected areas as fields, facilitating subsequent use by the control and scheduling unit.

[0067] Through the above processing, the status analysis unit 103 realizes the transformation from precision-related data to quality evaluation indicators, so that complex multi-source data relationships can be summarized into quality status information with clear engineering significance.

[0068] Furthermore, the state analysis unit is specifically used for: Based on the accuracy-related dataset, the deviation data of the target segment measurement points are extracted, and the segment assembly connection boundary is divided into boundary area and internal area to generate regional deviation feature data. Based on the regional deviation feature data and the deviation data of the corresponding connection boundary of adjacent segments, the consistency of deviation direction and the closeness of amplitude are judged, and the boundary transmission judgment result is generated. Based on the boundary transfer judgment results and the segment merging order, the deviation between the target segment and the preceding segment is continuously compared in stages to generate cumulative deviation feature data. Based on the deviation accumulation feature data and the deviation consistency judgment results of the corresponding segments between vehicle deck layers, inter-layer coupling feature data is generated. Based on regional deviation characteristic data, boundary transfer judgment results, deviation accumulation characteristic data, and inter-layer coupling characteristic data, the dominant deviation source of the target segment is determined according to the preset deviation dominant judgment rule, and quality evaluation index is generated according to the dominant deviation source and the combination relationship of each characteristic data.

[0069] In this embodiment, the state analysis unit is used to perform multi-dimensional analysis of the flatness deviation of the target segment based on the accuracy-related data set, and generate quality evaluation indicators that can directly reflect the construction quality status and risk sources. The accuracy-related data set has been formed by the preceding processing and includes the measurement point deviation data of the target segment in the current process stage, the deviation data of adjacent segments at the corresponding connection boundary positions, and historical data of the preceding construction stages. Therefore, it can completely reflect the spatial environment and temporal evolution process of the target segment.

[0070] In specific implementation, firstly, all measurement point deviation data of the target segment are extracted from the accuracy-related data set. This measurement point deviation data represents the deviation information of each measurement point relative to the design reference plane, which has already been obtained in the previous processing. To identify the spatial distribution characteristics of the deviations in the structure, the target segment needs to be divided into regions. The so-called segment assembly connection boundary refers to the edge area where the segment directly connects with adjacent segments during construction; this area typically corresponds to welded joints or structural splicing locations. In this embodiment, a boundary region can be formed by extending a certain distance into the segment based on the segment boundary line defined in the design model. This distance can be preset based on actual engineering experience, for example, ranging from several tens to several hundred millimeters, to cover areas that may be affected by assembly. The remaining area, excluding this boundary region, is defined as the internal region. Subsequently, the measurement point deviation data belonging to the boundary region and the measurement point deviation data of the internal region are statistically analyzed separately to form regional deviation characteristic data. This characteristic data can include the concentration of deviations within the boundary region, such as the proportion of measurement points with large deviations within this region, and the dispersion of deviations within the internal region, such as the spatial uniformity of the deviation distribution.

[0071] After obtaining the regional deviation characteristic data, the deviation data of adjacent segments at the connection boundary are further introduced for comparative analysis. An adjacent segment refers to a segment that has a direct connection with the target segment in the segment assembly relationship, which has been determined by the associated construction unit. In specific implementation, the deviation data of the measurement points of adjacent segments at the corresponding connection boundary positions are extracted from the accuracy association data set and compared point-by-point or region-by-region with the deviation data within the boundary area of ​​the target segment. During the comparison, two aspects need to be determined: firstly, whether the deviation directions are consistent, i.e., whether the deviations of the two segments at corresponding positions are simultaneously higher or lower than the design datum plane; secondly, whether the deviation magnitudes are similar, i.e., whether the difference between the two is within the allowable range. For example, if the measurement points in a certain boundary area of ​​the target segment show an overall upward convex trend, and the corresponding positions of the adjacent segments also show an upward convex trend in the same direction, and the difference in their deviations is small, then it can be determined that the deviation directions are consistent and the magnitudes are similar. Based on this, a boundary transmission determination result is generated to characterize whether the error continues or is transmitted between segments.

[0072] Subsequently, based on the boundary transfer determination results and in conjunction with the segment closure sequence, the changes in deviation over time are analyzed. The segment closure sequence refers to the order in which each segment is installed and fixed during construction; this order determines the path of error transmission within the structure. In practice, the deviation data of the target segment at the current stage can be compared with its deviation data in the previous stage, and a comprehensive analysis can be performed in conjunction with the deviation data of its preceding segments during closure. If the deviation direction of the target segment remains consistent across multiple consecutive construction stages, and the deviation magnitude shows a gradually increasing trend, it can be determined as a deviation accumulation phenomenon. For example, if, in three consecutive construction stages of a segment, the deviation in its boundary area gradually increases from a small value to a large value, and the direction is consistent, then the deviation can be considered to have a continuous accumulation characteristic. Through the above comparative analysis, deviation accumulation characteristic data is generated to describe the evolution of deviation over time.

[0073] After completing the time-dimensional analysis, the impact of the inter-layer relationship of the vehicle deck on the deviation is further considered. The inter-layer relationship refers to the vertical correspondence between upper and lower layers in a multi-layer deck structure, formed by supporting components. In this embodiment, based on a preset inter-layer mapping relationship, the upper or lower layer segment corresponding to the target segment in its spatial projection position can be identified, and the deviation data of the corresponding segment within that region can be extracted. Subsequently, the current deviation of the target segment is compared and analyzed with the deviation of the corresponding segment. If, within the corresponding region, the deviation directions of the upper and lower layer segments are consistent and their trends are similar, it can be determined that an inter-layer coupling relationship exists, meaning that the deviation is transmitted between different layers through structural supports. For example, if a concave deviation occurs in a certain area of ​​the upper layer, and the corresponding area of ​​the lower layer also shows a similar trend, then the deviation can be considered to have cross-layer influence characteristics. Through the above analysis, inter-layer coupling characteristic data is generated.

[0074] After obtaining regional deviation characteristic data, boundary transfer judgment results, deviation accumulation characteristic data, and inter-layer coupling characteristic data, the state analysis unit comprehensively determines the source of deviation in the target segment. The so-called dominant source of deviation refers to the factor that plays a major role in the formation of the current deviation among various influencing factors. In this embodiment, a set of deviation dominance judgment rules is preset to prioritize different characteristics. For example, when the boundary transfer judgment result indicates a significant error continuation, and the deviation accumulation characteristic data indicates a continuous increase in deviation, it can be preferentially determined as a deviation caused by segment assembly transfer; when the inter-layer coupling characteristic data is significant, but the boundary transfer is not significant, it can be determined as a cross-layer deviation caused by structural support; when deviation exists only in the internal region and there are no obvious transfer or accumulation characteristics, it can be determined as a deviation caused by local processing or construction factors. Through the above rules, various characteristics are combined and judged to determine the dominant source of deviation in the target segment.

[0075] After identifying the primary source of deviation, the status analysis unit further generates quality evaluation indicators by combining the relationships between various characteristic data. These quality evaluation indicators not only include the risk level but can also encompass the type of deviation source and its impact range. For example, an evaluation result can be generated stating that "cumulative deviations caused by boundary transmission have a high risk level and primarily affect the connecting boundary area." This evaluation indicator can be stored in structured data format, including segment number, process stage, risk level, primary source of deviation, and affected area, thus providing a clear basis for subsequent control and scheduling units.

[0076] The state analysis process will be further described below with reference to a specific embodiment, so that those skilled in the art can more intuitively understand and implement the present invention.

[0077] Suppose that during the construction of a PCTC (Ro-Ro) ship, an analysis is conducted on a target section of the second vehicle deck, designated D2-S05. At this stage, the initial inspection phase following welding is underway. Deviation data from 120 measuring points on this section are collected using measurement equipment. The assembly connection boundary is determined based on the section design model, and the measuring points are divided according to the preset boundary range. 40 measuring points are located within the boundary area, while the remaining 80 are located in the inner area. Statistical analysis of the data reveals that 30 measuring points within the boundary area exhibit a significant upward convex deviation, with the deviation values ​​concentrated in a higher range. In contrast, the deviations at the measuring points in the inner area are more dispersed, showing no clear concentration trend. Therefore, the regional deviation characteristic data can be obtained, indicating that the deviations of this section are mainly concentrated in the boundary area.

[0078] Subsequently, data from adjacent segments directly connected to the current segment are extracted from the accuracy-related dataset. It is assumed that this segment is directly connected to two segments numbered D2-S04 and D2-S06. At the corresponding connection boundary, the deviation data of the measurement points of these two adjacent segments are extracted and compared with the data from the boundary region of the target segment. The comparison results show that at the boundary location connected to D2-S04, the measurement points of both segments exhibit upward convex deviations, and the deviation values ​​are relatively similar; at the location connected to D2-S06, no significant consistency is found. Based on this, it can be determined that there is an error propagation relationship at the connection boundary with D2-S04, while there is no significant propagation on the other side. In the time dimension, deviation data for the target segment in the previous process stage and the stage before that are further retrieved. For example, in the previous stage, the deviation in the boundary region of this segment was small, but it has increased significantly in the current stage, and shows a continuous increasing trend compared to earlier stages. Simultaneously, the deviation data of D2-S04 at its closure also shows a similar direction of deviation. By comparing continuous stages, it can be determined that the deviation has cumulative characteristics, and cumulative deviation characteristic data can be formed.

[0079] In the inter-layer relationship analysis, the corresponding segment in the third deck, for example, numbered D3-S05, was further identified. Deviation data of this upper segment within the same projection area was extracted, revealing that it also exhibits an upward convex deviation in the corresponding area, and its trend in the two most recent construction phases is consistent with that of the target segment. Based on this, it can be determined that the deviation exhibits cross-layer coupling characteristics. After obtaining the aforementioned regional deviation characteristics, boundary transmission judgment results, deviation accumulation characteristics, and inter-layer coupling characteristics, a comprehensive judgment is made according to the preset deviation-dominant judgment rules. Since this segment simultaneously satisfies three conditions—concentrated deviation in the boundary area, existence of error transmission relationship with adjacent segments, and continuous deviation accumulation—its dominant deviation source can be preferentially determined to be a cumulative deviation caused by segment assembly transmission. Furthermore, the inter-layer coupling characteristics further indicate that this deviation has a risk of expansion.

[0080] Ultimately, the generated quality evaluation index can be described as follows: this segment is in a high-risk state, with deviations mainly originating from error propagation at the connection with the preceding segment, accumulating continuously during construction, and showing a tendency to propagate to the upper structure. This evaluation result can be further used to guide subsequent construction scheduling, such as prioritizing correction of the connection boundary area and adjusting the construction sequence of related segments.

[0081] Furthermore, the state analysis unit is also used for: Based on regional deviation feature data, the set of measurement points on the connecting boundary of the target segment is extracted, and the set of measurement points is continuously segmented according to the spatial position of the measurement points on the connecting boundary to generate boundary measurement point segment data. Based on the segmented data of boundary measurement points and the deviation data of adjacent segments at the corresponding connecting boundary positions, the measurement points in each boundary measurement point segment are matched one by one, and it is determined whether the deviation directions between the matched measurement points are consistent, generating direction consistency judgment data. Based on the direction consistency determination data, the deviation difference of the matching measurement point pairs that meet the direction consistency is calculated, and it is determined whether the deviation difference is less than a preset range, thereby generating amplitude proximity determination data. Based on the direction consistency judgment data and amplitude proximity judgment data, the number of consecutive measurement points that meet the conditions of direction consistency and amplitude proximity is counted for each boundary measurement point segment. Only when the number of consecutive measurement points reaches the preset minimum number of consecutive points is it determined that there is a deviation transmission relationship in the boundary measurement point segment, and segment transmission judgment data is generated. Based on the segmented transfer judgment data corresponding to each boundary measurement point segment, the boundary transfer judgment result between the target segment and adjacent segments is comprehensively determined.

[0082] In this embodiment, after completing the basic deviation extraction and region division, the state analysis unit further refines the determination of whether there is a deviation transmission relationship along the connection boundary between the target segment and adjacent segments. This determination process takes spatial continuity as the core constraint, and through segmentation processing of measurement points on the connection boundary and point-by-point matching analysis, it ensures that the obtained results have clear structural meaning and repeatability, thereby avoiding the instability caused by judging based solely on single-point or local data.

[0083] In practice, the first step is to extract a set of measuring points located on the connection boundaries from the target segment based on the aforementioned regional deviation feature data. Measuring points on the connection boundaries refer to those points located near the segment boundaries during segment assembly and capable of reflecting the segment connection status. These measuring points are typically distributed along the segment boundary lines in the ship's coordinate system. To effectively organize these measuring points, they need to be sorted according to their spatial positions on the connection boundaries. Specifically, this can be achieved by projecting the measuring points onto the boundary line and arranging them in the order of their projection positions, thus forming a sequence of measuring points continuously distributed along the boundary.

[0084] After sorting, the measurement point sequence is divided into continuous segments. Continuous segmentation refers to dividing the measurement point sequence into several subsequences based on the spatial distance between adjacent measurement points. When the spatial distance between two adjacent measurement points is less than a preset distance, they are considered to belong to the same continuous segment; when the distance is greater than this distance, it is used as the starting point of a new continuous segment. For example, if there are ten measurement points on a boundary, where the first six measurement points are closely spaced and continuously distributed, while the last four measurement points are significantly spaced from the preceding points, then this set of measurement points can be divided into two continuous segments. Through this process, boundary measurement point segmentation data is generated, so that each segment represents a spatially continuous region on the boundary.

[0085] After obtaining the segmented boundary measurement point data, the deviation data of measurement points at the corresponding connecting boundary positions of adjacent segments are further introduced for matching processing. For each measurement point in the target segment, based on its spatial position in the ship's coordinate system, the measurement point with the closest spatial position in the corresponding boundary region of the adjacent segment is found as the matching object, thus forming a measurement point pair. This matching process is based on the proximity relationship of spatial positions, enabling the matched measurement points to reflect the state of the same or adjacent physical locations.

[0086] After completing the measurement point matching, the consistency of deviation direction is judged for each pair of matched measurement points. The deviation direction refers to the direction of deviation of the measurement point relative to the design reference plane, i.e., upward or downward deviation. In practice, when the deviations of two matched measurement points are both positive or both are negative, their deviation directions are considered consistent; when one is positive and the other is negative, their directions are considered inconsistent. By performing this judgment on each pair of measurement points, direction consistency judgment data is generated.

[0087] Based on this, the amplitude proximity of measurement point pairs that meet the directional consistency condition is further assessed. Specifically, the deviation difference is calculated for each pair of matched measurement points; this difference is the difference between the absolute values ​​of the deviations of the two measurement points. For example, if the deviation of a measurement point in the target segment is three millimeters, and the deviation of the corresponding measurement point in the adjacent segment is four millimeters, then the difference is one millimeter. Subsequently, this difference is compared with a preset range. If the difference is less than this range, the amplitudes are determined to be close; otherwise, they are determined to be not close. Through this process, amplitude proximity judgment data is generated, thereby filtering out measurement point pairs that meet both the directional consistency and amplitude proximity conditions.

[0088] After obtaining the data for directional consistency and amplitude proximity, statistical analysis is performed on each boundary measurement point segment. Specifically, in each continuous segment, the number of measurement points that simultaneously meet the conditions of directional consistency and amplitude proximity is counted, and it is further determined whether these measurement points form a continuous spatial distribution. If the number of measurement points meeting the conditions in a certain continuous segment reaches a preset minimum number of consecutive points, then a stable deviation transmission relationship is considered to exist within that segment. The so-called minimum number of consecutive points refers to the threshold used to determine continuity; for example, it can be set to no less than three consecutive measurement points to avoid misjudging a transmission relationship due to the occasional consistency of individual measurement points. Through the above statistics and judgment, segmented transmission determination data is generated, which is used to identify whether deviation transmission exists in each boundary segment.

[0089] Finally, based on the segmented transfer judgment data corresponding to each boundary measurement point segment, a comprehensive judgment is made on the overall boundary transfer relationship between the target segment and adjacent segments. When at least one boundary segment is determined to have a deviation transfer relationship, it can be considered that a boundary transfer relationship exists between the target segment and adjacent segments; when none of the segments meet the condition, it is determined that no transfer relationship exists. This comprehensive result is the boundary transfer judgment result, which is used for subsequent deviation source analysis and scheduling decisions.

[0090] Through the above implementation method, the state analysis unit not only considers single-point deviation, but also judges the deviation transmission through spatial continuity constraints, so that the analysis results can truly reflect the structural influence relationship between segments.

[0091] Furthermore, the state analysis unit is also used for: Based on the boundary transfer determination results, the connecting boundary regions with boundary transfer relationships in the target segment are screened out, and the measurement point deviation data of the connecting boundary regions in the current process stage and at least two preceding process stages are extracted to generate stage deviation sequence data. Based on the stage deviation sequence data, the measurement point deviation data of each stage are aligned according to the segment merging order, so that the measurement points with the same location are in a one-to-one correspondence in different process stages, and a stage deviation sequence with position alignment is generated. Based on the position-aligned stage deviation sequence, the deviation direction change of each measuring point in the continuous process stage is judged, and the set of measuring points with consistent deviation direction in multiple continuous stages is selected to generate directionally stable measuring point data. Based on the directional stability measurement point data, the deviation change of each measurement point in adjacent process stages is calculated, and the set of measurement points with continuously increasing changes is selected to generate cumulative measurement point data. Based on the accumulated measurement point data, the measurement points are grouped according to the connecting boundary regions, and the number of measurement points that meet the conditions and their spatial continuous distribution in each group are counted. The boundary group that meets the conditions and has the most measurement points is selected as the dominant accumulation region. Only when the number of measurement points in the dominant accumulation region reaches the preset minimum number is it determined that there is a deviation accumulation relationship in the target segment, and deviation accumulation feature data is generated.

[0092] In this embodiment, the state analysis unit, after determining the boundary transfer relationship, further determines whether the deviation of the target segment accumulates continuously during construction, in order to identify key areas that may lead to a gradual amplification of structural errors. This process, based on the boundary transfer relationship, achieves stable identification of the deviation evolution trend through unified alignment and continuous change analysis of data from different construction stages, thereby avoiding misjudgments caused by relying solely on data from a single stage.

[0093] In practice, the first step is to identify connected boundary regions with boundary transfer relationships from the target segments based on the boundary transfer determination results. A connected boundary region refers to the boundary portion determined in the aforementioned analysis to have a stable deviation transfer relationship with adjacent segments. For this region, the deviation data of its measurement points in the current process stage and at least two preceding process stages are extracted from the accuracy correlation dataset. A process stage refers to the different operational stages that a segment undergoes sequentially during construction, such as assembly, welding, straightening, and re-measurement. By extracting data from multiple consecutive process stages, a stage deviation sequence data reflecting the change of deviation over time can be formed. For example, for a certain connected boundary region, the deviation data of the measurement points in this region are obtained in three consecutive stages, thus forming a sequence containing three time nodes.

[0094] After obtaining the stage deviation sequence data, it is necessary to perform position alignment processing on the data from different stages. Position alignment refers to matching measurement points belonging to the same spatial location in different process stages, creating a one-to-one correspondence in the sequence. In this embodiment, this alignment process relies on the position identifiers generated in the aforementioned data access unit. Since each measurement point has a unique position identifier, data from different stages can be directly matched based on this identifier. For example, when a certain position identifier has corresponding measurement points in all three stages, these three measurement points can be arranged in chronological order to form a deviation change sequence for that position. By processing all position identifiers, a position-aligned stage deviation sequence is generated, enabling subsequent analysis to compare data based on the same physical location.

[0095] After completing the position alignment, the deviation direction changes of each measuring point in consecutive process stages are judged. The deviation direction refers to the direction of deviation of the measuring point relative to the design reference plane, i.e., upward or downward deviation. In practice, for multiple stages of data corresponding to the same position marker, it is determined whether the deviation sign remains consistent. For example, if the deviation of a measuring point is positive in three consecutive stages, it is considered that the direction of the measuring point remains consistent within that time period; if there are alternating positive and negative values, the direction is considered unstable. By performing this judgment on all measuring points, a set of measuring points with consistent deviation direction across multiple consecutive stages is selected, generating directionally stable measuring point data. This set reflects the region where the deviation has a continuous trend.

[0096] Based on this, further analysis of the variation in directionally stable measuring points is conducted. The variation in deviation refers to the magnitude of the change in deviation value of the same measuring point between adjacent process stages. For example, if the deviation of a measuring point in three stages is two millimeters, three millimeters, and five millimeters, then the variations in adjacent stages are one millimeter and two millimeters, respectively. In this embodiment, the variation in deviation between adjacent stages is calculated for each directionally stable measuring point, and it is determined whether the variation shows a continuously increasing trend. When the variation of a measuring point gradually increases in consecutive stages, it indicates that its deviation not only persists but also increases at an accelerating rate, thus it can be considered to have cumulative characteristics. By selecting measuring points that simultaneously meet the conditions of consistent direction and increasing variation, cumulative measuring point data is formed.

[0097] After obtaining the cumulative measurement point data, the measurement points are further grouped according to the boundary regions. Specifically, based on the spatial location of the measurement points on the boundary, they are divided into several continuously distributed sub-regions, each corresponding to a measurement point group. Within each group, the number of measurement points that meet the accumulation criteria is counted, and it is determined whether these measurement points form a continuous spatial distribution. A continuous distribution means that the spatial distance between adjacent measurement points is less than a preset range, thus reflecting that the area is a whole affected area. Through this statistical analysis, the cumulative number of measurement points and their spatial distribution in each group can be obtained.

[0098] Among multiple groups, the boundary group with the largest number of measurement points that meets the criteria is selected as the dominant accumulation region. The dominant accumulation region refers to the region that has the greatest impact on the overall cumulative deviation among all candidate regions. In practice, this can be determined by comparing the number of measurement points that meet the criteria in each group. When the number of measurement points in a certain group is significantly higher than in other groups, it is selected as the dominant region. For example, if three, five, and eight measurement points meet the criteria in three groups respectively, then the group containing eight measurement points is selected as the dominant accumulation region.

[0099] After determining the dominant accumulation region, it is also necessary to determine whether this region meets a preset minimum number condition. This minimum number is used to avoid misjudging an accumulation relationship due to a small number of measurement points accidentally meeting the condition. In practical applications, this threshold can be set according to the segment size and measurement point density, for example, requiring at least three or more consecutive measurement points to meet the condition. When the number of measurement points in the dominant accumulation region reaches this threshold, it is determined that the target segment has a deviation accumulation relationship in the connection boundary region, and deviation accumulation feature data is generated; if it does not reach the threshold, it is not determined to be accumulation.

[0100] Through the above implementation methods, the state analysis unit can perform fine analysis of the evolution of deviation in the time dimension based on the boundary transfer relationship, and achieve reliable identification of deviation accumulation phenomena through spatial continuity constraints and quantity threshold judgment.

[0101] The control and scheduling unit 104 is used to receive quality evaluation indicators, combine them with preset construction resource constraints, process execution rules and flatness control thresholds, generate management instruction information including process scheduling plan, construction resource allocation plan and quality control decision, and store the management instruction information and corresponding quality evaluation indicators in the accuracy database to realize quality traceability.

[0102] In this embodiment, the control and scheduling unit 104 is used to make specific scheduling decisions and quality control over the construction process based on the quality evaluation indicators generated by the status analysis unit, combined with the resource conditions of the construction site, the process execution requirements, and the flatness control standards. It also associates and stores the relevant decision results with the original quality evaluation data, thereby forming a complete closed-loop quality management mechanism. The core function of this unit is to transform the quality status information obtained from the preceding analysis into executable construction instructions, enabling the data analysis results to directly affect construction organization and resource allocation.

[0103] In practice, the control and scheduling unit 104 first receives quality evaluation indicators from the status analysis unit. These indicators describe the flatness status, deviation risk level, and trend of each segment at the current construction stage. Based on this, the control and scheduling unit 104 introduces construction resource constraints. These constraints refer to various resource limitations available for scheduling during actual construction, including but not limited to the number of construction personnel, equipment availability, tooling and fixture configuration, work time windows, and work space conflicts. For example, the limited number of available welding stations within a certain time period, or the fact that a critical piece of equipment can only serve one segment at a time, are all examples of resource constraints.

[0104] Meanwhile, the control and scheduling unit 104 also considers process execution rules. Process execution rules refer to the technological sequence and operational constraints that must be followed during construction. For example, certain sections must be welded before installation can proceed, or certain correction procedures must be performed only after measurement confirmation. These rules can originate from construction process documents or empirical rules and are pre-stored in the system as a set of rules.

[0105] Based on the aforementioned resource constraints and process rules, the control and scheduling unit 104 generates specific scheduling decisions according to quality evaluation indicators. For segments with quality status within the normal range, the original construction plan can be maintained; for segments in a warning state, priority can be given to arranging retesting or partial adjustment processes; for segments assessed as being in a risk state, resources need to be allocated first for corrective processing. For example, when a segment is determined to be in a high-risk state and its deviation is concentrated in the boundary area, priority can be given to arranging construction teams with corrective capabilities to perform local heating or mechanical adjustment in that area, while delaying the installation of subsequent segments associated with it in the scheduling process to avoid further propagation of errors.

[0106] When generating a specific scheduling plan, the following logic can be followed: First, sort all construction segments according to quality evaluation indicators, prioritizing segments with higher risk levels. Second, consider resource constraints to determine the number and type of work that currently available resources can support. Then, while meeting the process execution rules, allocate corresponding resources to higher-priority segments and determine their construction time periods. Segments that are not allocated resources are postponed to subsequent time windows. In this way, a process scheduling plan containing the specific execution order, resource allocation, and time arrangement can be formed.

[0107] Simultaneously, the control and scheduling unit 104 also generates a construction resource allocation plan. This plan clarifies the allocation relationship of each type of resource among different segments. For example, it specifies the segment served by a particular surveying device at different time periods, or the specific tasks undertaken by a construction team at different work stages. Through this resource allocation plan, resource conflicts can be avoided, and resource utilization efficiency can be improved.

[0108] Based on this, the control and scheduling unit 104 further generates quality control decision information. This decision information includes not only specific construction adjustment measures, but also suggestions for subsequent construction strategies, such as increasing measurement frequency, adjusting assembly sequence, or strengthening quality monitoring in a certain area. This type of information can be represented in a structured form, such as recording target segments, suggested measure types, execution priorities, and expected effects in the form of fields, thereby facilitating understanding and implementation by construction management personnel.

[0109] After generating the aforementioned process scheduling plan, resource allocation plan, and quality control decisions, the control and scheduling unit 104 organizes this information into management instruction information and stores it in association with the corresponding quality evaluation indicators. Specifically, each management instruction information can be appended with a quality evaluation identifier of its source, such as the corresponding segment number, process stage, and risk level, thereby forming a one-to-one correspondence in the database. In this way, when it is necessary to trace a quality problem later, the scheduling decision generated at that time and its execution background can be directly found, thus realizing full-process quality traceability.

[0110] Through the above implementation methods, the control and scheduling unit 104 realizes the transformation from quality evaluation to construction decision-making, enabling the system to not only have data analysis capabilities, but also actual construction management capabilities.

[0111] Based on the above implementation methods, the workflow of the control and scheduling unit 104 will be further described in detail below with reference to a specific embodiment.

[0112] Assuming that during the construction of a PCTC (Plan-Do-Check-Act) roll-on / roll-off (Ro-Ro) vessel, the current stage is the segment assembly of the second vehicle deck, the system contains segments D2-S01, D2-S02, D2-S03, and D2-S04 to be constructed. The status analysis unit has generated corresponding quality evaluation indicators for these segments. D2-S03 is classified as a high-risk state, with the dominant deviation source being boundary transfer, and the deviation concentrated in the connection boundary area with D2-S02; D2-S02 is classified as a medium-risk state, showing a certain trend of deviation accumulation; D2-S01 and D2-S04 are both in normal states.

[0113] At this stage, the resource constraints at the construction site are as follows: there is only one hydraulic adjustment device for deck straightening, two groups of construction personnel, and one surveying team; at the same time, the construction process stipulates that the segment positioning and initial welding must be completed first before the straightening process can be carried out, and after the straightening is completed, a retest must be conducted for confirmation before the next segment assembly process can begin.

[0114] During the specific scheduling process, the control and scheduling unit 104 first sorts the segments based on quality evaluation indicators. Since D2-S03 is in a high-risk state and the deviation originates from boundary propagation, it is prioritized first; followed by D2-S02; the other two segments are ranked later. Subsequently, based on resource constraints, the system determines that only one segment can currently undergo correction. Therefore, the only available hydraulic adjustment equipment is preferentially allocated to D2-S03, along with a team of construction workers, and a measurement team is arranged to conduct a re-measurement after the correction is completed.

[0115] After resource allocation, the system further verifies the process execution rules. Since D2-S03 has already completed initial welding and meets the correction conditions, its scheduling is valid. For D2-S02, although its risk is higher, its operation is postponed to the next time window because its resources are currently occupied by D2-S03. Simultaneously, the system checks the status of its preceding processes and confirms that it still requires partial welding reinforcement; therefore, correction work is not scheduled for the time being.

[0116] After determining the construction sequence, the control and scheduling unit 104 generates a specific work process scheduling plan. For example, during the current time period, D2-S03 is scheduled to perform boundary area correction work, which is expected to take two hours. After the correction is completed, a retest will be conducted immediately, and a decision will be made on whether to make a second adjustment based on the retest results. During this period, another group of construction workers will be arranged to prepare D2-S01 for the next work process to improve the overall construction efficiency.

[0117] Simultaneously, the system generates a construction resource allocation plan, which clearly records that the hydraulic equipment corresponds to service segment D2-S03 in the current time period. After the measurement team completes the calibration, it will re-measure this segment, while another group of construction personnel will perform auxiliary tasks. This plan ensures that the allocation of various resources in time is clear and conflict-free.

[0118] Regarding quality control decisions, for the case of D2-S03, the system generates the following decision information: perform key correction treatment in the connection boundary area between D2-S03 and D2-S02, and increase the measurement frequency in this area during subsequent construction; at the same time, it is recommended to postpone the closure operation of D2-S02 until it is confirmed that the deviation of D2-S03 has been effectively controlled, so as to prevent the error from being transmitted to subsequent segments.

[0119] After completing the above scheduling and decision-making, the control and scheduling unit 104 integrates the generated process scheduling plan, resource allocation plan, and quality control measures into a single management instruction. For example, this instruction might include the following: the target segment is D2-S03, the execution time is the current time window, the resources used are hydraulic equipment and construction team A, the operation to be performed is boundary correction and retesting, and the quality control requirement is to focus on monitoring the connection boundary area. This management instruction also includes its corresponding quality evaluation indicators, including risk level and sources of deviation.

[0120] Finally, the system associates and stores the management instruction information with the quality evaluation indicators and writes it into the accuracy database. Through this association, the quality status of the segment at a specific point in time and the corresponding scheduling measures taken can be traced during subsequent construction or quality analysis. For example, if an abnormal deviation is subsequently found in D2-S02, the database can be queried to obtain the handling status of D2-S03 in previous stages, thereby analyzing whether there is a propagation relationship in the error.

[0121] Furthermore, the control and scheduling unit is specifically used for: Based on quality evaluation indicators, the construction segments are sorted according to the deviation risk level and dominant deviation source of each segment, and segment priority sequence data is generated. Based on the segmented priority sequence data and the preset construction resource constraints, construction resources are allocated to segments with higher priority. When multiple segments compete for the same construction resource, the segment with a higher risk level and whose dominant deviation source is boundary propagation or deviation accumulation is selected as the sole execution object, and the remaining segments are processed later to generate unique resource allocation data. Based on unique resource allocation data and process execution rules, the construction sequence of each segment is constrained and verified. When there is a process dependency conflict, the sequence of segments that do not meet the prerequisite conditions is adjusted to generate scheduling sequence data that satisfies the constraints. Based on the scheduling sequence data and the flatness control threshold, the construction time and execution order of each segment are determined, and the process scheduling scheme data is generated. In the process scheduling scheme, the connection boundary processing process is given priority to the segment where the main deviation source is boundary propagation, so as to suppress the continued propagation of deviation. Based on the process scheduling scheme data, the risk level and dominant deviation source of the delayed processing segment are recalculated, feedback correction data is generated, and the segment priority sequence data is updated according to the feedback correction data to form dynamic scheduling update data. Based on dynamic scheduling update data and process scheduling scheme data, management instruction information containing construction sequence, resource allocation and quality control measures is generated, and the management instruction information is associated with the corresponding quality evaluation indicators and stored in the accuracy database.

[0122] In this embodiment, the control and scheduling unit is used to make comprehensive decisions on the segmented execution sequence, resource allocation and quality control measures in the construction process based on the quality evaluation indicators generated by the status analysis unit, and to form executable management instructions. At the same time, it realizes dynamic linkage between the scheduling process and the quality status, thereby ensuring that the deck flatness control target can be effectively implemented in the actual construction process.

[0123] In practice, the system first receives quality evaluation indicators from the status analysis unit. These indicators include the deviation risk level and dominant deviation source for each segment at the current construction stage. The deviation risk level refers to the severity of the impact of the current deviation on the overall structural quality, categorized as low, medium, or high risk. The dominant deviation source refers to the factor that plays a major role in the current deviation among various contributing factors, such as boundary transfer, deviation accumulation, or local processing errors. Based on these indicators, all segments to be constructed are sorted, prioritizing risk level followed by dominant deviation source. For example, when two segments have the same risk level, the segment with boundary transfer or deviation accumulation as the dominant deviation source is prioritized, as these types of deviations tend to spread or amplify. This sorting process generates a segment priority sequence, reflecting the processing priority order of each segment during the current construction process.

[0124] After obtaining the segmented priority sequence data, construction resource constraints are introduced for resource allocation. These constraints refer to the limitations of available resources on the actual construction site, including the number of construction personnel, equipment, workstations, and time windows. For example, if only one piece of equipment for correcting deck flatness is available during a certain time period, this equipment constitutes a resource constraint. In practice, resources are allocated starting with the segment with the highest priority, according to the segmented priority sequence. When multiple segments simultaneously require the same resource, resource competition occurs. In this case, according to preset rules, only one segment is allowed to obtain the resource, selected based on a combination of risk level and dominant deviation source. For example, if one segment is high-risk and its dominant deviation source is boundary propagation, while another segment is medium-risk and its source is local error, the former should be prioritized as the sole execution target. Unselected segments are marked for delayed processing and enter the subsequent scheduling phase. Through this process, unique resource allocation data is formed, ensuring that each type of critical resource corresponds to only one execution target within the same time period, thereby avoiding resource conflicts.

[0125] After resource allocation is completed, the construction sequence is further constrained and verified. The process execution rules refer to the sequential relationships that must be followed during construction. For example, a segment can only be joined after its preceding segment has been welded, or a testing process can only be performed after correction is completed. In this embodiment, the unique resource allocation data is compared with the process execution rules. If a segment has acquired resources but does not meet the conditions of its preceding process, it is determined to be a process dependency conflict. For example, if a segment has been allocated welding resources, but its corresponding positioning or assembly process has not yet been completed, then that segment cannot be executed at present. In this case, the execution order of the segment needs to be adjusted, postponing it to the time point when the conditions are met, while advancing the execution of subsequent segments that meet the conditions. Through continuous verification and adjustment, scheduling sequence data that satisfies all process dependencies is generated.

[0126] After obtaining the scheduling sequence data, the specific construction time and execution order are determined by combining the flatness control threshold. The flatness control threshold refers to the allowable deviation range; when the deviation exceeds this range, corrective measures must be taken. In this embodiment, specific construction time periods are allocated to each segment based on its risk level and deviation status, forming a complete process scheduling plan. For example, for high-risk segments, corrective actions can be scheduled to be performed immediately within the current time window, while low-risk segments can be scheduled for execution in subsequent time periods. Simultaneously, for segments where the primary source of deviation is boundary propagation, the scheduling plan prioritizes boundary-connecting processing actions, such as prioritizing boundary welding adjustments or local corrections, to prevent further propagation of deviation to adjacent segments. In this way, the scheduling plan not only meets resource and process constraints but also proactively controls the deviation propagation path.

[0127] After the aforementioned scheduling scheme is formulated, the segments whose processing was delayed are re-evaluated. Since changes in the scheduling order may affect the development trend of deviations—for example, delayed processing may lead to further accumulation or spread of deviations—it is necessary to recalculate the risk level and dominant sources of deviation for these segments. In practice, the delayed segments can be re-entered into the state analysis logic, and their quality evaluation indicators can be updated by combining their latest time position and the status of adjacent segments, generating feedback correction data. For example, if a segment's adjacent segments have completed correction after a period of delay, its risk may decrease; conversely, it may increase.

[0128] Based on feedback correction data, the original segmented priority sequence is updated, thus forming dynamic scheduling update data. This update process enables the scheduling system to continuously adjust its decisions according to real-time changes during construction, rather than generating a fixed plan all at once. In practical applications, this update process can be executed periodically, for example, by re-evaluating and updating the scheduling sequence after each batch of construction tasks is completed, thereby forming a dynamic scheduling mechanism.

[0129] Finally, based on dynamically updated scheduling data and process scheduling scheme data, complete management instruction information is generated. This management instruction information includes the construction sequence of each segment, corresponding resource allocation, and specific quality control measures, such as whether correction, retesting, or enhanced monitoring is required. Simultaneously, this management instruction information is associated with corresponding quality evaluation indicators and stored in a precision database according to segment number and process stage. Through this associated storage, the quality background and execution basis corresponding to a specific decision can be accurately obtained when quality traceability is needed later, thereby achieving full-process traceability management of the construction process.

[0130] Through the above implementation methods, the control and scheduling unit realizes a closed-loop transformation from quality evaluation to construction decision-making, and continuously optimizes the scheduling results through a dynamic feedback mechanism, enabling construction organization and quality control to be carried out in a coordinated manner.

[0131] Although this application discloses preferred embodiments as described above, it is not intended to limit this application. Any person skilled in the art can make possible changes and modifications without departing from the spirit and scope of this application. Therefore, the scope of protection of this application should be determined by the scope defined in the claims of this application.

Claims

1. A construction quality data management and scheduling system based on PCTC (Planetary Controlled Towing and Turn-On) ship deck flatness, characterized in that, include: The data access unit is used to collect deck flatness measurement data, construction process parameters and section spatial location information corresponding to each process during the construction of the roll-on / roll-off ship sections. It also performs unified identification and format conversion processing on the data to generate structured precision data with time stamps and spatial coordinates, and stores the structured precision data in the precision database. The associated construction unit is used to read structured accuracy data from the accuracy database, perform association mapping on accuracy data of different times and different segments based on segmented assembly relationships and construction sequence, construct accuracy association datasets and merge them back into the accuracy database; The status analysis unit is used to perform correlation analysis and deviation level assessment on the flatness deviation of each segment based on the accuracy correlation data set, according to the segment assembly sequence and the interlayer relationship of the vehicle deck, and generate quality evaluation indicators that characterize the quality status and deviation risk level of each process. The control and scheduling unit is used to receive quality evaluation indicators, combine them with preset construction resource constraints, process execution rules and flatness control thresholds, generate management instruction information that includes process scheduling plan, construction resource allocation plan and quality control decision, and store the management instruction information and corresponding quality evaluation indicators in the accuracy database to realize quality traceability. Specifically, the state analysis unit is used for: Based on the accuracy-related dataset, the deviation data of the target segment measurement points are extracted, and the segment assembly connection boundary is divided into boundary area and internal area to generate regional deviation feature data. Based on the regional deviation feature data and the deviation data of the corresponding connection boundary of adjacent segments, the consistency of deviation direction and the closeness of amplitude are judged, and the boundary transmission judgment result is generated. Based on the boundary transfer judgment results and the segment merging order, the deviation between the target segment and the preceding segment is continuously compared in stages to generate cumulative deviation feature data. Based on the deviation accumulation feature data and the deviation consistency judgment results of the corresponding segments between vehicle deck layers, inter-layer coupling feature data is generated. Based on regional deviation feature data, boundary transfer judgment results, deviation accumulation feature data and inter-layer coupling feature data, the dominant deviation source of the target segment is determined according to the preset deviation dominant judgment rule, and quality evaluation index is generated according to the dominant deviation source and the combination relationship of each feature data. The state analysis unit is further configured to: Based on regional deviation feature data, the set of measurement points on the connecting boundary of the target segment is extracted, and the set of measurement points is continuously segmented according to the spatial position of the measurement points on the connecting boundary to generate boundary measurement point segment data. Based on the segmented data of boundary measurement points and the deviation data of adjacent segments at the corresponding connecting boundary positions, the measurement points in each boundary measurement point segment are matched one by one, and it is determined whether the deviation directions between the matched measurement points are consistent, generating direction consistency judgment data. Based on the direction consistency determination data, the deviation difference of the matching measurement point pairs that meet the direction consistency is calculated, and it is determined whether the deviation difference is less than a preset range, thereby generating amplitude proximity determination data. Based on the direction consistency judgment data and amplitude proximity judgment data, the number of consecutive measurement points that meet the conditions of direction consistency and amplitude proximity is counted for each boundary measurement point segment. Only when the number of consecutive measurement points reaches the preset minimum number of consecutive points is it determined that there is a deviation transmission relationship in the boundary measurement point segment, and segment transmission judgment data is generated. Based on the segmented transfer judgment data corresponding to each boundary measurement point segment, the boundary transfer judgment result between the target segment and the adjacent segment is comprehensively determined. The state analysis unit is further configured to: Based on the boundary transfer determination results, the connecting boundary regions with boundary transfer relationships in the target segment are screened out, and the measurement point deviation data of the connecting boundary regions in the current process stage and at least two preceding process stages are extracted to generate stage deviation sequence data. Based on the stage deviation sequence data, the measurement point deviation data of each stage are aligned according to the segment merging order, so that the measurement points with the same location are in a one-to-one correspondence in different process stages, and a stage deviation sequence with position alignment is generated. Based on the position-aligned stage deviation sequence, the deviation direction change of each measuring point in the continuous process stage is judged, and the set of measuring points with consistent deviation direction in multiple continuous stages is selected to generate directionally stable measuring point data. Based on the directional stability measurement point data, the deviation change of each measurement point in adjacent process stages is calculated, and the set of measurement points with continuously increasing changes is selected to generate cumulative measurement point data. Based on the accumulated measurement point data, the measurement points are grouped according to the connecting boundary regions, and the number of measurement points that meet the conditions and their spatial continuous distribution in each group are counted. The boundary group that meets the conditions and has the most measurement points is selected as the dominant accumulation region. Only when the number of measurement points in the dominant accumulation region reaches the preset minimum number is it determined that there is a deviation accumulation relationship in the target segment, and deviation accumulation feature data is generated.

2. The construction quality data management and scheduling system based on the flatness of the PCTC roll-on / roll-off ship deck as described in claim 1, characterized in that, The associated construction unit is specifically used for: Based on the structured precision data stored in the precision database, the spatial position and deviation data of each segment measurement point are extracted according to the segment number, and the direct connection relationship between segments is determined according to the spatial boundary information of the segments in the ship coordinate system, thus generating segment adjacency relationship data. Based on the segmented adjacency relationship data, the set of measurement points in the corresponding area of ​​each adjacent segment in the connection boundary is extracted. According to the spatial position of the measurement points on the connection boundary, the target segment measurement points are matched with the adjacent segment measurement points one by one. For each target measurement point, the adjacent measurement point with the smallest spatial distance and within the same boundary position range is selected as the unique matching object, and unique boundary matching data is generated. Based on the time stamps and process identifiers in the unique boundary matching data and structured precision data, the matching measurement points are arranged according to the construction sequence. The matching measurement point data of the same connection boundary at different process stages are combined according to the time sequence to generate boundary association sequence data containing unique spatial correspondence and temporal evolution relationship. Based on boundary-related sequence data, a subset of related data is constructed for each target segment, which includes its own measurement point data, unique matching data of adjacent segments and data of its preceding process stages. The source type and boundary position of each data in the subset of related data are marked to generate a precision-related data set. The accuracy-related data set is stored back into the accuracy database according to segment number and process stage.

3. The construction quality data management and scheduling system based on PCTC roll-on / roll-off ship deck flatness according to claim 1, characterized in that, The control and scheduling unit is specifically used for: Based on quality evaluation indicators, the construction segments are sorted according to the deviation risk level and dominant deviation source of each segment, and segment priority sequence data is generated. Based on the segmented priority sequence data and the preset construction resource constraints, construction resources are allocated to segments with higher priority. When multiple segments compete for the same construction resource, the segment with a higher risk level and whose dominant deviation source is boundary propagation or deviation accumulation is selected as the sole execution object, and the remaining segments are processed later to generate unique resource allocation data. Based on unique resource allocation data and process execution rules, the construction sequence of each segment is constrained and verified. When there is a process dependency conflict, the sequence of segments that do not meet the prerequisite conditions is adjusted to generate scheduling sequence data that satisfies the constraints. Based on the scheduling sequence data and the flatness control threshold, the construction time and execution order of each segment are determined, and the process scheduling scheme data is generated. In the process scheduling scheme, the connection boundary processing process is given priority to the segment where the main deviation source is boundary propagation, so as to suppress the continued propagation of deviation. Based on the process scheduling scheme data, the risk level and dominant deviation source of the delayed processing segment are recalculated, feedback correction data is generated, and the segment priority sequence data is updated according to the feedback correction data to form dynamic scheduling update data. Based on dynamic scheduling update data and process scheduling scheme data, management instruction information containing construction sequence, resource allocation and quality control measures is generated, and the management instruction information is associated with the corresponding quality evaluation indicators and stored in the accuracy database.

4. The construction quality data management and scheduling system based on PCTC roll-on / roll-off ship deck flatness according to claim 1, characterized in that, The data access unit is specifically used for: Based on the spatial location data of deck measuring points collected by the measuring equipment and the construction process parameters, coordinate transformation is performed on each measuring point according to the unified ship coordinate system, and the measuring point data obtained by different equipment is mapped to the same coordinate reference to generate unified coordinate measuring point data. Based on unified coordinate measurement point data and preset segmented spatial boundary information, the segmentation and affiliation of each measurement point are determined, and a unique location identifier is assigned to each measurement point according to its spatial projection position within the segment, so that the corresponding positions of the same segment at different acquisition times have consistent identifiers, and initial identifier data with location identifiers is generated. Based on the initial identification data, the actual measured height of each measuring point is compared with the corresponding design reference height to determine the deviation direction and deviation value of each measuring point. For measuring points with abnormal deviation changes, a consistency verification process is performed to generate valid deviation data. Based on the effective deviation data and location identifiers, the measuring points in the same segment and at the same process stage are fixedly sorted according to the location identifiers to generate structured measuring point sequence data with a unique spatial sequence. Based on structured measurement point sequence data, the measurement point data, segment number, process identifier, time marker and location identifier are combined to generate structured precision data with unique spatial correspondence, and the structured precision data is stored in the precision database.

Images