Railway maintenance safety monitoring method and system based on electronic ink screen

Abstract

The invention discloses a railway maintenance safety monitoring method and system based on an electronic ink screen, and the method comprises the steps: obtaining the information of a maintenance region, positioning the electronic ink screen covering the maintenance region through combining the deployment state information of the electronic ink screen, and determining an edge calculation node based on the processing priority; the edge computing node obtains meteorological environment information corresponding to the maintenance area to carry out risk prediction, and carries out risk grade division and screening on the maintenance area based on the generated prediction risk information; carrying out maintenance path planning based on the maintenance area information and the prediction risk information, generating maintenance planning information, and carrying out space-time conflict detection on the maintenance planning information; the edge computing node acquires the maintenance operation information in real time to perform exception identification and exception event grade judgment, and adopts a corresponding exception handling strategy for the exception event based on the exception grade information; the defects that traditional tool management depends on manual registration, dynamic early warning cannot be achieved when tools are lost, and management and control by workers are difficult are overcome.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of rail transit technology, in particular to a railway maintenance safety monitoring method and system based on electronic ink screen. BACKGROUND

[0002] Railway maintenance, as the core link to ensure the safe operation of the rail transit system, has three characteristics of high risk, strong dynamics and complex spatial distribution. The traditional railway maintenance monitoring system has many defects in the following aspects: from manual counting, personnel scheduling and other data using paper records to abnormal event handling in the maintenance process, and monitoring data display equipment.

[0003] 1. Tool management defects: Maintenance operations involve a large number of special tools, and the traditional paper work order management mode has significant drawbacks. Manual registration is prone to omissions, and tool omissions are common, and it is difficult to quickly locate lost tools through existing technology. The lack of tool state monitoring makes it impossible to real-time perceive abnormal conditions, such as tool drop or environmental factors causing performance changes.

[0004] 2. Personnel positioning defects: Insufficient spatial positioning accuracy makes it difficult to track personnel dynamically, and cannot meet the needs of high-precision operations. Personnel entering and exiting the construction area relies on manual registration, and the risk of missed detection is high, especially in emergency evacuation scenarios, it is difficult to verify the safety status of all personnel through existing data, the dangerous area control mechanism is weak, the false alarm rate of electronic fence is high, and it is impossible to effectively prevent personnel from entering high-risk areas.

[0005] 3. Display device defects: The display screen has poor visibility in strong light environments, and the night reflection is serious, resulting in low efficiency of key information transmission, in addition, the display content of the device lacks dynamic adaptation ability, and the device has not optimized energy consumption for the railway maintenance scene. SUMMARY

[0006] In order to solve the problems mentioned in the background art, the present application provides a railway maintenance safety monitoring method and system based on electronic ink screen.

[0007] The above invention object of the present application is achieved by the following technical scheme:

[0008] A railway maintenance safety monitoring method based on electronic ink screen, comprising the steps of:

[0009] Obtaining maintenance area information, positioning the electronic ink screen covering the maintenance area in combination with the deployment state information of the electronic ink screen, and determining the edge computing node based on the processing priority;

[0010] The edge computing node obtains meteorological environment information corresponding to the maintenance area to perform risk prediction, and performs risk level division and screening on the maintenance area based on the generated prediction risk information.

[0011] Maintenance path planning is performed based on the maintenance area information and the prediction risk information, maintenance planning information is generated, and spatiotemporal conflict detection is performed on the maintenance planning information.

[0012] The edge computing node obtains maintenance operation information in real time to perform abnormality identification and level determination of abnormal events, and adopts corresponding abnormality processing strategies based on the abnormality level information.

[0013] By using the above scheme, maintenance area information is obtained from the maintenance operation scheduling system, and electronic ink screens that completely cover or partially cover the maintenance area are screened out based on the maintenance area information and the deployment state information of the electronic ink screens. An electronic ink screen serving as an edge computing node is determined based on the processing priority. The electronic ink screen serving as the edge computing node obtains meteorological environment information of the maintenance area corresponding to the maintenance area information, performs risk prediction on the meteorological environment information, performs risk level division and screening on the maintenance area based on the generated prediction risk information, performs maintenance path planning on the maintenance area operation based on the maintenance area information and the prediction risk information, generates maintenance planning information, and performs spatiotemporal conflict detection on the maintenance planning information and maintenance planning information corresponding to other maintenance areas. The edge computing node obtains maintenance operation information in real time from the start to the end of the maintenance operation, performs abnormality identification based on the maintenance operation information, determines the level of abnormal events, and finally adopts different abnormality processing strategies for abnormal events of different levels. By using edge collaborative computing combined with meteorological environment information and dynamic path planning combined with spatiotemporal conflict detection, an intelligent monitoring system covering the whole process of railway maintenance is constructed. The intelligent monitoring system realizes localized decision-making of risk prediction and resource scheduling, and guarantees the maintenance efficiency and operation safety of railway maintenance operations.

[0014] In a preferred example, the application can be further configured to: the step of obtaining the maintenance area information, locating the electronic ink screen covering the maintenance area based on the deployment state information of the electronic ink screen, and determining the edge computing node based on the processing priority, includes the steps of:

[0015] The maintenance area information is matched with the deployment state information of the electronic ink screen to obtain a candidate edge computing node.

[0016] The candidate edge computing node is evaluated based on a preset multi-dimensional evaluation model to obtain the edge computing node.

[0017] By adopting the technical solutions, the maintenance area information and the deployment state information of the electronic ink screen are matched to filter out all candidate edge computing nodes, and a preset multi-dimensional evaluation model is used to score each dimension of all candidate edge computing nodes to obtain an evaluation result of the processing priority, and finally the optimal edge computing node is selected based on the comparison of the evaluation results. The application matches the maintenance area with the coverage range of the electronic ink screen in space, and selects the best edge computing node by using a multi-dimensional evaluation model, which realizes dynamic selection of the optimal electronic ink screen as an edge computing node, supports data processing of the maintenance area and real-time response, and ensures the localization and low delay of data processing of the maintenance area.

[0018] In a preferred example, the application can be further configured to: the prediction risk information includes tool failure risk information and worker safety risk information, and the edge computing node obtains the meteorological environment information corresponding to the maintenance area for risk prediction, and based on the generated prediction risk information, the steps of risk level division and screening of the maintenance area include steps of:

[0019] The edge computing node obtains the historical risk information of the maintenance work, and establishes a mapping relationship between the meteorological parameters and the maintenance risk based on the historical risk information, thereby establishing a risk prediction model;

[0020] The meteorological environment information of the maintenance area is input into the risk prediction model to generate prediction risk information;

[0021] Based on the prediction risk information, a risk matrix is constructed, the risk level of the maintenance area is divided based on the generated matrix distribution map, and the risk level information is mapped to the edge computing node for screening of the maintenance area.

[0022] By adopting the technical solutions, the electronic ink screen as an edge computing node obtains the historical risk information of the maintenance work to establish a mapping relationship between the meteorological parameters and the maintenance risk, thereby establishing a risk prediction model based on the mapping, inputting the meteorological environment information corresponding to the maintenance area into the risk prediction model for risk prediction of tool failure and worker safety, thereby generating prediction risk information, constructing a risk matrix based on the prediction risk information to generate a matrix distribution map, dividing the risk level of the maintenance area according to the matrix distribution map, and finally the edge computing node screens the maintenance area based on the risk level information. The application integrates meteorological environment information and railway maintenance scene characteristics to dynamically predict tool failure and worker safety risk, and divides the risk level to guide the adjustment of the maintenance strategy, which ensures the stability of the tool performance and the safety of the personnel operation.

[0023] The application can be further configured in a preferred example: the space-time conflict includes path conflict and resource conflict, the step of planning a maintenance path based on the maintenance area information and the predicted risk information, generating maintenance planning information, and detecting space-time conflict of the maintenance planning information, includes the steps of:

[0024] Constructing a regional topology graph based on the maintenance area information, and adding hard constraint conditions to the regional topology graph;

[0025] Planning a maintenance path based on an optimization algorithm combined with the regional topology graph and the predicted risk information, generating maintenance planning information with multiple candidate paths;

[0026] Detecting space-time conflict of the maintenance planning information, and resolving conflict of the maintenance planning information based on the generated conflict list information.

[0027] By adopting the above technical solution, a regional topology graph is constructed based on the maintenance area information, and hard constraint conditions such as the starting point and the ending point must pass through an electronic ink screen are added to the regional topology graph. A maintenance path is planned through an optimization algorithm combined with predicted risk information, thereby generating maintenance planning information with multiple candidate paths. Space-time conflict including path overlap conflict and resource conflict is detected for the maintenance planning information, and conflict resolution of the maintenance planning information is performed based on the generated conflict list information. The application generates a safe and efficient maintenance path, avoids space-time conflict and resource deficiency of the maintenance planning information, and ensures the safety and feasibility of the maintenance task.

[0028] The application can be further configured in a preferred example: the abnormality identification includes tool abnormality identification and worker behavior abnormality identification, the step of the edge computing node acquiring maintenance job information in real time for abnormality identification and level determination of abnormal events, and taking corresponding abnormal handling strategies based on abnormal level information, includes the steps of:

[0029] The edge computing node acquires maintenance job information in real time for abnormality identification, generating abnormal event information;

[0030] Abnormal level determination of the abnormal event information is performed based on a preset determination standard, generating abnormal level information;

[0031] Priority division of the maintenance task is performed based on the abnormal level information and the maintenance job information, and corresponding abnormal handling strategies are taken based on the priority and the abnormal level information.

[0032] By adopting the technical scheme, the edge computing node acquires the maintenance operation information in real time to identify tool abnormalities and worker behavior abnormalities, generates abnormal event information, performs abnormal level determination on the abnormal event information based on a preset determination standard, generates abnormal level information, performs priority division of the maintenance task based on the abnormal level information and the maintenance operation information, and adopts a corresponding abnormal processing strategy based on the divided priority and the abnormal level information; by identifying abnormal events in the maintenance operation process in real time, hierarchical response is performed according to the severity of the abnormal events and the priority of the maintenance task, and the safety of the operation is ensured while the efficiency of the maintenance operation is ensured.

[0033] The application can be further configured in a preferred example as follows: the display control step of the electronic ink screen includes:

[0034] The display content information is acquired, and a pixel-level charge distribution map is generated based on the display content information;

[0035] A differential refresh instruction set is generated based on the pixel-level charge distribution map according to a differential refresh algorithm;

[0036] Based on the differential refresh instruction set, a voltage signal is sent to the changed pixel area through a staged driving technology.

[0037] By adopting the technical scheme, display content information from the edge computing node is acquired, a pixel-level charge distribution map for driving microcapsules is generated based on the display content information, a differential refresh instruction set is generated based on the pixel-level charge distribution map by using a differential refresh algorithm, and finally a pixel area coordinate in the differential refresh instruction set is applied with a pulse signal of different voltages at different times through a staged driving technology, so as to change the display content of the electronic ink screen; by using a low-power driving technology, efficient display and energy consumption control of the electronic ink screen are realized, the display quality and energy consumption demand are balanced, and low-power and high-reliability display of the electronic ink screen in the railway maintenance scene is realized.

[0038] The second application purpose is realized by the following technical scheme:

[0039] A railway maintenance safety monitoring system based on an electronic ink screen includes:

[0040] A node determination module is configured to acquire maintenance area information, locate an electronic ink screen covering the maintenance area in combination with deployment state information of the electronic ink screen, and determine an edge computing node based on a processing priority.

[0041] An environmental risk prediction module is configured to acquire meteorological environment information corresponding to the maintenance area by the edge computing node to perform risk prediction, and divide and screen the maintenance area based on generated prediction risk information.

[0042] The path planning module is configured to plan a maintenance path based on the maintenance area information and the predicted risk information, generate maintenance planning information, and perform time-space conflict detection on the maintenance planning information.

[0043] The abnormality monitoring module is configured to acquire the maintenance operation information in real time to identify abnormalities and determine the levels of abnormal events, and take corresponding abnormality processing strategies based on the abnormality level information.

[0044] The display control module is configured to generate a pixel-level charge distribution map based on the display content information, and generate a refresh instruction set based on a differential refresh algorithm to send a voltage signal to a changed pixel area.

[0045] In summary, the present application has at least one of the following beneficial technical effects:

[0046] 1. Based on the edge computing node, meteorological data is collected in real time, combined with historical tool failure cases in maintenance records, and tool failure prediction is realized through meteorological parameter and historical risk association modeling, solving the defects of traditional tool management relying on manual registration and dynamic warning of tool loss.

[0047] 2. Through multi-level brightness self-adaptive adjustment and differential refresh technology of electronic ink screen, the defects of poor visibility under strong light, serious reflection at night, short endurance and frequent battery replacement of outdoor display equipment are solved.

[0048] 3. Based on the regional topology graph and the optimization algorithm, multiple candidate paths are generated and the risk weight is evaluated, the path conflict is detected in real time, and the path is dynamically adjusted through the conflict resolution strategy, solving the defects of traditional path planning, low efficiency and high risk. BRIEF DESCRIPTION OF DRAWINGS

[0049] Figure 1 is a flowchart of an embodiment of a railway maintenance safety monitoring method based on an electronic ink screen according to the present application;

[0050] Figure 2 is an implementation flowchart of step S20 in an embodiment of a railway maintenance safety monitoring method based on an electronic ink screen according to the present application;

[0051] Figure 3 is an implementation flowchart of step S30 in an embodiment of a railway maintenance safety monitoring method based on an electronic ink screen according to the present application;

[0052] Figure 4 is an implementation flowchart of step S40 in an embodiment of a railway maintenance safety monitoring method based on an electronic ink screen according to the present application;

[0053] Figure 5is an implementation flowchart of step S01 in an embodiment of a railway maintenance safety monitoring method based on an electronic ink screen. DETAILED DESCRIPTION

[0054] The technical solutions of the present application will be described clearly and completely below in conjunction with the drawings. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of protection of the present application.

[0055] In an embodiment, as shown in Figure 1 A railway maintenance safety monitoring method based on an electronic ink screen is disclosed, which specifically comprises the following steps:

[0056] S10: Obtain maintenance area information, locate the electronic ink screen covering the maintenance area in combination with the deployment state information of the electronic ink screen, and determine the edge computing node based on the processing priority;

[0057] In this embodiment, the maintenance area information is multi-source information generated by a maintenance work scheduling system, including geographic coordinates, coverage range, and maintenance equipment types of the maintenance area, etc. The electronic ink screen is a device for displaying maintenance work related content, and each electronic ink screen can be regarded as an edge computing node. The deployment state information is information including deployment position coordinates, coverage radius, device state, and current load, etc. The processing priority is a measure of the data processing capacity of the electronic ink screen as an edge computing node, which includes reference to the communication quality, computing load, energy state, and historical reliability of the edge computing node, so as to select the electronic ink screen with the best processing capacity in the maintenance area as the edge computing node. The edge computing node is a processing platform constructed on the network edge side close to the maintenance area.

[0058] Specifically, the maintenance area information is obtained from the maintenance work scheduling system, the electronic ink screen that can completely cover or partially cover the maintenance area is located in combination with the deployment state information of the electronic ink screen, and the priority is selected based on the processing capacity of each edge computing node, so as to select the electronic ink screen with the best processing capacity in the maintenance area as the edge computing node.

[0059] S20: The edge computing node obtains the meteorological environment information corresponding to the maintenance area for risk prediction, and divides and screens the risk level of the maintenance area based on the generated prediction risk information;

[0060] In the embodiment, the meteorological environment information is current environment corresponding to the maintenance area and future meteorological prediction data, the prediction risk information is tool failure risk and worker safety risk possibly existing in the generated maintenance range, and the risk level is a risk level determined by combining the tool failure risk and the worker safety risk, including three levels of low risk, medium risk and high risk.

[0061] Specifically, the electronic ink screen of the edge computing node acquires meteorological environment information corresponding to the maintenance area to perform risk prediction on tool failure and worker safety, and divides the maintenance area into risk levels based on the generated prediction risk information, so as to screen out the maintenance area that can continue to perform maintenance work.

[0062] S30: planning a maintenance path based on the maintenance area information and the prediction risk information, generating maintenance planning information, and performing time-space conflict detection on the maintenance planning information;

[0063] In the embodiment, the maintenance planning information is a maintenance planning scheme containing path starting point, ending point, and path node information, and the time-space conflict is a conflict that the maintenance planning scheme may overlap in time or space with other schemes, causing the maintenance work to be unable to continue.

[0064] Specifically, the maintenance path is planned based on the maintenance area information and the prediction risk information, the maintenance planning information containing the starting point and the ending point and avoiding the high-risk area is generated, and the maintenance planning information is compared with other planning schemes to detect whether there is a conflict in the time-space dimension or resource competition.

[0065] S40: The edge computing node acquires maintenance work information in real time to perform abnormality identification and level determination of abnormal events, and adopts a corresponding abnormal handling strategy based on the abnormal level information.

[0066] In the embodiment, the abnormal event is an abnormal phenomenon deviating from the normal state in the maintenance work process, such as tool abnormality, worker not wearing a safety helmet, etc., the maintenance work information is real-time data generated in the maintenance work process, including device operating parameters, worker operation records, etc., the abnormal level information is data information classified into different levels according to abnormal confidence and influence degree, and the abnormal handling strategy is different response actions for different level abnormal events.

[0067] Specifically, the edge computing node acquires maintenance work information in real time to perform abnormality identification on tools and workers, determines the level of the abnormal event obtained by abnormality identification, and adopts different response actions based on the generated abnormal level information to handle the abnormal event.

[0068] In an embodiment, step S10 includes the steps of:

[0069] S101: Obtain the maintenance area information and the deployment state information of the electronic ink screen, and perform matching, to obtain candidate edge computing nodes through screening;

[0070] S201: Perform processing priority evaluation on the candidate edge computing nodes based on a preset multi-dimensional evaluation model, to obtain edge computing nodes.

[0071] In this embodiment, the candidate edge computing nodes are edge devices that may be responsible for processing data related to the maintenance task after location matching and state screening, the multi-dimensional evaluation model is an index system for comprehensively evaluating the performance of the nodes, including processing capability, network delay, etc., and the processing priority evaluation is to dynamically select the best edge computing node according to the model score and the urgency of the maintenance work.

[0072] Specifically, the maintenance area information and the deployment state information of the electronic ink screen are obtained and matched, the electronic ink screens covering the maintenance area are screened out through a spatial matching algorithm, and the edge nodes thereof are associated, to obtain the candidate edge computing nodes. The candidate edge computing nodes are given priority scores based on a preset multi-dimensional evaluation model, and the best edge computing node is selected based on the priority score and the urgency of the maintenance work.

[0073] In an embodiment, as shown in FIG. 2, the prediction risk information includes tool failure risk information and worker safety risk information, and step S20 includes steps of: Figure 2

[0074] S201: The edge computing node obtains historical risk information of the maintenance work, and establishes a mapping relationship between meteorological parameters and maintenance risks based on the historical risk information, to establish a risk prediction model;

[0075] S202: Obtain meteorological environment information of the maintenance area and input the information into the risk prediction model, to generate prediction risk information;

[0076] S203: Construct a risk matrix based on the prediction risk information, divide the maintenance area into risk levels based on the generated matrix distribution map, and map the risk level information to the edge computing node for screening of the maintenance area.

[0077] In this embodiment, the historical risk information is risk data related to tool failure and worker safety recorded in past maintenance work, the meteorological parameter is real-time or forecast environment data of the maintenance area, the risk prediction model is a machine learning algorithm for predicting the risk probability of the maintenance task under different meteorological conditions, and the risk matrix is a two-dimensional visual table combining meteorological parameters and risk scores.

[0078] ​Specifically, the edge computing node analyzes the correlation between meteorological parameters and risk events in the current maintenance area, thereby building a risk prediction model, and obtaining real-time meteorological data of the maintenance area and inputting it into the model to generate predicted risk information. Finally, a risk matrix is ​​constructed to divide risk levels, and the edge computing node is used to filter and sort priority or delayed maintenance areas according to the risk level information.

[0079] In one embodiment, if Figure 3 As shown, the time-space conflict includes path conflict and resource conflict. Step S30 includes the following steps:

[0080] S301: constructing a regional topology map based on the maintenance area information, and adding hard constraints to the regional topology map;

[0081] S302: Perform maintenance route planning based on an optimization algorithm combined with the regional topology map and predicted risk information to generate maintenance planning information with multiple candidate routes;

[0082] S303: Performing spatiotemporal conflict detection on the maintenance planning information, and resolving conflicts on the maintenance planning information based on the generated conflict list information.

[0083] In this embodiment, the regional topology map is a graph structure in which nodes represent key locations and edges represent path connectivity, which is used to describe spatial topological relationships. Hard constraints are inviolable path planning restrictions, such as the starting point and end point must be nodes, and high-risk areas must be avoided. The optimization algorithm is an intelligent algorithm for path generation, such as a genetic algorithm. The candidate paths are multiple feasible paths generated by the algorithm that meet the constraints. Conflict resolution is to resolve conflicts through priority adjustment, path replanning, or resource reallocation to generate a final feasible solution.

[0084] Specifically, a regional topology map is constructed with maintenance area information as input, and hard constraints are added to it. Optimization algorithms such as genetic algorithms are used to optimize the input regional topology map and predicted risk information to generate maintenance planning information with multiple candidate paths. Based on tool inventory status, worker task allocation table, etc., spatiotemporal conflict detection is performed on the maintenance planning information, and conflicts are resolved based on the detection results.

[0085] In one embodiment, if Figure 4 As shown, abnormality identification includes tool abnormality identification and worker behavior abnormality identification, step S40, including the steps of:

[0086] S401: The edge computing node obtains maintenance operation information in real time to identify abnormalities and generate abnormal event information;

[0087] S402: performing abnormality level determination on the abnormal event information based on a preset determination standard to generate abnormality level information;

[0088] S403: Prioritize the maintenance tasks based on the abnormality level information and the maintenance operation information, and adopt corresponding abnormality handling strategies based on the priorities.

[0089] In this embodiment, the abnormal event information is the abnormal data and abnormal behavior records detected by the algorithm, and the maintenance task priority is the priority dynamically assigned according to the abnormality level and the urgency of the maintenance task.

[0090] Specifically, the edge computing node obtains maintenance operation information in real time to detect abnormal events, and makes abnormal level judgments on the generated abnormal event information based on preset judgment criteria, outputs abnormal level information with marked levels, prioritizes based on the abnormal level information and maintenance operation information, ranks abnormal events by risk, and adopts different abnormality handling strategies based on the priority ranking results.

[0091] In one embodiment, if Figure 5 As shown, the display control step S01 of the electronic ink screen includes:

[0092] S011: Acquire display content information, and generate a pixel-level charge distribution map based on the display content information;

[0093] S012: generating a differential refresh instruction set based on a pixel-level charge distribution map according to a differential refresh algorithm;

[0094] S013: Based on the differential refresh instruction set, a voltage signal is sent to the changed pixel area through a phased driving technology.

[0095] In this embodiment, the display content information is the original data of text, icons or images to be presented on the electronic ink screen. The pixel-level charge distribution diagram is a matrix diagram used to describe the charge state of each pixel of the electronic ink screen, which determines the display color of the pixel. The differential refresh algorithm is used to generate difference instructions that only update the pixel area where the content has changed by comparing the new and old charge distribution diagrams. The differential refresh instruction set is an instruction set generated by the differential refresh algorithm and includes the pixel coordinates to be updated, the target charge value and the drive timing parameters. The voltage signal is a DC or pulse voltage used to change the pixel charge. The staged drive signal is a pulse signal that applies different voltages in time, which is used to gradually change the pixel charge state.

[0096] Specifically, the display content information is converted into a pixel matrix, and the target charge value of each pixel is calculated to generate a pixel-level charge distribution map. The differential refresh algorithm is used to generate a differential refresh instruction set based on the pixel-level charge distribution map. Finally, the phased driving technology is used to apply pulse signals of different voltages to the pixel area coordinates within the differential refresh instruction set in a time-sharing manner.

[0097] It should be understood that the size of the serial number of each step in the above embodiment does not mean the order of execution, and the execution order of each process should be determined according to its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0098] In an embodiment, a railway maintenance safety monitoring system based on an electronic ink screen is provided, which corresponds one-to-one to the above-mentioned railway maintenance safety monitoring method based on an electronic ink screen.

[0099] The railway maintenance safety monitoring system based on an electronic ink screen comprises:

[0100] A node determination module is configured to acquire maintenance area information, locate the electronic ink screen covering the maintenance area in combination with the deployment state information of the electronic ink screen, and determine an edge computing node based on a processing priority.

[0101] An environmental risk prediction module is configured to acquire meteorological environment information corresponding to the maintenance area by the edge computing node to perform risk prediction, and divide and screen the maintenance area based on the generated prediction risk information.

[0102] A path planning module is configured to perform maintenance path planning based on the maintenance area information and the prediction risk information, generate maintenance planning information, and perform time-space conflict detection on the maintenance planning information.

[0103] An abnormality monitoring module is configured to acquire maintenance operation information in real time by the edge computing node to perform abnormality identification and grade determination of abnormal events, and take corresponding abnormality processing strategies for abnormal events based on abnormality grade information.

[0104] A display control module is configured to generate a pixel-level charge distribution map based on display content information, and generate a refresh instruction set based on a differential refresh algorithm to send a voltage signal to a changed pixel area.

[0105] For specific limitations of the railway maintenance safety monitoring system based on an electronic ink screen, refer to the limitations of the railway maintenance safety monitoring method based on an electronic ink screen in the above, which will not be repeated here. Each module in the above railway maintenance safety monitoring system based on an electronic ink screen can be realized by software, hardware and their combinations in whole or in part. The above modules can be embedded in or independent of the processor in the computer device in hardware form, or stored in the memory in the computer device in software form, so as to be called and executed by the processor to perform the operations corresponding to the above modules.

[0106] The above describes the present application and its embodiments, which are not limited, and the drawings only show one of the embodiments of the present application, and the actual structure is not limited thereto. In general, if a person skilled in the art is inspired by it, without departing from the purpose of the present application, without creative design, similar structure and embodiments of the technical solution are not creative, and should belong to the protection scope of the present application.

Claims

1. A railway maintenance safety monitoring method based on an electronic ink screen, characterized by: Including steps: Obtain maintenance area information, locate the E-ink screens covering the maintenance area based on the E-ink screen deployment status information, and determine the edge computing node based on processing priority; The edge computing node obtains the meteorological environment information corresponding to the maintenance area to perform risk prediction, and classifies and screens the maintenance area according to the risk level based on the generated predicted risk information; Plan maintenance routes based on maintenance area information and predicted risk information, generate maintenance planning information, and perform spatiotemporal conflict detection on the maintenance planning information; The edge computing node obtains maintenance operation information in real time to identify abnormalities and determine the level of abnormal events, and adopts corresponding abnormal handling strategies for abnormal events based on the abnormal level information.

2. The railway maintenance safety monitoring method based on an electronic ink screen according to claim 1, characterized in that: The step of obtaining maintenance area information, locating the electronic ink screen covering the maintenance area in combination with the deployment status information of the electronic ink screen, and determining the edge computing node based on the processing priority includes the following steps: Obtain maintenance area information and match it with the deployment status information of the electronic ink screen to screen out candidate edge computing nodes; The processing priority of the candidate edge computing nodes is evaluated based on the preset multi-dimensional evaluation model to obtain the edge computing nodes.

3. The railway maintenance safety monitoring method based on an electronic ink screen according to claim 1, characterized in that: The predicted risk information includes tool failure risk information and worker safety risk information. The edge computing node obtains meteorological environment information corresponding to the maintenance area to perform risk prediction, and divides and screens the maintenance area into risk levels based on the generated predicted risk information, including the following steps: The edge computing node obtains historical risk information of maintenance operations and establishes a mapping relationship between meteorological parameters and maintenance risks based on the historical risk information, thereby establishing a risk prediction model; Obtain meteorological environment information of the maintenance area and input it into the risk prediction model to generate predicted risk information; A risk matrix is ​​constructed based on the predicted risk information, and the maintenance areas are divided into risk levels based on the generated matrix distribution map. The risk level information is then mapped to the edge computing node to screen the maintenance areas.

4. The railway maintenance safety monitoring method based on an electronic ink screen according to claim 1, characterized in that: Spatiotemporal conflicts include path conflicts and resource conflicts. The steps of performing maintenance path planning based on maintenance area information and predicted risk information, generating maintenance planning information, and performing spatiotemporal conflict detection on the maintenance planning information include the following steps: Construct a regional topology map based on the maintenance area information and add hard constraints to the regional topology map; Based on the optimization algorithm, regional topology and predicted risk information are combined to plan maintenance paths and generate maintenance planning information with multiple candidate paths; Perform spatiotemporal conflict detection on maintenance planning information, and resolve conflicts based on the generated conflict list information.

5. The railway maintenance safety monitoring method based on electronic ink screen according to claim 1, characterized in that: Abnormal identification includes tool abnormality identification and worker behavior abnormality identification. The edge computing node obtains maintenance operation information in real time to perform abnormality identification and abnormal event level determination. The steps of adopting corresponding abnormality handling strategies for abnormal events based on abnormal level information include the following steps: The edge computing node obtains maintenance operation information in real time to identify abnormalities and generate abnormal event information; Based on the preset judgment criteria, the abnormal event information is judged at an abnormal level and abnormal level information is generated; Prioritize maintenance tasks based on abnormality level information and maintenance operation information, and adopt corresponding abnormality handling strategies based on the priority.

6. The railway maintenance safety monitoring method based on an electronic ink screen according to claims 1-5, characterized in that: The display control steps of the electronic ink screen include: Acquiring display content information and generating a pixel-level charge distribution map based on the display content information; generating a differential refresh instruction set based on a pixel-level charge distribution map according to a differential refresh algorithm; Based on the differential refresh instruction set, voltage signals are sent to the changing pixel areas through phased driving technology.

7. A railway maintenance safety monitoring system based on an electronic ink screen, characterized by: include: A node determination module is used to obtain maintenance area information, locate the E-ink screens covering the maintenance area based on the deployment status information of the E-ink screens, and determine the edge computing node based on the processing priority; The environmental risk prediction module is used by edge computing nodes to obtain meteorological and environmental information corresponding to the maintenance area for risk prediction, and to classify and screen the maintenance area according to the generated predicted risk information; The path planning module is used to plan the maintenance path based on the maintenance area information and predicted risk information, generate maintenance planning information, and perform spatiotemporal conflict detection on the maintenance planning information; The anomaly monitoring module is used by edge computing nodes to obtain maintenance operation information in real time to identify anomalies and determine the level of abnormal events, and adopt corresponding abnormality handling strategies for abnormal events based on the abnormality level information; The display control module is used to generate a pixel-level charge distribution diagram based on display content information, and to generate a refresh instruction set based on a differential refresh algorithm to send a voltage signal to the changed pixel area.

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