Railway construction data processing method and system

Abstract

The invention relates to the technical field of data processing, and discloses a railway construction data processing method and system. The method comprises the steps of performing semantic annotation on track laying, line measurement and construction monitoring data through RDF semantic association to obtain a semantic data set; setting smoothness constraint screening qualified data according to the gauge deviation and the elevation deviation; qualified data are input into an improved firework algorithm, and track geometric parameters are optimized through a curvature self-adaptive explosion mechanism; adopting improved Kriging interpolation to complement the track center line, the track surface elevation and the track gauge change data; and establishing a construction quality constraint matrix, and cooperatively adjusting track line shape, elevation control and track gauge precision through a sequential quadratic programming algorithm. According to the method, the technical problems of lack of multi-source data semantic association, insufficient professional algorithm adaptability and insufficient parameter collaborative optimization capability in railway construction are solved.

Description

Technical Field

[0001] This application relates to the technical field of data processing, and particularly to a method and system for processing railway construction data. Background Art

[0002] Existing railway construction data processing technologies mainly rely on traditional data collection and analysis methods. Measuring data at the construction site is collected through devices such as total stations and GPS, and a database management system is used for storage and query. During the data processing process, statistical analysis methods are generally used to evaluate the quality of track geometric parameters, interpolation algorithms are used to complete missing data, and construction deviations are adjusted through a single-parameter optimization method. These technologies have played an important role in railway construction quality control and progress management, providing data support for construction decision-making.

[0003] However, the phenomenon of data islands is serious, and it is difficult to effectively integrate and share data between different systems and departments; the data quality is uneven, with problems such as incomplete, inaccurate, and untimely data; the data processing efficiency is relatively low, relying heavily on manual operations and having a low degree of automation; there is a lack of unified data standards and specifications, resulting in inconsistent data formats and making it difficult to conduct effective comparison and analysis; the level of intelligence needs to be improved, lacking the application of advanced data mining and artificial intelligence technologies; there is an interference phenomenon during the parameter adjustment process, and the independent adjustment of a single parameter often leads to the deterioration of the quality of other parameters.

[0004] Based on the above analysis of the limitations of the existing technology, deeper technical problems can be inferred: due to the lack of a semantic association mechanism for multi-source heterogeneous data, the logical relationship between data cannot be effectively established; due to the lack of a dedicated optimization algorithm for the characteristics of railway engineering, data processing cannot adapt to the professional constraints of track geometry; due to the lack of a multi-parameter collaborative adjustment mechanism, problems of attending to one thing and losing another occur during the parameter optimization process, and the overall improvement of track geometric quality cannot be achieved. Summary of the Invention

[0005] This application provides a method and system for processing railway construction data, which is used to solve the technical problems of the lack of semantic association of multi-source data, insufficient adaptability of professional algorithms, and insufficient parameter collaborative optimization ability in railway construction.

[0006] In a first aspect, the present application provides a method for processing railway construction data. The method for processing railway construction data includes: performing semantic annotation processing on track laying data, line measurement data, and construction monitoring data through RDF semantic association to obtain a track construction semantic data set; performing quality screening processing on the construction data by setting track smoothness constraints according to the gauge deviation and elevation deviation in the track construction semantic data set to obtain qualified construction data; inputting the qualified construction data into an improved fireworks algorithm and performing optimization processing on track geometric parameters through an adaptive explosion mechanism of track curvature to obtain optimized track parameter data; performing complementation processing on the track center line, rail surface elevation, and gauge change by using an improved Kriging interpolation method according to the optimized track parameter data to obtain complete track geometric data; and establishing a construction quality constraint matrix based on the complete track geometric data and performing collaborative adjustment processing on track alignment, elevation control, and gauge accuracy through a sequential quadratic programming algorithm to obtain construction data results.

[0007] In a second aspect, the present application provides a system for processing railway construction data. The system for processing railway construction data includes: a labeling module, configured to perform semantic annotation processing on track laying data, line measurement data, and construction monitoring data through RDF semantic association to obtain a track construction semantic data set; a screening module, configured to perform quality screening processing on the construction data by setting track smoothness constraints according to the gauge deviation and elevation deviation in the track construction semantic data set to obtain qualified construction data; an optimization module, configured to input the qualified construction data into an improved fireworks algorithm and perform optimization processing on track geometric parameters through an adaptive explosion mechanism of track curvature to obtain optimized track parameter data; a complementation module, configured to perform complementation processing on the track center line, rail surface elevation, and gauge change by using an improved Kriging interpolation method according to the optimized track parameter data to obtain complete track geometric data; a collaborative module, configured to establish a construction quality constraint matrix based on the complete track geometric data and perform collaborative adjustment processing on track alignment, elevation control, and gauge accuracy through a sequential quadratic programming algorithm to obtain construction data results.

[0008] In a third aspect, there is provided a device for processing railway construction data, including: a memory and at least one processor, wherein instructions are stored in the memory; the at least one processor invokes the instructions in the memory so that the device for processing railway construction data executes the above-mentioned method for processing railway construction data.

[0009] Fourthly, a computer-readable storage medium is provided. Instructions are stored in the computer-readable storage medium, and when it runs on a computer, the computer is enabled to execute the above-mentioned railway construction data processing method.

[0010] In the technical solution provided by this application, by using the RDF semantic association technology to perform semantic annotation processing on track laying data, line measurement data, and construction monitoring data, the technical problem of difficult unified management of multi-source heterogeneous data in the field of railway construction is effectively solved, the logical association relationship between data is established, and the data island phenomenon in the traditional method is eliminated. At the same time, by setting track smoothness constraints to perform quality screening processing on construction data, a unified data quality evaluation standard is established, avoiding the problem of uneven data quality. The application of the improved fireworks algorithm combined with the track curvature adaptive explosion mechanism enables the algorithm to dynamically adjust the search strategy according to the track alignment characteristics, perform fine search in the small curvature radius section, and perform rough search in the straight section, significantly improving the accuracy and efficiency of parameter optimization. The improved Kriging interpolation method dynamically adjusts the interpolation parameters through the track curvature radius, enabling the interpolation process to better adapt to the geometric characteristics of railway engineering, effectively solving the problem of insufficient accuracy of traditional interpolation methods under complex alignment conditions. The introduction of the sequential quadratic programming algorithm realizes the coordinated adjustment of track alignment, elevation control, and gauge accuracy parameters, eliminates the mutual interference caused by single-parameter adjustment, and ensures the integrity and consistency of track geometric quality.

[0011] The triple structure of the RDF semantic association algorithm is particularly suitable for describing the complex relationships between entities in railway engineering. Its subject-predicate-object expression can accurately reflect the associations between objects such as track slabs, measurement points, and construction techniques, providing a structured semantic basis for subsequent data processing. The core contribution of the track curvature adaptive mechanism in the improved fireworks algorithm lies in combining the search behavior of the algorithm with the professional constraints of railway engineering, enabling the algorithm to have domain professionalism when processing track geometric parameters and avoiding the limitation that general algorithms are difficult to adapt to professional requirements. The dynamic parameter adjustment feature of the improved Kriging interpolation method enables the interpolation accuracy to adaptively change according to the complexity of the track alignment, significantly improving the reliability of the interpolation results while ensuring the calculation efficiency. The iterative solution feature and constraint handling ability of the sequential quadratic programming algorithm make multi-parameter coordinated optimization possible. Its characteristic of gradually approaching the optimal solution is particularly suitable for dealing with the complex constraint relationships between parameters in railway construction, achieving a coordinated effect that cannot be achieved by traditional single-parameter optimization methods. Description of the Drawings

[0012] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0013] Figure 1 It is a schematic diagram of an embodiment of the railway construction data processing method in the embodiments of the present application; Figure 2 It is a schematic diagram of an embodiment of the railway construction data processing system in the embodiments of the present application; Figure 3 It is a structural schematic block diagram of the railway construction data processing device in the embodiments of the present invention. Detailed implementation manners

[0014] The embodiments of the present application provide a railway construction data processing method and system. The terms "first", "second", "third", "fourth", etc. (if any) in the specification, claims and above-mentioned drawings of the present application are used to distinguish similar objects, and do not necessarily need to be used to describe a specific order or sequence. It should be understood that such data used can be interchanged under appropriate circumstances so that the embodiments described here can be implemented in an order other than that illustrated or described here. In addition, the terms "include" or "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device including a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0015] For ease of understanding, the following describes the specific process of the embodiments of the present application. Please refer to Figure 1 , an embodiment of the railway construction data processing method in the embodiments of the present application includes: Step S101: Perform semantic annotation processing on track laying data, line measurement data, and construction monitoring data through RDF semantic association to obtain a track construction semantic data set; Step S102: According to the gauge deviation and elevation deviation in the track construction semantic data set, perform quality screening processing on the construction data by setting track smoothness constraints to obtain qualified construction data; Step S103: Input the qualified construction data into the improved fireworks algorithm, and perform optimization processing on the track geometric parameters through the track curvature adaptive explosion mechanism to obtain optimized track parameter data; Step S104: According to the optimized data of track parameters, the improved Kriging interpolation method is used to complete the track center line, track surface elevation, and gauge change, and obtain the complete track geometry data; Step S105: Based on the complete track geometry data, a construction quality constraint matrix is established, and the track alignment, elevation control, and gauge accuracy are coordinately adjusted through the sequential quadratic programming algorithm to obtain the construction data results.

[0016] It can be understood that the execution subject of this application can be a railway construction data processing system, or a terminal or a server, and specific limitations are not made here. In this embodiment of the application, the server is used as the execution subject for illustration.

[0017] Specifically, the multi-source heterogeneous railway construction data is uniformly semantically annotated through RDF semantic association. RDF, that is, the Resource Description Framework, uses a triple structure of subject-predicate-object to describe the relationship between data. The track laying data includes information such as the position coordinates of the track slab and the laying time series, and is converted into semantic labels through ontology category mapping. For example, "Track slab number TB001 is located at mileage K10+500" is mapped into a semantic triple. The line measurement data covers spatial information such as mileage coordinates and elevation coordinates, and a spatio-temporal relationship triple structure is established to map the coordinate points to the mileage stake numbers. The construction monitoring data includes quality parameters such as gauge measurement values and elevation detection results, and attribute value annotation processing is performed to establish an association between the numerical data and the quality standards. After the three types of data are fused and processed by the RDF graph database, a unified multi-source data semantic mapping table is formed, and finally a track construction semantic data set is established.

[0018] Quality screening is performed on the gauge deviation and elevation deviation in the semantic data set. The gauge deviation refers to the difference between the measured gauge and the standard gauge of 1435 mm, and the elevation deviation refers to the difference between the measured track surface elevation and the designed elevation. Through numerical range determination processing, the deviation data is classified according to the magnitude. The track smoothness constraint conditions include the allowable range of gauge deviation and the allowable range of elevation deviation, which are set based on railway technical standards. The deviation values of each measuring point are compared and screened point by point, and the data exceeding the allowable range is marked as a constraint violation. The Boolean logic judge performs a binary evaluation on the marked data, marking the qualified data as true and the unqualified data as false. According to the evaluation results, the qualified construction data that meets the quality requirements is extracted from the original data.

[0019] The improved fireworks algorithm is used to optimize the track geometric parameters. The fireworks algorithm is a swarm intelligence optimization algorithm that conducts global search by simulating the process of fireworks explosion. The track geometric parameters in the qualified construction data are used as the initial position coordinates of the fireworks individuals to form a population. The track curvature adaptive explosion mechanism dynamically adjusts the explosion radius according to the track curvature radius. A smaller explosion radius is used for fine search in the small curvature radius section, and a larger explosion radius is used to expand the search range in the large curvature radius section. Each fireworks individual randomly generates multiple spark positions within its explosion radius to form a candidate solution set. The track superelevation gradual change constraint condition is used to evaluate the fitness of the spark positions, and the sparks with a superelevation gradient rate exceeding the limit are given a lower fitness value. The excellent sparks are selected according to the fitness ranking to update the positions of the fireworks, and the optimized track parameter data is obtained after multiple iterations.

[0020] The improved Kriging interpolation method is used to complete the track geometric data. Kriging interpolation is a spatial interpolation method based on the theory of regionalized variables, which uses the spatial correlation of known points to predict the values of unknown points. First, the track centerline coordinates, rail surface elevation, and gauge change data are separated and extracted from the optimized data. The Kriging interpolation parameters are dynamically adjusted according to the track curvature radius. The range parameter controls the action distance of the spatial correlation, and the nugget effect parameter reflects the influence of measurement errors. A semi-variance function is established to describe the spatial correlation structure, and the spatial correlation matrix between known measurement points is calculated. The interpolation weight coefficients are solved through matrix operations, and the weight size reflects the contribution degree of each known point to the interpolation point. The missing positions on the track line are weighted and averaged according to the weight coefficients to obtain the completed track geometric data.

[0021] A construction quality constraint matrix is established for collaborative adjustment. The track alignment parameters, elevation control parameters, and gauge accuracy parameters are extracted from the complete geometric data for grouping. There are mutual constraint relationships between the parameters. For example, the change in the position of the track centerline will affect the gauge value, and the elevation adjustment will affect the track superelevation. These constraint relationships are organized in matrix form, and the matrix elements represent the constraint coefficients between the parameters. The sequential quadratic programming algorithm is a numerical method for dealing with constrained optimization problems, which gradually approaches the optimal solution by constructing a quadratic programming subproblem. The constraint ranges of the track centerline coordinates, rail surface elevation, and gauge change are set as the optimization boundary conditions. The algorithm obtains the adjustment increment values of each parameter through iterative calculation, and the increments are superimposed on the original parameter values to complete the numerical update, and finally the construction data results meeting the quality requirements are obtained.

[0022] In a specific embodiment, the process of executing step S101 may specifically include the following steps: Perform ontology category mapping processing on the track slab position information and laying time sequence information in the track laying data to obtain the semantic labels of the track entities; Establish a spatio-temporal relationship triple structure based on the mileage coordinates and elevation coordinates in the line measurement data to obtain a semantic relationship graph of the measurement data; Perform attribute value annotation processing based on the quality inspection parameters and construction process parameters in the construction monitoring data to obtain a semantic attribute set of the construction process; Input the track entity semantic label, the semantic relationship graph of the measurement data, and the semantic attribute set of the construction process into the RDF graph database for fusion processing to obtain a multi-source data semantic mapping table; Perform subject-predicate-object relationship description processing on each data source according to the multi-source data semantic mapping table to obtain a semantic data set for track construction.

[0023] Specifically, the ontology category mapping processing of the track slab position information and the laying time sequence information uses a predefined railway engineering ontology library for data conversion. The track slab position information includes attributes such as the track slab number, installation coordinates, and mileage stake number, and the laying time sequence information records the installation order and time stamp of the track slab. The ontology category mapping reorganizes these original data according to the semantic classification standard. The track slab number is mapped to an entity identifier, the installation coordinates are mapped to a spatial attribute, the mileage stake number is mapped to a position attribute, and the installation time is mapped to a time attribute. Through the ontology mapping rule, the original data "Track slab TB001 is installed at mileage K15+200, and the coordinates are (125.6, 89.3)" is converted into semantic labels "Entity: TB001 Attribute: Position Value: K15+200" and "Entity: TB001 Attribute: Coordinates Value: (125.6, 89.3)". This mapping processing converts the unstructured text description into a standardized semantic expression form and establishes a clear corresponding relationship between the track entity and its attributes.

[0024] The spatio-temporal relationship triple structure of mileage coordinates and elevation coordinates forms a four-dimensional data model by combining spatial position information with time information. The mileage coordinate represents the longitudinal position on the track line, the elevation coordinate represents the vertical height of the track surface relative to the reference plane, and the time information records the specific moment of measurement or construction. The triple structure adopts the expression of "subject-relationship-object". The subject is usually the mileage stake number, the relationship describes the connection in space or time, and the object is the specific coordinate value or time mark. For example, the elevation measurement data at mileage K10+500 is constructed as triples "K10+500 - elevation value - 245.67 meters" and "K10+500 - measurement time - March 15, 2024, 10:30". Multiple triples form an associated network through common subjects or objects, constituting a semantic relationship graph of measurement data. This relationship graph can clearly express the spatial adjacency relationship and time sequence relationship between different measurement points, solving the problem of unclear spatial relationship in traditional data tables. The annotation processing of the attribute values of quality inspection parameters and construction process parameters semantically describes numerical data through a preset attribute classification system. Quality inspection parameters include geometric indexes such as gauge measurement value, alignment deviation, vertical profile deviation, and level deviation. Construction process parameters cover process information such as track slab laying process, fine adjustment operation process, and inspection and acceptance process. Attribute value annotation associates numerical data with quality standards and process specifications. For example, the gauge measurement value of 1437 mm is annotated as "deviation type: positive deviation, deviation amplitude: 2 mm, quality grade: qualified". The fine adjustment operation in construction process parameters is annotated as "process type: geometric adjustment, adjustment object: elevation, adjustment amplitude: 3 mm, adjustment direction: upward adjustment". Through attribute annotation processing, the originally isolated numerical values obtain clear engineering meanings and quality evaluations, forming a structured semantic attribute set of construction processes.

[0025] The RDF graph database fusion process integrates three types of semantic data into a unified knowledge graph structure. The semantic labels of track entities are used as nodes in the graph, the semantic relationship graph of measurement data is used as edges in the graph, and the semantic attribute set of construction processes is used as the attribute information of nodes and edges. The fusion process first identifies duplicate records that describe the same track entity in different data sources, and eliminates data redundancy through entity alignment algorithms. Then, cross-reference relationships between data sources are established. For example, a track slab entity is associated with its corresponding measurement data and construction process data. The fusion algorithm uses graph merging technology to combine scattered semantic fragments into a connected knowledge network, forming a multi-source data semantic mapping table. This mapping table records the corresponding relationships and attribute inheritance relationships between entities in each data source, supporting semantic queries and inferences across data sources.

[0026] The subject-predicate-object relationship description process converts the relationship information in the mapping table into the standard RDF triple format. The subject represents specific entities in track construction, such as track slabs, measurement points, construction operations, etc.; the predicate describes the type of relationship between entities, such as "is located at", "is measured at", "is constructed at", etc.; the object represents the target object or attribute value of the relationship. The relationship description process traverses each record in the mapping table, extracts the entity identifier, relationship type, and associated object, and generates triples according to the RDF specification. For example, "Track slab TB001 is located at mileage K15+200" is described as the triple <TB001, is located at, K15+200>, and "The elevation of mileage K15+200 is 245.67 meters" is described as <K15+200, elevation, 245.67>. Through the relationship description process, all track construction data is converted into a unified triple representation, forming a track construction semantic dataset. This dataset has good semantic expression ability and query and reasoning ability, and solves the problems of inconsistent original data formats and unclear semantic relationships.

[0027] In a specific embodiment, the process of executing step S102 may specifically include the following steps: Extract the gauge deviation value and elevation deviation value from the track construction semantic dataset for numerical range determination processing to obtain the deviation parameter classification result; Set the allowable range of gauge deviation and the allowable range of elevation deviation according to the deviation parameter classification result for constraint condition establishment processing to obtain the track smoothness constraint condition set; Based on the track smoothness constraint condition set, perform point-by-point comparison and screening processing on the gauge deviation value and elevation deviation value to obtain the constraint violation identification data; Input the constraint violation identification data into a Boolean logic judge for qualification evaluation processing to obtain the construction data quality evaluation result; Extract the qualified data from the original construction data according to the construction data quality evaluation result to obtain the qualified construction data.

[0028] Specifically, the extraction and processing of gauge deviation values and elevation deviation values identify and extract key geometric parameters from the track construction semantic dataset. The gauge deviation value refers to the difference between the measured gauge and the standard gauge of 1435 mm, which is obtained by parsing the gauge measurement triples in the semantic dataset. For example, the deviation value of positive 2 mm is calculated from "<Measurement point K20+300, Gauge measurement value, 1437>". The elevation deviation value refers to the difference between the measured track surface elevation and the designed elevation, which is obtained by comparing the semantic data of the measured elevation and the designed elevation. The numerical range determination process classifies the data into different categories according to the magnitude of the deviation amplitude. Small deviation data is classified into the fine-tuning range, medium deviation data is classified into the adjustment range, and large deviation data is classified into the rework range. The determination algorithm traverses all deviation values and automatically classifies them according to the preset threshold intervals to form the deviation parameter classification result, which contains the deviation amplitude, deviation direction, and category information of each measurement point. The setting process of the allowable range of gauge deviation and the allowable range of elevation deviation determines the numerical boundary based on railway technical standards and construction specifications. The allowable range of gauge deviation is determined according to the line grade and train operation speed. The gauge deviation of high-speed railways is controlled within plus or minus 2 mm, and that of ordinary railways is relaxed to plus or minus 3 mm. The allowable range of elevation deviation is set according to the track type and accuracy requirements. The elevation deviation of ballastless tracks is controlled within plus or minus 1 mm, and that of ballasted tracks is relaxed to plus or minus 2 mm. The constraint condition establishment process converts these allowable ranges into logical expressions recognizable by a computer to establish a set of track smoothness constraint conditions. This set of constraint conditions includes three types: upper limit constraint, lower limit constraint, and combined constraint. The upper limit constraint restricts the maximum value of the deviation, the lower limit constraint restricts the minimum value of the deviation, and the combined constraint takes into account the mutual influence relationship between the gauge and the elevation.

[0029] The point-by-point comparison and screening process checks each measurement point's deviation value against the set of constraint conditions one by one. The comparison algorithm reads the gauge deviation and elevation deviation values of the measurement point and compares them numerically with the corresponding allowable ranges to determine whether the constraint boundary is exceeded. Measurement points that exceed the allowable range of gauge deviation are marked as gauge constraint violations, measurement points that exceed the allowable range of elevation deviation are marked as elevation constraint violations, and measurement points that violate both constraints are marked as combined constraint violations. The screening process uses a point-by-point scanning method to sequentially check the deviation conditions of each measurement point according to the mileage order and records the specific types and deviation values of the constraint violations. The constraint violation identification data is represented in binary coding. The gauge violation is marked as 1, the elevation violation is marked as 2, the combined violation is marked as 3, and the qualified data is marked as 0 to form a digital quality evaluation identifier.

[0030] The Boolean logic judge performs logical operation processing on the constraint violation identification data, converting the numerical identification into a Boolean quality evaluation. The judge uses basic operations such as logical AND, logical OR, and logical NOT to establish the judgment rules for quality evaluation. The pass / fail evaluation processing inputs the identification data into the preset logical judgment rules. The data with an identification value of 0 outputs a true value indicating passing, and the data with a non-zero identification value outputs a false value indicating failing. The judge also performs composite logical operations. For example, when deviations occur continuously at multiple points within a certain measuring point segment, the entire measuring point segment is marked as failing. The evaluation algorithm considers the spatial continuity and numerical magnitude of the deviations, establishing a hierarchical evaluation system. Slight violations are marked as the warning level, and severe violations are marked as the failing level, forming the construction data quality evaluation result. The qualified data extraction processing screens out the data records that meet the requirements from the original construction data according to the quality evaluation result. The extraction algorithm traverses the evaluation results of all measuring points, retains the measuring point data evaluated as true, and eliminates the measuring point data evaluated as false. The extraction process not only considers the pass / fail of individual points but also the continuity between adjacent measuring points, avoiding data sequence breaks caused by the elimination of individual abnormal points. The algorithm uses the sliding window technique to maintain the spatial continuity of the data while ensuring data quality. The qualified construction data includes the track gauge measurement values, elevation measurement values, and their corresponding mileage position information that pass the quality screening.

[0031] In a specific embodiment, the process of executing step S103 may specifically include the following steps: Use the track geometric parameters in the qualified construction data as the initial positions of the fireworks individuals for population initialization processing to obtain the initial fireworks population; Perform adaptive adjustment processing on the corresponding explosion radius size according to the track curvature radius value of each fireworks individual in the initial fireworks population to obtain the curvature adaptive explosion radius; Based on the curvature adaptive explosion radius, perform random position explosion generation processing on each fireworks individual within the search space to obtain the set of spark position coordinates; Substitute the set of spark position coordinates into the track superelevation gradual change constraint condition for fitness value evaluation processing to obtain the spark fitness evaluation result; According to the spark fitness evaluation result, select the optimal spark for position update and algorithm iteration processing according to the quality ranking to obtain the optimized track parameter data.

[0032] Specifically, the track geometric parameters are used for population initialization of the initial positions of the fireworks individuals, converting qualified construction data into numerical vectors that can be processed by the algorithm. The track geometric parameters include the abscissa and ordinate of the track center line, the elevation of the rail surface, and the gauge value. The geometric parameter combinations of each measurement point form a multi-dimensional vector, which is regarded as a fireworks individual in the algorithm. The population initialization process arranges the geometric parameter vectors of qualified measurement points in sequence according to the mileage order, forming a set of fireworks individuals. The position coordinates of each fireworks individual consist of four components, corresponding to the spatial geometric characteristics of the track. The determination of the initial position directly affects the search starting point and convergence direction of the algorithm. Therefore, the population initialization adopts a uniform distribution strategy to ensure good dispersion of the fireworks individuals in the solution space. The population size is determined according to the length of the construction section and the measurement point density, usually set to one-tenth of the number of measurement points, which not only ensures the sufficiency of the search but also controls the computational complexity. The numerical calculation of the track curvature radius corresponding to the adaptive adjustment process of the explosion radius size reflects the professional characteristics of the present invention for railway engineering. The track curvature radius is calculated from the coordinates of three adjacent measurement points, reflecting the degree of bending of the track line. The smaller the curvature radius, the sharper the bend. The adaptive adjustment mechanism dynamically sets the explosion radius of each fireworks individual according to the size of the curvature radius. A small curvature radius corresponds to a small explosion radius, and a large curvature radius corresponds to a large explosion radius. The adjustment algorithm first calculates the track curvature radius at the position of each fireworks individual, and then determines the explosion radius value according to the preset mapping relationship. The mapping relationship uses a piecewise linear function. When the curvature radius is less than the straight line segment threshold, the explosion radius is reduced proportionally. When the curvature radius is greater than the transition curve threshold, the explosion radius is enlarged proportionally. This adaptive mechanism enables the algorithm to conduct fine searches in sharp curve sections of the track and rough searches in straight sections, conforming to the engineering practice of railway geometric design.

[0033] The generation process of random position explosions is the core operation of the fireworks algorithm, exploring the solution space by generating randomly distributed sparks around each fireworks individual. The explosion generation process takes the current position of the fireworks individual as the center and the curvature-adaptive explosion radius as the range, randomly generating new position coordinates in the four-dimensional geometric parameter space. The random generation algorithm uses a Gaussian distribution to ensure that the spark positions show a normal distribution characteristic within the explosion radius, with a higher spark density in the central region and a lower spark density in the edge region. The number of sparks generated by each fireworks individual is dynamically adjusted according to its fitness. Individuals with higher fitness generate more sparks, and individuals with lower fitness generate fewer sparks. The spark position coordinates need to satisfy the physical constraints of the track geometry. For example, the gauge cannot exceed the technical standard range, and the elevation cannot have excessive slope changes. Sparks outside the constraint range are regenerated or corrected to a reasonable range.

[0034] The fitness numerical evaluation process for the gradient constraint condition of track superelevation converts the position coordinates of the sparks into quality evaluation indicators. Track superelevation refers to the elevation of the outer side of the track relative to the inner side, which is used to balance the centrifugal force when the train runs on the curve. Gradient superelevation refers to the change rate of the superelevation value along the longitudinal direction of the line. The fitness evaluation algorithm calculates the track geometric indicators corresponding to each spark position, including line continuity, elevation smoothness, and gauge consistency. In the evaluation process, geometric parameters such as track curvature, slope, and twist are first calculated based on the coordinate data of the sparks, and then these parameters are compared with the railway technical standards. The gradient superelevation constraint requires that the change in superelevation between adjacent measuring points does not exceed the specified limit, and the sparks that violate the constraint are given a lower fitness value. The fitness is calculated by weighted summation, combining multiple geometric indicators into a single value, and the weight coefficients are determined according to the importance of the indicators. The higher the fitness value, the better the track geometric quality at the spark position, and the lower the fitness value, the worse the geometric quality.

[0035] The mechanism of evolution of the fireworks algorithm is to select the optimal sparks for position update and algorithm iteration through sorting by quality. The sorting algorithm arranges all the sparks in descending order according to the fitness value, and selects the excellent sparks with higher rankings as candidates for the next generation of fireworks individuals. The selection strategy adopts the elitist retention principle, directly retaining several sparks with the highest fitness to ensure that excellent solutions will not be lost during the evolution process. At the same time, a random selection mechanism is introduced to randomly select some individuals from the sparks with medium fitness to maintain the diversity of the population. The position update process assigns the coordinates of the selected sparks to the corresponding fireworks individuals to complete an iteration cycle. The iteration termination conditions include reaching the preset number of iterations, the improvement amplitude of the fitness being less than the threshold, or no obvious improvement for several consecutive generations. After the algorithm converges, the position of the fireworks individual with the highest fitness is the optimized track parameter data, which represents the optimal combination of geometric parameters under the given constraint conditions.

[0036] In a specific embodiment, the process of performing the step of adaptively adjusting the corresponding explosion radius according to the track curvature radius value of each fireworks individual in the initial fireworks population may specifically include the following steps: Extract the track curvature radius values corresponding to each fireworks individual from the initial fireworks population for numerical reading processing to obtain a set of curvature radius parameters; Perform curvature type classification determination processing according to the numerical sizes in the set of curvature radius parameters to obtain the curvature classification identification result; Based on the curvature classification identification result, set different explosion radius scaling coefficients for the small curvature radius segment and the large curvature radius segment for coefficient allocation processing to obtain the curvature adaptive scaling coefficient; Perform a multiplication operation on the curvature adaptive scaling coefficient and the basic explosion radius parameter to obtain the individual-specific explosion radius value; Assign the explosion radius to each individual firework according to the individual-specific explosion radius value to obtain the curvature-adaptive explosion radius.

[0037] Specifically, the reading process of the orbital curvature radius value calculates the geometric curvature feature from the position coordinates of each firework individual. The position coordinates of the firework individual include the abscissa and ordinate information of the orbital center line, and the orbital curvature radius at this position is calculated through the coordinate relationship of three adjacent points. The calculation process uses the curvature calculation formula in geometry and obtains the curvature radius value by using the principle of determining an arc with three points. The reading process traverses each individual in the initial firework population, extracts its position coordinates one by one and calculates the corresponding curvature radius, and organizes the calculation results into a curvature radius parameter set in the order of the firework individual numbers. This parameter set not only contains numerical information but also retains the corresponding relationship with the firework individual position, ensuring the consistency of the curvature information and the spatial position in subsequent processing.

[0038] The curvature type classification and determination process classifies the curvature radius according to the alignment classification standard in railway engineering. Railway alignments are usually divided into types such as straight sections, transition curve sections, and circular curve sections, and different types correspond to different curvature radius ranges. The determination process sets the curvature classification threshold and compares the values in the curvature radius parameter set with the threshold. The curvature radius of the straight section is close to infinity, the curvature radius of the transition curve section changes within a certain range, and the curvature radius of the circular curve section is a fixed value. The classification algorithm uses an interval determination method to classify each curvature radius value into the corresponding alignment type. The curvature classification identification result is represented by a digital code, with the straight section identified as type 1, the transition curve section identified as type 2, and the small-radius circular curve section identified as type 3. Each firework individual obtains the corresponding curvature type identification.

[0039] The coefficient assignment process determines the adjustment strategy of the explosion radius according to the characteristics of different curvature types. The small curvature radius section corresponds to the sharp bend area of the track, and higher precision is required for the adjustment of geometric parameters. Therefore, a smaller scaling coefficient is assigned to reduce the explosion radius and concentrate the search range near the current position. The large curvature radius section corresponds to the straight or gentle bend area of the track, and the geometric constraints are relatively loose. A larger scaling coefficient is assigned to expand the explosion radius and cover a wider space. The coefficient assignment adopts a piecewise function form, and the corresponding scaling coefficient value is determined by looking up the table according to the curvature classification identification result. The scaling coefficient of the small-radius circular curve section is set to a smaller value, the scaling coefficient of the straight section is set to a larger value, and the scaling coefficient of the transition curve section is set to a medium value. The curvature-adaptive scaling coefficient set contains the scaling values corresponding to each firework individual, reflecting the differential processing strategy of the algorithm for different alignment characteristics.

[0040] The product operation process multiplies the scaling factor by the base explosion radius parameter to obtain the explosion radius value exclusive to each individual. The base explosion radius parameter is the preset standard search range in the fireworks algorithm, representing the search ability of the algorithm under normal circumstances. The product operation calculates the exclusive explosion radius of each fireworks individual by multiplying one by one. The operation result not only maintains the search characteristics of the original algorithm but also incorporates the professional constraints of the track geometry. The magnitude of the explosion radius value exclusive to each individual directly affects the search range of that fireworks individual during the next explosion. A smaller value indicates a more refined search, while a larger value indicates a more extensive search. The operation process maintains numerical precision to avoid affecting the subsequent search effect due to calculation errors.

[0041] The explosion radius assignment process assigns the calculated explosion radius value exclusive to each individual to the corresponding fireworks individual. The assignment process is carried out in the order of the fireworks individual numbers to ensure that each individual obtains an explosion radius that matches its position characteristics. After the assignment, the fireworks individuals at different positions have different search abilities. The individuals located in the small curvature radius section will conduct a refined search, while the individuals located in the large curvature radius section will conduct a rough search. The curvature-adaptive explosion radius becomes an important attribute of the fireworks individual and plays a key role in the subsequent explosion operations, enabling the search behavior of the algorithm to adapt to the complex changes in the track alignment.

[0042] In a specific embodiment, the process of executing step S104 may specifically include the following steps: Extract the track centerline coordinate points, track surface elevation values, and track gauge change values from the track parameter optimization data for data separation processing to obtain geometric parameter classification data; Dynamically adjust the Kriging interpolation range parameter and nugget effect parameter according to the track curvature radius value in the geometric parameter classification data to obtain the interpolation parameter configuration result; Based on the interpolation parameter configuration result, establish a semivariogram function structure to calculate the spatial correlation between interpolation points to obtain a spatial correlation matrix; Perform a weight coefficient solution operation on the spatial correlation matrix and the known measurement point data to obtain an interpolation weight coefficient set; Perform a weighted interpolation calculation on the missing track geometric parameters according to the interpolation weight coefficient set to obtain complete track geometric data.

[0043] Specifically, the data separation processing of the track centerline coordinate points, the track surface elevation values, and the gauge change values classifies the multi-dimensional geometric parameters according to the attribute types. The optimized track parameter data contains various geometric attributes of each measurement point. The data separation disassembles the mixed data into independent parameter sequences through an attribute recognition algorithm. The track centerline coordinate points include two components, the abscissa and the ordinate, which reflect the spatial position of the track in the horizontal plane; the track surface elevation value represents the vertical height of the track surface relative to the reference plane, reflecting the longitudinal section characteristics of the track; the gauge change value describes the change in the inner spacing between the two rails, reflecting the cross-section characteristics of the track. The separation processing establishes a classification index according to the data attributes, merges the parameter values of the same type into the corresponding data sequences, and forms three independent geometric parameter classification data sets. Each data set contains parameter values, the corresponding mileage position, and the measurement time information, ensuring that the correlation between the data is not destroyed. The dynamic adjustment processing of the Kriging interpolation range parameter and the nugget effect parameter according to the track curvature radius value reflects the professional adaptability of the present invention to railway engineering. Kriging interpolation is an interpolation method based on spatial statistics. The range parameter controls the acting distance of spatial correlation, and the nugget effect parameter reflects the influence of measurement errors and small-scale variations. The dynamic adjustment mechanism determines the values of the interpolation parameters according to the track alignment characteristics. Larger range parameters are used in straight sections and large-radius curve sections, while smaller range parameters are used in small-radius curve sections and transition sections. The adjustment algorithm first calculates the track curvature radius at each position in the geometric parameter classification data, and then determines the corresponding interpolation parameter values according to the preset mapping function. The nugget effect parameter of the ballastless track is set to a smaller value, reflecting its higher construction accuracy; the nugget effect parameter of the ballasted track is set to a larger value, reflecting its relatively lower construction accuracy. The configuration result of the interpolation parameters includes the range parameter, the nugget effect parameter, and the arch height parameter for each interpolation section. The combination of these parameters determines the accuracy and adaptability of the interpolation algorithm.

[0044] The calculation and processing of the spatial correlation between interpolation points by the semivariogram function structure is the core step of Kriging interpolation. The semivariogram function describes the statistical characteristics of the numerical differences between two points at a certain distance in space, reflecting the spatial continuity and variation law of the data. The calculation and processing first determine the mathematical model of the semivariogram function. Commonly used models include the spherical model, the exponential model, and the Gaussian model. The appropriate model type is selected according to the spatial distribution characteristics of the track geometric data. The spherical model is suitable for the case where the spatial correlation has a clear boundary, the exponential model is suitable for the case where the correlation gradually decays, and the Gaussian model is suitable for the case where the correlation changes smoothly. The semivariogram calculation uses the empirical semivariogram estimation method to statistically analyze the numerical differences between measurement point pairs at different distance intervals and establish a relationship curve between distance and semivariogram. The spatial correlation matrix is calculated through the semivariogram function. The matrix elements represent the spatial correlation degree between any two known measurement points. The higher the correlation degree, the more similar the numerical values of the two points.

[0045] The solution operation of the spatial correlation matrix and the weight coefficients of the known measurement point data converts the spatial correlation information into the weight distribution of the interpolation calculation. The weight coefficient reflects the contribution degree of each known measurement point to the value of the interpolation point. The measurement points that are close and have strong correlation obtain larger weights, while the measurement points that are far and have weak correlation obtain smaller weights. The solution operation adopts the matrix solution method of the Kriging equations, establishes a linear equation system with the spatial correlation matrix as the coefficient matrix, and the solution of the equation system is the weight coefficient of each measurement point. The operation process includes basic mathematical operations such as matrix inversion, matrix multiplication, and vector operations, and obtains the set of interpolation weight coefficients through numerical calculation. The weight coefficient has the normalization property, that is, the sum of all coefficients is equal to 1, ensuring the unbiasedness of the interpolation result. The weight distribution also needs to meet the constraint conditions of Kriging interpolation to ensure that the interpolation process meets the theoretical requirements of spatial statistics.

[0046] The weighted interpolation calculation of the interpolation weight coefficient set for the missing position track geometry parameters is the implementation stage of data completion. The weighted interpolation calculation performs weighted averaging on the parameter values of each known measurement point according to the corresponding weight coefficient to obtain the parameter estimated value of the missing position. The calculation process traverses all positions that need to be interpolated, and calculates the interpolation results of the track centerline coordinates, track surface elevation, and gauge for each position respectively. The interpolation of the track centerline coordinates calculates the abscissa and ordinate independently to ensure the geometric continuity of the interpolated line shape. The interpolation of the track surface elevation considers the continuity of the longitudinal slope change to avoid excessive slope mutations. The interpolation of the gauge maintains the numerical stability to avoid abnormal values exceeding the technical standards. The interpolation calculation also includes error estimation, calculates the confidence interval of the interpolation result through the Kriging variance, and evaluates the reliability of the interpolation accuracy. The complete track geometry data integrates the original measurement point data and the interpolated and completed data to form a continuous track geometry parameter sequence.

[0047] In a specific embodiment, the process of executing step S105 may specifically include the following steps: Extract the track line shape parameters, elevation control parameters, and gauge accuracy parameters from the complete track geometry data for parameter grouping processing to obtain a set of collaborative adjustment parameters; Establish a constraint condition table according to the mutual constraint relationship between the parameters in the set of collaborative adjustment parameters for matrix arrangement processing to obtain a construction quality constraint matrix; Based on the construction quality constraint matrix, set the constraint range of the track centerline coordinates, the constraint range of the track surface elevation, and the constraint range of the gauge change for the optimization target establishment processing to obtain a multi-parameter collaborative optimization target; Input the multi-parameter collaborative optimization target into the sequential quadratic programming algorithm for step-by-step iterative solution processing to obtain the parameter adjustment increment value; Numerically update and adjust the track alignment, elevation control, and gauge accuracy parameters according to the parameter adjustment increment values to obtain the construction data results.

[0048] Specifically, the parameter grouping process of the track alignment parameters, elevation control parameters, and gauge accuracy parameters classifies and organizes the complete track geometric data according to engineering attributes. The track alignment parameters include geometric quantities such as the azimuth, curvature, and torsion of the track centerline, which describe the alignment characteristics and control the planar and vertical orientations of the track. The elevation control parameters cover values such as the rail surface elevation, longitudinal slope, and vertical curve radius, which reflect the elevation changes of the track and determine the longitudinal section shape of the track. The gauge accuracy parameters include cross-section characteristic quantities such as the gauge value, gauge change rate, and gauge deviation, which affect the lateral geometric quality of the track. The grouping process extracts and classifies various parameters in the complete data through an attribute recognition algorithm, establishing the correspondence between the parameter types and numerical sequences. Each group of parameters is arranged in mileage order to maintain the continuity of spatial positions, and at the same time, the logical relationships between the parameters are recorded to form a set of coordinated adjustment parameters. This set not only contains the numerical information of the parameters but also the importance weights and adjustment priorities of the parameters, laying a data foundation for subsequent coordinated optimization.

[0049] The matrix arrangement process of establishing the constraint condition table for the mutual constraint relationship converts the engineering constraints between parameters into a mathematical expression form. There are various parameter constraint relationships in track engineering. For example, a change in the position of the track centerline will affect the measurement reference of the gauge, and an adjustment of the rail surface elevation will affect the calculation of superelevation and cross-level of the track. The constraint condition table records these mutual influence relationships in matrix form. The rows of the matrix correspond to the constrained parameters, and the columns correspond to the parameters of the constraint source. The matrix elements represent the strength and type of the constraint relationship. The arrangement process determines the values of the matrix elements according to the tightness and influence range of the constraints. A strong constraint relationship is assigned a larger value, and a weak constraint relationship is assigned a smaller value. The matrix also contains the directional information of the constraints. A positive value indicates a positive correlation constraint, a negative value indicates a negative correlation constraint, and a zero value indicates no constraint relationship. The construction quality constraint matrix combines various constraint relationships through matrix operations to form a comprehensive matrix that describes the constraint characteristics of the entire track geometric system. This matrix provides a mathematical basis for the coordinated optimization algorithm.

[0050] The optimization objective establishment process for the coordinate constraint range of the track center line, the elevation constraint range of the track surface, and the gauge change constraint range converts engineering technical standards into the boundary conditions of mathematical optimization. The coordinate constraint range of the track center line is determined according to the track design alignment and the allowable value of construction error. The constraint range limits the offset amplitude of the center line coordinates to prevent the track alignment from deviating from the design requirements. The elevation constraint range of the track surface is set based on the track design elevation and the elevation control accuracy, and the elevation of the track surface is constrained to vary within a reasonable range near the design value. The gauge change constraint range is formulated according to the gauge technical standard and the measurement accuracy requirement, and the gauge value is controlled within the allowable deviation range around the standard gauge. The optimization objective establishment process integrates these constraint ranges into the objective function of a multi-objective optimization problem. The objective function consists of two parts: an error minimization term and a constraint satisfaction term. The error minimization term aims to minimize the deviation from the ideal design value after parameter adjustment, and the constraint satisfaction term ensures that the adjusted parameters meet all engineering constraint conditions. The multi-parameter collaborative optimization objective forms a comprehensive objective function by weighted combination of each single objective, and the weight coefficients are determined according to the engineering importance and adjustment difficulty of the parameters.

[0051] The step-by-step iterative solution process of the sequential quadratic programming algorithm is a numerical method for dealing with constrained optimization problems. The sequential quadratic programming algorithm decomposes the original non-linear constrained optimization problem into a series of quadratic programming sub-problems, and approximates the optimal solution of the original problem by gradually solving the sub-problems. The iterative solution process first establishes a quadratic approximation model at the current iteration point, which includes the quadratic approximation of the objective function and the linear approximation of the constraint function. Then, the quadratic programming sub-problem is solved to obtain the search direction, which points to the direction where the objective function value decreases and the constraint conditions are improved. Next, a line search is performed to determine the step size, and the choice of the step size takes into account both the reduction amplitude of the objective function and the reduction degree of constraint violation. The algorithm uses the Lagrange multiplier method to handle equality constraints and the penalty function method to handle inequality constraints to ensure that the constraint conditions are satisfied during the iterative process. The convergence criteria include multiple indicators such as the improvement amplitude of the objective function, the degree of constraint violation, and the gradient norm. The algorithm terminates when all indicators meet the convergence requirements. The parameter adjustment increment value is the difference between the current parameter value and the initial parameter value after the algorithm converges, which reflects the amount of parameter adjustment required to meet the constraint conditions and optimization objectives.

[0052] The numerical update and adjustment process of the parameter adjustment increment values for track alignment, elevation control, and gauge accuracy parameters is the implementation stage of collaborative optimization. The numerical update adopts an incremental superposition method, adding the adjustment increment values to the original parameter values to obtain the adjusted parameter values. The adjustment of track alignment parameters focuses on the continuity and smoothness of curve elements to ensure that the adjusted alignment meets the geometric continuity conditions. The adjustment of elevation control parameters considers the rationality of longitudinal slope changes and the smoothness of vertical curves to avoid excessive slope mutations or unreasonable vertical curve radii. The adjustment of gauge accuracy parameters keeps the gauge values within the technical standards and controls the gauge change rate within the allowable limits. The adjustment process also includes parameter rationality verification, evaluating the engineering feasibility of the adjusted parameters, and parameter values outside the reasonable range need to be re-optimized or manually intervened. The construction data results include all track geometric parameters that have been collaboratively adjusted, and these parameters not only meet their respective technical requirements but also maintain coordination and consistency with each other.

[0053] The above describes the railway construction data processing method in the embodiments of the present application. Next, the railway construction data processing system in the embodiments of the present application will be described. Please refer to Figure 2 , an embodiment of the railway construction data processing system in the embodiments of the present application includes: A marking module for performing semantic marking processing on track laying data, line measurement data, and construction monitoring data through RDF semantic association to obtain a track construction semantic data set; A screening module for performing quality screening processing on construction data by setting track smoothness constraints according to the gauge deviation and elevation deviation in the track construction semantic data set to obtain qualified construction data; An optimization module for inputting the qualified construction data into an improved fireworks algorithm and performing optimization processing on track geometric parameters through an orbit curvature adaptive explosion mechanism to obtain optimized track parameter data; A complementing module for complementing the track center line, rail surface elevation, and gauge change according to the optimized track parameter data by using an improved Kriging interpolation method to obtain complete track geometric data; A collaborative module for establishing a construction quality constraint matrix based on the complete track geometric data and performing collaborative adjustment processing on track alignment, elevation control, and gauge accuracy through a sequential quadratic programming algorithm to obtain construction data results.

[0054] The above Figure 2 The railway construction data processing system in the embodiments of the present invention is described in detail from the perspective of modular functional entities. Next, the railway construction data processing device in the embodiments of the present invention will be described in detail from the perspective of hardware processing.

[0055] Refer to Figure 3, an embodiment of the present invention further provides a railway construction data processing device, which can be a server, and its internal structure can be as Figure 3 shown. The railway construction data processing device includes a processor, a memory, a display screen, an input device, a network interface, and a database connected through a system bus. Among them, the processor designed by this computer is used to provide computing and control capabilities. The memory of the railway construction data processing device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the railway construction data processing device is used to store the corresponding data in this embodiment. The network interface of the railway construction data processing device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the above method is implemented.

[0056] Those skilled in the art can understand that Figure 3 the structure shown in is only a block diagram of some structures related to the solution of the present invention, and does not constitute a limitation on the railway construction data processing device to which the solution of the present invention is applied.

[0057] The present invention also provides a computer-readable storage medium, which can be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions run on a computer, the computer is caused to execute the steps of the railway construction data processing method.

[0058] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the above-described system, system, and unit can refer to the corresponding processes in the foregoing method embodiments, and will not be elaborated herein.

[0059] When the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a railway construction data processing device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs that can store program codes.

[0060] The above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for processing railway construction data, characterized in that, The method includes: Performing semantic annotation processing on track laying data, line measurement data, and construction monitoring data through RDF semantic association to obtain a track construction semantic dataset; According to the gauge deviation and elevation deviation in the track construction semantic dataset, performing quality screening processing on the construction data by setting track smoothness constraints to obtain qualified construction data; Inputting the qualified construction data into an improved fireworks algorithm, and performing optimization processing on track geometric parameters through an adaptive explosion mechanism of track curvature to obtain optimized track parameter data; According to the optimized track parameter data, using an improved Kriging interpolation method to complete the track centerline, rail surface elevation, and gauge change to obtain complete track geometric data; Based on the complete track geometric data, establishing a construction quality constraint matrix, and performing collaborative adjustment processing on track alignment, elevation control, and gauge accuracy through a sequential quadratic programming algorithm to obtain construction data results.

2. The railway construction data processing method according to claim 1, wherein The performing semantic annotation processing on track laying data, line measurement data, and construction monitoring data through RDF semantic association to obtain a track construction semantic dataset includes: Performing ontology category mapping processing on the track slab position information and laying time sequence information in the track laying data to obtain track entity semantic labels; Establishing a spatio-temporal relationship triple structure based on the mileage coordinates and elevation coordinates in the line measurement data to obtain a measurement data semantic relationship graph; Performing attribute value annotation processing based on the quality inspection parameters and construction process parameters in the construction monitoring data to obtain a construction process semantic attribute set; Inputting the track entity semantic labels, the measurement data semantic relationship graph, and the construction process semantic attribute set into an RDF graph database for fusion processing to obtain a multi-source data semantic mapping table; According to the multi-source data semantic mapping table, performing subject-predicate-object relationship description processing on each data source to obtain a track construction semantic dataset.

3. The railway construction data processing method according to claim 1, wherein The according to the gauge deviation and elevation deviation in the track construction semantic dataset, performing quality screening processing on the construction data by setting track smoothness constraints to obtain qualified construction data includes: Extracting the gauge deviation value and elevation deviation value from the track construction semantic dataset for numerical range determination processing to obtain a deviation parameter classification result; Setting the allowable range of gauge deviation and the allowable range of elevation deviation according to the deviation parameter classification result for constraint condition establishment processing to obtain a track smoothness constraint condition set; Based on the track smoothness constraint condition set, performing point-by-point comparison and screening processing on the gauge deviation value and elevation deviation value to obtain constraint violation identification data; Inputting the constraint violation identification data into a Boolean logic judge for qualification evaluation processing to obtain a construction data quality evaluation result; According to the construction data quality evaluation result, performing qualified data extraction processing on the original construction data to obtain qualified construction data.

4. The railway construction data processing method according to claim 1, wherein The inputting the qualified construction data into an improved fireworks algorithm, and performing optimization processing on track geometric parameters through an adaptive explosion mechanism of track curvature to obtain optimized track parameter data includes: Taking the track geometric parameters in the qualified construction data as the initial positions of individual fireworks for population initialization processing to obtain an initial fireworks population; Calculating the corresponding explosion radius size according to the track curvature radius value of each individual firework in the initial fireworks population for adaptive adjustment processing to obtain a curvature adaptive explosion radius; Based on the curvature adaptive explosion radius, performing random position explosion generation processing on each individual firework within the search space to obtain a set of spark position coordinates; Substituting the set of spark position coordinates into the track superelevation gradient constraint condition for fitness value evaluation processing to obtain a spark fitness evaluation result; According to the spark fitness evaluation result, sorting by quality and selecting the optimal spark for position update and algorithm iteration processing to obtain optimized track parameter data.

5. The railway construction data processing method according to claim 4, characterized in that The calculating the corresponding explosion radius size according to the track curvature radius value of each individual firework in the initial fireworks population for adaptive adjustment processing to obtain a curvature adaptive explosion radius includes: Extracting the track curvature radius value corresponding to each individual firework from the initial fireworks population for numerical reading processing to obtain a set of curvature radius parameters; Performing curvature type classification determination processing according to the numerical values in the set of curvature radius parameters to obtain a curvature classification identification result; Based on the curvature classification identification result, setting different explosion radius scaling coefficients for the small curvature radius segment and the large curvature radius segment for coefficient allocation processing to obtain a curvature adaptive scaling coefficient; Performing a multiplication operation on the curvature adaptive scaling coefficient and the basic explosion radius parameter to obtain an individual-specific explosion radius value; Assigning the explosion radius to each individual firework according to the individual-specific explosion radius value to obtain a curvature adaptive explosion radius.

6. The railway construction data processing method according to claim 1, wherein The using the improved Kriging interpolation method to complement the track center line, track surface elevation, and gauge change according to the optimized track parameter data to obtain complete track geometric data includes: Extracting the track center line coordinate points, track surface elevation values, and gauge change values from the optimized track parameter data for data separation processing to obtain classified geometric parameter data; Dynamically adjusting the Kriging interpolation range parameter and nugget effect parameter according to the track curvature radius value in the classified geometric parameter data to obtain an interpolation parameter configuration result; Based on the interpolation parameter configuration result, establishing a semi-variance function structure to calculate the spatial correlation between interpolation points to obtain a spatial correlation matrix; Performing a weight coefficient solving operation on the spatial correlation matrix and the known measurement point data to obtain a set of interpolation weight coefficients; Performing weighted interpolation calculation on the missing track geometric parameters according to the set of interpolation weight coefficients to obtain complete track geometric data.

7. The railway construction data processing method according to claim 1, characterized in that The establishing a construction quality constraint matrix based on the complete track geometric data and performing collaborative adjustment processing on the track alignment, elevation control, and gauge accuracy through a sequential quadratic programming algorithm to obtain construction data results includes: Extracting the track alignment parameters, elevation control parameters, and gauge accuracy parameters from the complete track geometric data for parameter grouping processing to obtain a set of collaborative adjustment parameters; Establish a constraint condition table according to the mutual constraint relationship among the parameters in the collaborative adjustment parameter set for matrix arrangement processing to obtain a construction quality constraint matrix; Based on the construction quality constraint matrix, set the coordinate constraint range of the track center line, the elevation constraint range of the track surface, and the gauge change constraint range for establishing the optimization objective processing to obtain a multi-parameter collaborative optimization objective; Input the multi-parameter collaborative optimization objective into the sequential quadratic programming algorithm for step-by-step iterative solution processing to obtain the parameter adjustment increment value; According to the parameter adjustment increment value, perform numerical update adjustment processing on the track alignment, elevation control, and gauge accuracy parameters to obtain the construction data results.

8. A railway construction data processing system, characterized in that, For implementing the railway construction data processing method described in any one of claims 1-7, the railway construction data processing system includes: A marking module, configured to perform semantic marking processing on the track laying data, line measurement data, and construction monitoring data through RDF semantic association to obtain a track construction semantic data set; A screening module, configured to perform quality screening processing on the construction data by setting track smoothness constraints according to the gauge deviation and elevation deviation in the track construction semantic data set to obtain qualified construction data; An optimization module, configured to input the qualified construction data into an improved fireworks algorithm and perform optimization processing on the track geometric parameters through an adaptive explosion mechanism of track curvature to obtain optimized track parameter data; A completion module, configured to perform completion processing on the track center line, track surface elevation, and gauge change according to the optimized track parameter data by using an improved Kriging interpolation method to obtain complete track geometric data; A collaborative module, configured to establish a construction quality constraint matrix based on the complete track geometric data and perform collaborative adjustment processing on the track alignment, elevation control, and gauge accuracy through a sequential quadratic programming algorithm to obtain construction data results.

9. A railway construction data processing device, characterized in that, It includes a memory and a processor. The memory stores a computer program that can run on the processor. When the processor executes the computer program, it implements the railway construction data processing method described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is run by the processor, the processor is caused to execute the railway construction data processing method described in any one of claims 1 to 7.

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