A method for constructing an integrated middle platform for railway infrastructure operation and maintenance

Abstract

The present application relates to the technical field of data processing, in particular to a kind of integrated platform construction method for railway infrastructure operation and maintenance, comprising: the configuration information and communication log of infrastructure are automatically scanned by data probe, and the preliminary integrated basic node of information control unit is set;Based on the actual data delay of communication link, the association between basic nodes is established, and the virtual space node of railway operation and maintenance is aggregated to form;When the data of virtual space node is updated, based on the update event corresponding to virtual space node, the degree of mutual constraint between infrastructure is quantified, and update path diagram is formed;According to the data preference of update path diagram, the target equipment group under service mapping is determined according to the topology structure of data preference degree and update path diagram;Distributed resource planning is carried out to target equipment group, and the authority boundary of each update is determined, and integrated architecture after authority mapping is formed.The processing efficiency of data integration and the operation and maintenance efficiency of railway infrastructure are improved.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, specifically a method for constructing an integrated middleware platform for railway infrastructure operation and maintenance. Background Technology

[0002] With the rapid expansion of my country's high-speed railway network, the scale, complexity, and operational density of railway infrastructure continue to increase, posing significant challenges to data processing and storage. In the process of infrastructure integration, a bias towards decentralized data processing has led to severe data silos, unclear boundaries between multiple facilities, and consequently, blind data updates, gradually highlighting the contradiction between resource consumption and efficiency.

[0003] For example, Chinese Patent Publication No. CN119415087A discloses a method, apparatus, and medium for constructing a digital scenario-driven intelligent public service platform. The method includes: performing a four-dimensional mapping of business scenarios to obtain a list of scenario elements; performing value stream analysis and capability decomposition on the scenario element list to obtain a scenario value stream map and a scenario capability map; converting the scenario value stream map and the scenario capability map into a four-layer architecture model through layered processing; classifying the four-layer architecture model into information-type and design-type asset models; componentizing the asset models to form three types of component libraries; and integrating the component libraries into an intelligent public service platform through microservice integration.

[0004] For example, Chinese Patent Publication No. CN119621689A discloses a data integration method for a fusion host for rail trains, including: collecting multi-dimensional time-series data of rail trains during operation and preprocessing it; determining the target dimension between target trains and non-target trains based on the preprocessed multi-dimensional time-series data, obtaining the corresponding specificity through the target dimension, constructing a target dimension set, obtaining the weight of each dimension of the target train, and determining the non-standard factor of the target train; determining the first attention and second attention for each initial time period based on the non-standard factor and the multi-dimensional time-series data, combining the first attention and second attention to construct the attention sequence of the target train from the starting point to the end point, obtaining multiple optimal time periods and their corresponding attention; and encoding all dimensions of time-series data corresponding to the optimal time period in parallel, with the attention corresponding to the optimal time period as a label, to complete the data integration.

[0005] In existing technologies, data is classified into components based on business scenarios to manage multi-dimensional data in a static scenario; or data is integrated based on the train's focus period. Existing technologies tend to integrate data in a single-dimensional scenario, which lacks the ability to monitor real-time data streams. They are prone to neglecting the boundary control and data update integration between infrastructures, resulting in problems such as data silos and lack of integration in global data updates. Summary of the Invention

[0006] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is: an integrated middleware construction method for railway infrastructure operation and maintenance, including: S1, automatically scanning the configuration information and communication logs of the infrastructure through data probes, and setting up the basic nodes for the initial integration of information control units.

[0007] S2 establishes the association between basic nodes based on the actual data delay of the communication link, and aggregates them to form virtual space nodes for railway operation and maintenance.

[0008] S3, when data is updated in virtual space nodes, quantifies the degree of mutual constraint between infrastructures based on the update events corresponding to the virtual space nodes, and forms an update path diagram.

[0009] S4. Locate data preferences based on the update path map, and determine the target device group under the service mapping based on the degree of data preference and the topology of the update path map.

[0010] S5 performs distributed resource planning on the target device group, determines the permission boundaries for each update, and forms an integrated architecture after permission mapping.

[0011] The beneficial effects of this invention are as follows: First, this invention automatically scans the configuration information and communication logs of the entire infrastructure using data probes, and cuts out independent information control units based on a single signal control source and all terminal devices it directly controls. Each unit is uniquely identified architecturally as a basic node for initial integration. This clarifies the initial integration content under data integration, avoids integration errors caused by unclear boundaries and complex types of devices, and improves the accuracy of data integration.

[0012] Second, this invention utilizes the actual data delay between different basic nodes to calculate correlation indicators. Basic nodes with correlation exceeding a threshold, spatially adjacent, and jointly serving the same train operation function are aggregated into status nodes. These status nodes are then attached to virtual spaces corresponding one-to-one with physical railway lines according to their geographical scope, forming virtual space nodes. This mapping of the actual location of the physical railway to its function breaks down the data silos of independent device states under functional aggregation, providing a unified data foundation for global status monitoring.

[0013] Third, this invention classifies update events based on infrastructure-based physical, communication, and business constraints, quantifies the constraint degree of each node, generates a priority ranking table, and generates an update path diagram containing the scope of impact and update order according to the upstream and downstream connection order of control flow and data flow. This clarifies the relative order of each data update, ensuring the rationality and stability of the update process, avoiding business interruptions caused by incorrect update order, and improving the efficiency and reliability of data updates.

[0014] Fourth, this invention assigns an initial preference weight to each node based on device type and update event, propagates unidirectionally along the update path graph topology to obtain the data preference degree of each node, and uses the average preference degree of all nodes as a threshold to filter core nodes. Combined with service matching capabilities, it generates independently operable target device groups. This further divides multi-device aggregation scenarios into multi-unit forms of service execution, improving the operation and maintenance response speed and service efficiency in update scenarios.

[0015] Fifth, this invention defines device-level, service-level, and inter-group-level permission boundaries based on the service mapping content of the target device group. It performs real-time permission matching according to the data flow process, automatically identifies sensitive data access and erroneous data under multi-service integration, uses this abnormal data as the detection subject, and constructs a dynamically optimized integration architecture based on the operating status of the target device group. This achieves adaptive integration of the structure under data analysis, improving the flexibility, stability, and security of the data integration architecture. Attached Figure Description

[0016] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0017] Figure 1 This is a flowchart illustrating a method for building an integrated middleware platform for railway infrastructure operation and maintenance. Figure 2 This is a flowchart illustrating step S2 of an integrated middleware platform construction method for railway infrastructure operation and maintenance. Figure 3 This is a flowchart illustrating step S3 of an integrated middleware platform construction method for railway infrastructure operation and maintenance. Figure 4 This is a flowchart illustrating step S4 of an integrated middleware platform construction method for railway infrastructure operation and maintenance. Figure 5 This is a flowchart illustrating step S5 of an integrated middleware platform construction method for railway infrastructure operation and maintenance. Detailed Implementation

[0018] The embodiments of the present invention are described in detail below. The embodiments described below are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention. Where specific techniques or conditions are not specified in the embodiments, they shall be performed in accordance with the techniques or conditions described in the literature in the art or in accordance with the product manual.

[0019] See Figure 1 A method for building an integrated middleware platform for railway infrastructure operation and maintenance includes: S1, automatically scanning the configuration information and communication logs of the infrastructure through data probes, and setting up the basic nodes for the initial integration of the information control unit.

[0020] S2 establishes the association between basic nodes based on the actual data delay of the communication link, and aggregates them to form virtual space nodes for railway operation and maintenance.

[0021] S3, when data is updated in virtual space nodes, quantifies the degree of mutual constraint between infrastructures based on the update events corresponding to the virtual space nodes, and forms an update path diagram.

[0022] S4. Locate data preferences based on the update path map, and determine the target device group under the service mapping based on the degree of data preference and the topology of the update path map.

[0023] S5 performs distributed resource planning on the target device group, determines the permission boundaries for each update, and forms an integrated architecture after permission mapping.

[0024] In one embodiment of the present invention, based on the data format transmitted by each infrastructure, a data probe is used to traverse all uploaded data content to form a preliminary integrated basic node, in order to interpret the real-time movement range of trains on each railway track and the working status of each infrastructure on the corresponding line, and to integrate the data into a dynamic data set for the entire process, so as to characterize the data mapping combination of multiple locations under train operation.

[0025] The infrastructure mainly includes communication equipment for signal transmission, including track circuits, transponders, cameras along the line, signal lights, and other communication equipment. The current solution will maintain each device based on its full life cycle usage attributes, maintenance records, and continuous output information to form an integrated architecture that includes causal relationships.

[0026] Furthermore, the data probe uses the basic communication protocol to traverse and process the data information transmitted by each infrastructure, and selects the basic nodes for initial integration according to the data output range.

[0027] Specifically, one implementation of step S1 includes: S11, scan the configuration information and communication logs of all infrastructure to determine the signal control source for each data reception. The signal control source represents the smallest signal control entity that can independently issue control commands and whose control range has a clear physical boundary.

[0028] S12, using the infrastructure corresponding to a single signal control source as the dividing boundary, cut out information control units. At this point, for the parts where there is a direct and uninterrupted signal transmission relationship between different infrastructures, and the corresponding equipment has only a single uplink communication interface to the outside world, divide them into multiple information control units; such as signal controllers + the 3 track circuit sections they control + 2 transponders; section cameras + their associated video analysis servers + transmission switches, etc.

[0029] S13. After identifying the architecture of each information control unit and recording attributes such as the response time range, equipment list, data output frequency and historical fault records of each unit, each information control unit is used as a node in the architecture and serves as the basic node for initial integration.

[0030] The basic node will divide the boundaries according to the communication logic based on the continuous communication process of each unit, and regard each divided node as the distinguishing subject for subsequent communication control.

[0031] In step S1, the logic of IoT edge differentiation is used. Each identified signal control source is equivalent to an edge node in the IoT scenario. Through the logic of centralized uploading from the edge nodes, all devices are divided into the smallest control units. Based on the relative aggregation of each control unit, the initial integration of data is achieved, ensuring the integrity and interpretability of the data under the uploading analysis.

[0032] In one embodiment of the present invention, based on the communication associations of the infrastructure, the railway line is divided into discrete state nodes representing geographical, functional, and operational status. Data within each section is uniformly attached to the corresponding virtual space node to characterize the overall operational status of the entire line. Based on this, the data from each infrastructure is integrated through logical association, and the operational status of each virtual space node is recorded in real time, thereby explaining the distribution of trains on the line and simultaneously confirming the real-time communication and control status of each train.

[0033] The virtual space here essentially refers to the mapping of the railway's physical space, representing a specific spatial location for railway operation and maintenance. It's like controlling the entire railway system as a group of trains, achieving virtual flexible connections. This can be achieved by dividing the railway into virtual space units every kilometer of railway line according to the distribution of infrastructure. The boundaries of the virtual space must be aligned with the boundaries of physical infrastructure such as stations, tunnel entrances, and signal lights. At the same time, special structures such as bridges, tunnels, and turnout groups are divided into independent virtual spaces, and the boundaries of the virtual spaces must not cross the jurisdiction of the radio block center. The data of these infrastructures are uniformly attached to the corresponding virtual space nodes to represent the overall operational mapping.

[0034] Furthermore, when setting up virtual space nodes, multiple virtual spaces are set up along the railway line based on the current train route. Each virtual space corresponds to a virtual space node, and the geographical location of the node is the geographical center point of the corresponding virtual space.

[0035] Specifically, the line is initially divided into several virtual spaces according to physical characteristics (stations, sections, tunnels) and control domain (the jurisdiction of the radio block center); each virtual space corresponds to at least one state node, including parameters such as average delay, correlation, and number of devices.

[0036] At this point, the virtual space nodes are logical nodes set at the center point of each range on the railway line after the range is defined according to physical characteristics and the affiliation is defined according to the control domain.

[0037] like Figure 2 As shown, one implementation of step S2 includes: S21. When calculating the correlation index of basic nodes by utilizing the data delay between different basic nodes, the data delay represents the time difference from when the sender sends a message to when the receiver receives the message. It is necessary to perform message interaction on all basic nodes to determine the overall device data delay. First, calculate the ratio of the average data delay to the maximum allowable delay of the entire link, and then subtract the ratio from 1 to reflect the processing scenario where the larger the value, the stronger the correlation.

[0038] Furthermore, the maximum permissible delay can be quantified using the maximum data delay of the current communication link during historical data operation.

[0039] S22, based on the value range of the correlation index, aggregate spatially adjacent basic nodes to construct state nodes.

[0040] When constructing state nodes, the basic nodes with low communication latency are first selected based on the range of correlation index values. That is, basic nodes with a correlation index threshold are selected. After selecting multiple basic nodes that can be aligned in space, they are aggregated and processed step by step. The correlation index threshold is selected as 0.7 or the average correlation index value among all basic nodes. Data with a value greater than this is considered as processed data.

[0041] Then, check the deployment location of the infrastructure corresponding to the basic node. When the deployment locations are spatially adjacent and each infrastructure serves the same driving function, aggregate the corresponding basic nodes into a status node.

[0042] The train operation function refers to equipment that assists in signal acquisition and distribution within the same track, as well as equipment that controls the management of branch sections, ensuring the logical correlation of data within status nodes and facilitating subsequent detection of changes in railway data.

[0043] S23. According to the geographical range corresponding to the status node, attach each status node to the virtual space to obtain multiple virtual space nodes. Through the virtual space nodes that represent geographical locations, associate the data changes in each status node with the actual geographical location to form a connection relationship of virtual space node → status node → basic node → information control unit → device, so as to generate a change log containing the association of the entire life cycle of the device, and record the cycle of data changes after each attachment to determine the dynamic changes of the entire process data.

[0044] In one embodiment of the present invention, for data updates of virtual space nodes, the update status is summarized according to the affected devices, the update status of all nodes is summarized, the data grouping and order of each update are planned, and an update path including the direction of change is formed.

[0045] Specifically, for the number of information control units corresponding to each virtual space node, the number of information control units that change in each scenario is determined based on the current railway operation and maintenance scenario; the number of changed units is then summarized according to the node locations of aggregation and migration to aggregate the update status of the virtual space nodes.

[0046] During special periods such as emergency dispatch, machine failure shutdown, or construction, the jurisdiction of the signal control source may be temporarily adjusted, resulting in the addition, deletion, or merging of the number of information control units. By processing the changes in the number of information control units, the communication operation status of each piece of equipment on the railway line can be further verified.

[0047] Each virtual space node synchronously records data on changes in status such as train entry / departure, equipment failure, and speed limit changes. This data is aggregated according to the structure at each level, and the correlation between data at each level is synchronously integrated to output the relative change cycle under architecture updates or coupling.

[0048] like Figure 3 As shown, one implementation of step S3 includes: S31, based on the constraint type corresponding to the infrastructure, classifies update events into event levels and sets the constraint propagation scope.

[0049] The types of constraints include physical constraints arising from the physical deployment and functional dependencies of infrastructure, communication constraints arising from data transmission links and communication delays, and business constraints arising from railway traffic rules and business processes. Physical constraints originate from the functions and deployment status of the status nodes identified in step S2 and the integration constraints of the information control units in step S1. When two infrastructures belong to the same information control unit or are mounted under the same status node, they are considered to have physical constraints. Communication constraints originate from the communication status and data delays of the basic nodes. Business constraints originate from the business processes used by each infrastructure.

[0050] When updating events and classifying them, examine the infrastructure that has experienced state changes. This includes state changes that do not affect driving, state changes that affect driving efficiency, and state changes that directly affect driving safety. These are then set to levels 3, 2, and 1, respectively. Rule matching is performed on the infrastructure to convert the state changes into semantic descriptions, and the semantic descriptions corresponding to each infrastructure are recorded.

[0051] It should be noted that during rule matching, the main process involves matching the data showing status changes with the preset inspection rules in the database, and outputting information such as slow turnout switching or signal malfunction.

[0052] Based on the semantic description of each infrastructure, and according to the impact of each infrastructure on train operation, event classification is configured.

[0053] The infrastructure that has completed event classification is connected to other infrastructures according to constraint types. Each connected infrastructure must satisfy at least one constraint type. The connected infrastructures need to traverse the facility list based on the virtual space node, starting from the updated event, to obtain multiple other infrastructures connected to the current infrastructure, forming the constraint propagation range.

[0054] S32, for each infrastructure within the constraint propagation range, prioritize events based on the constraint level and event level of each node, and determine the priority ranking table for update events.

[0055] Among them, priority ranking represents the link ranking of the impact range of all infrastructures based on the event level; the constraint degree is set according to the constraint type currently assigned; communication constraints rely on the normalized weighted sum of communication latency, packet loss rate, and bandwidth between infrastructures, with the weights based on the ratio of the three to the historical average; physical constraints rely on the normalized value of the straight-line spatial distance between infrastructures, emphasizing the relative dependence under adjacent spatial deployment; business constraints rely on the relative operation of each infrastructure to quantify numerical values, divided into five levels: hard interlocking, strong dependence, medium dependence, weak dependence, and no dependence, with values ​​of 1, 0.8, 0.6, 0.3, and 0.1 respectively; hard interlocking means that the two infrastructures need to work synchronously; strong dependence means that the response interval between the two is less than 100ms, and they are almost synchronous; medium dependence means that the response interval between the two is larger, allowing for short-term service degradation; weak dependence means that the two can work asynchronously and are not directly affected by the failure of the previous device; no dependence means that they are relatively independent.

[0056] When there is only one type of constraint between infrastructures, the value of that type of dependency is output directly. If there are two or more, the corresponding degree values ​​are added together and output to emphasize the impact on the infrastructure.

[0057] During the subsequent sorting process, the data at different event levels are first sorted according to the event level, and then the data at the same event level are sorted according to the degree of constraint to explain the current order affected by the corresponding devices and relative order.

[0058] S33 connects multiple infrastructures corresponding to the priority sorting table into an update path graph according to the upstream and downstream connection order.

[0059] After prioritizing, a list of the impacts of each infrastructure is obtained. These facilities are then linked to the virtual space nodes in three layers: status nodes → basic nodes → information control units. Based on this, the upstream and downstream connection order between facilities is determined according to the aggregated pointing relationship within each node. Finally, an updated path diagram is generated to represent the linear path of fault propagation within the virtual space nodes.

[0060] Specifically, for a given node that experiences an update event, the data aggregated will be linked to related facilities along the upward (direction of continued data aggregation) and downward (direction of gradually dispersing the smallest control unit and related equipment) paths to characterize the path relationships of multiple devices in the geospatial environment under the aggregation process.

[0061] In one embodiment of the present invention, for the output update path map, the infrastructure contained in the update path map is transformed into the preferred content of the region mapping under different state changes, and the coupling relationship between different infrastructures is further verified according to the change in the number of preferred content. The coupling relationship is regarded as the integration content under a specific scenario to determine the integration status of infrastructure during each maintenance, operation or construction.

[0062] like Figure 4 As shown, one implementation of step S4 includes: S41. For each node in the update path graph, assign a preference weight to each node according to the device type and update event corresponding to the node.

[0063] S42 updates the preference weights along the topology of the update path graph and outputs the degree of data preference for each node.

[0064] S43, based on the arithmetic mean of data preference, filters nodes, performs service integration on the filtered nodes, and obtains the output target device group.

[0065] The preference weights are assigned based on the node corresponding to each update event in the update path graph. First, inherent weight values ​​are set according to the equipment type of the corresponding node. For example, the inherent weight of equipment such as signal machines, turnout machines, and track circuits corresponding to the main train control equipment is set to 1; the inherent weight of equipment such as section cameras and intrusion detection systems corresponding to safety monitoring equipment is set to 0.7; the inherent weight of equipment such as catenary monitoring equipment and track status sensors corresponding to operation and maintenance monitoring equipment is set to 0.4; and the inherent weight of equipment such as switches and routers corresponding to auxiliary communication equipment is set to 0.2.

[0066] Secondly, examine the constraint degree value between the source node and other nodes of the current update event across multiple nodes. The ratio of the number of times the combination of the source node's device type, the current node's device type, and the constraint degree value between the two has occurred in history to the total number of update events corresponding to the source node's device type in history is regarded as the correction value set at this time. The weighted value of the correction value and the inherent weight is regarded as the preference weight value of each node.

[0067] Then, each node in the update path graph is iteratively updated from upstream to downstream according to its connection topology. For each update, a decay factor is defined, which can be initially set to 0.7 to indicate how much the decay occurs after passing through an edge. The maximum value of the product of the upstream node's preference weight and the decay factor, and the current node's preference weight, is selected as the preference weight for this update. The updated preference weight is the data preference degree of each node. In addition to propagating from upstream nodes where problems occur, the data preference degree can also be obtained from downstream to upstream, with the same implementation method as upstream propagation.

[0068] Specifically, the update propagates unidirectionally along the directed edges of the update path graph, starting from the source node that triggered the update event.

[0069] If the node corresponding to the update event is an upstream node in the update path graph, the preference weights are iteratively updated based on the propagation from upstream to downstream nodes, and each updated preference weight is output as the degree of data preference.

[0070] If the node corresponding to the update event is a downstream node in the update path graph, output the data preference degree based on the propagation from the downstream node to the upstream node.

[0071] When a branch path is encountered, it is propagated to all branches simultaneously; when a node has multiple upstream propagation sources, the maximum value of all upstream propagation values ​​is taken to participate in the weight update of the current node; the final updated preference weights of each node are output as the degree of data preference.

[0072] Then, regarding the output data preference level, the devices scattered across different branches of the update path graph are examined. Devices that logically conform to the topology are connected to form multiple target device groups. Within each target device group, the data preference level of each device is greater than the arithmetic mean of all nodes, and updates between different groups can be executed relatively independently. Devices that meet this condition are grouped into a single target device group. Ultimately, this achieves an organic combination of subjective preferences and objective dependency structures, outputting data integration tailored to specific tasks.

[0073] Furthermore, for each output target device group, the service mapping process of the target devices is determined according to the user's preferences; the devices in each group need to record the services they can provide, and classify them into normal services, degraded services and isolated services to explain whether the relevant devices corresponding to each infrastructure are still available under the current update event. Based on the content of these states, they are integrated into the output target device group to determine the service characteristics of the combination of infrastructures under collaborative work.

[0074] In one embodiment of the present invention, based on the changes and updates of the target device group under service integration, any integrated data portion is mapped in a closed loop according to its execution permission boundary to determine sensitive data and erroneous data under multi-mapping integration, and these data are used as the detection subject of the integration architecture, ultimately realizing the deployment of cluster-optimized operation and maintenance scenarios.

[0075] like Figure 5 As shown, one implementation of step S5 includes: S51 defines the permission boundaries of the target device group based on the service mapping content of the target device group; its permission boundaries represent the read and write permissions, data access scope and operation permissions of each device, which are used to explain the total permissions required for joint operation of multiple groups of associated devices after each update under multiple updates.

[0076] S52 performs permission matching based on the flow of the target device group under the update event, and identifies sensitive data and erroneous data under multi-service integration.

[0077] In this process, the data and communication logs of each device in the target device group during the update are matched with the permissions restricted by the permission boundaries according to the actual operation. Sensitive data accessed beyond the permission boundaries and erroneous operation data that does not conform to normal business logic are viewed. This data is used as part of the current architecture integration to characterize the operation under data update.

[0078] S53 takes sensitive data and erroneous data from each update as the detection subject, combines them with the operating status of the target device group, and outputs an integrated architecture.

[0079] Here, the identified abnormal data is transformed into reusable detection topics, defined as structured content for detection in different scenarios, and the data is synchronously output to the external system interrupt as a structured data structure through the device operating status under each permission mapping and service mapping, so as to complete the structure integration in the data update scenario, thereby helping to improve the integration optimization efficiency and early warning response rate of railway operation and maintenance.

[0080] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention, which are still covered within the protection scope of the present invention.

Claims

1. A method for constructing an integrated middleware platform for railway infrastructure operation and maintenance, characterized in that, include: S1 automatically scans the infrastructure's configuration information and communication logs using data probes to set up the basic nodes for the initial integration of the information control unit; S2, based on the actual data delay of the communication link, establishes the association between basic nodes and aggregates them to form virtual space nodes for railway operation and maintenance; S3, when data is updated in virtual space nodes, based on the update events corresponding to virtual space nodes, quantifies the degree of mutual constraints between infrastructures and forms an update path diagram; S4. Locate data preferences based on the update path map, and determine the target device group under the service mapping based on the degree of data preference and the topology of the update path map. S5 performs distributed resource planning on the target device group, determines the permission boundaries for each update, and forms an integrated architecture after permission mapping.

2. The method for constructing an integrated middleware platform for railway infrastructure operation and maintenance according to claim 1, characterized in that, The implementation methods of the basic node in step S1 include: S11 scans the configuration information and communication logs of all infrastructure to determine the signal control source for each data reception; S12, using the infrastructure corresponding to a single signal control source as the dividing boundary, cuts out the information control unit; S13, each information control unit is architecturally identified as a basic node for initial integration.

3. The method for constructing an integrated middleware platform for railway infrastructure operation and maintenance according to claim 1, characterized in that, The implementation methods of virtual space nodes include: Based on the current train route, multiple virtual spaces are set up along the railway line. Each virtual space corresponds to a virtual space node, and the geographical location of the node is the geographical center point of the corresponding virtual space.

4. The method for constructing an integrated middleware platform for railway infrastructure operation and maintenance according to claim 1, characterized in that, One implementation of step S2 includes: S21, Calculate the correlation index of basic nodes by utilizing the data latency between different basic nodes; S22, based on the value range of the correlation index, aggregate spatially adjacent basic nodes to construct state nodes; S23. According to the geographical range corresponding to the state node, attach each state node to the virtual space to obtain multiple output virtual space nodes.

5. The method for constructing an integrated middleware platform for railway infrastructure operation and maintenance according to claim 4, characterized in that, The methods for constructing state nodes include: Select basic nodes that are greater than the correlation index threshold, check the deployment location of the infrastructure corresponding to the basic nodes, and when the deployment locations are spatially adjacent and each infrastructure serves the same driving function, aggregate the corresponding basic nodes into status nodes.

6. The method for constructing an integrated middleware platform for railway infrastructure operation and maintenance according to claim 1, characterized in that, The implementation methods for updating the path graph in step S3 include: S31, based on the constraint type corresponding to the infrastructure, classify update events into event categories and set the constraint propagation scope; S32, For each infrastructure within the constraint propagation range, prioritize events based on the constraint degree and event level of each node, and determine the priority ranking table for update events; S33 connects multiple infrastructures corresponding to the priority sorting table into an update path graph according to the upstream and downstream connection order.

7. A method for constructing an integrated middleware platform for railway infrastructure operation and maintenance according to claim 6, characterized in that, When updating events and classifying them, the implementation methods include: Examine the infrastructure that has undergone state changes, perform rule matching on the infrastructure, convert the state changes into semantic descriptions, and record the semantic descriptions corresponding to each infrastructure. Based on the semantic description of each infrastructure, and according to the impact of each infrastructure on train operation, event classification is configured; The infrastructure that has completed event classification is connected to other infrastructures according to constraint types. Each connected infrastructure satisfies at least one constraint type, forming a constraint propagation scope.

8. A method for constructing an integrated middleware platform for railway infrastructure operation and maintenance according to claim 1, characterized in that, The implementation methods of the target device group in step S4 include: S41, For each node in the update path graph, assign a preference weight to each node according to the device type and update event corresponding to the node; S42, update the preference weights along the topology of the update path graph and output the degree of data preference for each node; S43, based on the arithmetic mean of data preference, filters nodes, performs service integration on the filtered nodes, and obtains the output target device group.

9. A method for constructing an integrated middleware platform for railway infrastructure operation and maintenance according to claim 8, characterized in that, When updating preference weights along the topology of the update path graph, the implementation methods include: If the node corresponding to the update event is an upstream node in the update path graph, the preference weights are iteratively updated based on the propagation from upstream to downstream nodes, and each updated preference weight is output as the degree of data preference. If the node corresponding to the update event is a downstream node in the update path graph, output the data preference degree based on the propagation from the downstream node to the upstream node.

10. A method for constructing an integrated middleware platform for railway infrastructure operation and maintenance according to claim 1, characterized in that, The implementation methods of the integration architecture in step S5 include: S51, Define the permission boundaries of the target device group based on the service mapping content of the target device group; S52 performs permission matching for the target device group according to the flow process under the update event, and identifies sensitive data and erroneous data under the integration of multiple services. S53 takes sensitive data and erroneous data from each update as the detection subject, combines them with the operating status of the target device group, and outputs an integrated architecture.

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