Disease repair strategy optimization method and system for improving toughness of rail transit network

Abstract

The invention relates to the technical field of emergency management, and particularly discloses a disease repair strategy optimization method and system for improving the toughness of a rail transit network, and the method comprises the steps: receiving a disease report request, forwarding the request to a total terminal, receiving a disease processing scheme sent by the total terminal, and calculating the network performance parameters of the disease processing scheme; clustering the disease reporting requests to obtain each type of disease reporting requests; comparing the network performance parameters of the disease processing schemes corresponding to the disease reporting requests in the same kind of disease reporting requests, and selecting an optimal scheme; and when a new disease report request is received, querying the optimal scheme as a reference scheme, and feeding back the reference scheme to the general end. According to the method, the network topology characteristics and the actual operation characteristics of the infrastructure of the rail transit line are fused, the maintenance process and the work efficiency corresponding to common diseases are combined, the weighted interval trafficability evaluation index is established, and the evaluation effect is excellent.

Description

Technical Field

[0001] The present invention relates to the technical field of emergency management, and specifically to a method and system for optimizing the disease repair strategy to enhance the resilience of rail transit networks. Background Art

[0002] During the construction and maintenance management of rail transit line infrastructure, a large number of new technologies, new equipment, new materials and new processes have been adopted, the construction and maintenance management level has been increasing day by day, and the track maintenance equipment is generally stable and reliable. However, due to the vast territory of our country, complex climate environments and topographical and geological conditions, and diverse types of track maintenance equipment, there are some diseases in individual sections that affect the stability and durability of track infrastructure. These diseases cause the running speed of trains to decrease within the section, reduce the passing capacity of rail transit, and affect daily transportation tasks. Improving the recovery ability of the rail transit line infrastructure system has attracted much attention from industry practitioners and domestic and foreign researchers in recent years.

[0003] The resilience of the rail transit line infrastructure network is the ability of the rail transit line infrastructure network to quickly recover from diseases or potential disturbances. During the resilience recovery period, the section cannot operate at the original designed speed when put into use, and the passing capacity is also restricted to a certain extent. Evaluating the resilience of the rail transit line infrastructure network and simulating the performance of the network under various maintenance strategies are convenient for managers to make correct decisions, which is of great significance to the daily operation of the rail transit line infrastructure network.

[0004] Large and complex construction projects often contain multiple optional construction sequencing schemes. The currently commonly used construction sequencing scheme algorithms mainly optimize the construction period. Insufficient consideration is given to the construction effect, that is, the passing capacity of the line during maintenance construction; currently, the network resilience evaluation model usually calculates the link length, node degree or average efficiency, etc., ignoring the differences in the importance of different links. Therefore, it is necessary to select the best maintenance method for multi-point disasters under the condition of limited construction capacity and speed limit in the section to restore the network performance as quickly as possible. Summary of the Invention

[0005] The purpose of the present invention is to provide a method and system for optimizing the disease repair strategy to enhance the resilience of rail transit networks, so as to solve the problems raised in the above background art.

[0006] To achieve the above purpose, the present invention provides the following technical solutions:

[0007] A method for optimizing the disease repair strategy to enhance the resilience of rail transit networks, the method comprising:

[0008] When a disease reporting request is received, forward it to the master terminal, receive the disease treatment plan sent by the master terminal, and calculate the network performance parameters of the disease treatment plan; the disease reporting request includes the disease type, disease location, and disease time.

[0009] Cluster the disease reporting requests to obtain each type of disease reporting request.

[0010] Compare the network performance parameters of the disease treatment plans corresponding to each disease reporting request in the same type of disease reporting requests, and select the optimal plan.

[0011] When a new disease reporting request is received, query the optimal plan, use it as a reference plan, and feedback it to the master terminal.

[0012] As a further solution of the present invention: the steps of receiving the disease reporting request, forwarding it to the master terminal, receiving the disease treatment plan sent by the master terminal, and calculating the network performance parameters of the disease treatment plan include:

[0013] Receive the disease type and disease location based on a preset request receiving port, and use the information receiving time as the disease time.

[0014] Forward the disease type, disease location, and disease time to the master terminal.

[0015] Receive the disease treatment plan sent by the master terminal, and calculate the passing capacity of each line and station in the disease treatment plan.

[0016] Calculate the network performance parameters of the disease treatment plan according to the passing capacity.

[0017] As a further solution of the present invention: the steps of clustering the disease reporting requests to obtain each type of disease reporting request include:

[0018] Read the disease location of the disease reporting request, and take the disease location as the center to obtain the lines and stations within a preset radius.

[0019] Convert the lines and stations into a graph structure; the lines correspond to connection lines, and the stations correspond to nodes.

[0020] Obtain the average daily passing quantity of the lines, adjust the width of the connection lines based on the average daily passing quantity, obtain the scale of the stations, and adjust the radius of the nodes based on the scale of the stations.

[0021] Cluster the disease reporting requests based on the graph structures of each disease reporting request to obtain each type of disease reporting request.

[0022] Among them, the distance parameter in the clustering process uses the difference degree of the graph structure, and the difference degree uses the image difference degree.

[0023] As a further solution of the present invention: in the comparison of the reported requests for the same type of disease, the steps of selecting the optimal solution from the network performance parameters of the disease treatment solutions corresponding to each disease reported request include:

[0024] For any reported request for a certain type of disease, query the network performance parameters of the disease treatment solution corresponding to each disease reported request;

[0025] Compare the network performance parameters and select the optimal network performance parameter;

[0026] Query the disease treatment solution corresponding to the optimal network performance parameter as the optimal solution.

[0027] As a further solution of the present invention: the method further includes:

[0028] Install sensors in the track to obtain track data;

[0029] Determine the spatial range according to the disease location in the disease reported request, query the sensors within the spatial range, and establish a connection channel with the sensors;

[0030] Determine the time range according to the disease time in the disease reported request, and query the track data within the time range in the data of the sensors; the track data is stored in the form of a three-dimensional matrix, and the three-dimensional matrix is a set of matrices based on the time sequence in the time dimension of a two-dimensional matrix, and the row and column positions in the two-dimensional matrix represent the installation positions of the sensors;

[0031] Read all the track data of the reported requests for the same type of disease, calculate the mean value at each position, and after the calculation is completed, the obtained average track data is called the data feature;

[0032] Perform disease prediction on the track based on the data feature, read the optimal solution, generate a prompt message containing the optimal solution, and feedback it to the main terminal.

[0033] As a further solution of the present invention: the steps of performing disease prediction on the track based on the data feature, reading the optimal solution, generating a prompt message containing the optimal solution, and feedbacking it to the main terminal include:

[0034] Generate prediction instructions regularly;

[0035] Traverse the entire track area based on a preset spatial radius to locate the sensors;

[0036] Read data in the located sensors based on a preset time radius to construct track data;

[0037] Compare the track data with all the data features to determine the data feature with the highest matching degree;

[0038] When the highest matching degree reaches the preset matching degree threshold, read the optimal solution of the disease reporting request corresponding to the data feature, generate a prompt message containing the optimal solution, and feedback it to the main terminal.

[0039] The technical solution of the present invention also provides a system for optimizing the disease repair strategy to enhance the resilience of the rail transit network, and the system includes:

[0040] A solution analysis module, which is used to forward the disease reporting request to the main terminal when receiving the disease reporting request, receive the disease treatment solution sent by the main terminal, and calculate the network performance parameters of the disease treatment solution; the disease reporting request includes the disease type, disease location and disease time.

[0041] A request clustering module, which is used to cluster the disease reporting requests to obtain each type of disease reporting request.

[0042] An optimal solution selection module, which is used to compare the network performance parameters of the disease treatment solutions corresponding to each disease reporting request in the same type of disease reporting requests and select the optimal solution.

[0043] A solution feedback module, which is used to query the optimal solution as a reference solution and feedback it to the main terminal when receiving a new disease reporting request.

[0044] As a further solution of the present invention: the solution analysis module includes:

[0045] An information receiving unit, which is used to receive the disease type and disease location based on a preset request receiving port, and use the information receiving time as the disease time.

[0046] An information forwarding unit, which is used to forward the disease type, disease location and disease time to the main terminal.

[0047] A passing capacity calculation unit, which is used to receive the disease treatment solution sent by the main terminal and calculate the passing capacity of each line and station in the disease treatment solution.

[0048] A performance parameter calculation unit, which is used to calculate the network performance parameters of the disease treatment solution according to the passing capacity.

[0049] As a further solution of the present invention: the request clustering module includes:

[0050] A point location obtaining unit, which is used to read the disease location of the disease reporting request, and take the disease location as the center to obtain the lines and stations within a preset radius.

[0051] A graph structure generation unit, which is used to convert the lines and stations into a graph structure; the lines correspond to connecting lines, and the stations correspond to nodes.

[0052] A graph structure update unit, configured to obtain the average daily traffic volume of a line, adjust the width of a connection line based on the average daily traffic volume, obtain the scale of a station, and adjust the radius of a node based on the scale of the station;

[0053] A clustering execution unit, configured to cluster disease reporting requests based on the graph structure of each disease reporting request to obtain each type of disease reporting request;

[0054] Wherein, the distance parameter in the clustering process adopts the difference degree of the graph structure, and the difference degree adopts the image difference degree.

[0055] As a further solution of the present invention: the optimal solution selection module includes:

[0056] A parameter query unit, configured to query the network performance parameters of the disease treatment plan corresponding to each disease reporting request for any type of disease reporting request;

[0057] A parameter comparison unit, configured to compare the network performance parameters and select the optimal network performance parameters;

[0058] A solution query unit, configured to query the disease treatment plan corresponding to the optimal network performance parameter as the optimal solution.

[0059] Compared with the prior art, the beneficial effects of the present invention are as follows: By integrating the network topology characteristics and actual operation characteristics of rail transit line infrastructure, combining the corresponding maintenance processes and work efficiencies of common diseases, the present invention establishes a weighted interval passing capacity evaluation index, and the evaluation effect is excellent; In addition, the best resilience recovery strategy is not only a means to deal with diseases, but also an important guarantee for the operation safety of rail transit in the face of sudden natural disasters or accidents. In the face of common diseases or accidents, the resilience recovery ability of the rail transit line infrastructure network under the random recovery strategy is relatively low, and it takes a long time to recover to the normal operation state. For the same traffic volume, the higher the speed of the rail line, the higher the comprehensive resilience index value, the smaller the impact of the passing capacity lost due to disease disturbance on the network, and the better the operation stability; For rail transit lines with the same maximum speed, the greater the flow weight, the lower the comprehensive resilience index value and the worse the operation stability. Generally speaking, in the face of the threat of diseases or accidents, according to the disease repair strategy optimization method proposed by the present invention aiming at improving the resilience of the rail transit line infrastructure network, analyzing the network performance during the maintenance period and after repair, and monitoring the evolution process of network resilience can, to a certain extent, improve the resilience of the rail transit network, adapt to the threat of diseases to the rail transit line infrastructure, and enhance the safety and stability. Description of the Drawings

[0060] To more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the accompanying drawings required in the embodiments or the description of the prior art. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention.

[0061] Figure 1 Flow chart of the method for optimizing the disease repair strategy to enhance the resilience of the rail transit network.

[0062] Figure 2 The first sub-flow chart of the method for optimizing the disease repair strategy to enhance the resilience of the rail transit network.

[0063] Figure 3 The second sub-flow chart of the method for optimizing the disease repair strategy to enhance the resilience of the rail transit network.

[0064] Figure 4 The third sub-flow chart of the method for optimizing the disease repair strategy to enhance the resilience of the rail transit network.

[0065] Figure 5 Block diagram of the composition structure of the system for optimizing the disease repair strategy to enhance the resilience of the rail transit network. Detailed implementation manners

[0066] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present invention more clearly understood, the following further details the present invention with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0067] Figure 1 In the embodiments of the present invention, for the flow chart of the method for optimizing the disease repair strategy to enhance the resilience of the rail transit network, a method for optimizing the disease repair strategy to enhance the resilience of the rail transit network includes:

[0068] Step S100: When receiving a disease reporting request, forward it to the main terminal, receive the disease treatment plan sent by the main terminal, and calculate the network performance parameters of the disease treatment plan; the disease reporting request includes the disease type, disease location and disease time;

[0069] The disease reporting request includes the disease type and disease location uploaded by the section management party (usually front-line personnel), and the upload time is used as the disease time. The disease refers to the factors on the section that affect the normal passage of vehicles. The execution entity of this method is equivalent to a central control terminal, which is used to manage all sections in the entire area, receive the disease reporting request uploaded by the section management party at any section, and then forward the disease reporting request to the main terminal. The main terminal is a higher-level management terminal. After receiving the disease reporting request, the main terminal issues a disease treatment plan, and the execution entity of this method then forwards the disease treatment plan to the section management party.

[0070] In practical applications, each road section has a corresponding road section management party. The road section management parties in an area (such as a town) correspond to a central control terminal, that is, the execution entity of this method. The general terminal has a higher level and may be set in a county, city or higher-level area. One general terminal corresponds to multiple central control terminals, and one central control terminal corresponds to multiple road section management parties.

[0071] Step S200: Cluster the disease reporting requests to obtain each type of disease reporting request;

[0072] Over a relatively long time span, the disease reporting requests are diverse. By comparing the disease reporting requests pairwise, the disease reporting requests of the same type can be grouped into one category to obtain different types of disease reporting requests.

[0073] Step S300: Compare the network performance parameters of the disease treatment plans corresponding to each disease reporting request among the disease reporting requests of the same type, and select the optimal plan;

[0074] For each type of disease reporting request, read the network performance parameters of the disease treatment plan corresponding to the disease reporting request, compare them pairwise, determine the optimal network performance parameters, and select the optimal plan.

[0075] Step S400: When a new disease reporting request is received, query the optimal plan, use it as a reference plan, and feedback it to the general terminal;

[0076] After the internal comparison of the disease reporting requests of the same type, there is an optimal plan for each type of disease reporting request. When a new disease reporting request uploaded by the road section management party is received, the execution entity of this method (central control terminal) will determine which type the disease reporting request belongs to, query the corresponding optimal plan, which represents the best plan for similar disease reporting requests in historical data, use it as a reference plan, and feedback it to the general terminal, which can assist the general terminal in making a disease repair strategy more quickly; in the actual scenario, the general terminal is generally a signal transmitter. For the execution entity of this method, it only receives the plan sent by the general terminal. Specifically, the working conditions at the general terminal need to be roughly described. In the face of diseases, the staff at the general terminal need to hold meetings to discuss and determine the final plan, and then send it to the central control terminal, which takes a lot of time. Under the architecture of the technical solution of the present invention, if time is tight, the general terminal only needs to review whether there are obvious differences in the reference plan and can directly use it, which is equivalent to having a guaranteed better plan.

[0077] Figure 2 The first sub-process block diagram of the disease repair strategy optimization method for enhancing the resilience of the rail transit network. The steps of forwarding the received disease reporting request to the general terminal, receiving the disease treatment plan sent by the general terminal, and calculating the network performance parameters of the disease treatment plan include:

[0078] Step S101: Receive the disease type and disease location based on a preset request receiving port, and take the information receiving moment as the disease moment;

[0079] Step S102: Forward the disease type, disease location, and disease moment to the master terminal;

[0080] Step S103: Receive the disease treatment plan sent by the master terminal, and calculate the passing capacities of each line and station in the disease treatment plan;

[0081] Step S104: Calculate the network performance parameters of the disease treatment plan according to the passing capacity;

[0082] In an example of the technical solution of the present invention, a specific scheme evaluation process is provided. Receive the disease type and disease location based on a preset request receiving port, take the information receiving moment as the disease moment, and forward the disease type, disease location, and disease moment to the master terminal; the master terminal issues a disease treatment plan, and the execution subject of this method will calculate the passing capacities of each line and station in the disease treatment plan, and then calculate the network performance parameters.

[0083] Regarding the calculation process of the passing capacity and network performance parameters in the disease treatment plan, the specific description is as follows:

[0084] The disease treatment plan contains a maintenance cycle and a speed limit method. Calculate the passing capacity of the rail transit line during maintenance according to the maintenance cycle and speed limit method; then, evaluate the resilience of the rail transit line infrastructure network. In view of the current situation that it is difficult for the rail transit line to maintain the maximum passing capacity after the disease occurs and during maintenance, according to the infrastructure resilience theory, describe the passing capacity - time change process and evaluate the resilience of the rail transit line.

[0085] Further, regarding the speed limit method, it includes interval speed limit for the line and station speed limit for the station. The ways of calculating the passing capacity for both are different:

[0086] 1. Interval speed limit:

[0087] Under the condition of local speed limit, the train running process in the interval includes a deceleration process, a speed limit process, an acceleration process, and a normal running process, and their times are respectively , , and . The calculation method of the interval speed limit running time is: .

[0088] Section speed limits occur on the track between two stations. That is, when a train is running in a certain section, it needs to decelerate to the specified speed. When the train is running in this section, it goes through four processes: the deceleration process (from the normal speed to the speed limit), the speed-limited operation process (maintaining the speed limit), the acceleration process (restoring from the speed limit to the normal speed), and the normal driving process (running after restoring the normal speed). After calculating The calculation process of the traffic capacity under section speed limit conditions is as follows:

[0089] ; represents the time of the rail transit maintenance skylight period (the time available for passage), represents the time for the train to complete the entire section operation under the speed limit state, represents the traffic capacity under the speed limit condition (the number of trains passing through per unit time).

[0090] 2. Station speed limits:

[0091] Station speed limits are divided into two categories: non-stop and stop. For the speed limit levels of stations, calculate the additional running time caused by the speed limit :

[0092] ; Among them, is the length of the main line within the station, is the station speed limit; is the vehicle speed without speed limit.

[0093] The reduction in traffic capacity caused by the train stopping at the station when the station is speed-limited The calculation method is:

[0094] ;

[0095] Among them, represents the stop time, and respectively represent the additional stop time and the additional start time when the station is speed-limited; is the minimum tracking interval time of the high-speed railway;

[0096] The traffic capacity under the station speed limit condition The calculation method is:

[0097] ;

[0098] Among them, is the number of speed-limited stations, is the number of speed-limited stations with stops, is the time of the rail transit maintenance skylight period, is the number of stops, The reduction in passing capacity caused by the train's stop at the station during normal operation; among them, 1440 is the total number of minutes in a day, which is obtained by Calculated; m represents the number of speed-limited sections or speed-limited stations. The above calculation process needs to calculate the sum of the increased time at each speed-limiting point. The speed-limiting points are set to m, and m is the total number of these speed-limiting points traversed, which is used to sum the increased time for each section.

[0099] Furthermore, the change in the rail transit line network performance function has three stages, including:

[0100] The first stage: the disease occurrence stage. The destructive power of the disease is reflected in the local speed limit of the rail transit line, and the rail transit network performance function decreases; the second stage: the maintenance stage, and the rail transit line network performance function increases with the increase of maintenance time; the third stage: the recovery stage. After the maintenance activity ends, the speed of the rail transit line gradually increases from the speed limit value to the normal value, and the rail transit line network performance function increases with the increase of maintenance time. The calculation method of the rail transit line network performance function within t time during normal operation is:

[0101] ; Is the passing capacity of the line or station during normal operation Passing capacity.

[0102] The calculation method of the rail transit line network performance function within t time when speed-limited is:

[0103] ;

[0104] Among them, Is the passing capacity of the line or station when speed-limited Passing capacity.

[0105] In an example of the technical solution of the present invention, the network resilience can also be evaluated. The rail transit line network evaluation method is:

[0106] ; Represents the final evaluation score, and A represents the total time from the occurrence of the disease to the speed recovery.

[0107] In the above content, the core part is the calculation process of the passing capacity during the speed-limiting process, which is specifically described as follows:

[0108] Section speed limit: It occurs between stations. The calculation method considers the whole process of the train decelerating - running at a speed limit - accelerating - running normally. Station speed limit: It occurs within the station and mainly affects the train's arrival, stop, and departure times, resulting in a decrease in passing capacity. The calculation process of passing capacity is:

[0109] Section speed limit passing capacity = skylight period time / speed-limited running time;

[0110] The speed limit passing capacity of the station = the passing capacity after considering the additional stop time.

[0111] It is worth mentioning that at the master end, the generated disease treatment plan may not be unique at one time. With the above calculation process and combined with the mixed integer linear programming (MILP) method, an optimization model for the maintenance decision of the rail transit line under multi-point speed limit conditions can be established. By using the mixed integer linear programming method, the best maintenance strategy with the fastest performance recovery can be formed, and then a disease treatment plan can be determined and fed back to the execution entity of this method. At this time, the master end also belongs to an autonomous intelligent device and does not require excessive human intervention; actually, no matter what method the master end uses to determine the final disease treatment plan, for the execution entity of this method, only one disease treatment plan is received and only the receiving action is executed.

[0112] Figure 3 The second sub-process block diagram of the disease repair strategy optimization method for enhancing the resilience of the rail transit network. The steps of clustering the disease reporting requests to obtain each type of disease reporting request include:

[0113] Step S201: Read the disease location of the disease reporting request, and take the disease location as the center to obtain the lines and stations within the preset radius range.

[0114] Step S202: Convert the lines and stations into a graph structure; the lines correspond to connection lines, and the stations correspond to nodes.

[0115] Step S203: Obtain the daily average passing quantity of the lines, adjust the width of the connection lines based on the daily average passing quantity, obtain the station scale of the stations, and adjust the radius of the nodes based on the station scale.

[0116] Step S204: Cluster the disease reporting requests based on the graph structure of each disease reporting request to obtain each type of disease reporting request.

[0117] Among them, the distance parameter in the clustering process uses the difference degree of the graph structure, and the difference degree uses the image difference degree.

[0118] In an example of the technical solution of the present invention, the clustering process of the disease reporting request is described. The disease location of the disease reporting request is read. Taking the disease location as the center, the lines and stations within a preset radius are obtained. The lines and stations are converted into a graph structure. The graph structure includes nodes and connecting lines between the nodes. The connecting lines in the graph structure correspond to the lines, and the stations in the graph structure correspond to the nodes. Then, the average daily traffic volume of the lines is obtained, the width of the connecting lines is adjusted based on the average daily traffic volume, the scale of the stations is obtained, and the radius of the nodes is adjusted based on the scale of the stations. The adjusted graph structure can be used as the feature of the disease reporting request. Essentially, the graph structure is two-dimensional data and can be compared using an image comparison algorithm. Comparing the graph structures of the disease reporting requests is equivalent to comparing the disease reporting requests. The similarity is calculated during the comparison process, and a subtraction function is introduced to obtain the difference degree. The difference degree can be used as the distance in the clustering algorithm. The data is clustered, and in this application, it is to cluster the disease reporting requests. The clustering algorithm can use a conventional clustering algorithm, such as the K-means clustering algorithm with variable K value, etc.

[0119] Figure 4 The third sub-process block diagram of the method for optimizing the disease repair strategy to improve the resilience of the rail transit network. In comparing the disease handling plans corresponding to each disease reporting request among the same type of disease reporting requests, the steps for selecting the optimal plan include:

[0120] Step S301: For any type of disease reporting request, query the network performance parameters of the disease handling plan corresponding to each disease reporting request.

[0121] Step S302: Compare the network performance parameters and select the optimal network performance parameters.

[0122] Step S303: Query the disease handling plan corresponding to the optimal network performance parameters as the optimal plan.

[0123] In an example of the technical solution of the present invention, for any type of disease reporting request, query the network performance parameters of the disease handling plan corresponding to each disease reporting request, compare the network performance parameters, select the optimal network performance parameters, and the disease handling plan corresponding to the optimal network performance parameters is the optimal plan.

[0124] As a preferred embodiment of the technical solution of the present invention, the method further includes:

[0125] Install sensors in the track to obtain track data.

[0126] Determine the spatial range according to the disease location in the disease reporting request, query the sensors within the spatial range, and establish a connection channel with the sensors.

[0127] Determine the time range according to the disease time in the disease reporting request, and query the track data within the time range in the sensor data; the track data is stored in the form of a three-dimensional matrix, and the three-dimensional matrix is a set of matrices based on time order in the time dimension of a two-dimensional matrix. The row and column positions in the two-dimensional matrix represent the installation positions of the sensors;

[0128] Read all the track data of the same type of disease reporting request, calculate the mean value at each position. After the calculation is completed, the obtained average track data is called the data feature;

[0129] Based on the data features, perform disease prediction on the track, read the optimal solution, generate a prompt message containing the optimal solution, and feedback it to the main terminal.

[0130] In an example of the technical solution of the present invention, sensors are installed on the track, and the sensors can actually obtain data. At different times, different sensors can obtain data, such as pressure and temperature, etc. The present application does not specifically limit these data, which are collectively referred to as track data; determine the spatial range according to the disease location in the disease reporting request. The disease location is the center of the circle, and the preset value is the radius. The created fixed-position circular area is the spatial range. Query the sensors within the spatial range, establish a connection channel with the sensors, and determine the time range according to the disease time in the disease reporting request. Generally, the time range takes the disease time as the end point and obtains a preset span of time forward as the time range. Query the track data within the time range in the sensor data; specifically, the track data is stored in the form of a three-dimensional matrix, and the three-dimensional matrix is a set of matrices based on time order in the time dimension of a two-dimensional matrix. The row and column positions in the two-dimensional matrix represent the installation positions of the sensors; at this time, for each position in the three-dimensional matrix, it is represented by the three-dimensional coordinates (x, y, t). It is worth mentioning that regarding its specific value, it can also be represented by an array, such as (A1, A2,..., An), and each array element represents a type of sensor. This enables all types of sensor data to be represented by the same three-dimensional matrix, but this method is more troublesome. In actual applications, generally, the same type of sensors are analyzed separately. At this time, the specific value is the sensing data, and the unit problem is not involved.

[0131] Based on the above content, for each disease reporting request, the same data extraction method is adopted to construct a three-dimensional matrix of the same dimension. For any position in the three-dimensional matrix, calculate the mean value of the data at this position in all three-dimensional matrices. After calculating the mean value of each position, what is obtained is a three-dimensional matrix containing the mean value, which can be used as the data feature. At this time, one type of disease reporting request corresponds to one data feature and also corresponds to one optimal solution. Establishing the connection between the data feature and the optimal solution, the detection data of the sensor can be used as the independent variable to obtain the optimal solution; and the sensor can obtain data in real time. Analyze the data obtained by the sensor based on the data feature, and then predict the diseases of the track, read the optimal solution, generate a prompt message containing the optimal solution, and feedback it to the main terminal. This provides an early warning solution.

[0132] Specifically, the steps of predicting the diseases of the track based on the data feature, reading the optimal solution, generating a prompt message containing the optimal solution, and feedbacking it to the main terminal include:

[0133] Generate prediction instructions regularly;

[0134] Traverse the entire track area based on a preset spatial radius to locate the sensors;

[0135] Read data in the located sensors based on a preset time radius to construct track data;

[0136] Compare the track data with all data features to determine the data feature with the highest matching degree;

[0137] When the highest matching degree reaches the preset matching degree threshold, read the optimal solution of the disease reporting request corresponding to the data feature, generate a prompt message containing the optimal solution, and feedback it to the main terminal.

[0138] In an example of the technical solution of the present invention, according to a preset time period, generate prediction instructions regularly, traverse the entire track area based on a preset spatial radius to locate the sensors, where the spatial radius is the same as the radius for determining the spatial range, read data in the located sensors based on a preset time radius to construct track data, where the time radius is the same as the time span within the determined time range; compare the track data with all data features to determine the data feature with the highest matching degree. When the highest matching degree reaches the preset matching degree threshold, it indicates that the corresponding disease reporting request is very likely to occur. Although the disease reporting request uploaded by the section management party has not been received at this time, a prompt message can also be reported to the main terminal in advance, and the optimal solution is feedbacked at the same time.

[0139] It should be noted that in the above content, one step is to traverse the entire track area based on a preset spatial radius to locate the sensors. The meaning of traversal is that the track area is very large, including all areas under the jurisdiction of the central control terminal, while the spatial radius is relatively small. Therefore, only a part of the track area can be extracted at a time, and the sensors in a part of the area are analyzed. The process of extracting a part of the area is the traversal process, which can be analogized to a window sliding in an image with a preset sliding step. Each time it slides, an analysis process is performed.

[0140] Figure 5 The block diagram of the composition of the system for optimizing the disease repair strategy to enhance the resilience of the rail transit network. In an embodiment of the present invention, a system for optimizing the disease repair strategy to enhance the resilience of the rail transit network, the system 10 includes:

[0141] The scheme analysis module 11 is configured to forward the disease reporting request to the central terminal when receiving the disease reporting request, receive the disease treatment plan sent by the central terminal, and calculate the network performance parameters of the disease treatment plan; the disease reporting request includes the disease type, disease location, and disease time.

[0142] The request clustering module 12 is configured to cluster the disease reporting requests to obtain each type of disease reporting request.

[0143] The optimal scheme selection module 13 is configured to compare the network performance parameters of the disease treatment plans corresponding to each disease reporting request among the disease reporting requests of the same type, and select the optimal scheme.

[0144] The scheme feedback module 14 is configured to query the optimal scheme as a reference scheme and feedback it to the central terminal when receiving a new disease reporting request.

[0145] Further, the scheme analysis module 11 includes:

[0146] The information receiving unit is configured to receive the disease type and disease location based on a preset request receiving port, and use the information receiving time as the disease time.

[0147] The information forwarding unit is configured to forward the disease type, disease location, and disease time to the central terminal.

[0148] The passing capacity calculation unit is configured to receive the disease treatment plan sent by the central terminal and calculate the passing capacity of each line and station in the disease treatment plan.

[0149] The performance parameter calculation unit is configured to calculate the network performance parameters of the disease treatment plan according to the passing capacity.

[0150] Specifically, the request clustering module 12 includes:

[0151] The point location acquisition unit is used to read the disease location of the disease reporting request, and taking the disease location as the center, acquire the lines and stations within a preset radius;

[0152] The graph structure generation unit is used to convert the lines and stations into a graph structure; the lines correspond to connecting lines, and the stations correspond to nodes;

[0153] The graph structure update unit is used to acquire the average daily traffic volume of the lines, adjust the width of the connecting lines based on the average daily traffic volume, acquire the scale of the stations, and adjust the radius of the nodes based on the scale of the stations;

[0154] The clustering execution unit is used to cluster the disease reporting requests based on the graph structures of each disease reporting request to obtain each category of disease reporting requests;

[0155] Wherein, the distance parameter in the clustering process adopts the difference degree of the graph structure, and the difference degree adopts the image difference degree.

[0156] Furthermore, the optimal solution selection module 13 includes:

[0157] The parameter query unit is used to query the network performance parameters of the disease treatment solutions corresponding to each disease reporting request for any category of disease reporting requests;

[0158] The parameter comparison unit is used to compare the network performance parameters and select the optimal network performance parameters;

[0159] The solution query unit is used to query the disease treatment solution corresponding to the optimal network performance parameter as the optimal solution.

[0160] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An optimization method for disease repair strategies to enhance the resilience of rail transit networks, characterized in that, The method includes: When a disease reporting request is received, forward it to the main terminal, receive the disease treatment plan sent by the main terminal, and calculate the network performance parameters of the disease treatment plan; the disease reporting request includes the disease type, disease location, and disease time. Cluster the disease reporting requests to obtain each type of disease reporting request. Compare the network performance parameters of the disease treatment plans corresponding to each disease reporting request in the same type of disease reporting requests, and select the optimal plan. When a new disease reporting request is received, query the optimal plan, use it as a reference plan, and feedback it to the main terminal.

2. The method for optimizing the disease repair strategy to enhance the resilience of the rail transit network according to claim 1, wherein, The steps of when a disease reporting request is received, forward it to the main terminal, receive the disease treatment plan sent by the main terminal, and calculate the network performance parameters of the disease treatment plan include: Receive the disease type and disease location based on a preset request receiving port, and use the information receiving time as the disease time. Forward the disease type, disease location, and disease time to the main terminal. Receive the disease treatment plan sent by the main terminal, and calculate the passing capacity of each line and station in the disease treatment plan. Calculate the network performance parameters of the disease treatment plan according to the passing capacity.

3. The method for optimizing the disease repair strategy to enhance the resilience of the rail transit network according to claim 1, wherein The steps of clustering the disease reporting requests to obtain each type of disease reporting request include: Read the disease location of the disease reporting request, and take the disease location as the center to obtain the lines and stations within a preset radius. Convert the lines and stations into a graph structure; the lines correspond to connecting lines, and the stations correspond to nodes. Obtain the daily average passing quantity of the lines, adjust the width of the connecting lines based on the daily average passing quantity, obtain the scale of the stations, and adjust the radius of the nodes based on the scale of the stations. Cluster the disease reporting requests based on the graph structures of each disease reporting request to obtain each type of disease reporting request. Among them, the distance parameter in the clustering process uses the difference degree of the graph structure, and the difference degree uses the image difference degree.

4. The method for optimizing the disease repair strategy for enhancing the resilience of the rail transit network according to claim 1, wherein The steps of comparing the network performance parameters of the disease treatment plans corresponding to each disease reporting request in the same type of disease reporting requests and selecting the optimal plan include: For any type of disease reporting request, query the network performance parameters of the disease treatment plan corresponding to each disease reporting request. Compare the network performance parameters and select the optimal network performance parameter. Query the disease treatment plan corresponding to the optimal network performance parameter as the optimal plan.

5. The method for optimizing the disease repair strategy for enhancing the resilience of the rail transit network according to claim 1, wherein The method further includes: Install sensors in the track to obtain track data. Determine the spatial range according to the disease location in the disease reporting request, query the sensors within the spatial range, and establish a connection channel with the sensors. Determine the time range according to the disease time in the disease reporting request, and query the track data within the time range in the data of the sensors; the track data is stored in the form of a three-dimensional matrix, and the three-dimensional matrix is a set of matrices of a two-dimensional matrix in the time dimension based on the time sequence, and the row and column positions in the two-dimensional matrix represent the installation positions of the sensors. Read all the track data of the disease reporting requests of the same type, calculate the mean value at each position, and after the calculation is completed, the obtained average track data is called the data feature. Perform disease prediction on the track based on the data feature, read the optimal plan, generate a prompt message containing the optimal plan, and feedback it to the main terminal.

6. The method for optimizing the disease repair strategy for enhancing the resilience of a rail transit network according to claim 5, wherein The steps of predicting track diseases based on data features, reading the optimal solution, generating a prompt message containing the optimal solution, and feeding it back to the master terminal include: Generating a prediction instruction regularly; Traversing the entire track area based on a preset spatial radius to locate sensors; Reading data within the located sensors based on a preset time radius to construct track data; Comparing the track data with all data features to determine the data feature with the highest matching degree; When the highest matching degree reaches the preset matching degree threshold, reading the optimal solution of the disease reporting request corresponding to the data feature, generating a prompt message containing the optimal solution, and feeding it back to the master terminal.

7. A disease repair strategy optimization system for enhancing the resilience of rail transit networks, characterized in that, The system includes: A solution analysis module, which is used to forward the disease reporting request to the master terminal when receiving it, receive the disease treatment solution sent by the master terminal, and calculate the network performance parameters of the disease treatment solution; the disease reporting request includes the disease type, disease location, and disease time; A request clustering module, which is used to cluster the disease reporting requests to obtain each type of disease reporting request; An optimal solution selection module, which is used to compare the network performance parameters of the disease treatment solutions corresponding to each disease reporting request among the same type of disease reporting requests and select the optimal solution; A solution feedback module, which is used to query the optimal solution as a reference solution and feed it back to the master terminal when receiving a new disease reporting request.

8. The disease repair strategy optimization system for enhancing the resilience of rail transit networks according to claim 7, characterized in that The solution analysis module includes: An information receiving unit, which is used to receive the disease type and disease location based on a preset request receiving port and use the information receiving time as the disease time; An information forwarding unit, which is used to forward the disease type, disease location, and disease time to the master terminal; A passing capacity calculation unit, which is used to receive the disease treatment solution sent by the master terminal and calculate the passing capacity of each line and station in the disease treatment solution; A performance parameter calculation unit, which is used to calculate the network performance parameters of the disease treatment solution according to the passing capacity.

9. The disease repair strategy optimization system for enhancing the resilience of rail transit network according to claim 7, wherein, The request clustering module includes: A point location obtaining unit, which is used to read the disease location of the disease reporting request and obtain the lines and stations within a preset radius centered on the disease location; A graph structure generating unit, which is used to convert the lines and stations into a graph structure; the lines correspond to connecting lines, and the stations correspond to nodes; A graph structure updating unit, which is used to obtain the daily average passing quantity of the line, adjust the width of the connecting line based on the daily average passing quantity, obtain the station scale of the station, and adjust the radius of the node based on the station scale; A clustering execution unit, which is used to cluster the disease reporting requests based on the graph structures of each disease reporting request to obtain each type of disease reporting request; Among them, the distance parameter in the clustering process uses the difference degree of the graph structure, and the difference degree uses the image difference degree.

10. The disease repair strategy optimization system for enhancing the resilience of rail transit networks according to claim 7, characterized in that The optimal solution selection module includes: A parameter query unit, which is used to query the network performance parameters of the disease treatment solution corresponding to each disease reporting request for any type of disease reporting request; A parameter comparison unit, which is used to compare the network performance parameters and select the optimal network performance parameter; A solution query unit, which is used to query the disease treatment solution corresponding to the optimal network performance parameter as the optimal solution.

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