Intelligent Transportation and Data Analysis Method Based on EMU Gravity Energy Storage and Related Equipment

Abstract

This application relates to the technical field of bullet train transportation, and provides an intelligent transportation and data analysis method and related equipment based on bullet train gravity energy storage. By obtaining the first target data of the bullet train set and the second target data of the station in real time, the arrival and departure data set of the bullet train set is obtained by analyzing the first target data according to the bullet train operation analysis method; according to the arrival and departure time threshold and the arrival and departure data set, the overlapping information of the operation routes between the bullet train sets about to enter the same station is obtained; according to the overlapping information and the second target data, a load adjustment instruction for the stations in the corresponding operation path is generated; according to the load adjustment instruction and the first target data, an operation scheduling instruction for the bullet train set is generated, and finally the instructions are respectively sent to the corresponding stations and bullet train sets, so that the stations and bullet train sets are dynamically adjusted according to the instructions. This application conducts operation analysis based on real-time data, so as to make a rapid response according to the actual situation to achieve the dynamic adjustment of the transportation network.

Description

Technical Field

[0001] This application relates to the technical field of bullet train transportation, and in particular, to an intelligent transportation and data analysis method and related equipment based on bullet train gravity energy storage. Background Art

[0002] As a high-speed electric bullet train, the multiple unit train is usually used on intercity and high-speed routes, and can provide a higher running speed and travel cost performance than traditional internal combustion motor vehicles, thus becoming the preferred means of transportation for most intercity and long-distance passengers.

[0003] At present, the applied bullet train dispatching system obtains the running data of the multiple unit trains through sensors, so as to realize the dispatching of the incoming multiple unit trains at the same station and avoid the situation of congestion when the multiple unit trains enter the station on the same track. However, the existing dispatching system does not combine the running data between the multiple unit trains with the passenger data for the operation dispatching of the multiple unit trains. Therefore, the existing multiple unit trains still rely on the preset departure schedule for the planning of entering and leaving the station and the running route. It is impossible to perform intelligent transportation and data analysis based on bullet train gravity energy storage according to real-time data, and traffic anomalies such as the late arrival of the multiple unit trains will occur. Summary of the Invention

[0004] In view of this, this application provides an intelligent transportation and data analysis method and related equipment based on bullet train gravity energy storage to solve the problems of intelligent transportation and data analysis based on bullet train gravity energy storage.

[0005] The first aspect of this application provides an intelligent transportation and data analysis method based on bullet train gravity energy storage, and the method includes:

[0006] Real-time obtain the first target data of each multiple unit train in the target area and the second target data of each station in the target area;

[0007] According to the preset multiple unit train operation analysis method and the first target data, perform operation analysis on each multiple unit train in the target area to obtain a stop data set;

[0008] According to the preset in-and-out station time threshold and the stop data set, obtain the overlap information of the running routes between each multiple unit train in the target area;

[0009] Generate a load adjustment instruction for each station in the target area according to the overlap information and the second target data;

[0010] Generate an operation dispatching instruction for each multiple unit train in the target area according to the load adjustment instruction and the first target data;

[0011] Send the load adjustment instruction and the operation scheduling instruction to the corresponding station and EMU respectively, so that the station and the EMU perform dynamic adjustment according to the corresponding instructions.

[0012] In an alternative embodiment, the operation analysis of each EMU in the target area according to the preset EMU operation analysis method and the first target data to obtain the stop data set includes:

[0013] Obtain the planned route of the EMU according to the train number marked by the first target data;

[0014] According to the EMU operation analysis method, the speed of the EMU, and the current positioning information of the EMU, mark the current position of the EMU on the planned route and mark the entry and exit times of the stations to be passed along the way to generate the stop data of the EMU at each station in the target area, where the first target data includes the speed of the EMU and the current positioning information of the EMU;

[0015] Synthesize the stop data corresponding to each EMU in the target area into the stop data set.

[0016] In an alternative embodiment, the obtaining of the overlapping information of the operation routes between each EMU in the target area according to the preset entry and exit time threshold and the stop data set includes:

[0017] Obtain the information of the stations to be entered by each EMU according to the coordinate marks and speed directions of each EMU, where the stop data set includes the coordinate marks and the speed directions;

[0018] Classify the stop data set according to the station information and the preset entry and exit time threshold to obtain the associated stop data set for the same station;

[0019] Obtain the overlapping information of the operation routes between each EMU in the target area according to the associated stop data set for the same station.

[0020] In an alternative embodiment, the generating of the load adjustment instruction for each station in the target area according to the overlapping information and the second target data includes:

[0021] Classify the second target data according to the preset boarding time threshold and the travel information of the waiting load to obtain the adjustment target information, where the second target data includes the travel information;

[0022] Generate load adjustment instructions for each station within the target area according to the coincidence information and the adjustment target information.

[0023] In an alternative embodiment, the scheduling target information includes the number of stations along the line. When the number of stations along the line is greater than 1, generating the load adjustment instructions for each station within the target area according to the coincidence information and the adjustment target information includes:

[0024] Obtain the historical throughput information of the stations along the line according to the current time;

[0025] Generate load adjustment instructions for each station within the target area according to a preset adjustment model, the adjustment target information, and the coincidence information.

[0026] In an alternative embodiment, generating the operation scheduling instructions for each EMU within the target area according to the load adjustment instructions and the first target data includes:

[0027] Generate inbound docking information according to the load adjustment instructions;

[0028] Generate operation scheduling instructions for each EMU within the target area according to the inbound docking information and the first target data.

[0029] In an alternative embodiment, the method further includes:

[0030] Obtain the energy recovery points on the planned route according to the operation scheduling instructions;

[0031] Generate energy recovery instructions for each EMU within the target area according to the energy recovery points.

[0032] A second aspect of the present application provides an intelligent transportation and data analysis device based on EMU gravity energy storage, and the device includes:

[0033] A data acquisition module, configured to acquire the first target data of each EMU within the target area and the second target data of each station within the target area in real time;

[0034] An operation analysis module, configured to perform operation analysis on each EMU within the target area according to a preset EMU operation analysis method and the first target data to obtain a docking data set;

[0035] A route matching module, configured to obtain the coincidence information of the operation routes between each EMU within the target area according to a preset inbound and outbound time threshold and the docking data set;

[0036] A load adjustment module, configured to generate load adjustment instructions for each station in the target area according to the coincidence information and the second target data;

[0037] An operation scheduling module, configured to generate operation scheduling instructions for each EMU in the target area according to the load adjustment instructions and the first target data;

[0038] An instruction sending module, configured to send the load adjustment instructions and the operation scheduling instructions to the corresponding stations and EMUs respectively, so that the stations and the EMUs perform dynamic adjustment according to the corresponding instructions.

[0039] A third aspect of the present application provides a server, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the steps of the intelligent transportation and data analysis method based on EMU gravity energy storage as described above are implemented.

[0040] A fourth aspect of the present application is a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the intelligent transportation and data analysis method based on EMU gravity energy storage as described above are implemented.

[0041] In summary, the present application at least includes the following beneficial technical effects:

[0042] 1. By collecting data in real time and generating dynamic adjustment instructions, it can quickly respond to changes in EMUs and stations in the target area, improving the operation efficiency.

[0043] 2. By analyzing the coincidence of operation routes and generating load adjustment instructions, the transportation efficiency of EMUs is optimized, the waiting time of passengers can be reduced, and the travel experience of passengers can be optimized.

[0044] 3. According to the load demand of stations and the operation status of EMUs, reasonable operation scheduling instructions are generated to avoid waste of energy resources and traffic congestion of EMUs, and improve the operation efficiency of EMUs.

[0045] 5. Respond to various emergencies and abnormal situations in real time, such as delays, failures, etc., and ensure the stable operation of the entire EMU transportation network through dynamic adjustment. Description of the Drawings

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0047] Figure 1 It is a flowchart of an intelligent transportation and data analysis method based on dynamic train gravity energy storage provided by an embodiment of the present application;

[0048] Figure 2 It is a functional module diagram of an intelligent transportation and data analysis device based on dynamic train gravity energy storage provided by an embodiment of the present application;

[0049] Figure 3 It is a schematic structural diagram of a server provided by an embodiment of the present application. Detailed implementation manners

[0050] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without creative efforts shall fall within the protection scope of the present application.

[0051] The intelligent transportation and data analysis method based on dynamic train gravity energy storage provided by the embodiments of the present application is executed by a server. Correspondingly, the intelligent transportation and data analysis device based on dynamic train gravity energy storage runs in the server. When the server implements the intelligent transportation and data analysis method based on dynamic train gravity energy storage of the present application, it can realize the transportation and allocation of passengers and / or goods. Next, the intelligent transportation and data analysis method based on dynamic train gravity energy storage provided by the embodiments of the present application will be described from the perspective of the server in combination with the process of adjusting the freight train traffic network.

[0052] As Figure 1 shown, it is a flowchart of the intelligent transportation and data analysis method based on dynamic train gravity energy storage provided by the embodiments of the present application. The intelligent transportation and data analysis method based on dynamic train gravity energy storage provided by the embodiments of the present application includes the following steps.

[0053] Step S11, obtain the first target data of each train in the target area and the second target data of each station in the target area in real time.

[0054] It should be understood that in order to cope with the large scale of the train route network and the complexity of the transportation situation, and to meet the needs of improving the train utilization efficiency, optimizing resource allocation, facilitating maintenance and meeting the travel needs of passengers, etc., the train traffic network adopts a zoning management method, and each server is responsible for the train scheduling in a target area.

[0055] Among them, the first target data is used to display the operation and carrying data of each EMU, including but not limited to the speed of the EMU, current positioning information, load conditions, and load loading and unloading information; the second target data is used to display the facilities and waiting data of each station, including but not limited to travel information, station waiting information, station coordinate information, and inbound and outbound planned route information. When the second target data is passenger information, the station waiting information includes but not limited to ticket sales information and inbound waiting information within the current time period.

[0056] The server of this application obtains the first target data through sensors on the EMU (such as Beidou satellite locators) in a wired communication and / or wireless communication manner, and at the same time obtains the second target data through monitoring systems, ticket checking systems, and ticket sales systems at stations. Among them, to distinguish the data between each EMU and the data between each station in the target area, the data of the same EMU is marked with the train number of the corresponding EMU, and the data of the same station is marked with the positioning coordinates corresponding to the station.

[0057] Step S12, perform operation analysis on each EMU in the target area according to the preset EMU operation analysis method and the first target data to obtain a stop dataset.

[0058] Among them, the stop dataset is composed of each stop data, and the stop data is the inbound and outbound times of the corresponding planned route predicted by the server based on the real-time operation status of the EMU (such as speed).

[0059] The server of this application obtains the corresponding train number by identifying the first target data, and thus obtains the planned route from the information management system or database of the railway system according to the train number and the current time. Among them, the planned route records the detailed information and operation plan of the EMU, including but not limited to the driving planned route, the starting and ending stations, the passing stations, and the inbound time and departure time of each station.

[0060] After obtaining the planned route of the multiple unit train, to reduce the complexity of data processing, the server of the present application screens the information of the planned route according to the coordinate threshold of the target area it manages, so as to obtain a planned route that only includes the route and station-related information within the target area. At the same time, the real-time speed data and the current positioning information of the multiple unit train are obtained by parsing the first target data, and the current position of the multiple unit train is marked with coordinates on the planned route. And the running state of the multiple unit train is analyzed in real time through a preset running analysis method, so as to predict the arrival and departure times of the stations to be passed along the way according to the speed and distance information of the multiple unit train and mark the predicted arrival and departure times on the planned route. By integrating the current coordinate mark and the arrival and departure time mark of the multiple unit train on the planned route, the stop data of the multiple unit train at each station within the target area is generated, and the stop data corresponding to all multiple unit trains within the target area is integrated into a unified stop data set.

[0061] By adopting the above implementation manner, by obtaining the running data of the multiple unit train in real time and combining with a preset running analysis method, the arrival and departure times of the multiple unit train can be accurately predicted, ensuring the high real-time performance and accuracy of the stop data set, and at the same time providing important data support for the intelligent operation system.

[0062] Step S13, according to a preset arrival and departure time threshold and the stop data set, obtain the overlapping information of the running routes between the multiple unit trains within the target area.

[0063] Among them, the overlapping information includes but is not limited to the list of multiple unit trains stopping at the same station, the estimated arrival time range, and the overlapping part of the running route after departure.

[0064] According to the coordinate mark and the speed direction in the stop data set, determine the station information that each multiple unit train is about to enter. Then, according to the station information and a preset arrival and departure time threshold, classify the stop data set. Specifically, the stop data of the multiple unit trains that are expected to arrive at the same station and within the time threshold are grouped into one category to form an associated stop data set for the same station. Finally, by analyzing the running routes of the multiple unit trains in the associated stop data set for the same station, determine the overlapping part of the running routes between the multiple unit trains.

[0065] Exemplarily, the target area includes multiple stations such as Station A, Station B, Station C, etc., and a 10-minute inbound and outbound time threshold is preset. There are three groups of EMUs, namely EMU 1, EMU 2, and EMU 3. The server analyzes and obtains from the docking data set including the three groups of EMUs: EMU 1: It is expected to enter Station A at 10:00 and will go to Station B after leaving the station; EMU 2: It is expected to enter Station A at 10:05 and will go to Station C after leaving the station; EMU 3: It is expected to enter Station A at 10:12 and will also go to Station B after leaving the station. Since the expected inbound times of EMU 1 and EMU 3 at Station A are both between 10:00 and 10:10 (i.e., 10:00 ± 10 minutes), they are classified into one category to form an associated docking data set for the same station. The inbound time of EMU 2 is not within this time threshold, so it is not considered to overlap with EMU 1 and EMU 3 for the time being. EMU 1 and EMU 3 both go to Station B after leaving the station, so there is an overlap in their running routes from Station A to Station B. Therefore, the first target data of EMU 1 and EMU 3 are obtained and the overlaps of the first target data on the running route are captured to obtain overlap information.

[0066] By adopting the above implementation manner, the EMUs can be reasonably dispatched according to the overlap information of the running routes, avoiding too many EMUs arriving or departing simultaneously at the same station or on the same section of the running route, thereby reducing possible conflicts and delays. Through the overlap analysis of the running routes of the EMUs, the passenger flow conditions at each station can be predicted more accurately, so as to reasonably allocate resources such as platforms and waiting rooms, and improve the resource utilization efficiency. The overlap information of the running routes can provide data support for the intelligent operation system. For example, based on this information, the system can automatically adjust train schedules, optimize passenger transfer plans, etc., and promote the development of the railway system towards the direction of intelligence and automation.

[0067] Step S14, generate a load adjustment instruction for each station in the target area according to the overlap information and the second target data.

[0068] The server of the present application classifies the second target data based on a preset boarding time threshold (for example, the cargo assembly time) and the travel information of the waiting load (for example, the destination station of the cargo transportation). To identify the load groups that are expected to arrive at the same station within the same time period according to the boarding time threshold, and determine their destination stations. Then, according to the classified load groups and the overlap information associated with the EMUs taken by the load groups, the second target data is screened to obtain adjustment target information. The dispatching target information includes, but is not limited to, the number of stations along the line.

[0069] After obtaining the scheduling target information, the server determines whether the EMU has excess carrying capacity to transport all load groups at this station based on the coincidence information. After determining the EMUs that meet the carrying requirements, the server determines which loads can be adjusted to the same EMU, as well as the running route and stopping stations of the adjusted EMU based on the target information and the coincidence information. Thus, based on the determined adjustment plan, the server generates specific load adjustment instructions. Among them, the load adjustment instructions include but are not limited to the load information to be adjusted, the EMUs and stations involved, the adjustment time and method, etc.

[0070] Exemplarily, there is a transportation line in the target area, and this transportation line includes stations A, B, C, and D in the order of arrival of the current EMUs. The server collects the second target data in station A, which includes that the destination of cargo A is station C and the expected boarding time is XX:XX; the destination of cargo B is station C and the expected boarding time is XX:XX + 5 minutes (i.e., 5 minutes after cargo A). The server classifies the second target data based on the preset boarding time threshold of 10 minutes and the destination stations of the cargos, and classifies cargo A and cargo B into one category to obtain their relevant adjustment target information. At the same time, the server discovers two EMUs (i.e., EMU 1 and EMU 2) that are about to enter station A according to the coincidence information. Among them, EMU 1 goes directly from station A to station C, while EMU 2 goes from station A through station B and finally goes to station C. EMU 1 has enough carrying capacity to transport both cargo A and cargo B at the same time, and EMU 2 does not need to unload cargo at station A. The server determines the following adjustment plan: adjust cargo B, which was originally to be transported by EMU 2, to EMU 1, and EMU 2 does not need to stop at station A and directly goes to station B. Based on the above adjustment plan, the server generates specific load adjustment instructions, the content of which includes: the load information to be adjusted: cargo B; the EMUs and stations involved: cargo B is adjusted to EMU 1 and goes directly from station A to station C; the adjustment time: before the expected boarding time of cargo A.

[0071] In summary, adopting the above implementation manner has at least the following beneficial effects:

[0072] 1. By classifying and screening the second target data and combining the coincidence information, the server can accurately determine which loads can be adjusted to the same EMU, thereby reducing the waiting time and empty running time of the EMUs and improving the overall transportation efficiency.

[0073] 2. After confirming that the EMU has enough carrying capacity, the server can reasonably allocate the loads, avoiding waste of resources. At the same time, it also ensures that each load can reach the destination in the shortest time.

[0074] 3. By optimizing the operation routes and stop stations of the multiple unit trains, unnecessary stops and travels are reduced, thereby lowering fuel consumption and vehicle wear and reducing operating costs.

[0075] 4. The server can dynamically adjust the transportation plan according to real-time load information and the status of multiple unit trains, enhancing the flexibility and adaptability of transportation.

[0076] In an optional embodiment, when the number of stations along the line is greater than 1, generating the load adjustment instructions for each station in the target area according to the coincidence information and the adjustment target information includes:

[0077] Obtain the historical throughput information of the stations along the line according to the current time;

[0078] Generate the load adjustment instructions for each station in the target area according to a preset adjustment model, the adjustment target information, and the coincidence information.

[0079] Exemplarily, in the embodiment of step S14, if the server determines an adjustment plan to transfer cargo A to multiple unit train 2. Since the number of stations along the line that cargo A and cargo B need to pass through is 2, the server needs to obtain the historical throughput information of station B on the same date last year from the database according to the current date, and screen out the predicted cargo quantity that needs to be transported from station B to station C by multiple unit train 2 according to the predicted arrival time of multiple unit train 2 at station B from the historical throughput information. The preset adjustment model in the server makes a judgment based on the predicted cargo quantity, the quantities of cargo A and cargo B in the adjustment target information, and the remaining carrying capacity of the current multiple unit train 2 in the coincidence information. When the remaining carrying capacity meets the sum of the quantities of cargo A, cargo B, and the predicted cargo, the server generates a load adjustment instruction for station A according to the adjustment plan; when the remaining carrying capacity does not meet the sum of the quantities of cargo A, cargo B, and the predicted cargo, the server interrupts the generation of the load adjustment instruction for station A.

[0080] In summary, adopting the above embodiment has at least the following beneficial effects:

[0081] 1. By considering the historical throughput information of the stations along the line, the server can more accurately predict and evaluate the possible load conditions that each station may face in a future period of time. This helps the server generate more accurate load adjustment instructions, ensuring that the cargo can be transported at the right time and on the right multiple unit train, and reducing resource waste or transportation delays caused by inaccurate prediction.

[0082] 2. When the remaining carrying capacity does not meet the sum of the quantities of cargo A, cargo B, and the predicted cargo, the server can interrupt the generation of the load adjustment instruction for station A, avoiding potential transportation safety hazards caused by overloading. This helps improve the reliability and safety of the entire transportation system.

[0083] Step S15: Generate the operation scheduling instructions for each multiple unit train in the target area according to the load adjustment instruction and the first target data.

[0084] After the server generates the load adjustment instruction for adjusting the site, it grabs the target information according to the load adjustment instruction to obtain the relevant information of the multiple unit trains that need to be scheduled, and generates new inbound stop information for the scheduled multiple unit trains. The inbound stop information includes, but is not limited to, the train numbers that need to be scheduled, the stations where they need to stop and the corresponding stop times, and the load information that needs to be loaded or unloaded at the corresponding stations. Exemplarily, according to the load adjustment instruction generated in the example of step S14, the server generates the following inbound stop information: Inbound vehicle information: Multiple unit train 1; Inbound station information: Station A; Inbound stop time: From the time point when multiple unit train 1 is expected to enter Station A to the departure time point of the original multiple unit train 2 from Station A.

[0085] After the server obtains the inbound stop information, it generates the operation scheduling instructions according to the inbound stop information and the first target data of the corresponding multiple unit trains. The first target data includes, but is not limited to, basic information such as the original operation plan, speed limit, and signal control of the multiple unit trains. Exemplarily, according to the inbound stop information generated in the example of step S15, the server grabs the time point when it is expected to enter Station A, the original operation plan, speed limit, signal control and other basic information from the first target information of multiple unit train 1 after obtaining the inbound stop information. At the same time, the server grabs the speed, current positioning information and the departure time point of the original Station A from the first target information of multiple unit train 2, and generates the operation scheduling instructions for each multiple unit train respectively.

[0086] Guide the basic equipment of the multiple unit train transportation network through the operation scheduling instructions for the scheduling of multiple unit trains, including, but not limited to, detailed information such as the specific actions and time requirements of the multiple unit trains at each station.

[0087] Step S16: Send the load adjustment instruction and the operation scheduling instruction to the corresponding station and multiple unit train respectively, so that the station and the multiple unit train perform dynamic adjustment according to the corresponding instructions.

[0088] Exemplarily, Station A receives the load adjustment instruction to transport Cargo A and Cargo B to the loading and unloading point of multiple unit train 1. Multiple unit train 1 receives the operation scheduling instruction and decelerates to enter the station according to the instruction, and at the same time sends an inbound code to Station A, and performs the inbound action at Station A according to the guidance of the signal light at Station A and the change of the driving track. Multiple unit train 2 receives the operation scheduling instruction and transports or accelerates to drive according to the current road to deviate from the inbound track direction of Station A.

[0089] In an alternative embodiment, the method further includes:

[0090] Obtain the energy recovery points on the planned route according to the operation scheduling instruction;

[0091] Generate energy recovery instructions for each multiple unit train in the target area according to the energy recovery points.

[0092] Among them, the energy recovery points are the coordinate points of braking and decelerating in the multiple unit train operation plan and the coordinate points where gravitational potential energy is released (i.e., downhill sections).

[0093] The server obtains the adjustment route corresponding to the multiple unit train through the operation scheduling instruction, generates an energy recovery instruction including the corresponding coordinate points according to the energy recovery points included in the adjustment route, and sends it to the corresponding multiple unit train. When the multiple unit train detects that the current positioning information includes the coordinate points in the energy recovery instruction, it controls the generator to access the transmission device of the wheel, or adjusts the motor to the generator mode. Thus, the energy recovery of converting kinetic energy and / or gravitational potential energy into electric energy during driving is realized.

[0094] This application is applied to the technical field of multiple unit train transportation. By obtaining the first target data of the multiple unit train and the second target data of the station in real time, the arrival and departure data set of the multiple unit train is obtained by analyzing the first target data according to the multiple unit train operation analysis method; the overlapping information of the operation routes between the multiple unit trains about to enter the same station is obtained according to the arrival and departure time threshold and the arrival and departure data set; the load adjustment instruction for the stations in the corresponding operation path is generated according to the overlapping information and the second target data; the operation scheduling instruction of the multiple unit train is generated according to the load adjustment instruction and the first target data, and finally the instructions are sent to the corresponding stations and multiple unit trains respectively, so that the stations and multiple unit trains are dynamically adjusted according to the instructions. This application conducts operation analysis based on real-time data, and thus makes a rapid response according to the actual situation to realize the dynamic adjustment of the transportation network.

[0095] As Figure 2 shown, it is a functional module diagram of an intelligent transportation and data analysis device based on multiple unit train gravity energy storage provided by an embodiment of this application.

[0096] In some embodiments, the intelligent transportation and data analysis device 2 based on multiple unit train gravity energy storage may include multiple functional modules composed of computer program segments. The computer programs of each program segment in the intelligent transportation and data analysis device 2 based on multiple unit train gravity energy storage can be stored in the memory of the server and executed by at least one processor to execute (see Figure 1 description) the functions of the intelligent transportation and data analysis method based on multiple unit train gravity energy storage.

[0097] In this embodiment, the intelligent transportation and data analysis device 2 based on EMU gravity energy storage can be divided into multiple functional modules according to the functions it performs. The functional modules may include: a data acquisition module 21, an operation analysis module 22, a route matching module 23, a load adjustment module 24, an operation scheduling module 25, an instruction sending module 26, and an energy recovery module 27. The module referred to in the present invention means a series of computer program segments that can be executed by at least one processor and can complete fixed functions, and are stored in the memory. In this embodiment, the functions of each module will be described in detail in subsequent embodiments.

[0098] The data acquisition module 21 is used to acquire the first target data of each EMU in the target area and the second target data of each station in the target area in real time.

[0099] The operation analysis module 22 is used to perform operation analysis on each EMU in the target area according to a preset EMU operation analysis method and the first target data to obtain a stop data set.

[0100] In an optional embodiment, the operation analysis module 22 is specifically used for:

[0101] Obtain the planned route of the EMU according to the train number marked by the first target data;

[0102] According to the EMU operation analysis method, the speed of the EMU, and the current positioning information of the EMU, mark the current position coordinates of the EMU on the planned route and mark the entry and exit times of the stations to be passed along the way to generate the stop data of the EMU at each station in the target area, where the first target data includes the speed of the EMU and the current positioning information of the EMU;

[0103] Synthesize the stop data corresponding to each EMU in the target area into the stop data set.

[0104] The route matching module 23 is used to obtain the coincidence information of the operation routes between each EMU in the target area according to a preset entry and exit time threshold and the stop data set.

[0105] In an optional embodiment, the route matching module 23 is specifically used for:

[0106] Obtain the information of the stations to be entered by each EMU according to the coordinate marks and speed directions of each EMU, where the stop data set includes the coordinate marks and the speed directions;

[0107] Classify the docking data set according to the site information and a preset inbound and outbound time threshold to obtain a site-associated docking data set;

[0108] Obtain the overlapping information of the running routes between each multiple unit train in the target area according to the site-associated docking data set.

[0109] The load adjustment module 24 is configured to generate a load adjustment instruction for each site in the target area according to the overlapping information and the second target data.

[0110] In an alternative embodiment, the load adjustment module 24 is specifically configured to:

[0111] Classify the second target data according to a preset boarding time threshold and the travel information of the waiting load to obtain adjustment target information, where the second target data includes the travel information;

[0112] Generate a load adjustment instruction for each site in the target area according to the overlapping information and the adjustment target information.

[0113] In an alternative embodiment, the scheduling target information includes the number of stations along the line. When the number of stations along the line is greater than 1, the load adjustment module 24 is specifically configured to:

[0114] Obtain the historical throughput information of the stations along the line according to the current time;

[0115] Generate a load adjustment instruction for each site in the target area according to a preset adjustment model, the adjustment target information, and the overlapping information.

[0116] The operation scheduling module 25 is configured to generate an operation scheduling instruction for each multiple unit train in the target area according to the load adjustment instruction and the first target data.

[0117] In an alternative embodiment, the operation scheduling module 25 is specifically configured to:

[0118] Generate inbound docking information according to the load adjustment instruction;

[0119] Generate an operation scheduling instruction for each multiple unit train in the target area according to the inbound docking information and the first target data.

[0120] The instruction sending module 26 is configured to send the load adjustment instruction and the operation scheduling instruction to the corresponding site and multiple unit train respectively, so that the site and the multiple unit train perform dynamic adjustment according to the corresponding instruction.

[0121] In an alternative embodiment, the intelligent transportation and data analysis device 2 based on the gravity energy storage of the bullet train further includes an energy recovery module 27, and the energy recovery module 27 is configured to:

[0122] Obtain energy recovery points on the planned route according to the operation scheduling instruction;

[0123] Generate energy recovery instructions for each bullet train in the target area according to the energy recovery points.

[0124] It should be understood that the various change modes and specific embodiments in the methods provided in the above embodiments are equally applicable to the intelligent transportation and data analysis device based on the gravity energy storage of the bullet train in this embodiment. Through the foregoing detailed description of the intelligent transportation and data analysis method based on the gravity energy storage of the bullet train, those skilled in the art can clearly know the implementation method of the intelligent transportation and data analysis device based on the gravity energy storage of the bullet train in this embodiment. For the sake of brevity of the specification, it will not be described in detail here.

[0125] As Figure 3 shown, it is a schematic structural diagram of the server provided by the embodiment of the present application.

[0126] In a preferred embodiment of the present invention, the server 3 may include, but is not limited to: a memory 31, at least one processor 32, and at least one communication bus 33.

[0127] Those skilled in the art should understand that Figure 3 the structure of the server 3 shown does not constitute a limitation on the embodiments of the present invention. The server 3 may further include more or fewer other hardware or software than shown, or different component arrangements.

[0128] In some embodiments, the server 3 is a device capable of automatically performing numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application specific integrated circuits, programmable gate arrays, digital processors, and embedded devices, etc.

[0129] It should be noted that the server 3 is only an example, and other existing or future possible electronic products that can be adapted to the present application should also be included in the protection scope of the present application and are included herein by reference.

[0130] In some embodiments, a computer program is stored in the memory 31, and when the computer program is executed by the at least one processor 32, all or part of the steps in the described intelligent transportation and data analysis method based on the gravity energy storage of the EMU are implemented. The memory 31 includes a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electrically-erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM), or other optical disc memories, magnetic disk memories, tape memories, or any other computer-readable medium capable of carrying or storing data. Further, the computer-readable storage medium mainly includes a program storage area and a data storage area. Among them, the program storage area can store an operating system, application programs required for at least one function, and the like.

[0131] In some embodiments, the at least one processor 32 is the control core (Control Unit) of the server 3, connecting various components of the entire server 3 through various interfaces and lines. By running or executing the programs or modules stored in the memory 31, and by calling the data stored in the memory 31, various functions of the server 3 are executed and data is processed. For example, when the at least one processor 32 executes the computer program stored in the memory 31, all or part of the steps in the intelligent transportation and data analysis method based on the gravity energy storage of the EMU described in the embodiments of the present application are implemented; or all or part of the functions of the intelligent transportation and data analysis device based on the gravity energy storage of the EMU are implemented. The at least one processor 32 may be composed of integrated circuits. For example, it may be composed of a single packaged integrated circuit, or may be composed of multiple integrated circuits with the same or different functions packaged, including a combination of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips.

[0132] In some embodiments, the at least one communication bus 33 is arranged to implement the connection communication between the memory 31 and the at least one processor 32, etc. Although not shown, the server 3 may further include a power supply (such as a battery) for powering each component. Preferably, the power supply can be logically connected to the at least one processor 32 through a power management device, so as to implement functions such as management of charging, discharging, and power consumption management through the power management device. The power supply may further include any components such as one or more DC or AC power supplies, a recharge device, a power failure detection circuit, a power converter or inverter, a power status indicator, etc. The server 3 may further include various sensors, a Bluetooth module, a Wi-Fi module, etc., which will not be elaborated here.

[0133] The integrated unit implemented in the form of a software functional module as described above can be stored in a computer-readable storage medium. The above software functional module is stored in a storage medium and includes several instructions for causing a server (which may be a personal computer, an electronic device, or a network device, etc.) or a processor to execute a part of the methods described in various embodiments of the present application.

[0134] In several embodiments provided by the present application, it should be understood that the disclosed apparatus and method can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation.

[0135] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units. They can be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0136] The above are all preferred embodiments of the present application. Without limiting the protection scope of the present application accordingly, therefore: all equivalent changes made according to the structure, shape, and principle of the present application should be covered within the protection scope of the present application.

Claims

1. An intelligent transportation and data analysis method based on the gravity energy storage of bullet trains, characterized in that, The method includes: Obtaining first target data of each multiple unit train in the target area and second target data of each station in the target area in real time. Among them, the first target data is used to display the operation and carrying data of each multiple unit train, including the speed of the multiple unit train, the current positioning information, the load condition, and the load loading and unloading information. The second target data is used to display the facilities and waiting data of each station, including travel information, station waiting information, station coordinate information, and inbound and outbound planned route information; Performing operation analysis on each multiple unit train in the target area according to a preset multiple unit train operation analysis method and the first target data to obtain a stop data set; Obtaining the coincidence information of the operation routes between each multiple unit train in the target area according to a preset inbound and outbound time threshold and the stop data set; Generating a load adjustment instruction for each station in the target area according to the coincidence information and the second target data; Generating an operation scheduling instruction for each multiple unit train in the target area according to the load adjustment instruction and the first target data; Sending the load adjustment instruction and the operation scheduling instruction to the corresponding station and multiple unit train respectively, so that the station and the multiple unit train perform dynamic adjustment according to the corresponding instructions; Among them, the performing operation analysis on each multiple unit train in the target area according to a preset multiple unit train operation analysis method and the first target data to obtain a stop data set includes: Obtaining the planned route of the multiple unit train according to the train number marked by the first target data; Marking the current position coordinates of the multiple unit train on the planned route and marking the inbound and outbound times of the stations to be passed along the way according to the multiple unit train operation analysis method, the speed of the multiple unit train, and the current positioning information of the multiple unit train to generate the stop data of the multiple unit train at each station in the target area. Among them, the first target data includes the speed of the multiple unit train and the current positioning information of the multiple unit train; Combining the stop data corresponding to each multiple unit train in the target area into the stop data set; Among them, the obtaining the coincidence information of the operation routes between each multiple unit train in the target area according to a preset inbound and outbound time threshold and the stop data set includes: Obtaining the information of the stations to be entered by each multiple unit train according to the coordinate marks and speed directions of each multiple unit train. Among them, the stop data set includes the coordinate marks and the speed directions; Classifying the stop data set according to the station information and a preset inbound and outbound time threshold to obtain an associated stop data set for the same station; Obtaining the coincidence information of the operation routes between each multiple unit train in the target area according to the associated stop data set for the same station; Among them, the generating a load adjustment instruction for each station in the target area according to the coincidence information and the second target data includes: Classify the second target data according to a preset boarding time threshold and travel information of the waiting load to obtain adjusted target information, where the second target data includes travel information of the waiting load; Generate load adjustment instructions for each station in the target area according to the coincidence information and the adjusted target information; Among them, the generating operation scheduling instructions for each multiple unit in the target area according to the load adjustment instructions and the first target data includes: Generate inbound stop information according to the load adjustment instructions; Generate operation scheduling instructions for each multiple unit in the target area according to the inbound stop information and the first target data.

2. The intelligent transportation and data analysis method based on the gravity energy storage of bullet trains according to claim 1, wherein The scheduling target information includes the number of stations along the line. When the number of stations along the line is greater than 1, the generating load adjustment instructions for each station in the target area according to the coincidence information and the adjusted target information includes: Obtain historical throughput information of the stations along the line according to the current time; Generate load adjustment instructions for each station in the target area according to a preset adjustment model, the adjusted target information, and the coincidence information.

3. The intelligent transportation and data analysis method based on the gravity energy storage of bullet trains according to claim 1, characterized in that, The method further includes: Obtain energy recovery points on the planned route according to the operation scheduling instructions; Generate energy recovery instructions for each multiple unit in the target area according to the energy recovery points.

4. An intelligent transportation and data analysis device based on the gravity energy storage of high-speed trains, characterized in that, For executing the steps of the intelligent transportation and data analysis method based on dynamic gravity energy storage according to any one of claims 1 to 3 above, the device includes: A data acquisition module, configured to acquire first target data of each multiple unit in the target area and second target data of each station in the target area in real time; An operation analysis module, configured to perform operation analysis on each multiple unit in the target area according to a preset multiple unit operation analysis method and the first target data to obtain a stop data set; A route matching module, configured to obtain coincidence information of the operation routes between each multiple unit in the target area according to a preset inbound and outbound time threshold and the stop data set; A load adjustment module, configured to generate load adjustment instructions for each station in the target area according to the coincidence information and the second target data; An operation scheduling module, configured to generate operation scheduling instructions for each multiple unit in the target area according to the load adjustment instructions and the first target data; An instruction sending module, configured to send the load adjustment instructions and the operation scheduling instructions to the corresponding stations and multiple units respectively, so that the stations and the multiple units perform dynamic adjustment according to the corresponding instructions.

5. A server, characterized in that, The server includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the intelligent transportation and data analysis method based on dynamic gravity energy storage according to any one of claims 1 to 3 above.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the intelligent transportation and data analysis method based on dynamic gravity energy storage according to any one of claims 1 to 3 above.

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