Intelligent matching method and system based on railway loading plan

Abstract

The invention relates to the technical field of rule matching, in particular to an intelligent matching method and system based on a railway loading plan, and the method comprises the following steps: extracting a section transport capacity construction trend sequence, judging a coherent generation path, extracting a time window, screening a continuous path, obtaining a state and a demand, carrying out the judgment and matching, and screening a borrowable path. And adaptive carriage space identification available sections, and performing statistical sorting to generate a priority list. According to the invention, through section transport capacity trend identification and path construction, resource scheduling dynamic optimization is realized, matching continuity and response speed are improved, operation time window intersection extraction is realized, loading time period screening is enabled to be more collaborative, and in combination with state availability and loading demand judgment, path screening precision and resource adaptation efficiency are improved. And space boundary and container parameter adaptive judgment is carried out, the carriage space identification depth is enhanced, the loading density is improved, the path priority is optimized through matching number sorting, the decision support accuracy is enhanced, and loading matching is promoted to be converted into intelligent distribution.

Description

Technical Field

[0001] This invention relates to the field of rule matching technology, and in particular to an intelligent matching method and system based on railway loading plans. Background Technology

[0002] Rule matching technology falls under the category of computer data processing technology. It primarily involves methods for automatically parsing, judging conditions, and performing matching operations on structured or unstructured information based on preset logical rules. This technology encompasses core aspects such as rule expression, rule parsing, rule execution, and decision output. It often achieves rapid comparison and selection of target data by constructing rule bases, matching engines, and result generation mechanisms. It is widely applied in scenarios such as scheduling optimization, intelligent recommendation, and resource allocation. Traditional railway loading matching refers to the activity of allocating containers to wagons according to the loading plan during railway freight operations. Methods include those without refined container location management, where the gantry crane operator manually records the wagon and container numbers on-site and then the freight handler completes the information registration; and those with container location management, where the freight handler manually judges and assigns containers based on the current container distribution. The content involves data collection and location allocation based on manual observation and on-site experience. The subcategories are container location recording and freight information allocation methods, relying on manual operation by operators for information transmission and matching decisions.

[0003] In traditional railway loading and matching processes, operators rely on manual observation to determine container positions and wagon status, lacking a systematic analysis of segment resource trends. This results in resource scheduling often operating in a static response state, unable to adapt promptly to changes in capacity between segments. For example, when multiple wagons are available simultaneously, it's difficult for operators to determine which segment is more suitable based on historical trends, leading to a passive and lengthy loading process. Regarding time management, current technology cannot accurately identify the overlap of operation times across multiple segments, relying solely on manual estimation of loading timing, often resulting in scheduling conflicts or wasted time resources due to planning deviations. In terms of path status assessment and resource matching, the lack of a data-driven real-time matching mechanism easily leads to the selection of unavailable or mismatched paths, impacting loading efficiency. Regarding space utilization, current methods fail to deeply identify the compatibility between remaining wagon space and standard container types, resulting in ineffective use of available space and wasted transportation resources. Path priority assessment is also limited to operational experience and manual sorting, lacking a data-driven statistical and comparative mechanism, leading to resource allocation deviations and chaotic operation sequences. In summary, existing technologies have significant shortcomings in resource trend perception, time window reconstruction, path status judgment, spatial adaptation identification, and priority ranking, which limit the system improvement of railway loading and matching efficiency. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of existing technologies by proposing an intelligent matching method based on railway loading plans.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: an intelligent matching method based on railway loading plans, comprising the following steps: S1: Extract the remaining TEU capacity of the scheduling segment and adjacent segments, construct a differential trend sequence based on the segment number order, determine the segment continuity based on the sign direction, and generate a continuous trend segment path set; S2: Based on the set of continuous trend segment paths, extract the operation time window of the path segment, identify the intersection of the time ranges of adjacent segments, filter the paths that meet the continuous execution conditions, and generate a sequence of overlapping loading time periods. S3: Based on the overlapping loading time period path sequence, obtain the operation status code and planned loading demand value of each path, determine the status availability and resource adaptability, filter out paths with borrowable conditions, and obtain a path resource borrowing matching reference set. S4: Based on the path resource borrowing and matching reference set corresponding to the carriage, extract the remaining space boundary value and standard container parameters for adaptation judgment, identify acceptable carriage space segments, and generate a list of acceptable container space segments; S5: Based on the path and carriage correspondence between the acceptable box-type space segment list and the path resource borrowing matching reference set, count the number of matching carriages under the path, sort them according to the number of matching carriages, and generate a path and space matching priority list.

[0006] As a further aspect of the present invention, the coherent trend segment path set includes a TEU remaining capacity difference sequence, a symbol direction sequence, and a segment number sequence; the overlapping loading time period path sequence includes the intersection of path operation time windows, continuous execution condition segments, and time period overlap matching segments; the path resource borrowing matching reference set includes path operation status codes, path loading demand values, and path resource adaptation status; the acceptable container space segment list includes remaining space boundary values, standard container parameters, and space segment adaptation types; and the path and space matching priority list includes the number of path matching wagons, path wagon matching relationships, and matching priority ranking results.

[0007] As a further aspect of the present invention, step S1 is as follows: S101: Based on the scheduling segment and the numbering order of adjacent segments, obtain the corresponding remaining TEU capacity value, arrange them in order of number, calculate the difference in remaining TEU capacity between adjacent segments, and obtain the TEU difference trend value sequence. S102: Call the TEU difference trend value sequence, judge the sign direction of each element one by one, extract the continuous sign consistent segment, and generate the sign direction coherence segment path segment set; S103: Based on the symbol direction continuity segment path set, detect the segment number span value, TEU difference change sum, and adjacent path number intersection in the continuity path to obtain the continuity trend strength value of the continuity path, sort the paths according to the continuity trend strength value, and obtain the continuity trend segment path set.

[0008] As a further aspect of the present invention, step S2 is as follows: S201: Based on the continuous trend segment path set, extract the operation time data of the segments in the path, integrate the time information in numerical order, and form a continuous segment operation time set. S202: Based on the set of continuous segment operation times, identify whether there is an overlap in the time range between adjacent segments within the path, filter paths with continuous operation time characteristics, and generate comparison results of overlapping time periods between paths; S203: Based on the comparison results of overlapping time periods between the paths, combined with the time range, number span and intersection quantity elements in the path, obtain the time overlap intensity value of each path under the conditions of time continuity and structural integrity, sort the results in descending order, and obtain the path sequence of overlapping loading time periods.

[0009] As a further aspect of the present invention, step S3 is as follows: S301: Based on the overlapping loading time period path sequence, extract the operation status information and planned loading demand information of the path, identify the set of paths that can be scheduled, construct the correspondence between paths and loading demands, and obtain the path operation demand structure set; S302: Based on the path operation demand structure set, combined with the current status of the path and loading demand information, determine whether the path meets the resource borrowing conditions, form a set of borrowable paths, and obtain a set of path borrowing feasibility difference intensity values. S303: Call the path borrowing feasibility difference intensity value set, filter paths that meet the conditions, extract the corresponding resource identifiers, establish a mapping structure between paths and resources, and obtain a path resource borrowing matching reference set.

[0010] As a further aspect of the present invention, step S4 is as follows: S401: Based on the path resource borrowing matching reference set, extract the carriage number corresponding to the path, call the carriage resource information table, obtain the three-dimensional remaining space boundary value of the carriage, establish the correspondence between the path and the space boundary value, and obtain the carriage space boundary value set; S402: Based on the set of boundary values ​​of the cargo compartment space, call the standard container parameter set, determine the adaptation relationship between the boundary values ​​of the cargo compartment space and the container size parameters, calculate the space adaptation deviation value between the cargo compartment and the container type, and filter according to the space adaptation threshold to obtain the cargo compartment adaptation deviation value structure set. S403: Call the carriage adaptation deviation value structure set, filter the carriage and box type combination information that meet the space adaptation conditions, extract the corresponding space segment number and box type identifier, establish a list structure, and obtain the list of acceptable box type space segments.

[0011] As a further aspect of the present invention, step S5 is as follows: S501: Based on the correspondence between the acceptable box-type space segment list and the path resource borrowing matching set, extract the associated carriage number under the path, and determine whether it exists in the carriage number recorded in the list. Summarize the number of successfully matched carriages according to the path number, and generate a set of matching carriage number information corresponding to the path. S502: Call the matching carriage quantity information set corresponding to the path, combine the carriage capacity information, carriage type matching quantity information and path level index bound to the path, classify and organize the path and establish a path matching index structure set; S503: Based on the path matching indicator structure set, extract the indicator structure content corresponding to the path number, establish a mapping structure between the path number and the sorting level according to the indicator relationship, and generate a path and space matching priority list.

[0012] As a further embodiment of the present invention, the remaining TEU capacity is the standard container transport capacity that has not been allocated in the scheduling section during the current scheduling cycle, and the unit is TEU. It is derived from the resource allocation system or station capacity management system in the railway transport scheduling system. The difference trend sequence is an ordered symbol sequence formed based on the changing direction of the remaining capacity value of TEUs between adjacent scheduling segments. The construction method is based on the segment number arrangement to determine the direction of the sequential difference. The direction of the symbol is a logical identifier representing the trend of TEU capacity value change. It is derived from the difference sign of TEU values ​​between adjacent segments. A positive value represents an increase in capacity, and a negative value represents a decrease in capacity. The TEU stands for Twenty-foot Equivalent Unit, which is a standard 20-foot container unit.

[0013] As a further aspect of the present invention, the operation time window is the time range of executable loading tasks allocated to each section in the scheduling system, including the start time and the end time, which is derived from the railway transportation operation plan table or the task instruction scheduling table. The continuous execution condition refers to the existence of a time intersection between the operation time windows of two adjacent segments, which meets the requirement of seamless connection of the operation process in time. The judgment method is based on the non-empty intersection of time intervals. The job status code is an identifier that identifies the current allocation status of path scheduling resources. It comes from the status control field in the railway dispatching system and the code indicates the status type: unassigned, pre-assigned, or locked. The planned loading demand value refers to the expected number of containers to be loaded within the target route segment, based on the transport order or loading task plan. The data source is the loading condition requirement document for railway transportation. The resource adaptability refers to the matching relationship between the current remaining TEU capacity value and the planned loading demand value of the path segment, and is determined based on the schedulable range between the two values. The remaining space boundary value is the length, width, and height parameters of the unoccupied portion of the current carriage. The data source is the number of containers that can be loaded, which is obtained by combining the vehicle model field in the current vehicle system data with the loading conditions. The planned loading demand value refers to the expected number of containers to be loaded within the target route segment based on the transport order or loading task plan. The data source is the railway transport planning system or the container loading management system. The resource adaptability refers to the matching relationship between the current remaining TEU capacity value and the planned loading demand value of the path segment, and is determined based on the schedulable range between the two values. The remaining space boundary value is the length, width, and height parameters of the unoccupied portion of the current carriage, and the data source is carriage layout information or automatic loading detection system; The standard container parameters are the length, width, and height dimensions of 20-foot and 40-foot containers conforming to ISO 668 standards, derived from the International Container Equipment Identification Specification. The path-car correspondence refers to the mapping relationship between the dispatched path segment and the actual assigned car, which originates from the shunting operation instruction or transportation marshalling plan and is the basic structure of loading task scheduling. The number of matched carriages refers to the statistical result of the number of carriages that meet the spatial adaptation conditions under a single scheduling path, which comes from the spatial recognition result of the loading and matching process; The path and space matching priority list is a numbered queue formed by sorting the number of matching carriages corresponding to the path.

[0014] An intelligent matching system based on railway loading plans includes: The trend extraction module obtains the scheduling segment number and the remaining TEU capacity value of adjacent segments, judges the direction of capacity difference according to the number order, filters consecutive numbered segments based on direction consistency, and generates a set of continuous trend segment paths. The time period intersection module extracts the segment operation time window in the path based on the continuous trend segment path set, identifies the intersection interval of adjacent segment time ranges, filters paths with continuous operation conditions, and generates a sequence of overlapping loading time period paths. Based on the overlapping loading time period path sequence, the status judgment module obtains the operation status code and planned loading requirements of the section under the path, judges whether the status is available and identifies resource matching relationship, filters the paths with resource borrowing conditions, and generates a path resource borrowing matching reference set. The spatial recognition module uses the path resource borrowing and matching reference set to extract the remaining space boundary value of the carriage and the standard parameters of the container, determines whether the space and the container type are compatible, identifies available carriage space segments, and generates a list of acceptable container space segments. The priority matching module, based on the list of acceptable container space segments and the path-carriage correspondence in the path resource borrowing matching reference set, counts the number of matching carriages under a path, sorts the path numbers in order of quantity, and generates a path-space matching priority list.

[0015] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In this invention, by identifying the segmental transport capacity trend and constructing routes, dynamic optimization of resource scheduling is achieved, improving matching continuity and response speed. Extraction of the intersection of operation time windows makes the selection of loading time periods more collaborative. Combining status availability and loading demand judgment improves the accuracy of route selection and resource adaptation efficiency. Spatial boundary and container parameter adaptation judgment enhances the depth of carriage space identification, increases loading density, and optimizes route priority by matching quantity sorting, thereby enhancing the accuracy of decision support and promoting the transformation of loading matching to intelligent allocation. Attached Figure Description

[0016] Figure 1 This is a flowchart of the main steps of the present invention; Figure 2 This is a flowchart of step S1 of the present invention; Figure 3 This is a flowchart of step S2 of the present invention; Figure 4 This is a flowchart of step S3 of the present invention; Figure 5 This is a flowchart of step S4 of the present invention; Figure 6 This is a flowchart of step S5 of the present invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0018] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, in the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0019] Please see Figure 1 The intelligent matching method based on railway loading plans includes the following steps: S1: Extract the remaining TEU capacity of the scheduling segment and adjacent segments, construct a differential trend sequence based on the segment number order, determine the segment continuity based on the sign direction, and output the path set of continuous trend segments; The remaining capacity of TEU is the standard container transport capacity that has not been allocated in the dispatching section during the current dispatching cycle. The unit is TEU, and it comes from the resource allocation or station capacity management in the railway transport dispatching system. The difference trend sequence is an ordered symbol sequence formed based on the changing direction of the remaining capacity of TEUs between adjacent scheduling segments. It is used to characterize the changing trend of resources in the scheduling path. The construction method is based on the segment number arrangement and the direction of the sequential difference is determined. The sign direction is a logical identifier used to indicate the trend of TEU capacity value changes. It is derived from the difference sign of TEU values ​​between adjacent segments. A positive value represents an increase in capacity, and a negative value represents a decrease in capacity. It is used to determine the trend of path continuity. TEU is an abbreviation for Twenty-foot Equivalent Unit, which is an international standard unit of measurement used to measure the volume of container transport. It is used in rail and sea container transport systems as a basic indicator of capacity and transport capacity. S2: Based on the continuous trend segment path set, extract the operation time window of the path segment, identify the intersection of the time range of adjacent segments, filter the paths that meet the continuous execution conditions, and generate the path sequence of overlapping loading time periods. The operation time window is the time range within which an executable loading task is allocated to each section in the dispatching system, including the start time and the end time, and is derived from the railway transport operation plan table or task instruction dispatch table. The continuous execution condition refers to the existence of a time intersection between the operation time windows of two adjacent segments, which meets the requirement of seamless connection of the operation process in time. The judgment method is based on the non-empty intersection of time intervals. S3: Based on the route sequence of overlapping loading time periods, obtain the operation status code and planned loading demand value of each route, determine the status availability and resource adaptability, filter the routes with borrowing conditions, and obtain the route resource borrowing matching reference set. The job status code is an identifier used to identify the current allocation status of route scheduling resources. It comes from the status control field in the railway dispatching system and the code indicates the status type: unassigned, pre-assigned, or locked. Planned loading demand refers to the expected number of containers to be loaded within the target route segment based on transport orders or loading task plans. The data source is the railway transport loading condition requirement document. Resource adaptability refers to the matching relationship between the current remaining capacity of TEUs in the path segment and the planned loading demand value. It is used to determine whether the resources can meet the current loading task, and is based on the schedulable range judgment rule between the two values. S4: Based on the path resource borrowing and matching of the corresponding carriages in the set of paths, extract the remaining space boundary values ​​and standard container parameters for adaptation judgment, identify acceptable carriage space segments, and generate a list of acceptable container space segments; The remaining space boundary value is the length, width, and height parameters of the unoccupied portion of the current cargo compartment. The data source is the vehicle model field in the current vehicle system data combined with the loading conditions to determine the number of containers that can be loaded. The remaining space boundary value is the length, width, and height parameters of the unoccupied portion of the current cargo compartment, used to describe the boundary range of the available loading space. The data source is cargo compartment layout information or automatic loading detection system. Standard container parameters are the length, width, and height dimensions of 20-foot and 40-foot containers conforming to ISO 668 standards, used to perform spatial adaptation comparisons, and are derived from the International Container Equipment Identification Specification. S5: Based on the path and carriage correspondence of the acceptable box-type space segment list and the path resource borrowing matching reference set, count the number of matching carriages under the path, sort them according to the number of matching carriages, and generate a path and space matching priority list. The correspondence between routes and carriages refers to the mapping relationship between the dispatched route segment and the actual assigned carriages. It originates from shunting operation instructions or transportation marshalling plans and is the basic structure for loading task scheduling. The number of matched carriages refers to the statistical result of the number of carriages that meet the spatial adaptation conditions under a single scheduling route. It is used to reflect the correspondence between the route and the carriage space and comes from the spatial recognition result of the loading and matching process. The path and space matching priority list is a numbered queue formed by sorting the number of matching carriages corresponding to each path. It is used to guide the scheduling system to prioritize path schemes with high suitability and serves as a reference for generating scheduling instructions.

[0020] The coherent trend segment route set includes the TEU remaining capacity difference sequence, symbol direction sequence, and segment number sequence. The route sequence with overlapping loading time periods includes the intersection of route operation time windows, continuous execution condition segments, and time period overlap matching segments. The route resource borrowing matching reference set includes the route operation status code, route loading demand value, and route resource adaptation status. The list of acceptable container space segments includes the remaining space boundary value, standard container parameters, and space segment adaptation type. The route and space matching priority list includes the number of matching carriages for the route, the route carriage matching relationship, and the matching priority ranking result.

[0021] Please see Figure 2 Step S1 is as follows: S101: Based on the scheduling segment and the numbering order of adjacent segments, obtain the corresponding remaining TEU capacity value, arrange them in order of number, calculate the difference in remaining TEU capacity between adjacent segments, and obtain the TEU difference trend value sequence. Based on the numbering order of scheduling segments and their adjacent segments, the scheduling area is first divided into several paths. Each path consists of a series of segments, each uniquely identified by a number. These numbers must be arranged in ascending order to ensure continuity within the path. In practical applications, if path "i=1" contains segments numbered 5, 6, and 7, it is recorded as path i=1 with the numbers 5, 6, and 7 respectively. Once the corresponding numbering order is clear, the remaining TEU capacity value of each segment needs to be extracted. The remaining TEU capacity value can be obtained from the real-time database of the scheduling system or collected by on-site terminal equipment. As an example, in path i=1, segments numbered 5, 6, and 7 have remaining TEU capacity values ​​of 80, 70, and 63 respectively. The difference value is... =80−70=10, =70−63=7, and so on. The difference between each pair of adjacent segments in the path is calculated to obtain the TEU difference sequence. For example, the difference sequence for path i=1 is [10, 7]. The number span S_ij is the difference between the numbers of adjacent segments. When the numbering order is continuous, the span for each segment is 1. If there is a jump in the numbering, the span is calculated based on the jump value. For example, if there is no number 6 between numbers 5 and 7, then the span S_ij=2. To improve processing efficiency, the TEU difference values ​​and number spans in each path are uniformly organized into a structured table, forming the following data: Table 1 Parameters for Calculating Continuous Paths

[0022] As shown in Table 1, each path contains an array of TEU difference values, an array of number spans, an array of overlapping segments with adjacent paths, and an array of path spans. These parameters provide the basis for subsequent path continuity trend judgment. The TEU difference value is obtained by subtracting the remaining capacity value of adjacent segments, the number span is calculated by the difference of segment numbers, the number of overlapping segments O_ik is obtained by statistically analyzing the intersection of path numbers, and the path span S_ik is determined by calculating the difference of the numbers at both ends of the intersection path. All parameters are derived from the real-time data of the scheduling system and the path topology processing module, forming a sequence of TEU difference trend values.

[0023] S102: Call the TEU difference trend value sequence, judge the sign direction of each element one by one, extract the continuous sign consistent segment, remove the segment whose first and last signs do not meet the sign continuity, and generate the sign direction continuity segment path segment set. The TEU difference trend value sequence generated in paragraph 1 is used to determine the directionality of the difference value array in each path. Operationally, each path is processed individually. Taking path i=1 as an example, its difference values ​​are [8, 10, 7]. The sign of each difference value is analyzed sequentially. If all difference values ​​in the sequence are positive or all are negative, the path is considered to have directional continuity. For example, all items in [8, 10, 7] are positive, indicating a consistent direction and positive continuity. If there is a change in direction, the path needs to be divided into multiple segments according to the point of sign change, retaining only the segments with consistent direction. A direction judgment threshold δ is set during the judgment process to exclude the influence of small fluctuations on the direction consistency judgment. Setting δ=2 means that when |ΔT_ij|<2, it is considered a fluctuation item. Merging is performed based on the preceding direction. For example, the difference value of path i=2 is [6, 1, 3], where the second term is 1<δ=2, so it can be merged to make the path still judged as a whole positive continuity. In actual implementation, the threshold δ can be set through statistical analysis of historical scheduling data. For example, select 1000 sample paths to calculate the standard deviation of the average fluctuation amplitude, and set the δ value at twice the standard deviation. This value will be further explained in the appendix of the attached figure in the manual. Its reasonable range is generally 1.5~3. After the segmentation process, the continuous path segments are recorded in the form of numbered intervals. For example, [5-7] indicates that the path number 5 to 7 is continuous. After each path is processed, all path segment intervals that meet the consistent sign direction are output, which are uniformly constituted as the symbol direction continuity interval path segment set.

[0024] S103: Based on the symbol direction continuity segment path segment set, the specific calculation formulas for detecting the segment number span value, the total TEU difference change, and the intersection of adjacent path numbers in a continuous path are as follows: ; The coherence trend strength value of the coherence path is obtained by calculation. The paths are extracted by sorting the coherence trend strength values ​​to obtain the coherence trend segment path set. in, Representing a path The strength value of the coherent trend, Representing a path The Middle The TEU difference between each segment and the previous segment. Representing a path The Middle The numbering span between each segment and the previous segment. Representing a path With path The number of intersections of the segment numbers, Representing a path With path The number span value, Representing a path Number of segments included Representing a path The number of adjacent paths, It represents a very small positive number and is used to prevent the formula from being undefined when the denominator is zero; Based on the symbol direction coherence segment path segment set, analyze the coherence trend strength value of the path segment, and call the TEU difference value, number span value and number intersection ratio with adjacent paths in the path segment; The numerator first calculates the TEU difference value of each segment multiplied by the square root of the segment's number span. Then, it sums the results of all segments to form the overall trend strength of the path. The denominator calculates the ratio of the numbered intersections of path i with all m_i adjacent paths, and sums these ratios as the overall connectivity weakening factor. If the number of adjacent paths is zero, the denominator is ε to avoid undefined values. The advantage of this formula is that by introducing the square root of the number span and the path intersection ratio, the trend strength evaluation takes into account both the internal structure of the path and the interference from adjacent paths, making the results more robust and stable. Now, let's take path i=1 as an example and substitute it into the formula for calculation: The difference values ​​are: [8, 10, 7]; The numbering span is: [4, 1, 2]; The number of overlapping segments is: [1, 1]; The path span is: [4, 3]; The molecular expansion is as follows: ; The denominator is expanded as follows: ; ; The strength value of the coherent trend is: ; This value represents the continuity strength of path i=1 under the TEU capacity difference structure and the intersection relationship with adjacent paths. The larger the result, the stronger the directional change within the path and the weaker the interference with other paths, making it easier to prioritize in subsequent path selection. All paths are sorted in descending order of trend strength values, and the top few paths are selected sequentially to form a set of paths with continuous trend segments. If the above calculations need to be incorporated into the specification, accompanying drawings, or claims, they can be further expanded according to the number of path segments and the intersection situation. The formula measures the cumulative intensity of capacity changes within a route by multiplying the absolute value of the TEU difference in each segment by the square root of the corresponding span in the numerator and summing the results. The absolute value ensures directional consistency, while the square root balances the impact of the span on the difference value. The denominator calculates the sum of the ratios of the number of overlapping segments between the route and adjacent routes to the span, reflecting the degree of structural overlap. This is used to suppress the weight of highly overlapping routes, thereby reducing their continuity trend value. The overall logic is that the more drastic the internal changes and the smaller the external interference, the higher the continuity trend strength value. The coherence trend strength value is a comprehensive indicator used to measure whether the TEU capacity difference trend of a route is continuous and concentrated under the segment numbering sequence. This value combines the capacity difference magnitude between each segment within the route and the numbering span to reflect the capacity fluctuation intensity within the route. At the same time, it introduces the degree of numbering overlap with adjacent routes as a correction factor to suppress the misjudgment of coherence caused by the overlap of route structures. Therefore, the higher the value, the more obvious and consistent the capacity change trend within the route is, and the less it is affected by the interference of adjacent routes. It can be used to identify high-intensity trend routes with continuous direction and independent structure in the scheduling network, providing a quantitative basis for route selection and resource allocation.

[0025] Please see Figure 3 Step S2 is as follows: S201: Based on the continuous trend segment path set, extract the operation time data of the segments in the path, integrate the time information in the order of number, and form the operation time set of continuous segments. Based on a set of continuous trend segment paths, the start and end times of all segments within each path are extracted sequentially. The start time represents the time when the segment enters the work preparation state, and the end time represents the deadline when the segment completes loading preparation. The time data of the complete path segment sequence is obtained through the scheduling system, and a path-level time series structure is constructed. The time data is assembled into a structured segment timetable according to the numbering order. For example, in path A, the start times of three consecutive numbered segments are 0, 30, and 60 minutes, and the end times are 20, 50, and 90 minutes, respectively. The time intervals for each operation are 0-20, 30-50, and 60-90. The time pairs of all segments are aggregated to form a set of operation time intervals. In the actual system, the start and end time fields of each segment can be obtained from the segment task registration table in the scheduling control module. If a segment is missing or the start and end times are incomplete, the path must be removed at this stage. After extracting the time data for each path, the set of segment time intervals for all valid paths is summarized in turn, and a mapping index table between the path and its corresponding operation time interval is constructed to obtain the set of continuous segment operation time intervals.

[0026] S202: Based on the set of continuous segment operation times, identify whether there is an overlap in the time range between adjacent segments within the path, filter paths with continuous operation time characteristics, and generate comparison results of overlapping time periods between paths; Based on the set of continuous segment operation time periods, adjacent segment time periods in the path are selected for pairwise comparison. The start and end times of each pair of segments are retrieved to determine if there is an intersection. The intersection condition is defined as the start time of the later segment being no less than the start time and end time of the earlier segment. If there is an intersection, the intersection time period is recorded as a valid time window; otherwise, it is judged as discontinuous and the path filtering process is exited. For example, in path A, the first segment is 0–20 minutes, and the second segment is 30–50 minutes. Since 30 > 20, there is no intersection, and this path does not meet the continuous overlap condition. If another… In a path, segment 1 is 10–30 minutes and segment 2 is 25–45 minutes. Since 25 < 30 and 25 ≥ 10, there is an intersection between the two. The segment number and the corresponding intersection time period of this path are retained. Intersections between segments are determined in this way for all paths. All intersection results are stored in the path intersection determination table. At the same time, the judgment threshold ε = 0.01 is set. When the total number of adjacent segment judgments is 0, the denominator is set to ε to avoid the formula being undefined. The path must meet the requirement of continuous intersection of the entire segment. All paths that meet the segment operation time intersection requirements and their intersection number information are summarized to obtain the comparison result of overlapping time periods between paths.

[0027] S203: Based on the comparison results of overlapping time periods between paths, and combined with the specific calculation formulas for time range, number span, and number of intersections in the path, the following formulas are used: ; The calculation obtains the time overlap intensity value of each path under the conditions of time continuity and structural integrity. Based on the calculation results, the paths are sorted in descending order to obtain the path sequence of overlapping loading time periods. in, Representing a path The time overlap intensity value, and Representing paths The Middle The end and start times of each segment, in minutes. Representing a path The number of consecutive segments included. Representing a path With path Number of intersections between the segment numbers Representing a path With path The number of segments where the operation times overlap. Representing a path With path Total number of overlapping comparisons Representation and path The number of paths that have task intersection judgment behavior. This represents a minimum positive value set to prevent the denominator from being zero; its value ranges from 0.01 to 0.1. Based on the comparison results of overlapping time periods between paths, the operation time periods of all continuous segments in each path are selected. The duration of each operation time is calculated by subtracting the start time from the end time of each segment, and the time difference is calculated and summed in sequence. For example, if the time periods of the three continuous segments in path A are 0–20, 30–50, and 60–90, the time differences are 20, 20, and 30 minutes respectively, and the total duration is 70 minutes, which is used as the numerator of the formula. Then, the number of number intersections, the number of corresponding operation time intersections, and the number of comparisons between the path and other paths are analyzed. The difference between the number of number intersections and the number of time intersections is calculated for each pair, divided by the value after adding one to the number of comparisons, and the ratios of each group are summed. The absolute value is then compared with the threshold ε to form the denominator. Calculate the time overlap intensity of path operations; in, Representing a path The time overlap intensity value, , Indicates the first in the path The end and start times of each segment, in minutes. Representing a path The number of consecutive segments contained To sum the segments, , , These represent the number of intersections between path i and path k, the number of time intersection segments, and the number of overlap comparisons, respectively. The number of paths participating in the comparison. A very small positive value is set to avoid a denominator of 0; Substituting the data for path A from "Table 1 Path Operation Time Analysis Table" into the formula, we get: Time difference calculation: ; Denominator calculation: ; Denominator = ; Calculation results: ; The results indicate that the work sections of route A have temporal overlap, and their corresponding route numbers are included in the route sequence of overlapping loading time periods. Table 2. Path Operation Time Analysis Table

[0028] As shown in Table 2, the relationship between the segment time and the number of intersections in path A satisfies the calculation conditions of the formula, and the time overlap intensity value of the path has been obtained through calculation, which can be used as the basis for path selection in the scheduling module. This formula sums the time differences between the sections in the path to reflect the total operation time of the entire path. Absolute value calculation is used to ensure that the time difference is non-negative. The denominator is constructed by the difference between the intersection of the numbers and the intersection of the time. This interference factor is then calculated by dividing it by the offset of the number of comparisons and summed. The absolute value is then taken to measure the degree of overlap between paths. The maximum value function is used to prevent the denominator from being zero. In the overall formula, addition is used to accumulate the relationship between each section or path, multiplication is used for the combination of time and number, division is used for intersection ratio calculation, and absolute value and maximum value function ensure the stability of the calculation. The time overlap intensity value is used to measure the relative relationship between the total operation time of a path in a continuous segment and the difference in time intersection generated when compared with other paths. The larger the value, the longer the coverage of the path in terms of its own operation time, and the lower the degree of overlap and interference with other paths, thus reflecting its higher time continuity and independence in the operation plan. This index comprehensively considers the length of continuous operation time inside the path and the degree of intersection and conflict outside, and can provide a quantitative basis for path selection based on time stability and overlap and conflict.

[0029] Please see Figure 4 Step S3 is as follows: S301: Based on the route sequence of overlapping loading time periods, extract the operation status information and planned loading demand information of the route, identify the set of routes that can be used for scheduling, construct the correspondence between routes and loading demands, and obtain the route operation demand structure set; Based on the route sequence of overlapping loading time periods, the operation status field and planned loading demand field of each route in the route set are extracted. Field matching is performed between the scheduling record table and the planning data table to summarize and integrate the route number, scheduling status, and loading demand, forming a structure corresponding to the route's operation status and demand. The operation status is enumerated data, distinguished by "schedulable" or "unschedulable" to indicate whether it is available for borrowing within the current period. The planned loading demand is expressed in units, representing the resource requirement of the route within the scheduling period. Combined with actual scheduling data, the initial information of routes X, Y, and Z can be obtained, as detailed in Table 3. Table 3 Path Operations and Resource Adaptation Table

[0030] By reading the above data, path X is marked as schedulable with a loading requirement of 48 units. Path Z is also schedulable with a loading requirement of 52 units. Path Y is not schedulable and will not participate in the subsequent processing. Only paths X and Z are retained for subsequent resource adaptation analysis. A unified index structure is established for this result to obtain the path job requirement structure set.

[0031] S302: Based on the path operation demand structure set, combined with the current status of the path and loading demand information, determine whether the path meets the resource borrowing conditions, form a set of borrowable paths, and obtain the set of path borrowing feasibility difference intensity values. Based on the path job demand structure set, determine whether the path meets the resource borrowing adaptation conditions. Two paths, X and Z, are selected as schedulable. For path X, extract the planned loading demand value of 48 units and the current resource capacity value of 50 units. The difference is 2 units, which is less than the borrowing matching benchmark value of 10 units. This path meets the difference condition. The status correction factor is 1.0, and the floating adjustment factor is 1.0. After multiplication, the result is 1.0. After proportional adjustment based on the difference value, it still meets the borrowing threshold. Path X is recorded as a borrowable path. Path Z corresponds to a loading requirement of 52 units and a resource capacity of 40 units, with a difference of 12 units, which is greater than the borrowing matching benchmark value. The status correction factor is 1.2, and the floating adjustment factor is 1.0. The product is 1.2, which, after adjustment and combination with the difference, exceeds the borrowing threshold range and does not meet the borrowing conditions. Path Y did not participate in this judgment because it is unschedulable. Therefore, it is confirmed that path X meets the resource borrowing adaptation judgment conditions. The number of path X is bound to the difference judgment result, and a difference intensity value index set is constructed to obtain the path borrowing feasibility difference intensity value set.

[0032] S303: Call the path borrowing feasibility difference strength value set, filter the paths that meet the conditions, extract the corresponding resource identifiers, establish the mapping structure between paths and resources, and obtain the path resource borrowing matching reference set; The system calls upon the set of feasibility difference strength values ​​for path borrowing, filters out paths marked as meeting the borrowing conditions, extracts path X as the only path meeting the borrowing conditions in the current period, and searches the resource identifier record currently associated with path X through the resource scheduling table. The resource number corresponding to this path is R001. A bidirectional pairing relationship between the path number and the resource number is established, forming a structured entry X→R001. Path Z does not enter the pairing process because it does not meet the borrowing conditions, and path Y is also excluded from the borrowing path set because its scheduling status does not meet the requirements. The pairing structure of path X and resource R001 is written into the borrowing mapping master table, a pairing relationship key-value index is constructed, and the path resource borrowing matching reference set is obtained.

[0033] Please see Figure 5 Step S4 is as follows: S401: Based on the path resource borrowing matching reference set, extract the carriage number corresponding to the path, call the carriage resource information table, obtain the three-dimensional remaining space boundary value of the carriage, establish the correspondence between the path and the space boundary value, and obtain the carriage space boundary value set; Based on the path and resource pairing information recorded in the path resource borrowing matching reference set, the carriage number bound to the path is extracted. Then, according to the carriage resource management data table, the remaining space parameters of each carriage are obtained. These space parameters should be the three-dimensional boundary values ​​of the free volume inside the carriage during the current scheduling cycle, corresponding to the available space in the carriage's internal structural length, width, and height. All parameters are uniformly in millimeters. The data structure is then bound together with the path number and carriage number. In actual execution, the path number A01 corresponds to carriage number C01. This is verified by checking the resource scheduling data table. The remaining three-dimensional space has a length of 1200, a width of 2300, and a height of 2600. Path A02 corresponds to carriage C02, with spatial boundaries of length 800, width 2500, and height 2800. Path A03 corresponds to carriage C03, with spatial boundary values ​​of length 1500, width 2200, and height 2400. By centrally storing and processing the above three sets of path and carriage data, a one-to-one mapping structure between path numbers and three-dimensional spatial boundary values ​​is constructed. The obtained values ​​are then subjected to structural integrity checks and dimensional uniform verification to obtain a set of carriage spatial boundary values.

[0034] S402: Based on the set of boundary values ​​of the cargo compartment space, call the standard container parameter set, determine the adaptation relationship between the boundary values ​​of the cargo compartment space and the container size parameters, calculate the space adaptation deviation value between the cargo compartment and the container type, and filter according to the space adaptation threshold to obtain the cargo compartment adaptation deviation value structure set. Based on the set of boundary values ​​for the cargo compartment space, the three-dimensional dimensions of each container type in the standard container parameter set are compared to determine whether the cargo compartment space meets the loading conditions. In this operation, 20-foot and 40-foot containers are used as the criteria. The standard dimensions of a 20-foot container are 5900 mm in length, 2350 mm in width, and 2390 mm in height, while the standard dimensions of a 40-foot container are 12100 mm in length. All other dimensions are the same. During the comparison, the three-dimensional boundary values ​​of each cargo compartment are compared item by item with the container dimensions. If the remaining length of the cargo compartment is greater than or equal to the container length, and the width and height also meet the matching conditions, it is marked as loadable. For example, cargo compartment C01 has a length of 1200 mm, which is much smaller than the standard length of a 20-foot container (5900 mm), thus it does not meet the matching conditions. Failure. Carriage C02, with a length of 800, also fails to meet the loading requirements. Carriage C03, with a length of 1500, also fails to match the 20-foot standard box type. In terms of width and height, the carriages can meet the dimensions of both 20-foot and 40-foot boxes. However, since all length dimensions are not met, all three carriages are identified as mismatched. During the calculation process, a spatial adaptation threshold needs to be set to accommodate small errors. The value range is set to a positive range of [5, 200] millimeters. This threshold is used to define the maximum acceptable error in the matching process. If the difference between the carriage space and the box type size exceeds this threshold during the comparison, it is considered that the conditions are not met. After matching screening and error comparison processing, a deviation comparison structure between the carriage and the box type is established, and a carriage adaptation deviation value structure set is obtained.

[0035] Table 4 Examples of Space Adaptation Between Carriage and Box-Type Vehicles

[0036] As shown in Table 4, none of the carriages corresponding to the three paths meet the carriage length requirements, and the spatial difference far exceeds the set range of the adaptation threshold. The width and height directions are within the allowable range, but an effective match cannot be formed. Therefore, only the information that does not meet the requirements is recorded in the deviation structure set for subsequent scheduling avoidance strategy design and loading early warning.

[0037] S403: Call the carriage adaptation deviation value structure set, filter the carriage and box type combination information that meet the space adaptation conditions, extract the corresponding space segment number and box type identifier, establish a list structure, and obtain the list of acceptable box type space segments. The system calls upon the carriage adaptation deviation value structure set, filters and screens the spatial difference structure between the recorded carriages and standard box types, identifies records whose deviation values ​​are within the positive range of the set spatial threshold, extracts the path number, carriage number, and standard box type code corresponding to these combinations, constructs a matching result list, and performs number indexing processing. The binding relationship between the carriage space segments that meet the loading matching requirements and the acceptable box types is integrated into structured data records, and a list of acceptable box type space segments is obtained.

[0038] Please see Figure 6The S5 steps are as follows: S501: Based on the correspondence between the acceptable container space segment list and the path resource borrowing matching set, extract the associated carriage number under the path, determine whether it exists in the carriage number recorded in the list, summarize the number of successfully matched carriages according to the path number, and generate the matching carriage number information set corresponding to the path. Based on the path-carriage correspondence between the acceptable container space segment list and the path resource borrowing matching set, the path number and its corresponding car number are obtained. All car numbers under each path are extracted and compared item by item with the loadable car numbers registered in the acceptable container space segment list. The presence of a car number in the list is checked; if it exists, it is marked as a matchable car. After comparison, the number of cars marked as matchable under each path is counted, and the corresponding number is recorded according to the path number. Further structural analysis is then performed. The process involves mapping route numbers to the number of matching carriages, using the route as the primary key and the number of matching carriages as the value. During implementation, route A01 has carriages C01 and C02. After comparing these with the carriage numbers in the inventory, it was found that both carriages were within the acceptable range. Only carriage C03 of route A02 matched the inventory, while carriages C04, C05, and C06 of route A03 all matched. The total number of matching carriages for routes A01 was 2, A02 was 1, and A03 was 3, as shown in the table below: Table 5 Example of Path Matching Statistics

[0039] As shown in Table 5, the number of matched carriages under the three paths has been counted, and the information set of the number of matched carriages corresponding to the paths has been obtained.

[0040] S502: Call the matching carriage quantity information set corresponding to the path, combine the carriage capacity information, carriage type matching quantity information and path level index bound to the path, classify and organize the path and establish a path matching index structure set; The system retrieves the matching carriage quantity information set corresponding to the path, reads the path number and the number of matching carriages in a unified manner, and combines the registered remaining capacity data of carriages, the number of matching carriage types, and the path level parameters under each path to construct a multi-dimensional matching index field. During the operation, the system first extracts the number of matching carriages according to the path number and reads the sum of the space capacity values ​​of all matching carriages for each path as the remaining capacity of the path. It then reads the number of acceptable carriage types marked in all carriages under the corresponding path as the carriage type matching index. Finally, it reads the level information marked in the path registration table, such as ordinary and urgent, as the scheduling level parameter. After the above data is extracted, it is uniformly written into the matching index field structure, and the structure is bound according to the path number to output the path matching index structure set.

[0041] S503: Based on the path matching indicator structure set, extract the indicator structure content corresponding to the path number, establish a mapping structure between the path number and the sorting level according to the indicator relationship, and generate a path and space matching priority list. Based on the path matching indicator structure set, read the combination of indicator fields corresponding to each path, arrange the path numbers according to the preset indicator field order, and combine the path numbers with the sorting results to construct a path sorting number list. In the structure list, the path number is used as the primary key field and the sorting level is used as the value field to complete the data structure entry. After the list is generated, it is synchronously updated to the path scheduling parameter set, and the matching fields are automatically bound to obtain the path and space matching priority list.

[0042] An intelligent matching system based on railway loading plans includes: The trend extraction module obtains the scheduling segment number and the remaining TEU capacity value of adjacent segments, judges the direction of capacity difference according to the number order, filters consecutive numbered segments based on direction consistency, and generates a set of continuous trend segment paths. The time period intersection module extracts the operation time window of the section in the path based on the continuous trend section path set, identifies the intersection interval of the time range of adjacent sections, filters the paths with continuous operation conditions, and generates the path sequence of overlapping loading time periods. The status judgment module obtains the operation status code and planned loading requirements of the section under the path based on the path sequence of overlapping loading time periods, judges whether the status is available and identifies resource matching relationship, filters the paths with resource borrowing conditions, and generates a path resource borrowing matching reference set. The spatial recognition module is based on the path resource borrowing and matching of the corresponding carriages in the set of paths. It extracts the boundary values ​​of the remaining space of the carriage and the standard parameters of the container, determines whether the space and the container type are compatible, identifies the available carriage space segments, and generates a list of acceptable container space segments. The priority matching module is based on the list of acceptable container space segments and the path-carriage correspondence in the path resource borrowing matching reference set. It counts the number of matching carriages under a path, sorts the path numbers in order of quantity, and generates a path-space matching priority list.

[0043] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. An intelligent matching method based on railway loading plans, characterized in that, Includes the following steps: S1: Extract the remaining TEU capacity of the scheduling segment and adjacent segments, construct a differential trend sequence based on the segment number order, determine the segment continuity based on the sign direction, and generate a continuous trend segment path set; S2: Based on the set of continuous trend segment paths, extract the operation time window of the path segment, identify the intersection of the time ranges of adjacent segments, filter the paths that meet the continuous execution conditions, and generate a sequence of overlapping loading time periods. S3: Based on the overlapping loading time period path sequence, obtain the operation status code and planned loading demand value of each path, determine the status availability and resource adaptability, filter out paths with borrowable conditions, and obtain a path resource borrowing matching reference set. S4: Based on the path resource borrowing and matching reference set corresponding to the carriage, extract the remaining space boundary value and standard container parameters for adaptation judgment, identify acceptable carriage space segments, and generate a list of acceptable container space segments; S5: Based on the path and carriage correspondence between the acceptable box-type space segment list and the path resource borrowing matching reference set, count the number of matching carriages under the path, sort them according to the number of matching carriages, and generate a path and space matching priority list.

2. The intelligent matching method based on railway loading plan according to claim 1, characterized in that, The coherent trend segment path set includes a TEU remaining capacity difference sequence, a symbol direction sequence, and a segment number sequence. The overlapping loading time period path sequence includes the intersection of path operation time windows, continuous execution condition segments, and time period overlap matching segments. The path resource borrowing matching reference set includes path operation status codes, path loading demand values, and path resource adaptation status. The acceptable container space segment list includes remaining space boundary values, standard container parameters, and space segment adaptation types. The path and space matching priority list includes the number of path matching wagons, path wagon matching relationships, and matching priority ranking results.

3. The intelligent matching method based on railway loading plan according to claim 1, characterized in that, The S1 step is as follows: S101: Based on the scheduling segment and the numbering order of adjacent segments, obtain the corresponding remaining TEU capacity value, arrange them in order of number, calculate the difference in remaining TEU capacity between adjacent segments, and obtain the TEU difference trend value sequence. S102: Call the TEU difference trend value sequence, judge the sign direction of each element one by one, extract the continuous sign consistent segment, and generate the sign direction coherence segment path segment set; S103: Based on the symbol direction continuity segment path set, detect the segment number span value, TEU difference change sum, and adjacent path number intersection in the continuity path to obtain the continuity trend strength value of the continuity path, sort the paths according to the continuity trend strength value, and obtain the continuity trend segment path set.

4. The intelligent matching method based on railway loading plan according to claim 3, characterized in that, The S2 step is as follows: S201: Based on the continuous trend segment path set, extract the operation time data of the segments in the path, integrate the time information in numerical order, and form a continuous segment operation time set. S202: Based on the set of continuous segment operation times, identify whether there is an overlap in the time range between adjacent segments within the path, filter paths with continuous operation time characteristics, and generate comparison results of overlapping time periods between paths; S203: Based on the comparison results of overlapping time periods between the paths, combined with the time range, number span and intersection quantity elements in the path, obtain the time overlap intensity value of each path under the conditions of time continuity and structural integrity, sort the results in descending order, and obtain the path sequence of overlapping loading time periods.

5. The intelligent matching method based on railway loading plan according to claim 4, characterized in that, The S3 step is as follows: S301: Based on the overlapping loading time period path sequence, extract the operation status information and planned loading demand information of the path, identify the set of paths that can be scheduled, construct the correspondence between paths and loading demands, and obtain the path operation demand structure set; S302: Based on the path operation demand structure set, combined with the current status of the path and loading demand information, determine whether the path meets the resource borrowing conditions, form a set of borrowable paths, and obtain a set of path borrowing feasibility difference intensity values. S303: Call the path borrowing feasibility difference intensity value set, filter paths that meet the conditions, extract the corresponding resource identifiers, establish a mapping structure between paths and resources, and obtain a path resource borrowing matching reference set.

6. The intelligent matching method based on railway loading plan according to claim 5, characterized in that, The S4 step is as follows: S401: Based on the path resource borrowing matching reference set, extract the carriage number corresponding to the path, call the carriage resource information table, obtain the three-dimensional remaining space boundary value of the carriage, establish the correspondence between the path and the space boundary value, and obtain the carriage space boundary value set; S402: Based on the set of boundary values ​​of the cargo compartment space, call the standard container parameter set, determine the adaptation relationship between the boundary values ​​of the cargo compartment space and the container size parameters, calculate the space adaptation deviation value between the cargo compartment and the container type, and filter according to the space adaptation threshold to obtain the cargo compartment adaptation deviation value structure set. S403: Call the carriage adaptation deviation value structure set, filter the carriage and box type combination information that meet the space adaptation conditions, extract the corresponding space segment number and box type identifier, establish a list structure, and obtain the list of acceptable box type space segments.

7. The intelligent matching method based on railway loading plan according to claim 6, characterized in that, The S5 step is as follows: S501: Based on the correspondence between the acceptable box-type space segment list and the path resource borrowing matching set, extract the associated carriage number under the path, and determine whether it exists in the carriage number recorded in the list. Summarize the number of successfully matched carriages according to the path number, and generate a set of matching carriage number information corresponding to the path. S502: Call the matching carriage quantity information set corresponding to the path, combine the carriage capacity information, carriage type matching quantity information and path level index bound to the path, classify and organize the path and establish a path matching index structure set; S503: Based on the path matching indicator structure set, extract the indicator structure content corresponding to the path number, establish a mapping structure between the path number and the sorting level according to the indicator relationship, and generate a path and space matching priority list.

8. The intelligent matching method based on railway loading plan according to claim 1, characterized in that, The remaining TEU capacity is the standard container transport capacity that has not been allocated in the scheduling section during the current scheduling cycle. The unit is TEU, and it comes from the resource allocation system or station capacity management system in the railway transportation scheduling system. The difference trend sequence is an ordered symbol sequence formed based on the changing direction of the remaining capacity value of TEUs between adjacent scheduling segments. The construction method is based on the segment number arrangement to determine the direction of the sequential difference. The direction of the symbol is a logical identifier representing the trend of TEU capacity value change. It is derived from the difference sign of TEU values ​​between adjacent segments. A positive value represents an increase in capacity, and a negative value represents a decrease in capacity. The TEU stands for Twenty-foot Equivalent Unit, which is a standard 20-foot container unit.

9. The intelligent matching method based on railway loading plan according to claim 1, characterized in that, The operation time window is the time range of executable loading tasks allocated to each section in the scheduling system, including the start time and end time, and is derived from the railway transportation operation plan table or task instruction scheduling table. The continuous execution condition refers to the existence of a time intersection between the operation time windows of two adjacent segments, which meets the requirement of seamless connection of the operation process in time. The judgment method is based on the non-empty intersection of time intervals. The job status code is an identifier that identifies the current allocation status of path scheduling resources. It comes from the status control field in the railway dispatching system and the code indicates the status type: unassigned, pre-assigned, or locked. The planned loading demand value refers to the expected number of containers to be loaded within the target route segment based on the transport order or loading task plan. The data source is the loading condition requirement document for railway transportation, the railway transportation planning system, or the container loading management system. The resource adaptability refers to the matching relationship between the current remaining TEU capacity value and the planned loading demand value of the path segment, and is determined based on the schedulable range between the two values. The remaining space boundary value is the length, width, and height parameters of the unoccupied portion of the current carriage. The data source is the number of containers that can be loaded, carriage layout information, or automatic loading detection system, obtained by combining the vehicle model field in the current vehicle system data with the loading conditions. The standard container parameters are the length, width, and height dimensions of 20-foot and 40-foot containers conforming to ISO 668 standards, derived from the International Container Equipment Identification Specification. The path-car correspondence refers to the mapping relationship between the dispatched path segment and the actual assigned car, which originates from the shunting operation instruction or transportation marshalling plan and is the basic structure of loading task scheduling. The number of matched carriages refers to the statistical result of the number of carriages that meet the spatial adaptation conditions under a single scheduling path, which comes from the spatial recognition result of the loading and matching process; The path and space matching priority list is a numbered queue formed by sorting the number of matching carriages corresponding to the path.

10. An intelligent matching system based on railway loading plans, characterized in that, The system is used to perform the method according to any one of claims 1-9, comprising: The trend extraction module obtains the scheduling segment number and the remaining TEU capacity value of adjacent segments, judges the direction of capacity difference according to the number order, filters consecutive numbered segments based on direction consistency, and generates a set of continuous trend segment paths. The time period intersection module extracts the segment operation time window in the path based on the continuous trend segment path set, identifies the intersection interval of adjacent segment time ranges, filters paths with continuous operation conditions, and generates a sequence of overlapping loading time period paths. Based on the overlapping loading time period path sequence, the status judgment module obtains the operation status code and planned loading requirements of the section under the path, judges whether the status is available and identifies resource matching relationship, filters the paths with resource borrowing conditions, and generates a path resource borrowing matching reference set. The spatial recognition module uses the path resource borrowing and matching reference set to extract the remaining space boundary value of the carriage and the standard parameters of the container, determines whether the space and the container type are compatible, identifies available carriage space segments, and generates a list of acceptable container space segments. The priority matching module, based on the list of acceptable container space segments and the path-carriage correspondence in the path resource borrowing matching reference set, counts the number of matching carriages under a path, sorts the path numbers in order of quantity, and generates a path-space matching priority list.

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